Studyguide_c17-gfm-msc-01_201802071553.pdf

STUDY GUIDE 2017-2019 Master of Science Degree Programme in Geo-information Science and Earth Observation for

Geoinformatics

C17-GFM-MSc-01 18 September 2017 - 22 March 2019 University of Twente, Faculty ITC Bureau Education and Research Support

COLOFON

UNIVERSITY OF TWENTE FACULTY OF GEO-INFORMATION SCIENCE AND EARTH OBSERVATION Bureau Education and Research Support DATE LAST MODIFIED

7 February 2018 PUBLISHED VERSION

Version 2.0 E-MAIL

[email protected] POSTAL ADDRESS

PO Box 217 7500 AE Enschede The Netherlands WEBSITE

www.itc.nl COPYRIGHT

© ITC, Faculty of Geo-Information Science and Earth Observation of the University of Twente, The Netherlands. Text and numerical material from this publication may be reproduced in print, by photocopying or by any other means with the permission of ITC if the source is mentioned. PUBLISHED BY

University of Twente Faculty of Geo-Information Science and Earth Observation Bureau Education and Research Support

FOREWORD DEAR STUDENTS IN THE MASTER'S PROGRAMME, Welcome to the Faculty ITC of the University of Twente. Having left your family and country, you have come to the Faculty ITC to further your education. We hope that the programme you have selected, will fulfil your expectations. Education at the Faculty ITC is characterised by:  a mixture of theory and practice, often including students' own experiences;  a common curriculum for Remote Sensing (RS) and Geo-information Systems (GIS);  deepening your knowledge in one of the specialisations;  acquiring academic and research skills;  choice options according to individual (research) interests. In the latest edition of the 'Keuzegids Master's', i.e. an overview of the quality of all Master's programmes in the Netherlands, the ITC's Master's programme was given the nomination 'Top Rated Programme' for the third time in a row. A great recognition of the quality of our programme. We are pleased to present you this study guide for the 2017/2018 Master's programme offered full-time at the Faculty ITC in Enschede. This study guide gives you information on the Master's programme, an overview of the blocks and the detailed structure of content of the programme modules.The Faculty ITC offers the Master's programme Geo-information Science and Earth Observations with the following specialisations:  Applied Earth Sciences (AES);  Geoinformatics (GFM);  Land Administration (LA);  Natural Resources Management (NRM);  Urban Planning and Management (UPM);  Water Resources and Environmental Management (WREM). You have arrived at a faculty of the University of Twente with more than 300 students from over 70 countries. Furthermore, Faculty ITC staff is originating from more than 25 countries: a truly international environment. However, there is more to life than just education. We would like to encourage you to actively participate in committees and other student activities and to make new friends from around the world. The Student Association Board and the Faculty ITC are organising all sorts of social, cultural and sports activities. Wellknown are the International Sports Tournament, the International Food Festival, the International Cultural Event and ITC's participation in the Batavierenrace. It is our ambition to continue to provide you with the quality of education that you may expect from this faculty of the University of Twente. We wish you the best of success during your studies and an enjoyable stay at the Faculty ITC, the University of Twente and in the Netherlands. Prof. Dr. Ir. A. Veldkamp Rector/Dean Faculty ITC

CONTENTS INTRODUCTION .................................................................................................................................................................................1 Programme structure ...........................................................................................................................................................................3 Teaching period ...................................................................................................................................................................................7 Events, holidays and breaks................................................................................................................................................................8 Roles within the curriculum..................................................................................................................................................................9 Programme learning outcomes..........................................................................................................................................................11 Teaching and learning approach .......................................................................................................................................................13 Sources of information.......................................................................................................................................................................16 BLOCK 1: CORE MODULES ...........................................................................................................................................................17 Geo-information science and earth observation: a systems-based approach...................................................................................19 BLOCK 2: COURSE MODULES ......................................................................................................................................................21 Databases and programming ............................................................................................................................................................23 Principles of spatial data quality ........................................................................................................................................................26 Programming skills ............................................................................................................................................................................28 Spatial data modelling and processing ..............................................................................................................................................30 Acquisition and processing of base data ...........................................................................................................................................32 Geovisualisation and web dissemination...........................................................................................................................................34 BLOCK 3: RESEARCH PROFILE ....................................................................................................................................................37 Research skills...................................................................................................................................................................................39 Advanced topic(s) ..............................................................................................................................................................................41 Geostatistics ......................................................................................................................................................................................43 Laser scanning ..................................................................................................................................................................................45 Spatial data for disaster risk management ........................................................................................................................................47 Field methods for earth sciences.......................................................................................................................................................50 Geovisual analytics............................................................................................................................................................................52 Spatial databases and their design....................................................................................................................................................54 The role of forests in climate change mitigation and the use of multi-sensor remote sensing to assess carbon ..............................56 Species distribution modelling and climate change impact ...............................................................................................................59 Remote sensing/geographic information systems analysis methods to support food and water security studies.............................62 Analysis of intra-urban, socio-spatial patterns ...................................................................................................................................65 Urban land use change and modelling ..............................................................................................................................................67 Advanced methods in transport planning ..........................................................................................................................................69 Water and health ...............................................................................................................................................................................71 Water availability from space.............................................................................................................................................................75 Water and ecosystems ......................................................................................................................................................................78 Advanced topic(s) ..............................................................................................................................................................................80 Advanced image analysis ..................................................................................................................................................................82 Unmanned aerial vehicles for earth observation ...............................................................................................................................84 Advanced geostatistics ......................................................................................................................................................................87 Unmanned aerial vehicles for scene understanding..........................................................................................................................89 Thermal infrared remote sensing: from theory to applications...........................................................................................................91 Spatial modelling for integrated watershed management..................................................................................................................94 Building infrastructures for geo-information sharing ..........................................................................................................................97 Spatio-temporal modelling and analytics ...........................................................................................................................................99 Spatial-temporal models for food and water security studies ..........................................................................................................101 Strategic Environmental Assessment (SEA) and Environmental Impact Assessment (EIA) applying systems analysis and spatial decision support tools ......................................................................................................................................................................104

Participatory mapping and the use of local spatial knowledge ........................................................................................................107 Land governance .............................................................................................................................................................................109 Collaborative planning and decision support systems applied in decision rooms ...........................................................................111 Satellite data for integrated water resource assessments and modelling........................................................................................113 Water, climate and cities..................................................................................................................................................................115 Research themes/MSc Research Proposal.....................................................................................................................................117 Getting real about your research: translating science into action ....................................................................................................121 Research preparation 4D-EARTH ...................................................................................................................................................124 Geodata and service provision in crisis situations: supporting United Nations peace-keeping operations .....................................126 Biomass estimation and carbon assessment ..................................................................................................................................128 Crop production modelling and monitoring ......................................................................................................................................131 Biodiversity mapping, vegetation change detection and species distribution ..................................................................................134 People, Land and Urban Systems (PLUS) research methods and techniques ...............................................................................136 Water cycle and climate...................................................................................................................................................................140 BLOCK 4: MSC RESEARCH..........................................................................................................................................................142 MSc Research .................................................................................................................................................................................144 Theme: Acquisition and quality of geo-spatial information (ACQUAL) ............................................................................................146 Theme: Spatio-temporal analytics, maps and processing (STAMP) ...............................................................................................147 Theme: 4D-EARTH..........................................................................................................................................................................148 Theme: Forest Agriculture and Environmental in the Spatial sciences (FORAGES) ......................................................................149 Theme: People, Land and Urban Systems (PLUS) .........................................................................................................................150 Theme: Water Cycle and Climate (WCC)........................................................................................................................................151

INTRODUCTION

INTRODUCTION

PROGRAMME STRUCTURE The Master's programme in Geo-information Science and Earth Observation is divided into four blocks. The blocks vary in length and are divided into three-week modules. The number of modules for this programme is 23.

BLOCK 1: CORE MODULES Block 1 is the common Core of all Faculty ITC educational programmes. It teaches the basic principles of Remote Sensing and Geographic Information Systems for studying processes in the system earth and its users. Module

Start

End

Title

1-3

2-10-17

1-12-17

Geo-information science and earth observation: a systems- Leventi, I. (Julia, MSc)

Coordinator

based approach

BLOCK 2: SPECIALISATION MODULES Block 2 is specific for the different specialisations within the Faculty ITC Master's programme (AES, GFM, LA, NRM, UPM, WREM). In this block the basic principles of the specialisation and application of Remote Sensing and Geographic Information Systems are taught and deepened. The student needs to select an MSc research profile and selects an MSc research theme and topic. An MSc fair is organised to support this. Module

Start

End

Title

Coordinator

4

4-12-17

22-12-17

Databases and programming

By, R.A. de (Rolf, dr.ir.)

5

8-1-18

26-1-18

Principles of spatial data quality

Persello, C. (Claudio, dr.)

5-10

8-1-18

9-5-18

Programming skills

Zurita Milla, R. (Raul, dr.)

6

29-1-18

16-2-18

Spatial data modelling and processing

Augustijn, P.W.M. (Ellen-Wien, ir.)

7-8

19-2-18

29-3-18

Acquisition and processing of base data

Peter, M.S. (Michael, dr.ing.)

9-10

3-4-18

9-5-18

Geovisualisation and web dissemination

Morales Guarin, J.M. (Javier, dr.)

BLOCK 3: RESEARCH PROFILE Block 3 prepares the student for their MSc Research phase by offering learning opportunities on research skills (module 11), advanced topics on specific research methods and tools which the student has to select (12 and 13), research themes in which the student works on their final MSc Research Proposal, and deepen knowledge and skills within the research activities of the scientific department (14 and 15). Module

Start

End

Title

Coordinator

11

22-5-18

8-6-18

Research skills

Sliuzas, R.V. (Richard, dr.)

12

11-6-18

29-6-18

Advanced topic(s)

Dopheide, E.J.M. (Emile, drs.)

12

11-6-18

29-6-18

Geostatistics

12

11-6-18

29-6-18

Laser scanning

Stein, A. (Alfred, prof.dr.ir.) Vosselman, M.G. (George, prof.dr.ir.)

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INTRODUCTION

12

11-6-18

29-6-18

Spatial data for disaster risk management

Westen, C.J. van (Cees, dr.)

12

11-6-18

29-6-18

Field methods for earth sciences

Ruitenbeek, F.J.A. van (Frank, dr.)

12

11-6-18

29-6-18

Geovisual analytics

Kraak, M.J. (Menno-Jan, prof.dr.)

12

11-6-18

29-6-18

Spatial databases and their design

By, R.A. de (Rolf, dr.ir.)

12

12

11-6-18

11-6-18

29-6-18

29-6-18

The role of forests in climate change mitigation and the use of

Hussin, Y.A.

multi-sensor remote sensing to assess carbon

(Yousif, dr.)

Species distribution modelling and climate change impact

Groen, T.A. (Thomas, dr.ir.)

12

12

11-6-18

11-6-18

29-6-18

29-6-18

Remote sensing/geographic information systems analysis

Bie, C.A.J.M. de

methods to support food and water security studies

(Cees, dr.ir.)

Analysis of intra-urban, socio-spatial patterns

Martinez Martin, J.A. (Javier, dr.)

12

11-6-18

29-6-18

Urban land use change and modelling

Sliuzas, R.V. (Richard, dr.)

12

11-6-18

29-6-18

Advanced methods in transport planning

Grigolon, A.B. (Anna, dr.)

12

11-6-18

29-6-18

Water and health

Salama, S. (Suhyb, dr.ir.)

12

11-6-18

29-6-18

Water availability from space

Velde, R. van der (Rogier, dr.ir.)

12

11-6-18

29-6-18

Water and ecosystems

Tol, C. van der (Christiaan, dr.ir.)

13

2-7-18

20-7-18

Advanced topic(s)

Dopheide, E.J.M. (Emile, drs.)

13

2-7-18

20-7-18

Advanced image analysis

Tolpekin, V.A. (Valentyn, dr.)

13

2-7-18

20-7-18

Unmanned aerial vehicles for earth observation

Nex, F.C. (Francesco, dr.)

13

2-7-18

20-7-18

Advanced geostatistics

Stein, A. (Alfred, prof.dr.ir.)

13

2-7-18

20-7-18

Unmanned aerial vehicles for scene understanding

Yang, Y. (Michael, dr.)

13

2-7-18

20-7-18

Thermal infrared remote sensing: from theory to applications

13

2-7-18

20-7-18

Spatial modelling for integrated watershed management

Hecker, C.A. (Christopher, dr.) Shrestha, D.B.P. (Dhruba, dr.)

13

2-7-18

20-7-18

Building infrastructures for geo-information sharing

Lemmens, R.L.G. (Robert, dr.ir.)

13

2-7-18

20-7-18

Spatio-temporal modelling and analytics

Zurita Milla, R. (Raul, dr.)

13

2-7-18

20-7-18

Spatial-temporal models for food and water security studies

Venus, V.

4

INTRODUCTION

(Valentijn, MSc) 13

2-7-18

20-7-18

Strategic Environmental Assessment (SEA) and Environmental

Looijen, J.M.

Impact Assessment (EIA) applying systems analysis and spatial

(Joan, drs.)

decision support tools 13

2-7-18

20-7-18

Participatory mapping and the use of local spatial knowledge

13

2-7-18

20-7-18

Land governance

Verplanke, J.J. (Jeroen, drs.) Todorovski, D. (Dimo, dr.)

13

13

13

2-7-18

2-7-18

2-7-18

20-7-18

20-7-18

20-7-18

Collaborative planning and decision support systems applied in

Boerboom, L.G.J.

decision rooms

(Luc, dr.ir.)

Satellite data for integrated water resource assessments and

Rientjes, T.H.M.

modelling

(Tom, dr.ing.)

Water, climate and cities

Timmermans, W.J. (Wim, dr.ir.)

14-15

30-7-18

7-9-18

Research themes/MSc Research Proposal

Dopheide, E.J.M. (Emile, drs.)

14-15

30-7-18

7-9-18

Getting real about your research: translating science into action

Stein, A. (Alfred, prof.dr.ir.)

14-15

30-7-18

7-9-18

Research preparation 4D-EARTH

Kerle, N. (Norman, prof.dr.)

14-15

14-15

30-7-18

30-7-18

7-9-18

7-9-19

Geodata and service provision in crisis situations: supporting

Drakou, E. (Valia,

United Nations peace-keeping operations

dr.)

Biomass estimation and carbon assessment

Leeuwen - de Leeuw, L.M. van (Louise, ir.)

14-15

30-7-18

7-9-18

Crop production modelling and monitoring

Venus, V. (Valentijn, MSc)

14-15

14-15

14-15

30-7-18

30-7-18

30-7-18

7-9-18

7-9-18

7-9-18

Biodiversity mapping, vegetation change detection and species

Westinga, E.

distribution

(Eduard, drs.)

People, Land and Urban Systems (PLUS) research methods and Richter, C. techniques

(Christine, dr.)

Water cycle and climate

Salama, S. (Suhyb, dr.ir.)

5

INTRODUCTION

BLOCK 4: MSC RESEARCH In Block 4 the student works individually on his MSc Research. It is required to have an approved MSc Research Proposal before entering this block. Formative feedback will be given at the Mid-term presentation. The MSc Research will be assessed at the MSc Research exam at the end of Module 23. Module

Start

End

Title

Coordinator

16-23

10-9-18

8-3-19

MSc Research

Dopheide, E.J.M. (Emile, drs.)

6

INTRODUCTION

TEACHING PERIOD Period

Time

1st period

08:45 - 10:30 Coffee/Tea Break

2nd period

10:45 - 12:30 Lunch break

3rd period

13:45 -15:30 Coffee/Tea Break

4th period

15:45 - 17:30

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INTRODUCTION

EVENTS, HOLIDAYS AND BREAKS 2017 Introduction weeks

18 September 2017 through 29 September 2017

Opening Academic Programme ITC

28 September 2017

Winter break

25 December 2017 through 5 January 2018

2018 MSc Research fair

7 February 2018

Public holiday

30 March 2018

Public holiday

2 April 2018

King's day

27 April 2018

Public holiday + bridging day

10 and 11 May 2018

Catch-up week I

14 May 2018 through 18 May 2018

Public holiday

21 May 2018

Catch-up week II

23 July 2018 through 27 July 2018

Proposal presentations

3 September 2018 through 7 September 2018

Mid-term presentations

19 November 2018 through 23 November 2018

Winter break

24 December 2018 through 04 January 2019

2019 Thesis submission

25 February 2019

Defences

11 March 2019 through 15 March 2019

Closing week

18 March 2019 through 22 March 2019

Graduation ceremony

21 and 22 March 2019

8

INTRODUCTION

ROLES WITHIN THE CURRICULUM Course Director Bakx, J.P.G. (Wan, drs.)

Room: ITC 2-003 Phone: +31534874357 Email: [email protected]

Course Secretary Scholten - Coelet, D.E. (Donny)

Room: ITC 1-105 Phone: +31534874334 Email: [email protected]

COURSE DIRECTOR The Course Director is authorised by and accountable to the Head of the Scientific Department as well as to the Programme Director. The Course Director is responsible for the specialisation-specific elements of the Master's programme and is the Study Advisor for the students in the specialisation.

CONFIDENTIAL ADVISOR The Faculty ITC is a strong, vibrant community that consists of people from all over the world. We expect all members of our community to respect the diversity of all students and staff. The Confidential Advisor plays an essential role in the faculty's response to harassment concerns. If you are affected by undesirable behaviour, such as bullying, aggression and unwanted sexual advances, you can turn to the Confidential Advisor for help, support and advice. The Confidential Advisor is authorized to receive complaints and will treat information discreetly and privately. You can find the Confidential Advisor, Ms. Annemarie AretsMeulman, in room 1-164 from Monday - Friday 09:00-14:00 and/or you can send an e-mail to [email protected]

COORDINATOR CORE MODULES The Coordinator of the Core Modules is in charge of the overall coordination of the Faculty ITC Core Modules 1-3 and has the status of Course Director reporting directly to the Programme Director.

COURSE SECRETARY The Course Secretary gives administrative and logistical support during the execution of the programme and courses and assists Course Directors and Course Coordinators as well as Module Coordinators. The Course Secretary is the first point of contact for students requiring information regarding the course. The Course Secretary is part of the Bureau Education and Research Support.

9

INTRODUCTION

EXAMINATION BOARD The Examination Board has to decide in an objective and professional manner whether a student has achieved all knowledge, skills and attitudes, as defined in the Education and Examination Regulations (EER) and the Rules and Regulations of the Examination Board (R&R) to award a degree, diploma or certificate of a specific programme or course. Therefore, the Examination Board monitors and is involved in all aspects of assessment; from policy on assessment (via appointment of Examiners), students' requests to the decision about complaints related to assessment.

EXAMINER The person who has been appointed by the Examination Board to hold exams and tests and determine their results.

MODULE COORDINATOR Each module is coordinated by a staff member of the Scientific Department. He is responsible for the organisation and execution of the entire module, and is first point of contact for staff and students when questions arise in regards to the module.

PROGRAMME COMMITTEE The Programme Committee advises the Programme Director on any matter pertaining to Faculty ITC's Master's level programme and credit bearing short courses, implemented by the Course Directors. This includes advice on the curricula, quality assurance, the Education and Examination Regulations and education policy. The Programme Committee is composed of both teacher and student members.

PROGRAMME DIRECTOR The Programme Director is the Dean's delegate on education matters, has been appointed to manage the Master's programme (in Dutch this person is called Opleidingsdirecteur or OLD) and is a member of the Management Team of the Faculty ITC. The Programme Director is responsible for preparation and implementation of education policy, monitoring the implementation of Faculty ITC's programs and courses by the Course Directors and their quality. Moreover, the Programme Director is responsible for the quality assurance of these programmes and courses.

PROPOSAL ASSESSMENT BOARD The Proposal Assessment Board is responsible for the assessment of the MSc Research Proposal exam at the end of Module 15 of the Master's programme.

STUDENT ADVISOR Each student is assigned a Student Advisor who will advise the student in study-related issues and will answer study-related questions. In many courses the Course Director or Course Coordinator has the role of Student Advisor.

SUPERVISOR All Master's students will be assigned to a Supervisor for the development of their MSc Research proposal and the execution of their MSc Research.

THESIS ASSESSMENT BOARD The Thesis Assessment Board is responsible for the assessment of the MSc Research exam at the end of the Master's programme.

10

INTRODUCTION

PROGRAMME LEARNING OUTCOMES MASTER'S PROGRAMME At successful completion of the Master's programme in Geo-information Science and Earth Observation, the student is able to: Domain/Academic field 1. Identify and understand principles, concepts, methods and techniques relevant for geo-information processing and earth observation; 2. Analyze problems and cases from a (geo-)spatial perspective; 3. Use and design models to simulate (or: study) processes in the system earth with a spatial component; 4. Apply principles, concepts, methods and techniques in the context of system earth, the user and an application domain to solve scientific and practical problems; 5. Independently design and carry out research in the domain according to acceptable scientific quality standards. Scientific 1. Analyse issues in an academic manner and formulate judgments based on this; 2. Analyse scientific and practical domain problems in a systematic manner and develop scientifically valid solutions for these problems in a societal context; 3. Communicate both orally and in writing on findings of research work to specialists and non-specialists; 4. Explore the temporal and social context of geo--information science and technology and be able to integrate these insights in his or her scientific work. Internationalization 1. Operate professionally in a multi-cultural environment, and act adequately on cultural differences; 2. Express himself adequately to colleagues of different nationalities. General 1. Critically reflect on his own and other's work; 2. Study in a manner that is largely self-directed and autonomous. These master's programme learning outcomes are translated into learning outcomes of the Geoinformatics specialisation. GEOINFORMATICS The objective of the Geoinformatics specialisation is to provide society with science-based expertise in the production of trustworthy geoinformation. Spurred by a wide variety of technological innovations, our field is rapidly changing, and the geoinformatics expert of the 21st century is expected to work in a context of a multitude of sensors, diverse and high-volume data, and advanced algorithmics. At the same time, the expert should maintain a focus on delivering products that are intuitive to use, that fit in the context of the stated purpose of decision making, and that help rationalizing decisions. Geoinformatics continues to be the science and engineering domain that uses location as the linking pin, though more and more time acts as such also, allowing us to study spatio-temporal processes. Geoinformatics is the domain in which data is acquired from sensors, is curated, stored, fused with other sources, visualized, interpreted and analysed to provide solid evidence and arguments to make spatial decisions. Innovation in our field is rapid: radar, lidar, cubesats, UAVs and smartphones bring us data of unprecedented resolutions-though often of unknown quality, the mobile revolution and the internet of things diversify the source points, and bring challenges of handling all data. Novel computational

11

INTRODUCTION

techniques like neural networks, deep learning and other methods coming from artificial intelligence are tools that we apply to our problems. Network- or cloud-based data storage and management techniques allow concurrent access by multiple users. Innovations in dissemination techniques enable a wide array of visual techniques, allowing us to design "the map" and other information products, in many different ways, always aiming for efficient and effective use by the client(s). A successful completion of the Geoinformatics specialisation means that the student: 1. Has deep knowledge of the core geospatial technologies of image processing, GIS, data analytics including data quality, data management and visualisation; 2. Understands the diversity of geoinformation workflows, and how to design its components to create forpurpose solutions to stated geospatial information needs from known users or communities; 3. Can choose appropriate methods, and design and realize associated techniques, using the above technologies, for geospatial data collection, curation, integration, processing and dissemination; 4. Has proficiency in various scripting/programming environments to realize workflow system components, and support research and development; 5. Knows how to evaluate the quality of developed solutions and how to compare against existing solutions, both in absolute terms and in context of intended use. These learning outcomes at programme and specialization level are detailed out as learning outcomes at module level.

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INTRODUCTION

TEACHING AND LEARNING APPROACH The academic profile of the Master's programme puts strong emphasis on the scientific discipline, a scientific approach, basic intellectual skills, co-operation and communication and the temporal and social context of research. The emphasis on doing research and/or designing or developing new methods or techniques depends on the application domain. Multidisciplinary research is an important focus for the Master's programme because (applied) research in practice seldom concerns one discipline but is more likely to be multidisciplinary. The student has to be prepared for that. Starting with a sound basis in their specialisation the student will be brought into learning situations in which students from different specialisations work together. It should be noted that most if not all research at the Faculty ITC is already multidisciplinary in nature. This is evident in the wide scope of expertise within departments, and the common denominator to carry out applied research contributing towards development related issues as specified in Faculty ITC's mission. In their profession, the graduate has to apply knowledge and skills independently. The Master's programme is therefore focused at handing over the control of the learning process to the student. At the beginning of the programme, the teacher will have the main control and the programme will contain some choices, especially concerning preparation for the MSc Research. The choices should be motivated, fit to the envisaged research trajectory, and be accepted by the Course Director. During the programme the teacher's role will develop towards the role of advisor. The student takes the lead in the learning process by developing a learning plan within the Master's framework and guidelines. The teacher supports this as a coach (while still passing on their experience).

BLOCK 1: MAINLY TEACHER LED In Block 1 the teacher takes the lead. He defines the content to be studied and learning tasks and exercises which have to be executed. The student can make limited choices between learning strategies and learning tasks. The number of contact hours between teacher and student is relatively large in this stage, mainly consisting of lectures and supervised practical exercises. Each student will be assigned a student advisor in Module 1 for advice on study related matters, especially the choice trajectory towards the MSc Research topic selection, but also for day-to-day problems, remedial self-study, etc. The student advisor is assigned for the whole Master's programme.

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INTRODUCTION

HANDING OVER CONTROL FROM THE TEACHER TO THE STUDENT

BLOCK 2: TEACHER AND STUDENT LED In Block 2 both the teacher and the student take the lead. The teacher defines the framework in which the student can make their choices about study tasks. The amount of choice options varies across the different specialisations. The student has to start thinking about an MSc Research topic and consult staff about its feasibility. The number of contact hours between teacher and student is reduced in favour of group work and independent study and assignments.

BLOCK 3: MAINLY STUDENT LED In Block 3 the student takes control by choosing advanced topics and a research theme which fit within their MSc Research topic. The student works on the final version of MSc Research Proposal and consults their student advisor and other specialised staff about its feasibility and quality. The final version of the MSc Research Proposal has to be presented and defended by the student for the Proposal Assessment Board. The number of contact hours between teacher and student is further reduced to make room for independent study by the student. Two MSc Research supervisors (first and second) are assigned for MSc Research supervision at the beginning of Block 3.

BLOCK 4: STUDENT LED In Block 4 the student works individually and independently on their MSc Research. This will be supported by meetings with the MSc Research supervisors and capita selecta meetings, organised by the research themes. The student is responsible for progress and quality of their MSc Research and its defence at the end. The number of contact hours between teacher and student is reduced to a minimum in this period. It is therefore wise to look for peer support and peer review opportunities in this phase, which is offered in the research theme where staff, PhD and Master students are together.

SPECIALISATION MODULES One important aspect of the selected teaching and learning methods in the specialization modules is that we offer Programming Skills parallel to the specialization modules. Geoinformatics research addresses

14

INTRODUCTION

usually cutting edge subjects and problems which require development of software/programming solutions. Learning to apply programming skills not only requires basic technical skills and language basics, but also analytical thinking. The exposure to these and other aspects over a prolonged period of time much better embeds the skills and competencies as compared to short intensive exposure. Similar reasons are at the basis for the two 6-week combined modules. The sometimes highly complex and technical subjects require more time for understanding and reflection than possible in a 3-week module. Assessments are, upon request of students, not cluttered in a final week but scheduled over the 6-week period.

15

INTRODUCTION

SOURCES OF INFORMATION STUDY GUIDE IN DIGITAL FORMAT www.itc.nl/studyguide

EDUCATION AND EXAMINATION REGULATIONS AND RULES AND REGULATIONS OF THE EXAMINATION BOARD www.itc.nl/regulations

FACULTY ITC www.itc.nl

UNIVERSITY OF TWENTE www.utwente.nl/en

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INTRODUCTION

BLOCK 1: CORE MODULES

17

BLOCK 1: CORE MODULES

GEO-INFORMATION SCIENCE AND EARTH OBSERVATION: A SYSTEMS-BASED APPROACH Module

1-3

Module code

P17-EDU-105

Period

2 October 2017 - 1 December 2017

EC

15

Module coordinator

Leventi, I. (Julia, MSc)

INTRODUCTION This block (Core Modules) consists of three modules that last for three (3) weeks each. The Core Modules form the basis of the Master of Science programme and the PGD course at the Faculty of Geo-Information Science and Earth Observation. The three modules are the following:  Geo-information science and modelling  Earth observation  Data integration: principles, approaches and user perspectives During the nine weeks that this block lasts for, the concepts and techniques of Geographic Information Science (GIS) and Earth Observation (EO) are addressed and put in context in relation to 'System Earth' and the user. As such the block consists of four interrelated parts:  A theoretical part which focuses on the main principles of system theory, GIS, EO, data integration, and the role of the user;  A practical part in which the knowledge gained can be applied and skills can be developed on operation of industry standard software and tools;  An application-oriented part in which participants learn how to individually design and carry out sequential data processing steps typical for the creation and use of basic GIS and EO methods;  Introduction and development of academic skills. The concepts and techniques introduced in this block will be further enhanced during subsequent modules within the Master of Science programme and the PGD course.

LEARNING OUTCOMES At the end of the block of these three modules the students will be able to interpret and generate information from Earth Observation, Remotely Sensed data and will have acquired GI Science-related knowledge that will enable them to analyse geodata for making meaningful conclusions. Moreover, they will be capable to identify the components of System Earth and their interrelations and thus, to support users like individuals and/or organizations to make beneficial plans for sustainable environments. Explicitly at the end of the block students must be able to: 1. Explain the main processes in system earth; 2. Use earth observation data that have been acquired remotely to extract geospatial information and/or produce date as part of system earth; 3. Process, generate, analyse and disseminate spatial data; 4. Understand the use of process and observation models to describe earth processes; 5. Describe the role of human beings as 'the users' at different levels of scale in the system earth; 6. Have basic academic level thinking, communication and learning skills.

19

BLOCK 1: CORE MODULES

CONTENT The block covers a wide range of topics offered through lectures, supervised and unsupervised practical sessions, guided discussions, fieldwork, workshops and a case study thats leads to the submission of an assessed technical report. Theoretical knowledge is transferred in combination with the development of skills in software handling and applications. The knowledge levels that the Core Modules are designed upon are remembering, understanding and applying information. For specific subjects the levels of analysis and evaluation are reached.

PREREQUISITES Admission to the Master of Science Programme, PGD course or short course

COMPULSORY TEXTBOOK(S) Tolpekin, V. & Stein, A. (eds) (2014): The core of GI Science: a systems-based approach, ITC, Enschede, The Netherlands.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

90

Supervised practicals

105

Unsupervised practicals

18

Individual assignment

48

Group assignment

0

Self study

145

Examination

12

Excursion

0

Fieldwork

14

ASSESSMENT Student performance evaluation during the Core Modules is done on the basis of a number of Individual Assignment Test Marks (IATM) and Digital Test Marks (DTM - online test) which will lead to three Final Module Marks (FMM):  For module 1 the FMM will be calculated from the weighted DTM on the Geo-information science and modelling subjects and from the weighted IATM (Poster);  For module 2 the FMM will be calculated from the weighted DTM on the Earth Observation subjects and from the weighted IATM (Digital Image Classification);  For module 3 the FMM will be calculated from the weighted DTM on the Data integration: principles, approaches and user percpectives, subjects and from the weighted IATM (project report/case study). All tests are marked from 1-10. The weights of the tests per module are given below: Module 1: FMM = 30%* IATM + 70%* DTM Module2: FMM = 30%* IATM + 70%* DTM Module3: FMM = 40%* IATM + 60%* DTM

20

BLOCK 1: CORE MODULES

BLOCK 2: COURSE MODULES

21

BLOCK 2: COURSE MODULES

DATABASES AND PROGRAMMING Module

4

Module code

M17-GFM-145

Period

4 December 2017 - 22 December 2017

EC

5

Module coordinator

By, R.A. de (Rolf, dr.ir.)

INTRODUCTION This module has two components: Databases and Programming Skills. These components are independent from each other, though they both address the domain of data and computation. The Databases component has approximately two weeks duration and the Programming component one week. In the Databases component, the module introduces the notion of database and data manipulation. We focus at this time on thematic (also known as attribute) databases, the relational data model, and queries in the query language SQL. We do pay some attention also to handling geospatial data. Database engineering is an important tool for any type of information management. We emphasize the role of mathematical logic in this domain, and help students to understand data mathematically. With an eye on the big data era and growing importance of map-reduce systems, we also discuss a number of computational abstractions in handling data sets. The techniques learnt in this module will be useful throughout the programme, and indeed later in professional life. In the Programming Skills component, the students are acquainted with the notions of algorithm and algorithm development. The Python language is the workhorse that we use, but the course should not be understood as a training in Python only. This component forms the start of a string of lectures and exercises that continue after the Christmas break in other modules, and that aim to bring the student sufficient skils in computational thinking and computer coding to realize computational solutions to problems. In this module, the type of problems addressed are mathematical, specifically related to linear algebra. Linear algebra topics covered are those useful for image processing, another post-Christmas educational target. Refresher lectures are included to bring all students to level, and focus is on relevant concepts such as vectors, matrices, solving linear equations and doing linear transformations.

LEARNING OUTCOMES Main objectives for Databases: To learn how a database management system works, what stored tables and queries are, and how to define queries in the standard language SQL, making use of predicate logic. To apply that knowledge to understand and improve an existing database, and extract information that was originally impossible to extract. Also, some first aspects of database design are introduced. Main objectives for Programming Skills: To build initial understanding and operational capability of coding computational solutions in the problem space of working with spatial data. Specifically also revive the understanding of linear algebra, and apply this in a programming context. Upon successful completion of this module, the student is able to:  Explain the fundamentals of the relational data model;  Formulate simple queries in mathematics and predicate logic;  Define, execute and verify SQL queries against an existing relational database;  Formulate and execute some simple spatial queries;  Explain the first principles of database design.

23

BLOCK 2: COURSE MODULES

And:  Understand fundamental programming and algorithm design concepts;  Have basic skills in programming, using Python;  Have basic skills in linear algebra to perform transformation tasks needed later in the programme.

CONTENT The databases component covers the following topics:  What purposes do databases serve? Database management systems;  The relational data model;  Set theory and predicate logic as a foundation for database querying;  Simple spatial computations in a database context;  Database querying using SQL;  Database maintenance;  Introduction to database design. And:  Programming Skills using Python;  Principles of Linear Algebra.

PREREQUISITES     

Core modules Basic computer handling skills Some familiarity with Windows software Secondary school discrete mathematics and linear algebra Ability to explore new software and new data sets

RECOMMENDED KNOWLEDGE Basic computer skills, basic mathematics.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

42

Supervised practicals

48

Unsupervised practicals

20

Individual assignment

0

Group assignment

0

Self study

26

Examination

8

Excursion

0

Fieldwork

0

ASSESSMENT At the end of the Database component, students will be assessed through an online test on their understanding of the terminology, the technology of the field of relational databases, and their theoretical understanding and practical skills in designing SQL queries. At the end of the Programming skills component, students will be assessed through a second online test, to test their skills in this domain, and as applied to linear programming challenges.

24

BLOCK 2: COURSE MODULES

The overall module mark is a weighted average (Database component 70% and Programming Skills component 30%) of the two results obtained.

25

BLOCK 2: COURSE MODULES

PRINCIPLES OF SPATIAL DATA QUALITY Module

5

Module code

M18-GFM-103

Period

8 January 2018 - 26 January 2018

EC

5

Module coordinator

Persello, C. (Claudio, dr.)

INTRODUCTION This module aims to cover the basic principles of spatial data quality (SDQ). SDQ is mentioned in several modules throughout the Master programme. This module will require the student to give critical attention to the meaning of SDQ. Greatest attention will be given to quantitative, probabilistic and statistical aspects of the subject. To do this we will revise and develop the basic mathematical and statistical concepts and computational tools which will be of more general value through the remainder of the Master programme. The statistical methods will be applied to important example applications in Remote Sensing and GIS.

LEARNING OUTCOMES At the end of the module, the student should have achieved the following learning outcomes. The student should be able to:  Explain basic concepts in probability and statistics and apply basic exploratory data analysis in R and appraise the results;  Explain confidence intervals and hypothesis testing, apply them to a simple dataset and evaluate the results;  Explain the principles of linear and non-linear regression and least squares, apply them using appropriate software tools and evaluate the results;  Apply analytical and numerical methods to compute derivatives of functions;  Describe impacts of uncertainty on the quality of spatial data products and evaluate quantitatively the impact of uncertainty on the output of a simple model;  Explain the terms "spatial data quality" and related terms validation, accuracy assessment, error and uncertainty and apply them appropriately;  Apply the core statistical concepts to attribute and positional uncertainty in the context of spatial data quality. To support achieving these learning outcomes the student will be provided with various short assignments. These take the form of group presentations, short-answer questions, problem sheets and computer practical assignments. Feedback will be given in class.

CONTENT Introduction to SDQ:  Definitions of SDQ;  Key components of SDQ. Quantitative mathematical and statistical analysis:  Basic concepts in probability and statistics;  Basic calculus for geoinformatics students;  Exploratory data analysis;  Linear regression and least squares;  Non-linear regression and non-linear adjustment;  Hypothesis testing and confidence intervals; 26

BLOCK 2: COURSE MODULES

 

Positional uncertainty; Attribute uncertainty (including themetatic uncertainty, scale issues and MAUP).

Modelling and uncertainty propagation:  Error propagation;  Accuracy assessment and validation of spatial datasets and model outputs.

PREREQUISITES Core modules, GFM specialisation module 4.

RECOMMENDED KNOWLEDGE Undergraduate classes in mathematics, statistics and data analysis.

COMPULSORY TEXTBOOK(S) Reading material will be provided in the lesson outlines. An important resource for statistics is http://onlinestatbook.com/.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

38

Supervised practicals

42

Unsupervised practicals

22

Individual assignment

8

Group assignment

0

Self study

32

Examination

2

Excursion

0

Fieldwork

0

ASSESSMENT The module mark will be a weighted average of the marks obtained for:  the individual assignment (25%)  closed-book written test at the end of the module (75%)

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BLOCK 2: COURSE MODULES

PROGRAMMING SKILLS Module

5-10

Module code

U18-GFM-107

Period

8 January 2018 - 9 May 2018

EC

0

Module coordinator

Zurita Milla, R. (Raul, dr.)

INTRODUCTION The objectives of this specialisation component are twofold: on the one side, it aims at providing a working knowledge of Python programming. Python is a general-purpose open-source computer programming language used by thousands of developers around the world, in areas as diverse as spatial modelling, internet scripting, user interfaces, product customization, and more. On the other side, it also aims at teaching students how to think and solve problems like a programmer. For this, we will use a variety of theoretical and applied examples that will gradually increase in complexity. Allocated time and EC are incorporated in modules 5 to 10. In these six modules there are 96 hours (approximately 3 EC) allocated to "Programming skills" (16 hours in each module).

LEARNING OUTCOMES After successful completion of this specialisation component, students are able to:  Translate computational problems into algorithms using general programming concepts;  Explain the basics of programming using the Python language;  Use Python to model, analyse and visualize (spatial) data;  Use Python to access both spatial and non-spatial databases;  Examine the pros and cons of various Python solutions;  Formulate your own Python algorithms to solve various computational problems;

CONTENT       

Python language basics Plotting graphs and geovisualization Scientific computing Image processing Geospatial development Database connectivity Server-side programming

PREREQUISITES Programming skills from GFM specialisation module 4.

COMPULSORY TEXTBOOK(S) The free ebook: Think Python: How to think like a computer scientist, A. Downey, Green Tea Press.Recommended books (available at the library):  Learning Geospatial Analysis with Python, J. Lawhead, Packt Publishing.  Python for Data Analysis, W. McKinney, O'Really.  Python Geospatial Development, 2nd edition, E. Westra, Packt Publishing.  Python scripting for ArcGis, P.A. Zandbergen, ESRI Press.

28

BLOCK 2: COURSE MODULES

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

38

Supervised practicals

11

Unsupervised practicals

11

Individual assignment

12

Group assignment

0

Self study

22

Examination

2

Excursion

0

Fieldwork

0

ASSESSMENT The assessment of this course component is integrated in the assessments of the GFM specialisation modules 6, 7-8 and 9-10. For more information (format, weights and type of marking) please see the corresponding module description.

29

BLOCK 2: COURSE MODULES

SPATIAL DATA MODELLING AND PROCESSING Module

6

Module code

M18-GFM-104

Period

29 January 2018 - 16 February 2018

EC

5

Module coordinator

Augustijn, P.W.M. (Ellen-Wien, ir.)

INTRODUCTION Managing large quantities of structured and unstructured (spatial) data is a primary function of information systems. Data models describe structured data for storage in data management systems such as relational databases. Data models also define constraints or limitations on the data placed within the structure. Process models allow the definition of series of analytical (spatial) operations that can be applied to structured and unstructured data. There are many different techniques for process modelling. In this module, the student will be exposed to a number of analytical and simulation techniques to enhance their understanding of the models available. Besides implementation issues, an important aspect of this module is conceptual modelling for both data models and process models.

LEARNING OUTCOMES Upon successful completion of this module, the student should be able to:  Explain and apply the fundamental concepts of object-orientation;  Use a data model to describe the structure of the data within a given application domain;  Derive and analyse user requirements for an application;  Conceptually model a process model using the ODD protocol;  Have an enhanced understanding of different types of (spatial) processing tasks;  Describe the fundamental principles of the main geocomputational methods (bloom level comprehension);  Sketch the use appropriate geocomputational methods to solve a particular spatio-temporal problem (bloom level application/analysis).

CONTENT The module covers the following topics:  Spatial databases & Spatial data standards;  Object-relational modelling using UML;  Transformational design;  ODD protocal;  Agent-based simulation;  Geocomputation.

PREREQUISITES Core modules, GFM specialisation modules 4-5.

RECOMMENDED KNOWLEDGE SQL Language, basic understaning of GIS analysis.

30

BLOCK 2: COURSE MODULES

COMPULSORY TEXTBOOK(S) none

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

23

Supervised practicals

26

Unsupervised practicals

26

Individual assignment

30

Group assignment

5

Self study

30

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT The assessment of the module consists of two tests: Written test (open book) with a weight of 55% (including 10% assessment of programming skills and competences) with a marking of 1-10. Individual assignment (report) with 3 sub-tasks. The assignment has a weight of 45% and a marking 1-10.

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BLOCK 2: COURSE MODULES

ACQUISITION AND PROCESSING OF BASE DATA Module

7-8

Module code

U18-GFM-108

Period

19 February 2018 - 29 March 2018

EC

10

Module coordinator

Peter, M.S. (Michael, dr.ing.)

INTRODUCTION The subject of this module is the acquisition and automated processing of data that can constitute the basis of a Spatial Data Infrastructure (SDI). The acquisition aspect relates to current technologies for the production of topographic maps, digital terrain models (DTM) and orthoimages. The automation aspect focuses on methods for image classification, segmentation and matching. The module also addresses the issue of uncertainty in data acquisition and processing, accuracy assessment procedures and fuzzy vs. crisp approaches to image analysis.

LEARNING OUTCOMES Upon successful completion of this module, students are able to:  Describe the standard processes of generating and updating base data, specifically topographic data, for large and medium scale mapping;  Explain the characteristics, strengths and weaknesses of existing sensor systems and data processing methods for mapping applications;  Apply image orientation procedures and generate topographic products such as maps, DTMs and orthoimages;  Make informed decision on the best way of data acquisition (type of imagery, overlap, resolution) and the (combination of) processing method(s) suitable for a given problem set or data type;  Evaluate and analyse the uncertainty in image data, and assess the quality of the derived products;  Implement image processing algorithms in a computer program.

CONTENT The module contains lectures and practicals on principles and techniques for acquisition and automated processing of base data. The main topics include: Data acquisition:  Short refresher on coordinate transformations;  Sensors for data acquisition: Digital aerial cameras, very high resolution satellite sensors and GPS;  Orientation of acquired images by indirect and direct georeferencing;  Derivation of standard topographic products from the images: digital terrain models, digital orthoimages and topographic databases. Automated processing:  Image segmentation;  Image classification including subpixel and fuzzy methods;  Image matching including area-based and feature-based methods.

PREREQUISITES  

Core modules GFM specialisation modules 4 and 5

32

BLOCK 2: COURSE MODULES

RECOMMENDED KNOWLEDGE     

Core concepts of remote sensing and topographic mapping Linear algebra Calculus Basic statistics Parameter estimation

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

74

Supervised practicals

44

Unsupervised practicals

16

Individual assignment

15

Group assignment

20

Self study

113

Examination

6

Excursion

0

Fieldwork

0

ASSESSMENT The assessment consists of the following four tests: 1. Orthorectification assignment (group), accounting for 35% of the mark of part 1 (data acquisition). Type of marking: 1-10. 2. Written test (closed book) covering part 1 (data acquisition), accounting for 65% of the mark of part 1. This test will also assess programming skills. Type of marking: 1-10. 3. Image processing assignment (individual), accounting for 25% of the mark of part 2 (automated processing). This individual assignment will also assess programming skills. Type of marking: 1-10 4. Written test (closed book) covering part 2 (automated processing), accounting for 75% of the mark of part 2. Type of marking: 1-10.  

The group assignment on orthorectification cannot be re-attempted since it is a practical of long duration. When you re-attempt a test, you re-attempt the complete test as specified above.

The group assignment on programming skills which is executed in parallel to the modules is formatively assessed; students can use this assessment and feedback to improve their programming skills and competences in preparation for the individual assignment. Detailed information on the tests and assignments will be provided at the start of the module.

33

BLOCK 2: COURSE MODULES

GEOVISUALISATION AND WEB DISSEMINATION Module

9-10

Module code

U18-GFM-109

Period

3 April 2018 - 9 May 2018

EC

10

Module coordinator

Morales Guarin, J.M. (Javier, dr.)

INTRODUCTION One of the most valuable skills of an information specialist is being able to provide insight into geographic phenomena whose behaviour is captured in large datasets. This will entail determining which part of the data needs to be visualised, in which form, for which target group and by which means. The result could be a paper map, a web animation, a mobile app, etc. Information can be presented as a map, a graph, an animation or a combination of thereof. Maps, for example, can help understanding the world around us effectively by, say, summarising or emphasising aspects of certain phenomena. Maps can also offer insight into spatial patterns and on relations among the represented phenomena. Whatever the choice of presentation, it will be the result of the application of the cartographic visualisation process. Nowadays, the common mechanism for dissemination and manipulation of information is the Web. Maps, for example, can not only be disseminated via web interfaces, but they can also be generated using analysis and representation functions accessible via the web. The mechanism behind this options is called web services. Proper understanding of the technology required for both creating and consuming web services enables information specialists to accurately target the needs of users or decision makers.

LEARNING OUTCOMES Upon successful completion of this module, students are able to: (Knowledge)  Explain the principles of web architectures and web services;  Explain the role of open systems and standards and use them for the generation and consumption of web services;  Select cartographic data analysis methods and design principles to map spatio-temporal data;  Understand the characteristics and differences of thematic and topographic maps types and use them appropriately;  Explain the basic principles of mapping time (among them animated maps). (Comprehension)  Provide examples of geo-information value chains in real life;  Compare and value client-side and server-side application development approaches;  Understand and reason about different data exchange standards;  Understand what roles maps play in a GIS on a desktop or Web environment. (Application)  Create static and dynamic geo-services;  Use web frameworks for the creation of web applications that consume geo-services;  Apply cartographic design principles to different kinds of spatio-temporal data to construct visualization services given the users' needs. 34

BLOCK 2: COURSE MODULES

(Problem solving)  Evaluate use cases and choose appropriate client/server components for implementation;  Judge the appropriateness of the application of design principles to maps;  Decide on the required components of an exploratory geo-visualisation environment.

CONTENT Principles of visualisation of spatio-temporal data in a desktop or web environment:  Maps, cartography and geo-visualisation  Cartographic data analysis and design guidelines  Thematic and topographic maps  Time and maps  Spatio-temporal data exploration Concepts and techniques for the creation and use of web services:  The geo-information value chain (providers, developers and consumers)  Open systems: principles and standards  Services technology (Distributed computing & Service oriented architecture)  Web application design  Server-side and client-side programming

PREREQUISITES Core modules, GFM specialisation modules 4-8.

COMPULSORY TEXTBOOK(S) Kraak, M.-J. & Ormeling, F.J., Cartography: Visualization of Spatial Data. Third edition. Abingdon, Oxon & New York, 2013, NY: Routledge. ISBN 9781317903116. It is assumed that students acquire this book themselves. It is available as e-book or in print. Please make sure that you order the right (3rd) edition. The e-book is also accesible through the Faculty ITC Library: http://ezproxy.utwente.nl:2048/login?url=http://www.dawsonera.com/depp/reader/protected/external/Abstra ctView/S9780273722809

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

60

Supervised practicals

45

Unsupervised practicals

50

Individual assignment

50

Group assignment

35

Self study

45

Examination

3

Excursion

0

Fieldwork

0

35

BLOCK 2: COURSE MODULES

ASSESSMENT The assessment of the module consists of 4 tests: 1. Open book test on Geovisualization & Programming (80% of module 9's mark) - (marking: 1-10) 2. Group assignment (20% of module 9's mark) - (marking: 1-10) 3. Open book test on Web Dissemination (60% of module 10's mark) - (marking: 1-10) 4. Individual assignment (40% of module 10's mark) - (marking: 1-10) Assessment of programming skills and competences is in Module 10 fully integrated into the Web Dissemination part.

36

BLOCK 2: COURSE MODULES

BLOCK 3: RESEARCH PROFILE

37

BLOCK 3: RESEARCH PROFILE

RESEARCH SKILLS Module

11

Module code

P18-EDU-101

Period

22 May 2018 - 8 June 2018

EC

5

Module coordinator

Sliuzas, R.V. (Richard, dr.)

INTRODUCTION In the MSc Research phase you must be able to execute scientific research and present it in a Thesis and defence. Your success in this phase depends, apart from skills and conceptual background in your scientific discipline, on the ability to adequately structure your MSc Research Proposal and Thesis. This module develops a set of research skills that you need for successful MSc Research. You will learn why research is structured as it is and to develop your ability to critically review scientific work, both of yourself and that of others. You learn how to analyze the structure, logic and quality of research with examples from your own scientific field. Also you will develop skills to structure scientific research and to write proper structured English. The module finally aims to create common understanding of what is expected of a MSc Research Proposal and how it will be assessed, to allow you to comply with these expectations. The module is structured as a series of common lectures, with per-specialisation breakout sessions through which the topics are examined in the light of domain-specific requirements. The common lectures are given mostly by the overall coordinator, with some topics covered by other faculty members and library staff. Specialisation coordinators will organize and teach the per-specialisation breakout sessions.

LEARNING OUTCOMES Upon completion of the module, students will be able to:  Identify the main characteristics of the scientific method and scientific argumentation;  Position their research project in the wider research context: UT/ITC, national, regional and global agendas;  Find, evaluate, and summarise relevant and up-to-date scientific literature to support research;  Store, manage and use bibliographic data in scientific writing;  Describe the type of framework used in a research paper;  Recognize and critically assess research quality in published work;  Recognize and follow ethical standards in research;  Write a well-structured and logically-argued essay explaining the importance of their research topic in accordance with scientific writing principles.

CONTENT         

The scientific enterprise and the Faculty ITC Master student's place in it; Logic and structure of scientific research; Inference in various scientific disciplines; Literature search, citation and bibliography; Conceptual and research frameworks; Abstracting and reviewing scientific research; Structured scientific writing and argumentation; How to structure a MSc Research Proposal; Ethics and professionalism in research.

Follow-up lectures given in the MSc Research phase (not part of this module) continue with related themes:

39

BLOCK 3: RESEARCH PROFILE

   

Preparing for the midterm and MSc Research exam; Research quality and thesis assessment; Structuring results, discussion and conclusions; Graphic presentation in a Thesis.

PREREQUISITES Before entering module 11 students should have identified their intended line of research based on topics that are provided during the MSc fair held in February. Proposed topics contain basic information on: the intended topic and rationale, available datasets, (optional) fieldwork planning and possible MSc supervisors. Before the start of module 11, students will have to select a MSc research theme and topic. In connection with this selection, students also select advanced MSc research topics (i.e. modules 12 and 13) through the Faculty ITC webpage. At the start of module 11 students must be able to:  Present and discuss research relevant to their field of interest in public (orally, supported by presentation slides);  Communicate about technical subjects in written English. Besides students are expected to have:  A background in at least one relevant scientific field (e.g. one of ITC's specialisations);  A critical/creative attitude.

COMPULSORY TEXTBOOK(S) Updated ITC Lecture notes based upon:  Rossiter, D. G. (2018). MSc research concepts and skills: Vol. 1. Concepts: text with self - test: lecture note (p. 180). Enschede: ITC.  Rossiter, D. G. (2018). MSc research concepts and skills: Vol. 2. Skills: text with self - test questions: lecture note (p. 212). Enschede: ITC.  Rossiter, D. G. (2018). MSc research concepts and skills: Vol. 3. The ITC thesis process: text with self - test questions: lecture note (p. 39). Enschede: ITC.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

12

Supervised practicals

22

Unsupervised practicals

0

Individual assignment

90

Group assignment

0

Self study

20

Examination

0

Excursion

0

Fieldwork

0

ASSESSMENT The module marks will be based upon three assignments: 1. Literature review (20%), marking 1-10 2. Critical reading (40%), marking 1-10 3. Argumentation (40%), marking 1-10

40

BLOCK 3: RESEARCH PROFILE

ADVANCED TOPIC(S) Module

12

Module code

P18-EDU-102

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Dopheide, E.J.M. (Emile, drs.)

INTRODUCTION After completing module 11 on research skills, the students follow two advanced topics. These topics are offered by the scientific departments in modules 12 and 13, and are designed to equip the students with specific tools, methods and applications that are important for their intended MSc Research. In selecting these two advanced topics, the students therefore have to make a logical choice that fits to their MSc Research that will be carried out during the MSc Research phase (modules 16-23). The choice of advanced topics has to be submitted before the start of module 11.

LEARNING OUTCOMES Specified per advanced topic.

CONTENT These are the advanced topics in module 12 that were offered in 2017: Module 12 M17-EOS-100 M17-EOS-101 M17-ESA-102 M17-ESA-103 M17-GIP-100 M17-GIP-101 M17-PGM-100 M17-PGM-101 M17-PGM-102 M17-NRS-100 M17-NRS-101

M17-WRS-100 M17-WRS-101

Title Geostatistics Laser scanning Field methods for earth science Spatial data for disaster risk management Geovisual analytics Spatial databases and their design Participatory mapping tools and approaches Urban land use change and modelling Advanced methods in transport planning Species distribution modelling and climate change impact Assessment of the effect of climate change on agro-ecological systems using optical and synthetic aperture radar remote sensing and geographic information system Water availability from space Water and ecosystems

The final list of advanced topics that will be offered in 2018 will be made available no later than January 2018.

PREREQUISITES Master's programme modules 1-11. Note that, for some topics, specific knowledge and skills may be required. Please consult the module description of the advanced topic. 41

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RECOMMENDED KNOWLEDGE Specified per advanced topic.

COMPULSORY TEXTBOOK(S) Specified per advanced topic.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures Supervised practicals Unsupervised practicals Individual assignment Group assignment Self study Examination Excursion Fieldwork

ASSESSMENT Specified per advanced topic.

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GEOSTATISTICS Module

12

Module code

M18-EOS-100

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Stein, A. (Alfred, prof.dr.ir.)

INTRODUCTION This module aims to provide an introduction to the theory and practice of geostatistics. By the end of the module you should have a good knowledge of basic theory AND be able to implement analysis using R and GIS. Geostatistics is statistical inference for data with known locations. The attentional to location is what differentiates the statistics that you study in this module from the classic statistics that you have studied previously. Location is fundamental to geodata, so geostatistics finds many applications in the different disciplines at ITC. As such, the module is relevant for students in all departments at ITC. Geostatistical analysis will be implemented mainly in the R environment. Where appropriate, we will also link to GIS software since this is important for using geostatistics. Geostatistics has wide application where mapping is required. Applications can be found in geoinformatics, water resources, soil science, ecology and disaster management. The content is learnt through a range of study approaches. We do use tradional lectures and practical exercises to deliver the key concepts and develop practical skills. These are complemented with group exercises, presentations and a project. We also use online and in-class quizzes to provide instant feedback and to generate discussion. We typically have students from a range of backgrounds. We recognize that many students are not very comfortable with mathematics so we focus on the key equations and explain, step-by-step, what they mean in an applied sense. Our experience is that such students can then develop a strong understanding of geostatistics and not just treat it as a "black box". Students who like mathematics can go into more detail in equations if they choose to do so. During practical exercises you will work range of datasets that are relevant to the different ITC domains. If you are interested in a particular type of data please contact us.

LEARNING OUTCOMES At the end of this module the student should be able to:  Explain and apply the linear model in the context of a geospatial analysis;  Explain the concept of auto-correlation and outline how this is described and modelled using the variogram and covariance function;  Calculate sample variograms and fit models to those sample variograms AND justify choices made during this process;  Apply ordinary kriging and interpret the results (mean and kriging variance);  Extend the ordinary kriging case to regression kriging through the use of appropriate covariates;  Describe and implement a geostatistical simulation;  Explain the concept of cross-covariance and the cross-variogram and apply these to co-kriging for a simple dataset;  Develop a thorough critical geostatistical analysis that leads to a written report and oral presentation;  Develop and enhance core skills in group work, oral presentations and scientific report writing. 43

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CONTENT The first week begins with a revision of standard regression modelling and the linear mixed model before moving on to study the concept of spatial auto-correlation and the regionalized variable. We then model autocorrelation using variograms and covariance functions and then apply the variogram for prediction using ordinary kriging. We conclude with a mapping exercise. We begin the second week by extending ordinary kriging to regression kriging. We then turn to geostatistical simulation, co-kriging and maximum-likelihood estimation. In the first two weeks students will work with a range of datasets. The third week is an assignment/mini-project, where you conduct geostatistical analysis. You will be provided with a dataset and a project brief and will be required to work this out fully and report your findings orally and in writing. We are open to requests to work with other datasets (e.g., your own data), but this should be agreed with the lecturers.

PREREQUISITES Modules 1-11 of the ITC Master programme. Where this has not been followed, we will assess the suitability of candidates on a individual basis.

RECOMMENDED KNOWLEDGE  

Insight and experience with quantitative geodata (GIS, Remote Sensing); Basic knowledge of probability (distributions) and statistics (including t-tests and linear 'regression'.

COMPULSORY TEXTBOOK(S) Compulsory reading material will be distributed or made availbable from the ITC library.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

20

Supervised practicals

18

Unsupervised practicals

18

Individual assignment

22

Group assignment

30

Self study

32

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT The assessment is split between a group exercise (10%), individual project work (40%) and a written test (50%). Type of marking: 1-10

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BLOCK 3: RESEARCH PROFILE

LASER SCANNING Module

12

Module code

M18-EOS-101

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Vosselman, M.G. (George, prof.dr.ir.)

INTRODUCTION Airborne, terrestrial and mobile laser scanning are modern technologies to acquire and monitor the geometry of the Earth's surface and objects above the surface like buildings, trees and road infrastructures. This module provides an overview on the state of the art of this technology, potential applications as well as methods to extract geo-information from the recorded point clouds.

LEARNING OUTCOMES After the module students should be able to:  Understand how point clouds are generated from GNSS, IMU, and range finder measurements and relative sensor registration;  Assess the applicability of laser scanning for various tasks, like surface reconstruction and 3D modelling;  Design survey plans to acquire point clouds taking into account the accuracy and point density requirements;  Evaluate the quality of laser scanning datasets;  Determine and apply optimal point cloud processing methods to extract surface descriptions for geometric modelling and point cloud classification;  Interpret and analyse point cloud processing results.

CONTENT        

Principles of airborne, terrestrial and mobile laser scanning; Sensor and point cloud properties; Point cloud registration, accuracy potential, error sources and correction methods, quality analysis; Comparison to other data acquisition techniques; Overview on various applications; General point cloud processing: visualisation, segmentation, classification; Digital terrain models: extraction of terrain points and break lines; Detection and modelling: 3D building modelling, 3D landscape modelling, extraction of vegetation characteristics, change detection with multi-temporal and single epoch data for map updating.

PREREQUISITES Completed Core Modules.

COMPULSORY TEXTBOOK(S) Lecture slides and chapters 1-4 of the book "Airborne and Terrestrial Laser Scanning". Vosselman, G., Maas, H.-G. (Eds.), 2010. Airborne and Terrestrial Laser Scanning, Whittles Publishing, ISBN 9781904445-87-6, 320 p.

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

23

Supervised practicals

7

Unsupervised practicals

2

Individual assignment

24

Group assignment

0

Self study

81

Examination

7

Excursion

0

Fieldwork

0

ASSESSMENT Oral report on individual assignment (20%) on the Tuesday of the third week and written test (80%) on the Friday of the third week. Type of marking: 1-10

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SPATIAL DATA FOR DISASTER RISK MANAGEMENT Module

12

Module code

M18-ESA-100

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Westen, C.J. van (Cees, dr.)

INTRODUCTION The world has experienced an increasing impact of disasters in the past decades. Many regions are exposed to natural hazards, each with unique characteristics. The risk due to natural disaster is changing, due to changes in population, land use and climate. This module studies the issues related to disaster risk: how it is analysed, how it can change and how it is used in decision making and planning. To reduce disaster losses, more efforts should be applied towards Disaster Risk Management, with a focus on hazard assessment, elements-at-risk mapping, vulnerability and risk assessment, all of which have an important spatial component. The use of Earth Observation (EO) products and Geographic Information Systems (GIS) has become an integrated approach in disaster-risk management. Hazard and risk assessments are carried out at multiple scales, ranging from global to a community level. These levels have their own objectives and spatial data requirements for hazard inventories, environmental data, triggering or causal factors, and elements-at-risk. This module provides an overview of various approaches used for hazard and risk assessment. We look at various methods for hazard assessment, depending on the data availability and objectives of the study. Several approaches are presented to generate elements-at-risk databases with emphasis on population and building information, as these are the most used categories for loss estimation. Vulnerability concepts are discussed, with emphasis on the methods used to define physical vulnerability of buildings and population, and indicator-based approaches used for a holistic approach, also incorporating social, economic and environmental vulnerability, and capacity. The use of multi-hazard risk for disaster risk reduction is also treated within this module, and we will look at different structural and non-structural measures for risk reduction, and also to the tools used for analysing optimal ones. Also risk governance and risk visualization are addressed.

LEARNING OUTCOMES At the end of this module students should be able to:  Understand the importance of spatial information in the various phases of Disaster Management, such as disaster mitigation, disaster preparedness planning, early warning systems, damage mapping, and reconstruction planning;  Specify the data requirements for hazard assessment, elements-at-risk mapping, vulnerability assessment and risk analysis;  Carry out multi-hazard risk assessment at two different scales: national and local scales, using a variety of methods for the analysis of economic and population risk;  Provide an overview of risk reduction measures, consisting of structural and non-structural measures, with a specific emphasis on eco-system based disaster risk reduction;  Analyse how risk reduction measures alter the risk and carry out cost-benefit analysis to select the optimal measures;  Analyse how risk may change over time due to climate change, land use change, population changes and other socio-economic changes; 47

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 

Understand how spatial information Earth Observation play a role in disaster preparedness planning, including early warning; Integrate what you have learned in a small project on the use of spatial information in a recent disaster.

CONTENT    







    





Risk Management framework, including aspects such as risk analysis, risk evaluation, risk perception and risk governance; Disaster risk reduction measures: structural and non-structural measures, with examples from the Netherlands and elsewhere. Eco-system based disaster risk reduction measures; Local scale multi-hazard risk assessment with project work; The analysis of risk reduction alternatives and changing risks. This is related to the results of the EU FP7 project CHANGES (www.changes-itn.eu). We will go through the various aspects that are involved in the development of a Spatial Decision Support System for Risk reduction analysis and changing risk analysis. Cost-Benefit analysis with project work. Role play exercise on stakeholder involvement in selecting disaster risk reduction alternatives; National scale multi-hazard risk assessment. Methods for multi-hazard risk assessment at a national scale, where we will take the dataset of Georgia as an example. Students can select various topics: analysis of historical disaster databases, hazard assessment for floods, landslides, earthquake, forest fires, etc., generation of elements at risk databases, physical, social, economic and environmental vulnerability assessment, exposure analysis, risk analysis. The generation of a hazard and risk atlas and the incorporation of the data in a Web-GIS. See also http://drm.cenn.org Use of spatial information for planning with examples from a Caribbean context. Evaluation of use cases on the application of hazard and risk information in spatial planning and critical infrastructure planning. Based on the CHARIM project. See also: http://www.charim.net Risk Governance: Users and providers of risk information. An analysis is given of the end users of risk information, their requirements, and the organizations that are involved in generating information for a risk assessment. Risk Perception: factors that are relevant for the perception of risk, and differences in perception depending on a number of criteria. Risk Evaluation: methods used for analysing the acceptability of risk, using societal and individual risk criteria; ALARP principle; problems involved; Risk Communication: communication process, examples of different methods for risk communication and the effectiveness; Excursion to organizations in the Netherlands that deal with the application of spatial information for early warning and emergency response planning; Simulation exercise on the use of spatial information for disaster response, where you work in a team and have to provide the right information to decision makers as a disaster is unfolding; Use of crowdsourcing methods in disaster risk management, with examples from Haiti; Image-based damage mapping, with overview of organizations involved in satellite -based damage assessment, and UAV-based damage assessment (e.g. GMES/Copernicus programme; Disaster risk cycle and corresponding service; SAFER, Emergency Management Service etc); Disaster reconstruction planning. We will evaluation of the earthquake disaster response and reconstruction efforts carried out in Haiti, through a video. We will also focus on the situation in Nepal, after the 2015 earthquake; Small final assignment where the students can integrate what they have learned in this module and apply it in a situation (e.g. their own country or a country where a recent disaster has taken place).

PREREQUISITES Open to all Master's students.

RECOMMENDED KNOWLEDGE Basic skills in GIS and remote sensing.

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COMPULSORY TEXTBOOK(S) Module folder with handouts, PowerPoint files, case study descriptions, background literature and examples of risk assessment studies and risk atlases will be provided.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

35

Supervised practicals

35

Unsupervised practicals

0

Individual assignment

0

Group assignment

30

Self study

32

Examination

0

Excursion

12

Fieldwork

0

ASSESSMENT The assessment is made based on the submission of a number of assignments and presentations, and does not include a test. Type of marking: 1-10

COMMENTS This module is also interesting for AES MSc students of the Natural Hazards and Disaster Risk Management specialization, as the components taught in this course are new with respect to the previous course components.

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FIELD METHODS FOR EARTH SCIENCES Module

12

Module code

M18-ESA-101

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Ruitenbeek, F.J.A. van (Frank, dr.)

INTRODUCTION In this three-week module you will carry out a research-oriented project that focuses on the combined use of remote sensing and geophysics together with field observations and measurements to make Earth Science interpretation of the Harz Mountains in Germany. In this integrated approach field-based methods play an important role in the investigations of the surface geological parameters at outcrop-scale and determinations of the relationships between the variations in remotely sensed and geophysical imagery and the geology on the ground. A variety of measurements will be acquired in the field during a short field campaign to the Harz Mountains in Germany and used to establish the relationships between the surface conditions and geology and interpretations that resulted from a desk study prior to the field campaign.

LEARNING OUTCOMES Upon completion of the module the student should be able to:  Make preliminary Earth Science interpretations from remotely sensed, geophysical imagery and other spatial data sets and plan field data acquisition and validation using a variety of geophysical, chemical and spectral field instruments;  Acquire field observations of the surface geology at outcrop scale and to use field instrumentation to measure chemical, physical, structural and mineralogical parameters;  Integrate the acquired field observations and measurements with the geological models and interpretations obtained using remote sensing and geophysics with the aim of validation and improvement of the preliminary interpretation;  Set up and run field campaigns with geological and geophysical instrumentation.

CONTENT The first week will consist of a preparation phase for the field campaign in which the student will select one data acquisition method for study and deepening of understanding and skills. This involves a theoretical component of literature study and a more practical component of preparing and interpretation of data sets for use in the field. Data acquisition methods include reflectance spectroscopy, gamma ray spectrometry, whole rock geochemistry with portable XRF, magnetic susceptibility measurements, etc. In the second week the field data acquisition campaign will take place in the Harz Mountains in Germany. Several rock outcrops and quarries will be visited during this field visit where field method will be practiced and data will be acquired. Together these locations will provide a comprehensive overview of the Harz area with its large diversity of rock types, Earth Science environments and mineralization. The third week is dedicated to integration of the data acquired in the field with the interpretations and models produced in the first week. The outcome will be presented in the form of project reports.

PREREQUISITES Modules 1-11 ITC Master's programme, relevant background in Earth Sciences.

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BLOCK 3: RESEARCH PROFILE

RECOMMENDED KNOWLEDGE The student must have a background in Earth Sciences. He must have affinity with the use of remote sensing and geophysics.

COMPULSORY TEXTBOOK(S) Excursion guide: Excursion to the Harz Mountains Handout - 14 pages Mueller, Andreas G. "The Rammelsberg shale-hosted Cu-Zn-Pb sulfide and barite deposit, Germany: Linking SEDEX and Kuroko-type massive sulfides." Slide presentation and explanatory notes (2008). slides 1 - 17. Reading material that is specific to the field methods that will be chosen by the students in the module will be distributed or made available from the ITC library.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

8

Supervised practicals

0

Unsupervised practicals

0

Individual assignment

24

Group assignment

18

Self study

48

Examination

6

Excursion

0

Fieldwork

40

ASSESSMENT Each students is working on and responsible for one type of field data acquisition method. One collective group report will be produced by the students to which each student contributes at least one chapter on the work conducted by the student, including the fieldwork preparation, field data acquisition and analysis of the acquired data. The performance of each student in the field will be assessed by assessment of the supervising staff and self- and peer-assessment. Weighting scheme of the assessment:  Individual field performance - assessed by supervising staff (25%),  Group performance - assessed by peer review (25%)  Group project report (50%). Type of marking: 1-10

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BLOCK 3: RESEARCH PROFILE

GEOVISUAL ANALYTICS Module

12

Module code

M18-GIP-100

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Kraak, M.J. (Menno-Jan, prof.dr.)

INTRODUCTION This module will cover basic aspects of geovisual analytics, and concentrates on the analyses and mapping of refugee and migration flows in the world. We work with a United Nations timeseries of 13 years with over 8 milion refugee movements and with multianual municipal migration data in Overijssel. In odd years you will work in close cooperation with Master's students from Penn State University (USA) who will physically join us in the last week of the module to finalize the tasks together. The objective is to develop, based on user-centred design an integrated visual environment with interactive and dynamic cartographic displays and alternative visual representations of time series to analyse relevant refugee/migration flows. The issue is that theory and tools to deal with the temporal component are less developed, despite the fact that time is a critical aspect of virtually all geo-problems. As part of the user-centered design the usability of the case study outcome will be evaluated with prospective users.

LEARNING OUTCOMES 1. Explain to peers the basics and usefulness of geovisual analytics in solving real world problems; 2. Explain and justify to peers the selection of components of a geovisual working environment in the context of a selected problem case; 3. Follow a user-centered design approach in selecting and applying appropriate visual representations to analyse a particular spatio-temporal problem (starting with a systematic requirement analysis); 4. Select and apply appropriate user research methods and techniques to evaluate a geovisual analytics environment. All above in context of the problem case selected (refugee/migration data).

CONTENT After overviews of the what and how of geovisual analytics and user-centered design of geoinformation tools the module will zoom in on a set of methods and techniques of geovisualization to deal with geospatial data which have a clear temporal component. Students are then expected to apply this knowledge in on the refuguee and/or migration data using off the shelf software tools and some scripting. The resulting prototype should be based on a solid requirement analysis and its usability will have to be investigated. A written report must be produced, describing, explaining and justifying the choices made in the usercentered design process, including the spatio-temporal problem which is addressed.

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PREREQUISITES Modules 1-11, ITC Master's programme. The knowledge gained in GFM Module "Geovisualisation and web dissemination" is advantageous, but it is not strictly necessary. Therefore, students from other specialisations are explicitly invited to join this elective module as well.

RECOMMENDED KNOWLEDGE Basic programming skills are recommended (scripting).

COMPULSORY TEXTBOOK(S) Chapters 2, 3 4 and 5 of 'Visualization of temporal origin-destination data (thesis 1786). Boyandin, I. Fribourg: University of Fribourg.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

24

Supervised practicals

24

Unsupervised practicals

0

Individual assignment

72

Group assignment

24

Self study

0

Examination

0

Excursion

0

Fieldwork

0

ASSESSMENT In this module, students work individually. The assessment is based on three main items:  A report describing, explaining and justifying the user centered design process (questions -10%- week 1 & report -35%- week 3) [ learning outcome 3 & 4 ];  A prototype of a geovisual analytics environement related to the case study (sketch map -15%- week 2 & map product -20%- week 3) [ learning outcome 1 & 2 ];  Final presentation & feedback provided on the work done by others (20%- week 3) [ learning outcome 1, 2, 3 & 4 ]. Type of marking: 1-10

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SPATIAL DATABASES AND THEIR DESIGN Module

12

Module code

M18-GIP-101

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

By, R.A. de (Rolf, dr.ir.)

INTRODUCTION Data management is key in the 21st century, in which mankind will be generating spatially explicit datasets in unprecedented volume. Whether coming off remote sensors, in situ sensors or on-the-person sensors, and whether it is raw, original data, or highly processed data in half- and end-products: our data holdings are so large now that, more even than before, we need methodical approaches and solid technology to make it all work. Spatial data has many disguises: we know the raster and vector distinction, but need to admit that other formats are also becoming of interest. For instance, genuine data that is spatio-temporal in nature. More and more, original spatial data snapshots are combined into spatial time series data, and this too needs to be properly managed in the store chosen. Data management comprises a number of activities: the design and preparation of the system to receive and hold large datasets, the design and realization of functions that operate over the stored data, and the execution of maintenance procedures that must ensure the data is secure and available, from a system that has performance characteristics that fit with the user needs. Current spatial database technology has many facilities on-board, amongst others various ways to store spatial data, loads of spatial functions very comparable to full-fledged GIS, as well as a variety of programming environments with which the data can be operated on. Novel technology includes hosted storage facilities, and these sometimes offer useful solutions. Database technology has also seen innovations in recent years, and some of these are becoming important for applications that handle really large datasets, or that are very computing-intensive. We will devote some time to cloud-based technology and how it can be made to work in application context.

LEARNING OUTCOMES The module aims to teach the students a number of skills, and aims to deepen their understanding of spatial data management. It also addresses the subsidiary skills of understanding technical manuals at appropriate operational levels. We also aim at the execution of a mini-research project around spatial database technology within the module, conducted by a small team of students. Pointwise the module has the following objectives:  deep operational knowledge on spatial database programming, with spatial SQL as well as a programming language that embeds SQL;  deep understanding of spatial database design, from conceptual model all the way to realized system;  proficiency in absorbing and digesting technical know-how from support manuals and standards;  build up some experience with cloud platforms;  experimental research project with database technology.

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CONTENT We will discuss architectural principles of spatial databases, standards for spatial data, database design theory, and execute a number of practical exercises in spatial database operation, extending spatial database functionality, GIS-like spatial data analysis and mapping, operating cloud-based storage platforms, and spatial database design. The module involves reading exercises, puzzles, and presentations by students, as well as execution of a database design project and a collaborative research project. We aim to conduct a highly interactive module in which students' interests may be specifically addressed.

PREREQUISITES    

Principles of GIS on spatial data, spatial reference systems, and generally GIS functions; Principles of databases on the fundamentals of the relational model, and the operation of SQL; Programming skills on the general understanding of algorithmics and algorithm development; Research skills on literature scanning and research project management.

RECOMMENDED KNOWLEDGE The fundamentals of GIS, database querying, and some experience in programming or scripting.

COMPULSORY TEXTBOOK(S) None. The module does have a fairly large reader, with articles and papers of interest.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

24

Supervised practicals

14

Unsupervised practicals

20

Individual assignment

16

Group assignment

30

Self study

38

Examination

2

Excursion

0

Fieldwork

0

ASSESSMENT The module is assessed in a number of ways, each giving a partial mark.  Students will be grouped to prepare a presentation on the basis of a reading assignment. Their presentation will be marked individually (20%);  Students will be assessed on their participation in class in discussions throughout the module. This will also be assessed individually (20%);  There will be a test that provides an individual mark (30%); we are currently studying whether it can be replaced by a personal project executed in context of the upcoming MSc thesis research; this may become an option for students who prefer that formula;  A mini-research project will be conducted also in a small group. This will give a group mark (30%). Type of marking: 1-10

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THE ROLE OF FORESTS IN CLIMATE CHANGE MITIGATION AND THE USE OF MULTI-SENSOR REMOTE SENSING TO ASSESS CARBON Module

12

Module code

M18-NRS-100

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Hussin, Y.A. (Yousif, dr.)

INTRODUCTION The greenhouse effects and the carbon cycle, in particular carbon emissions and carbon sequestration, are at the heart of climate change, one of the most pressing problems the earth is facing. Global instruments like the UNFCCC, Kyoto Protocol, CDM, and IPCC reports all address these, resulting in an explicit link with the International Environmental Agenda. The accurate quantification of the various components in the carbon cycle forms a core need for its assessment, monitoring, modelling, and the mitigation of adverse climate effects and, in the end, sustainability of livelihoods in many parts of the earth. The latter requires identification, analysis and development of policy instruments in order to handle the impacts of the foreseeable changes in the carbon cycle. Within the carbon cycle, forestry in the broad sense forms the principal scientific area for research including both emissions (sources) and sequestration (sinks). Afforestation, reforestation and deforestation are the current Kyoto focal areas, but sustainable forest management, including certification, and the assessment and prevention of forest degradation may well be considered in the so-called post-Kyoto period (see e.g., the REDD proposal). Due to size, inaccessibility of the forest resources, and international requirements for a uniform methodology, quantification of the carbon cycle components in both space and time leans heavily on remote sensing, GIS modelling and related statistical tools.

LEARNING OUTCOMES After the module students should be able to:  Understand carbon cycle and effect on climate change;  Assess and estimate forest, grass, shrubs and wetlands vegetation biomass;  Detect, monitor and model deforestation and forest degradation;  Model biomass from different vegetation types such as forest, grass, shrubs and wetlands vegetation and consequently model sequestrated carbon;  Model forest fire behaviour and consequently carbon emission;  Understand how deforestation, forest degradation, carbon sequestration and carbon emission affected climate change;  Understand the principles of SAR imaging system;  Interpret and analyze aircraft and satellite radar images;  Use radar images for modelling and mapping carbon and consequently model carbon.

CONTENT The application of optical and SAR Remote Sensing and GIS is an advanced subject introducing the principles of optical sensor system and Synthetic Aperture Radar Imaging Systems. It introduces the

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Carbon Cycle, Climate Response and the rule and effects of Deforestation and Forest Degradation (DD) on carbon and climate change. It discusses the new carbon strategy (REDD) Reducing Emission of Carbon from Deforestation and Forest Degradation accepted by UN countries as a continuation for its policy after Kyoto. It introduces the relationships between biophysical characteristics (e.g. biomass) of forest and other vegetation types such as grass, shrubs and wetland and optical and radar (reflectance or backscatter). It introduces the geo-information applications in deforestation and forest degradation by detecting, monitoring and modelling deforestation and forest degradation using Remote Sensing and GIS.Then it assesses methods of biomass assessment using field, Remote Sensing and GIS, which leads to the modelling and mapping biomass from all forest, grass, shrubs and wetland vegetation. Consequently, it presents methods and techniques of modelling carbon sequestration (CS). As far as carbon emission is concerned the module is first introducing forest fire. Then deals with modelling forest fire behaviour in order to present methods and techniques of modelling carbon emission (CE) from forest fire. Finally the module will discuss how Climate Change can be modelled in response to DD, CS and CE. As SAR data will be one of the remotely sensed most related to biomass, the module will go through all image pre-processing and processing techniques of radar data (e.g. enhancement, radiometric and geometric correction, etc.). The module explains how radar data can be fused with optical sensor system data and its applications in modelling carbon. The module will explain the techniques used to extract information from radar images. It will describe spatial, radiometric and temporal resolution of SAR Images.

PREREQUISITES Modules 1-11 of the ITC Master's programme.

RECOMMENDED KNOWLEDGE Remote sensing and GIS background.

COMPULSORY TEXTBOOK(S)  

Reader: Principles and Application of Imaging Radar (Henderson and Lewis 1998); Reader: Measurements and Estimations of Forest Stands Parameters Using Remote Sensing (Stellingwerf and Hussin, 1997).

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

20

Supervised practicals

20

Unsupervised practicals

0

Individual assignment

10

Group assignment

46

Self study

40

Examination

8

Excursion

0

Fieldwork

0

57

BLOCK 3: RESEARCH PROFILE

ASSESSMENT Summative assessment (test) theory and formative assessment of practical work. Type of marking: 1-10

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BLOCK 3: RESEARCH PROFILE

SPECIES DISTRIBUTION MODELLING AND CLIMATE CHANGE IMPACT Module

12

Module code

M18-NRS-101

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Groen, T.A. (Thomas, dr.ir.)

INTRODUCTION Interpolation of observations over space can be useful in many applications. For example, when observations are made of the occurrence of a rare and endangered species, it would be interesting for conservation to know all other places where this species can be found. Or, when a certain (invasive) pest species occurs somewhere, finding out which other places are vulnerable to similar outbreaks is useful. More generally said, it can be useful to make predictions of where a certain "event" (be it the presence of a rare species, a pest outbreak, a disease occurrence etc.) can occur. This kind of modelling is called distribution modelling. Or, in the case of modelling species, Species Distribution Modelling (SDM). A good way to make such predictions in space, is by looking at correlations of possible factors that can explain the occurrence of an event. If such relations exist, then continuous maps of those factors can be used to make predictions of the occurrence of the event. Apart from simple and multiple (logistic) regression, many more techniques exist, like boosted regression trees, maximum entropy (Maxent) or artificial neural networks. In this module you will learn in a hands on approach about the advantages and disadvantages of a number of these techniques. You will learn how to deal with explanatory variables that are highly correlated, and how to assess the accuracy of created models. Also, you will learn how you can, apart from making predictive maps, gain insights from the fitted relations themselves. Lastly, when you model these relations for a certain situation, but expect that this situation might change (for example as a result of climate change), then how can you use these models to make projections for the new situation. This module will teach you how to do so. Also you will get plenty of experience to practice all this on a dataset of yourself (or provided by us). You will be working with both ArcGIS (or any other GIS program at your liking) and the R-software. This module mainly aims at applications in the domain of natural resources, but in case your interest is in other domains where this can be applied (mapping land use change, mapping rare events of which limited observations exist etc..) this course can also be very useful for you. Besides, you will get hands on experience in using the open source R-graphical user interface, which is a skill that also outside the field of SDM can come in very usefull.

LEARNING OUTCOMES Upon completion of this module the student should be able to:  Select appropriate models for estimating species distribution and biodiversity, its relation to environmental parameters;  Know where to find relevant data sources online for modelling;  Assess the accuracy of models, and how to compare them;  Understand basic climate model output;  Apply these to real and future world situations. 59

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CONTENT 1. The module starts by introducing the R-package as a modelling environment and a number of advanced modelling techniques, such as logistic regression models, boosted regression trees, maximum entropy and expert system models; 2. Available environmental predictor variables are described; 3. Multi-collinearity diagnostics and spatial auto-correlation; 4. The techniques are applied to specific thematic application areas such as biodiversity modelling, species distribution probabilities and habitat requirements; 5. Trends and multi- and hyper temporal analysis; 6. The impact of Climate Change on the distribution of species; 7. Model calibration, validation, data quality and model comparison.

PREREQUISITES   

Basic knowledge of ecology and statistics (simple regression and inferential statistics); Basic GIS skills to work with raster data; Experience with R will be helpfull, but is not required.

RECOMMENDED KNOWLEDGE Excel, ArcGIS, R, basic statistics.

COMPULSORY TEXTBOOK(S) Lectures and the exercises during the module provide all the information needed to make the exam.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

20

Supervised practicals

30

Unsupervised practicals

30

Individual assignment

40

Group assignment

0

Self study

16

Examination

8

Excursion

0

Fieldwork

0

ASSESSMENT The module will be assessed in two steps, firstly there will be a presentation by each student on an individual assignment where the student demonstrates his ability to apply a suitable model to an example case study (will be provided) with all of its associated analysis and evaluation. The presentation will make up 50% of the final mark and should contain the following:     

Description of the species considered; A reflection on which environmental variables are likely to be important in determining the species' distribution given; A clear description of the data exploration process and the final selection of records and variables; At least 2 different models that are trained/calibrated, and subsequently validated using the right data subsetting technique; A reflection on the quality of both models, and the effect of prevalence on the end results;

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

A projection of the distribution of the species as the result of climatic change, and an analysis how much change is expected for the species.

The written test will account also for 50% and will assess the understanding of the student of the presented theory on:    

The different modelling techniques; The validation methods; Application of the entire approach to predict species distrubutions; The role of climatic data in the modelling.

Type of marking: 1-10

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REMOTE SENSING/GEOGRAPHIC INFORMATION SYSTEMS ANALYSIS METHODS TO SUPPORT FOOD AND WATER SECURITY STUDIES Module

12

Module code

M18-NRS-102

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Bie, C.A.J.M. de (Cees, dr.ir.)

INTRODUCTION Remote sensing and GIS are important tools to provide input to the spatial assessment of Food and Water Security. Such an assessment is important to both rainfed as irrigated agricultural systems and can be done at regional, national and continental scales. While a holistic study of food and water security requires many disciplinary inputs, at ITC's research and education three main fields are covered:   

Mapping of agro-ecosystems: mapping and characterization of crop production systems and area estimation (inputs for monitoring, modelling and planning); Monitoring agro-ecosystems: detecting past land cover and use changes, and assessing present land cover and crop conditions as for example affected by drought (early warning); Modelling agro-ecosystems: early prediction, quantified estimation of moisture conditions, canopy cover, biomass and yield, plus estimation of future impacts by anticipated climate change.

This module will cover the first two bullet points through use of satellite imagery, analytical tools, with emphasis on the space-time dimensions to map, monitor and estimate the systems conditions, behaviour and performance. A subsequent module titled "Spatial-temporal models for food and water security studies" will focus on the remaining bullet point. The two modules gradually change focus from inventories (mapping) and capturing changes and qualitative performance, to the use of the prepared maps and monitoring products (indices) to quantify performance using agro-ecological models. Research aspects that will be supported through this module concern (amongst others):  Use of hyper-temporal RS-imagery (SPOT-Vegetation, MODIS, PROBA-V, etc.) to stratify (map) and characterize cropping systems territories at good accuracies and with essential legend details on agroecosystems present, plus the use of data mining methods, that rely on secondary field data and/or existing tabular statistics;  Use of indices (NDVI, LAI, NDWI, etc.);  Mapping and monitoring gradual and abrupt land cover/use changes (probability algorithms);  Assessment of season specific performance variability (intensities, timing of planting-harvesting, droughts and other perils), as e.g. required to support index-based micro-insurance programs. In practice, gained knowledge serves (amongst others) a wide range of specialized advisory work:  Preparation of actual inventories and land cover/use maps;  Generation of spatial details of crop calendars and crop management, including production constraints and perils (yield gaps);  Quantified yield gap assessments for land use planning, specifications of advice for extension services, work agenda specifications by research stations, and policy-making considerations.

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LEARNING OUTCOMES After completing this module, students should be able:  To generate and explain the relation between agro-ecosystem components and RS-based indices like LAI, fAPAR, NDVI, NDWI, RFE's, ETa, SWI, SoS, FVC, BM, CC, etc., (explain their use, obtain them, and assess their value);  To access (pre-process) and present required imagery and indices through the GeonetCast toolbox and/or Spirits (optionally: Timesat, etc.);  To utilize hyper-temporal data for (agro-environmental stratification using ISODATA clustering;  To describe the strata through data-mining techniques of (i) high-resolution field maps, (ii) primary survey data, (iii) agricultural statistics, (iv) literature on followed crop-calendars, and (v) data on socioeconomic conditions by livelihood zones, etc.;  To generate agro-ecosystem maps, crop masks, cropping intensity maps, land use system characterizations;  To link prepared maps to information on farming systems, livelihood situations (vulnerability and coping conditions), and impact-response facts of past disasters, in order to support spatial Food Security issues and to support early response activities etc;  To use the time-series of imagery for the detection of anomalies and land cover/use changes.

CONTENT Week 1: day 1: Intro to (i) Food and Water Security and to (ii) Early Warning: Present day issues, state-ofthe-art, knowledge/application gaps, etc.; "four visions". Day 2-3: Primers on:  Contemporary indices in use to monitor agro-ecosystems: their purpose, basics and value;  Timescales of indices versus the availability of (hyper-temporal) imagery (SPOT-VGT, MODIS, Meris, MeteoSat (MSG), Proba-V, Sentinel, etc.);  Monitoring Vegetation from Space (eLearning: http://www.eumetrain.org/data/3/36/index.htm);  Discussion: value of RS-based measurements versus agro-ecological realities;  Practical: Tools to display (also in 3D) time-series data using Ilwis and nVis;  Geonetcast 'primer', with references to 52North manuals and reference materials. Day 4-5: Use of GeonetCast to obtain, (pre-)process, and display required time-series of imagery and indices (tool-skills), with advanced individual tasks for experienced users. Week 2: day 6-7: Skills and critical expert decisions needed for optimal spatial-temporal clustering of hyper-temporal data (ISODATA algorithm of Erdas). Advise/discussion on the small individual assignment (ref.day-14,15). Day 8: Key web-based imagery sources and tools and tricks to download, (pre-) process and import required timeseries of imagery, indices, and additional basic GIS-data. Day 9-10: Making agro-ecological sense of prepared stratifications: map-comparisons, data tabulations, surveying guide, data-mining, and statistical tricks; guided exercises of selected approaches to prepare crop masks, crop intensity maps, land use characterization, etc. Week 3: day 11: anomaly detection methods (services) and interpretation issues with discussions on new developments (partly eLearning). Day 12-15: Small individual assignment: implement, using required tools, a processing chain of selected spatial-temporal data to generate relevant Food and Water security information (to be submitted; graded exercise). Advice: initiate your project well in time. 63

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PREREQUISITES Skills in remote sensing and GIS (e.g. core modules of ITC Master's programme).

RECOMMENDED KNOWLEDGE Skills in remote sensing and GIS (e.g. core modules of ITC Master's programme).

COMPULSORY TEXTBOOK(S) None

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

29

Supervised practicals

40

Unsupervised practicals

25

Individual assignment

25

Group assignment

0

Self study

25

Examination

0

Excursion

0

Fieldwork

0

ASSESSMENT Assessment will be based on a submitted written paper (DOCX) describing the approved personal task plus the achieved results.This task will be specified on the basis of ones individual MSc interests [100% student centered]. The content and result of this individual task must be derived from guidance as provided by lectures, practicals, and individual discussions with staff. Elements of this DOCX that will be critically assessed are:  Test 1[2,5points]. Description of the Food/Water security issue/case selected versus the selection of the required RS-based indicator (plus why?). The issue/case must prefereably relate to the MSc topic selected or under consideration by the participant.  Test 2 [2,5points]. Select the source to collect the needed RS-based indicator data from; describe/capture/discuss the metadata; download data of the relevant period/area-window, import the data, carry out / describe / discuss required preprocessing steps.  Test 3 [2,5points]. Classify/cluster/stratify the data as required. Add the implemented annotated flowchart and discuss decision moments encounterred (options choosen). Provide the intermediate results.  Test 4 [2,5points]. Describe how the results link to follow-up work / studies, like e.g. sample scheme, data integration plan, hypothesis test, etc. as included/mentioned/suggested in the approved personal task. Include all assumptions made for test 1-4. Type of marking: 1-10

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ANALYSIS OF INTRA-URBAN, SOCIO-SPATIAL PATTERNS Module

12

Module code

M18-PGM-100

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Martinez Martin, J.A. (Javier, dr.)

INTRODUCTION This module explores on issues of socio-spatial diversity, differentiation and fragmentation that impact on the urban form and on the quality-of-life of urban dwellers. We concentrate on capturing and understanding intra-urban variations and differentials in quality-of-life conditions and access to social infrastructure. A better understanding of the resulting socio-spatial patterns is essential for targeting deprived areas and implementing area-based and regeneration policies. This module presents several methods under a mixed methods approach. Through a combination of lectures, reading assignments, exercises, and a final group work students learn to combine quantitatively derived patterns and measures with user generated data and perceptions.

LEARNING OUTCOMES Upon completion of this module the student should be able to:  Have an understanding of intra-urban socio-spatial patterns and the relation with current theoretical and empirical debates in urban studies;  Have knowledge and understanding of the importance of intra-urban patterns and inequality analysis in planning;  Have the ability to apply a combination of statistical and GIS-based spatial analytical methods to detect and analyse intra-urban variation patterns;  Understand the relevance of each method in the context of urban studies;  Have the capacity to reflect on the methodological choice and in the incorporation of both quantitative and qualitative data analysis;  Have ability to interpret results and relate these both to theoretical debates as well as policy implications.

CONTENT Context and application  Intra-Urban Socio-Spatial Patterns in Urban Studies;  Spatial Justice; Spatial Inequality;  Environmental Justice;  Quality of Life / Community Well-Being and Deprivation;  Targeting and Regeneration. Area-Based Policies. Methods  Data reduction, Factor Analysis;  Geodemographics ["analysis of people by where they live"], neighbourhood analysis and targeting. Cluster analysis. K-means;  Patterns and scale issues (MAUP);  Spatial autocorrelation; 65

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  

Intra-urban patterns and change; Patterns of user-generated data and qualitative data. Qualitative GIS. Mixed methods approach. "Objective" and "Subjective" measures; Spatial analysis of qualitative data. Geo / place quotation. ATLAS-ti software geocoding.

PREREQUISITES    

Modules 1-11 ITC Master's programme; Knowledge of GIS at level of Core Modules or higher; Ability to independently apply GIS software; Knowledge of basic statistics.

RECOMMENDED KNOWLEDGE ArcGIS or QGIS, SPSS software.

COMPULSORY TEXTBOOK(S) Lecture notes and provided articles.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

26

Supervised practicals

30

Unsupervised practicals

28

Individual assignment

18

Group assignment

20

Self study

18

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT Two tests submitted via BB by the end of the module:  40% Test 1: Group portfolio of completed exercises;  60% Test 2: individual reflection paper. Type of marking: 1-10 

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URBAN LAND USE CHANGE AND MODELLING Module

12

Module code

M18-PGM-101

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Sliuzas, R.V. (Richard, dr.)

INTRODUCTION Urban growth and land use change processes in less developed countries are typically rapid and extensive, and they often include a considerable proportion of unplanned or informal development. Urban land use change models can help to understand, analyse and simulate the outcomes of such processes, providing information that can inform policy development and planned interventions and action. This module develops the student's conceptual understanding of three methods for modelling urban land use change and their ability to select, develop and apply these methods in an appropriate manner. The module commences with introductory lectures, readings and discussions on the field of urban modelling, setting the stage for several workshops in which three types of models are studied more in depth and applied to case studies. The methods to be examined are: spatial logistic regression for identifying drivers of urban land use change, Cellular Automata models and Agent Based Models (ABMs).

LEARNING OUTCOMES Upon completion of the module students should be able to:  Explain the theoretic and modelling foundations of urban land use change and modelling;  Describe the functional requirements for three modelling methods for urban land use change and analysis;  Apply three methods for modelling urban growth and land use change;  Compare the three modelling methods with a SWOT analysis;  Propose, with justification, a suitable modelling method for a given problem situation.

CONTENT  

 

Urban land use modelling foundations - stories, models and plans; Urban land use change modelling:  Key parameters for developing land use models and scenarios  Workshop 1: Spatial Logistic Regression  Workshop 2: CA model  Workshop 3: ABM Positioning land use modelling in spatial planning practice. The module includes participation in a 2 day workshop (13-14 June 2018) to celebrate Fifty Years of Urban Studies at ITC, including the valedictory address of Prof Van Maarseveen and the inaugural address of Prof Pfeffer.

PREREQUISITES   

Knowledge of GIS and remote sensing at level of Core Modules or higher; Ability to independently apply GIS and remote sensing software; Knowledge of basic statistical methods and tests (e.g. regression analysis, etc).

RECOMMENDED KNOWLEDGE Familiarity with spatial planning and land use analysis in an urban/regional context. 67

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COMPULSORY TEXTBOOK(S) The papers below are used as an introduction to some key contextual issues in urban land use modelling. Students will be provided with an additional reading list consisting of key papers for each of the model types examined in the course.  Guhathakurta, S. (2002). Urban modeling as storytelling: using simulation models as a narrative. Environment and Planning B: Planning and Design, 29, 895 - 911. [17 pages];  Couclelis, H. (2005) Where has the future gone? Rethinking the role of integrated land-use models in spatial planning. Environment and Planning A, 37(8), 1353 - 1371. [18 pages];

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

30

Supervised practicals

50

Unsupervised practicals

20

Individual assignment

20

Group assignment

0

Self study

24

Examination

0

Excursion

0

Fieldwork

0

ASSESSMENT  

60% portfolio of completed practical assignments with SWOT analysis; 40% individual paper - selection and justification of models for given problem situations

The individual paper (max 1500 words) is a well-structured, clear and concise justification for selecting appropriate modelling methods to tackle two given problem situations. The paper should include a short introduction, a brief analysis of the problem, a justification for the most appropriate methods and a description of desired data requirements. Appropriate use of relevant literature is required to support the selection. Your paper shows how you have been able to link the literature, context and practice. Type of marking: 1-10

COMMENTS It is possible that the mode of assessment may change. In this case students will be informed of the assessment procedure at the start of the module.

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ADVANCED METHODS IN TRANSPORT PLANNING Module

12

Module code

M18-PGM-102

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Grigolon, A.B. (Anna, dr.)

INTRODUCTION Urban planners are faced with challenges in creating well-functioning fast growing urban regions. These challenges deal with decisions on where to develop urban functions and how to provide the infrastructure that is needed to enable people to access jobs and service locations. Increasingly, the concept of accessibility is used to analyse the land use and transport system in an integrated manner in order to make decisions on infrastructure provision on the one hand and the location of activities of different kind on the other. Key theories that underlie transport planning are spatial interaction theory and discrete choice theory. Spatial interaction models predict flows of people and goods between locations based on the degree of spatial separation and the attractivity of the (potential) activity/opportunity, assuming a decrease of flows with increasing distance or travel time. They are of relevance to the study of optimal service locations, accessibility analysis at various levels of detail, simulation and forecasting, and can also be used to optimize and manage network throughput. Discrete choice theory is used to describe, explain, and predict choices of individuals between two or more discrete alternatives. Discrete choice models have become essential in modelling individual behaviour. They are used in social sciences, transportation, health economics, medical research, marketing research, environmental and energy economics, and in many other disciplines. This advanced module covers important modelling foundations of networks and spatial interaction as a basis for accessibility analysis in GIS. In addition, discrete choice modelling (DCM) will be used as an approach to explain the various dimensions of choice behaviour and to predict market shares, for example, of transport-related services. Focus is on applications in the infrastructure, transportation, and services domain.

LEARNING OUTCOMES Upon completion of the students should be able to:  Explain the theoretical and modelling foundations of models in urban and regional planning and the role therein of networks and spatial interaction;  Describe the strengths and limitations of GIS in modelling networks and spatial interaction;  Apply models for network analysis, accessibility analysis and discrete choice analysis;  Analyse and reflect on model outcomes using a real world case study.

CONTENT 1. Introduction  Urban and regional modelling foundations 2. Spatial interaction theory  Challenges of modelling interaction in urban and regional planning

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

Gravity modelling

3. Network geography  Network geography and indicator development  Multimodal network modeling 4. Accessibility modeling  Various methods and approaches to accessibility modeling  GIS application in accessibility modeling  Case study on multimodal accessibility modeling 5. Discrete choice modeling  Principles of DCM  Theoretical framework of a Multinomial Logit model (MNL)  Estimate a MNL model (software Biogeme) 2. Case Study (Studio Project)

PREREQUISITES  

Knowledge of GIS at level of Core Modules or higher is preferred; Ability to independently apply GIS software.

COMPULSORY TEXTBOOK(S) Five to six scientific papers, approximately 75 pages.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

20

Supervised practicals

28

Unsupervised practicals

0

Individual assignment

16

Group assignment

60

Self study

16

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT   

Studio project written report (group mark): 40% Studio project presentation (individual mark): 30% Group discussion 6 articles (5 points each, individual mark) 30%

Type of marking: 1-10

COMMENTS Lecturers involved: Sherif Amer, Frans van den Bosch, Mark Brussel, Anna Grigolon

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WATER AND HEALTH Module

12

Module code

M18-WRS-100

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Salama, S. (Suhyb, dr.ir.)

INTRODUCTION Many regions in the world face increasing risks of new or re-emerging infectious diseases. Subsequently, there is a strong need in addressing increasing challenges for human and public health across the globe. Common to all water and vector-borne diseases is an urgent need to gain better understanding of spatiotemporal patterns in disease transmission and diffusion. The risk for outbreak of water or vectorborne diseases depends in most cases on climatic and environmental conditions which in turn can be best obtained from Earth Observation (EO) data. The provision of spatio-temporal information from current and future EO data, provide a unique opportunity for surveillance and risk mapping of water and vector-borne diseases. EO data can be analyzed to assess the spread of diseases in order to assist decision-makers and public health authorities to develop surveillance plans and control measures. Teaching method For this module we employ the project-based education method. Students work together on water & health related problems, from which they derive questions to guide their learning process. Integrating different perspectives is the default activity in project-groups. A project group consists of 3-4 persons working together on finalizing the project. The teaching staff acts as a scientific consultant ensuring a good introduction and create a more common knowledge base in the fields where this is relevant. In the course of the project, the students will increasingly be more autonomous, with teaching staff taking on a coaching role.

LEARNING OUTCOMES The objective of the module is teaching the students how to use suites of EO data for studying the spatial and temporal patterns of water and vector-borne disease and producing risk maps for mitigation measures. The following learning outcomes are expected from this module:  Identify water & health related problems in a specific region;  Retrieve and integrate different sources of EO data;  Link biotic and/or abiotic components of the earth's surface, retrieved from EO data, to the spreadingrisk of infectious diseases;  Produce risk indicator of infectious diseases;  Analyze infectious diseases' risk in terms of biotic and/or abiotic forcings;  Present the findings of the project.

CONTENT The module is structured as follows: (a) four frontal teaching lectures; (b) two progress meetings and (c) poster presentation of the final project. A. Lectures The module starts with an introductory lecture whereby the learning objectives and assessment method are highlighted to the students. In the second "theoretical" lecture, nr.2, we will explain water and vectorborne diseases and how EO data can be used to map their potential risks. Subsequently, in lecture nr.3 71

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different study cases will be presented that facilitate the definition of the project. In lecture nr.4 the requirements and expected outputs of the project will be presented and student groups will be identified with their study cases. From then on, it is the responsibility of the student groups to derive questions guiding their learning process. Each group will autonomously be working on: (i) problem definition; (ii) data retrieval and method selection; (iii) designing integrated solutions; (iv) analyzing and discussing the results; and (v) communicating the findings to the broader audience. B. Progress meetings Two progress meetings are foreseen to monitor and stimulate the progress of the students. The student groups will be presenting their progress, identifying the challenges and possible solutions. The students will have the opportunity to comments on each other's works and ask questions to the scientific consultants. C. The Project The project should covers at least one type of remote sensing observations (optical, thermal, microwave and altimetry) as applied for risk mapping of water and vector-borne diseases. One (or more) of the following subjects should be covered during the project:    

Optical remote sensing: human settlement, water bodies, wetlands, water quality, forest and vegetation products of MODIS;Thermal remote sensing: Human settlement, Land & water surface temperature products of MODIS; Passive microwave remote sensing: Soil moisture, flooding products of SMAP data; Active microwave remote sensing: Permanente water bodies mapping from Sentinel -1 , altimeter for water level height from TOPEX/Poseidon.

D. Poster presentation A poster will be designed whereby the project's objective, data, method, results and discussion are presented in a scientific and clear manner.

PREREQUISITES  

Good knowledge of EO data and processing models; Good skills in EO data retrieval and integrating different data sources;

RECOMMENDED KNOWLEDGE 

A common knowledge in EO data processing and knowledge in one of the following topics:

1. 2. 3. 4.

Water resources, hydrology; Vegetation, agriculture; Natural resources, environmental monitoring; Geospatial information processing, data mining.



Team player.

COMPULSORY TEXTBOOK(S) Some of these articles are found in the Reference folder of the Blackboard and serve as examples for relevant literature. Realizing the project may require other articles, which should be obtained by the students.

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

8

Supervised practicals

8

Unsupervised practicals

16

Individual assignment

16

Group assignment

40

Self study

40

Examination

8

Excursion

8

Fieldwork

0

ASSESSMENT 1. Method For this module we employ the project-based education method. Students work together in project-groups consisting of 3-4 persons with the objective of mapping water and/or vector- borne diseases using Earth Observation (EO) data and producing risk maps for mitigation measures. Each project should be presented in a poster form and defended. The assessment will be based on (table 1): (i) attendance of the progress meetings; (ii) evaluating the scientific quality of the project; (iii) answering the questions during the presentation. Table 1 shows the assessment matrix load that will be used in this module. Table 1: Assessment matrix load. Test Progress meeting Project Project Presentation

Assessment method Attendance Written group poster Presentation, question and answers

Percentage of the total 20% 40% 40%

2. Learning outcomes The objective of the module is teaching the students how to use suites of EO data for studying the spatial and temporal patterns of water and vector-borne disease and producing risk maps for mitigation measures. The following learning outcomes are expected from this module:      

Identify water & health related problems in a specific region; Retrieve and integrate different sources of EO data; Link biotic and/or abiotic components of the earth's surface, retrieved from EO data, to the spreadingrisk of infectious diseases; Produce risk indicator of infectious diseases; Analyze infectious diseases' risk in terms of biotic and/or abiotic forcings; Present the findings of the project.

The following criteria will be used in the assessment of the project: (i) scientific quality, (ii) creativity and utility of the proposed solutions (iii) poster presentation and (iv) answering the questions. Table 2 shows the load for these assessment criteria. Table 2: Loads of the assessment criteria.

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Scientific quality 40%

Creativity and utility 30%

Presentation 10%

Answering 20%

3. Assessment matrix The following assessment matrix will be used to evaluate the quality of the projects and presentations (table 3). Table 3 Assessment matrix for the project. Learning outcome Identify water & health related problems in a specific region.

Assessment Scientific quality

Level Comprehension

Retrieve and integrate different sources of EO data.

Creativity and utility

Application

10%

Application and

25%

Link biotic and/or abiotic Scientific quality, components of the Earth's surface to the spreading-risk of infectious creativity and utility diseases. Produce risk indicator of infectious Creativity and utility diseases

Perc of the total 10%

synthesis Application

Analyze infectious diseases' risk in Scientific quality, creativity Synthesis terms of biotic and abiotic forcings. and utility Present the findings of the project. Presentation and Presentation skills answering Type of marking: 1-10

20%

25% 10%

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WATER AVAILABILITY FROM SPACE Module

12

Module code

M18-WRS-101

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Velde, R. van der (Rogier, dr.ir.)

INTRODUCTION Deltas across the world experience increasing pressure on their water systems as a result of changes in supply and demand caused by socio-economic developments as well as climate variability. The grand challenge for regional water managers worldwide is to optimize the availability of fresh water of appropriate quality for all functions according to their respective needs. Indispensable for skilful management of these water systems is reliable up-to-date information on the current situation to evaluate the impact of control measures. In-situ measurements in combination with satellite observations are increasingly being studied for providing a spatially distributed quantification of water systems. In this module the student will exploit various types of data sources for estimating surface water budget components (e.g. water storage, rainfall, evapotranspiration) and water quality.

LEARNING OUTCOMES Upon completion of this module the student should be able to:  Explain the major physical processes governing the hydrological cycle of watersheds located in Deltas;  Explain the role of water management practices in optimizing water availability;  Apply EO data processing techniques in deriving individual components of the surface water budget;  Integrate various types of EO data sources for spatially distributed hydrological assessment;  Evaluate the water availability of a catchment-area based upon spatially distributed quantification of the system;  Participate effectively and share knowledge within a project team.

CONTENT A project-driven learning approach is selected for this module, whereby the catchment-area of the river 'Dinkel' will be adopted as study area. Three case studies are defined for the Dinkel watershed: two aiming at the quantification of a water balance component and one focusing on water quality. After the first introductory week, students will work, in groups, on a specific case study on a project basis with a focus on delivering the final product at the end of the second week. Each group will propose a framework in which the results from week 2 could be utilized for either operational or strategic water management. Throughout the course of the module the role of the staff evolves towards an advisory one. The three case studies are: 1. High resolution soil moisture mapping through combination of active (Sentinel-1, SAR) and passive microwave (NASA's SMAP, radiometer) satellite data; 2. Mapping floods from Sentinel-1 SAR data and determination of the flood depth using a high resolution digital elevation model; 3. Assessment of water quality in the Dinkel Catchment using earth observation techniques; Provisional schedule:

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Week 1: Introductions & project formulation Lectures on:  Introduction to case studies;  Water availability (Quality & Quantity);  Water management in the Dinkel catchment. An excursion to the Dinkel Management Area of the Regional Water Authority 'Vechtstromen'. Week 2: Case study implementation  Development of project plan;  Implementation;  Delivery of output. Week 3: Integration case study results  Exchange of data products;  Assessment of water availability;  Preparation report and reporting.

PREREQUISITES Modules 1 - 10 of a relevant specialisation in the ITC Master's programme or equivalent.

RECOMMENDED KNOWLEDGE Understanding of the hydrological cycle, a general overview of the available data products relevant for hydrology and strong skills in EO data retrieval, processing and integrating different data sources.

COMPULSORY TEXTBOOK(S) Reading materials: Arnold, G., Bos, H., Doef, R., Goud, R., Kielen, N., van Luijen, Water Management in the Netherlands, Rijkswaterstaat, Centre for Water Management, February 2011, pp 80. Available at https://staticresources.rijkswaterstaat.nl/binaries/Water%20Management%20in%20the%20Netherlands_tc m21-37646.pdf (last verified 20 december 2016). van der Velde, R., Salama, M.S., Eweys, O.A., Wen, J. and Wang, Q. (2015) Soil moisture mapping using combined active or passive microwave observations over the east of the Netherlands. In: IEEE Journal of selected topics in applied earth observations and remote sensing, 8 (2015), 9 pp. 4355-4372. Hostache, R., Matgen, P., Schumann, G., Puech, C., Hoffmann, L. and Pfister, L. (2009) Water Level Estimation and Reduction of Hydraulic Model Calibration Uncertainties Using Satellite SAR Images of Floods. In: IEEE Transactions on Geoscience and Remote Sensing, 47 (2009), 2, pp. 431-441. Salama, M.S., Radwan, M. and van der Velde, R.(2012) A hydro - optical model for deriving water quality variables from satellite images HydroSat : a case study of the Nile River demonstrating the future Sentinel2 capabilities. In: Physics and Chemistry of the Earth, Parts A/B/C, 50-52 (2012), pp. 224-232.

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

20

Supervised practicals

20

Unsupervised practicals

0

Individual assignment

5

Group assignment

43

Self study

32

Examination

4

Excursion

20

Fieldwork

0

ASSESSMENT   

10% weight: Performance within the group assessed via peer review using predefined rubrics supported with evidence (assessment type: individual & mark) 45% weight: Assessment of group work based on the scientific quality and oral defense of a poster (assessment type: group & mark); 45% weight: Oral exam (30 minutes) on the learning process with respect to learning outcomes 1) and 2) of the course for which the poster, lectures and excursion will be used as evidence for the discussion (assessment type: individual & mark);

Type of marking: 1-10

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WATER AND ECOSYSTEMS Module

12

Module code

M18-WRS-102

Period

11 June 2018 - 29 June 2018

EC

5

Module coordinator

Tol, C. van der (Christiaan, dr.ir.)

INTRODUCTION In this module you will get the bigger picture of ecosystem functioning and the cycles of water, carbon, nitrogen and phosphates, and get introduced to tools to analyse them with remote sensing. After a general introduction on biochemical cycles, you will focus on either a natural (forests, wetlands or savannah) or man-managed vegetation (agriculture) depending on your preference, and acquire satellite data for analysing the biological activity, vegetation characteristics and water and carbon fluxes from publicly available data sources. A tuturial will be provided to retrieve vegetation properties by inversion of the model ' Soil-CanopyObservation of Photosynthesis and Energy fluxes (SCOPE) from the Multi-Spectral Instrument (MSI) on Sentinel-2 or the Ocean and Land Color Instrument (OLCI) on Sentinel-3.

LEARNING OUTCOMES Upon successful completion of the module, you are be able to:  Describe feedbacks among vegetation, water and biochemical cycles;  Access various satellite data products via Geonetcast and perform basic operations with these data;  Apply basic retrieval techniques to obtain vegetation properties that are relevant for ecosystem functioning from visible and near-infrared reflectance;  Apply models to simulate evaporation and photosynthesis of vegetation using satellite derived inputs;  Analyze retrieved vegetation products and compare them to other satellite products;  Evaluate the applicability of different satellite products for water, vegetation and biochemical cycles.

CONTENT This module consists of the introduction to a study case, a limited number of lectures, complemented by background study material and excercises. Finally, the study case is worked out in small groups, each handling a specific part of an integrated ecosystem analysis. The following topics will be handled: Week 1:  Introduction lecture on the interactions between water quantity, quality and ecosystem functioning, including agricultural land and other human managed rural systems;  Paper reading exercise and presentation;  Instruction on modelling tools to retrieve vegetation properties from remote sensing;  Instruction on modelling tools for biochemical cycles in the ecosystem. Week 2 :  Tututorial on retrieval of vegetation properties from Sentinel-2 and Sentinel-3 data;  Tuturorial on data acces of time series data and satellite data repositories.

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Week 2-3:  A group assignment with individual component on the interactions between water quality and ecosystems. A selection of study areas and ancillary data will be supplied to choose from.

PREREQUISITES Basic knowledge of remote sensing and satellite data, image processing (ILWIS, ENVI, ARC-GIS, ERDAS or comparable), presentation skills. Students who did not follow the Master's programme or PGD course at ITC are recommended to contact the module coordinator for an individual study advice.

RECOMMENDED KNOWLEDGE Basic knowledge of hydrology, simulation models, and remote sensing data processing.

COMPULSORY TEXTBOOK(S) A syllabus will be made available through Blackboard on the first day of the module, which includes a summary of the material per learning objective, a selection of relevant research papers, instructions for the use of a vegetation remote sensing and a water quality model.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

14

Supervised practicals

32

Unsupervised practicals

0

Individual assignment

0

Group assignment

48

Self study

46

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT The learning objectives will be assessed as:  40% assessment of group work based on the quality and oral defense of a poster (group mark);  60% oral exam on the learning process and the contribution to the case study supported with evidence (individual mark). Type of marking: 1-10

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ADVANCED TOPIC(S) Module

13

Module code

P18-EDU-103

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Dopheide, E.J.M. (Emile, drs.)

INTRODUCTION After completing module 11 on research skills, the students follow two advanced topics. These topics are offered by the scientific departments in modules 12 and 13, and are designed to equip the students with specific tools, methods and applications that are important for their intended MSc Research. In selecting these two advanced topics, the students therefore have to make a logical choice that fits to their MSc Research that will be carried out during the MSc Research phase (modules 16-23). The choice of advanced topics has to be submitted before the start of module 11.

LEARNING OUTCOMES Specified per advanced topic.

CONTENT These are the advanced topics in module 13 that were offered in 2017: Module 13 M17-EOS-102 M17-EOS-104 M17-EOS-105 M17-ESA-101 M17-ESA-104 M17-GIP-102 M17-GIP-103 M17-PGM-103 M17-PGM-104 M17-PGM-105 M17-NRS-103 M17-WRS-102 M17-WRS-103

Title Advanced image analysis Unmanned aerial vehicles for earth observation Unmanned aerial vehicles for scene understanding Thermal infrared remote sensing: from theory to applications Spatial modelling for integrated watershed management Building infrastructures for geo-information sharing Spatio-temporal analytics and modelling Analysis of intra-urban, socio-spatial patterns Land governance Collaborative planning and decision support systems applied in decision rooms Strategic Environmental Assessment (SEA) and Environmental Impact Assessment (EIA) applying spatial decision support tools Satellite data for integrated water resource assessments and modelling Water, climate and cities

The final list of advanced topics that will be offered in 2018 will be made available no later than January 2018.

PREREQUISITES Master's programme modules 1-11. Note that, for some topics, specific knowledge and skills may be required. Please consult the module description of the advanced topic. 80

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RECOMMENDED KNOWLEDGE Specified per advanced topic.

COMPULSORY TEXTBOOK(S) Specified per advanced topic.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures Supervised practicals Unsupervised practicals Individual assignment Group assignment Self study Examination Excursion Fieldwork

ASSESSMENT Specified per advanced topic.

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ADVANCED IMAGE ANALYSIS Module

13

Module code

M18-EOS-102

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Tolpekin, V.A. (Valentyn, dr.)

INTRODUCTION Standard image analysis methods such as pixel-based crisp maximum likelihood classification do not take into account spatial correlations in images and therefore do not exploit information contained in images to full extent. In addition, such methods cannot correctly treat mixed pixels, uncertain class definitions and data from various sources. In this module we aim to treat more specialized image analysis methods, focusing on Markov random fields and kernel-based methods. These methods will be applied to analysis of optical and radar images on pixel as well as sub-pixel levels. The methods introduced in this module will be applied on real case studies.

LEARNING OUTCOMES Upon completion of this module students should be able to:  Summarize advanced image analysis methods;  Apply these methods to case studies using available software and data;  Be able to draw relevant conclusions from an image analysis.

CONTENT    

Analysis of Radar images, including polarimetry and interferometry; Kernel-based methods, semi-supervised learning, support vector machines; Markov Random Fields for classification on pixel and sub-pixel levels; Convolutional Neural Networks.

PREREQUISITES    

External students: background in Remote Sensing or image analysis; Master's programme students: modules 1-11; Basic programming skills (scripting level); Basic math skills.

RECOMMENDED KNOWLEDGE Proficiency in at least one of the fields:  Math  Image analysis  Programming

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

22

Supervised practicals

16

Unsupervised practicals

10

Individual assignment

30

Group assignment

0

Self study

62

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT Based on the presentation of a project and a following discussion. Depending on the number of participants, the project is either individual or group project. In case of a group project, marks are awarded individually, based on individual contribution to the project and to the discussion. Deliverables: oral presentation; presentation slides; program codes; A single mark is awarded (1-10) for the presentation.

COMMENTS Due to the teaching and assessment mode the number of participants is limited: max 25 internal participants and max 5 external participants.

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UNMANNED AERIAL VEHICLES FOR EARTH OBSERVATION Module

13

Module code

M18-EOS-103

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Nex, F.C. (Francesco, dr.)

INTRODUCTION Image-based modelling (IBM) refers to the techniques of acquiring 3D object information from two or more images. This includes three traditional photogrammetric algorithms (feature extraction and matching, Bundle Block Adjustment and orthophoto generation) and new techniques from the Computer Vision community (such as structure from Motion and Semi-Global Matching) to derive 3D point information from an image sequence. These techniques can be used to process both terrestrial and airborne images. Among the innovative platforms for data capture, Unmanned Aerial Vehicles (UAVs, better known as drones) are becoming a valid alternative to traditional Geomatics acquisition systems, as they close the gap between higher resolution terrestrial images and the lower resolution airborne and satellite data. UAVs can be remotely controlled helicopters, fixed wind airplanes or kites. Different sensors can be installed onboard to acquire data. Many applications ranging from 3D building modelling to crop and forest monitoring can profit from these data acquisition platforms. In this module the advanced IBM techniques and, in general, the 3D geo-information processing will be explained, with focus on the use of data acquired by UAVs. The module is composed by two main parts. In the first part (first week), the four main steps of the modern IBM process (image orientation, point cloud generation, orthophoto generation and quality assessment) to retrieve 3D information from images will be defined while during the second part (second and third week) the students will gain hands-on experience on the use of UAVs. This second part of the module will be given together with the module on "Unmanned aerial vehicles for scene understanding". Specifically, students will learn the principle of IBM methods and they will design three simple solutions (feature extraction, feature matching and relative orientation) by adopting these methods in simple Matlab codes. Lectures will be always coupled with demonstrations and practical sessions on the theory delivered. The second part of the module will allow the students to experience the UAV data acquisition and processing workflow. They will understand how a UAV-related project is planned and executed with their involvement to a real UAV acquisition project. Then, they will apply the learned IBM techniques using a commercial software (Pix4D - www.pix4d.com) to process the acquired data and extract 3D information. They will finally analyse and compare the data using the available ground truth and dedicated tools and software (Matlab scripts and CloudCompare) to evaluate their results. Multi-spectral and thermal image acquisitions from UAVs and their processing will be also part of the presented topics. Additional presentations will be finally provided to describe the use of UAVs in five different domains: urban monitoring, disaster mapping, cultural heritage, land administration and crop monitoring.

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Students do not need prior knowledge on the topics of the module. The students who have already attended module 7 and module 8 in the GFM specialisation are suggested to attend module 13 on "Unmanned aerial vehicles for scene understanding".

LEARNING OUTCOMES By the end of the module, the students will be able to:  Understand and describe the four main steps of the modern IBM process for UAV imagery;  Design three state-of-the-art IBM algorithms (in Matlab) for processing the given terrestrial and/or UAV imagery;  Apply the learned IBM techniques using the proposed commercial software (pix4D) for UAV data;  Analyse and evaluate the geometric quality of the previously generated data using the two available tools (Matlab and CloudCompare);  Describe the typical UAV data acquisition procedure and data processing for geo-information purposes, understanding the technical decisions usually adopted in real practical cases;  Identify the major pros and cons of the use of UAVs upon the gained experience and relate them with the five different domains;  Design at least one different possible application for UAVs beyond the experienced/learned ones;  Report and critically "discuss" the scientific outcomes by providing relevant referencing.

CONTENT Topics of the module are:  The IBM algorithms: feature extraction and matching, Structure from Motion, bundle block adjustment, dense image matching and orthophoto generation;  Use of existing simple libraries as well as commercial software to manage IBM techniques;  Analysis and evaluation of results generated by IBM techniques using simple tools and the available ground truth;  UAV image acquisition and processing for different geo-information purposes, using different camera sensors;  The use of UAVs in different domains: urban monitoring, disaster mapping, land administration, urban monitoring, crop monitoring.

PREREQUISITES Modules 1-11 of the ITC Master's programme. GFM students are recommended to attend the module on "Unmanned aerial vehicles for scene understanding".

RECOMMENDED KNOWLEDGE The module is addressed to students without any prior knowledge of Photogrammetry or UAVs.

COMPULSORY TEXTBOOK(S)     

Hartley and Zisserman (2004). Multiple view geometry in Computer Vision. Cambridge University Press (Only chapters 6 - 7 - 9 - 10); Foley (1981). Random Sample Consensus: a paradigm for model fitting with applications to image analysis and automated cartography; Lowe (2004). Distinctive image features from Scale-Invariant Keypoints. IJCV 2004; Remondino et al. (2014). State of the art in high density image matching. In: the Photogrammetric Record, 29(146); Nex et al. (2014). UAV for 3D mapping applications: a review. In Applied Geomatics, 6(1), 1-15.

The lecture notes, the presentations and other complementary material will be delivered during the module.

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

20

Supervised practicals

18

Unsupervised practicals

44

Individual assignment

0

Group assignment

28

Self study

24

Examination

2

Excursion

0

Fieldwork

8

ASSESSMENT  

Written test 60% Group assignment 40%.

Type of marking: 1-10

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BLOCK 3: RESEARCH PROFILE

ADVANCED GEOSTATISTICS Module

13

Module code

M18-EOS-104

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Stein, A. (Alfred, prof.dr.ir.)

INTRODUCTION Spatial statistics is a field that has come of age recently, and studies the modelling of spatial relations. Environmental data are usually collected by field samples, whereas a full coverage is required. Existing prior knowledge (like water and food security related knowledge) is most likely to be used in this context. Socioeconomic data, on the other hand, like the prevalence of aids and HIV data are usually aggregated data available within administrative units at various resolutions. To model such variation, different techniques are available. Remote sensing data are available as a lattice, some with a positive support (e.g. equal to the resolution of an image) or basically as a point (like in lidar data). Drawing valid inference requires a skilful use of the best possible methods. Also, several types of data are available as irregularly occurring points, such as fires in forests, earthquakes and disease outbreaks. In the topic 'Advanced geostatistics' we will deal with a broad and generic approach towards modelling and using spatial variation from different perspectives. We will both consider the spatial and the spatio-temporal issue. Random sets will be studied to also be able to consider aggregated data. Modern aspects of spatial statistics include copulas and max stable processes. Time will be spent on these new aspects of spatial statistics.

LEARNING OUTCOMES The students knows Bayesian model-based geostatistics, space-time geostatistics, lattice data, point patterns and spatial extremes. S/he learns to program in R and open source software. At the end of the module the student has learned to deal with spatial data of various characteristics. S/he has learned to distinguish different data types, learned to draw the most out of the data in terms of spatial modelling and modelling of spatial dependence and to draw valid inferences. The student has obtained basic knowledge of two methods for dealing with spatial extremes. S/he is able to interpret the results, and to present outcomes of an analysis in a report and in a presentation.

CONTENT Bayesian statistics  What is different from ordinary statistics, how can we include prior knowledge? Geostatistics  Model-based geostatistics; spatial simulations. Space-time geostatistics  Proportional variogram modelling in time. Lattice data  Techniques for clustering, spatial regression, SAR and CAR modelling.

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Point patterns  Intensities; the F-, G-, J- and K-functions; point process modelling. Spatial extremes  Copulas;  Max-stable processes. Use will be made of public domain software packages such as R and GeoDA. Use will be made of several texts available from the internet. Data from a wide range of different studies will be applied throughout. Students are also encouraged to bring their own data.

PREREQUISITES Knowledge of geostatistics (module 12); knowledge of statistical hypothesis testing, ordinary statistics (regression and correlation).

RECOMMENDED KNOWLEDGE Introductory geostatistics, module 12.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

40

Supervised practicals

32

Unsupervised practicals

12

Individual assignment

40

Group assignment

0

Self study

20

Examination

0

Excursion

0

Fieldwork

0

ASSESSMENT Assessment is done by means of assignments during the module that are judged. No formal examination session is given. Type of marking: 1-10

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BLOCK 3: RESEARCH PROFILE

UNMANNED AERIAL VEHICLES FOR SCENE UNDERSTANDING Module

13

Module code

M18-EOS-105

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Yang, Y. (Michael, dr.)

INTRODUCTION The introduction of low-cost and light digital cameras in the market has been facilitating the acquisition of large sets of images. Such cameras are very frequently installed on terrestrial and aerial vehicles and represent a valuable source of information to analyze the scene under investigation. Among the innovative platforms for data capture, Unmanned Aerial Vehicles (UAVs, better known as drones) are becoming a valid alternative to traditional Geomatics acquisition systems, as they close the gap between higher resolution terrestrial images and the lower resolution airborne and satellite data. UAVs can be remotely controlled helicopters, fixed wind airplanes or kites. This module deals with algorithms and techniques for scene information extraction from images. Both geometric (i.e. 3D reconstruction) and semantic (i.e. 2D image understanding) aspects are described in the module. In this module the 2D and 3D scene analysis will be explained, with focus on the use of data acquired by UAVs. The module is composed by two main parts. In the first part (first week), the students will focus on 2D scene analysis (semantic segmentation, object detection and tracking) while during the second part (second and third week) the students will gain hands-on experience on the use of UAVs. The second part of the module will be given together with the module on "Unmanned aerial vehicles for earth observation". Specifically, students will use an already available commercial solution (pix4D software - www.pix4d.com) and they will design three simple solutions (feature extraction, feature matching and relative orientation) by adopting photogrammetric methods. Lectures will be always coupled with demonstrations and practical sessions on the theory delivered. The second part of the module will allow the students to experience the UAV data acquisition and processing workflow. They will understand how a UAV-related project is planned and executed with their involvement to a real UAV acquisition project. Then, they will apply the learned techniques to process the acquired data and extract 3D information. They will finally analyse and compare the data using the available ground truth and dedicated tools and software (Matlab scripts and CloudCompare) to evaluate their results. Additional presentations will be provided to describe the use of UAVs in five different domains: urban monitoring, disaster mapping, cultural heritage, land administration and food security. At the end of the module the students will submit the output of a group assignment on the dealt topics, the quality of which will contribute to the module mark.

LEARNING OUTCOMES By the end of the module, the students will be able to:  Understand and describe three sematic segmentation algorithms for UAV imagery;  Apply the learned IBM techniques using the proposed commercial software (pix4D) for UAV data;  Analyse and evaluate the geometric quality of the previously generated data using the two available tools (GNUplot and CloudCompare);  Design two state-of-the-art object detection algorithms for processing given UAV imagery; 89

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   

Describe the typical UAV data acquisition procedure and data processing for 3D geo-information purposes, understanding the technical decisions usually adopted in real practical cases; Identify the major pros and cons of the use of UAVs upon the gained experience and relate them with the five different domains; Design at least one different possible application for UAVs beyond the experienced/learned ones; Report and critically "discuss" the scientific outcomes by providing relevant referencing.

CONTENT Topics of the module are:  The scene understanding algorithms: semantic segmentation, object detection and tracking;  Use of the commercial software to manage photogrammetric process;  Analysis and evaluation of results generated by photogrammetric process using simple tools and the available ground truth;  UAV image acquisition and processing for geo-information purposes;  The use of UAVs in different domains: urban monitoring, disaster mapping, land administration, urban monitoring, food security.

PREREQUISITES Modules 7 and 8 of the GFM specialisation.

RECOMMENDED KNOWLEDGE Basic understanding of the principles and techniques of computer vision.

COMPULSORY TEXTBOOK(S) There is one recommended course book "Computer Vision: Algorithms and Applications" by Richard Szeliski which can also be found online: http://szeliski.org/Book. In this book, Chapter 7 Stucture from Motion and Chapter 14 Recognition are highly relavant to this module.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

20

Supervised practicals

18

Unsupervised practicals

46

Individual assignment

0

Group assignment

28

Self study

24

Examination

2

Excursion

0

Fieldwork

6

ASSESSMENT  

Written test 60%; Group assignment 40%.

The mark of the group assignment will be equal combination of mark1 that will constitute the mark of a peer assessment and mark2 that will be the one given by the examiner. Type of marking: 1-10

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BLOCK 3: RESEARCH PROFILE

THERMAL INFRARED REMOTE SENSING: FROM THEORY TO APPLICATIONS Module

13

Module code

M18-ESA-102

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Hecker, C.A. (Christopher, dr.)

INTRODUCTION Remote sensing in the thermal infrared (TIR) spectral region is highly complementary to other remote sensing techniques, such as reflective remote sensing (VIS-SWIR) or microwave remote sensing (RADAR). TIR remote sensing measures the energy that is emitted by the studied objects themselves. By analyzing the TIR data we can gain insight on the objects' temperature as well as composition. These parameters are crucial when studying phenomena such as land and sea surface temperature, (geo-) thermal heat fluxes, crop health, urban heat islands and mineralogic composition of soils, rocks and drill cores. In this module we will take a multi-application look at thermal remote sensing. You will learn how TIR remote sensing works in theory and practice. You will hear about various applications from frontal teaching, as well as by finding, reading and discussing relevant literature on TIR applications of your interest. The module has a strong peer component, where you will learn from other student's experiences and vice-versa. You will get accustomed to several state-of-the-art TIR instruments in Faculty ITC's GeoScience Laboratory and you will get the chance to experiment and practice with the instruments yourselves. You also practice defining your own research question, and design an experiment to answer that question using TIR data or instruments. This mini-research will help you with defining your own MSc topic in the following modules. It is possible to focus part of the module on your own MSc topic if it is related to TIR remote sensing. Otherwise, you can choose from a list of possible topics and datasets to work on for this module based on your interest.

LEARNING OUTCOMES At the end of this module, you should be able to: 1. Understand the theory of TIR, emissivity and the concepts of radiant vs. kinetic temperature; 2. Know the usage of TIR data and its limitations in various application fields; 3. Independently operate state-of-the-art instruments under laboratory and field conditions, and to collect good quality data; 4. Retrieve and interpret qualitative and quantitative information from broadband or (hyper-) spectral TIR instrumentation; 5. Define experiments and methods with TIR instrumentation / data to answer relevant research questions;

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CONTENT This module covers the following topics:  Fundamentals of the thermal infrared; physical laws that govern the TIR;  Demonstrations and hands-on experience of thermal infrared instruments, including TIR cameras, radiant thermometers, TIR FTIR spectrometers (lab and field);  Working with TIR datasets from different platforms (ground, airborne, spaceborne) and different spectral resolution (broadband, multi-spectral, hyperspectral);  Corrections for atmosphere ("split window", MODTRAN, ISAC) and emissivity (ENorm);  Independent study of relevant TIR articles on a topic or application of choice, and presentation to the peers;  "capita selecta" presentations by Faculty ITC researchers on their work with TIR;  Defining a small research question and working on that question;  Evaluating experiments and analyses with TIR instruments and data of peers and provide constructive criticism. The content of the module can be grouped into three parts. In the first week, introductory lectures and demos will explain the theory as well as the instruments. In week 2, the students will independently work on a specific TIR topic or application of their choice. They will find relevant article(s) and study the material. They will explain their findings to their peers in the form of a short presentation or group discussion at the end of week 2. The third week will include a mini-research of choice. The students will define a TIR-related research question and will try to answer this with some support from staff. It may include acquiring a dataset with one of the instruments and working out the dataset to extract information. They can choose from a list of possible topics or bring an own research idea. The students will present the results and the lessons learnt in a short presentation at the end of week 3.

PREREQUISITES Knowledge of remote sensing and basic statistics, basic research skills; and knowledge of earth materials (atmosphere, water, soil, rocks, vegetation). This is typically covered in earlier modules of the Faculty ITC's Master's programme. The suitability of candidates who did not follow these modules will be assessed on an individual basis.

COMPULSORY TEXTBOOK(S) For reading assignments we will use the following book, which is available as eBook: Thermal Infrared Remote Sensing: Sensors, Methods, Applications / C. Kuenzer and S. Dech (eds.), Dordrecht : Springer ISBN 978-94-007-6639-6.

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

12

Supervised practicals

20

Unsupervised practicals

0

Individual assignment

40

Group assignment

8

Self study

48

Examination

8

Excursion

0

Fieldwork

8

ASSESSMENT The module exam consists of:  An open book, written test on the theory aspects of the module. It will cover the material discussed in lectures, exercises, reading assignments and discussions. This test covers learning outcomes 1 and 2 and counts for 50% of the final mark;  A marked presentation on the mini-project of week 3. This short (5-10 min) individual presentation is marked by staff and peers alike (75% vs 25%). This test covers learning outcomes 2,4,5 (and potentially 3) and counts for 50% of the final mark;  A formative test (not marked) with peer feedback on the review of journal articles in week 2. The result does not count towards the final mark of the module. Type of marking: 1-10

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SPATIAL MODELLING FOR INTEGRATED WATERSHED MANAGEMENT Module

13

Module code

M18-ESA-103

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Shrestha, D.B.P. (Dhruba, dr.)

INTRODUCTION Steadily increasing population pressure causes scarcity of land for growing food. This leads to cultivation on marginal lands, deforestation, and widespread changes in land cover/land use. Moreover, intensive use of marginal lands without proper conservation measures can trigger wide scale degradation of natural resources such as the degradation of vegetation and soils affecting groundwater recharge. In addition to this, climate change can have substantial effects on fundamental processes within natural and man-made ecosystems.Climate change studies have shown an increase of extreme rain events in the recent past, triggering many degradation processes e.g. high surface runoff, excessive soil erosion, reservoir sedimentation and flash flood problem in the low lying areas. On the other hand if an area receives consistently below average precipitation for a longer period it can have substantial impact on the ecosystem, can cause drought, can lead to crop failure and poverty in general. Analysis of degradation can be done for instance with analysis of multi-temporal satellite data, and modelling rainfall-runoff and its consequent effects on both upland (soil erosion) as well as in the lowland areas (sedimentation, flooding). Knowledge on the one hand of the degradation processes/rates and on the other hand of conservation measures, can help quantify the problem and find suitable solutions for controlling degradation. Soil and water conservation techniques, both scientific and indigenous, have been amassed over the last 50 years but successful implementation can only be based on acceptance and support by stakeholders. Guidelines for this are given by the WOCAT system (www.wocat.net) and the DESIRE project (www.desire-project.eu ). For conservation of the upslope areas an integrated watershed management is necessary. Spatial modelling techniques assist in analyzing the effects of land use change, extreme rain events and conservation measures. This helps in the preparation of proper watershed management plans.

LEARNING OUTCOMES At the end of the course students should be able to:  Analyze the influence of primary factors leading to natural resource degradation;  Apply RS/GIS and spatial modelling tools for mapping and monitoring of degradation processes;  Apply modelling techniques for assessing the effects of vegetation cover and rainfall intensity on soil erosion;  Understand the spatial implications of conservation measures for watershed management and discuss the methods developed to engage stakeholders, with examples from the DESIRE project (www.desireproject.eu);  Apply what you learn on a real life case study in tropical or semi-arid (dry) area.

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CONTENT The module teaches to identify and analyze the primary factors leading to the degradation of natural resources and their effects on upland-lowland processes and interactions. It consists of two weeks of theoretical explanations with exercises and one week of real life case study work. Theory and exercises (2 weeks):  Factors, process mechanisms and consequences of natural resource degradation (e.g. loss of biomass, disturbance of hydrological balance, land degradation);  Remote sensing techniques for land cover/land use change analysis;  Modelling rainfall-runoff, soil loss, and/or flash flood;  Mitigation measures and conservation planning for watershed management and discuss methods developed to engage stakeholders. Case studies using real life data (1 week):  Land degradation assessment (Morocco);  Land use change analysis and erosion modelling (Thailand/Indonesia);  Flash flood modelling (Thailand);  Soil and water conservation methods and modelling alternate land use scenarios.

PREREQUISITES  

Basic understanding of the principles of remote sensing and geographic information system; Background knowledge in natural sciences (earth sciences, natural resources, agriculture, forestry, hydrology, soil).

RECOMMENDED KNOWLEDGE Basic knowledge of modelling is recommended but not required to attend the module; the module takes learning by doing approach.

COMPULSORY TEXTBOOK(S)        

(Electronic) handouts on lecture materials (lecture slides); Practical exercise material with detailed description of corresponding physical processes (60 pages); Book chapter Soil water (15 pages) from Principles of Hydrology by R. C. Ward and M. Robinson; Book chapter Factors influencing erosion (22 pages) by R.P.C. Morgan; Relevant background; Scientific literature; Satellite images and digital databases; Open source software (PCRaster, Nutshell, SPAW Hydrology, OpenLisem, ILWIS).

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

44

Supervised practicals

34

Unsupervised practicals

22

Individual assignment

0

Group assignment

26

Self study

16

Examination

2

Excursion

0

Fieldwork

0

ASSESSMENT Individual assessment is based on:  Completion of exercises;  Written test (60%);  Case study (40%). Type of marking: 1-10

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BLOCK 3: RESEARCH PROFILE

BUILDING INFRASTRUCTURES FOR GEOINFORMATION SHARING Module

13

Module code

M18-GIP-102

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Lemmens, R.L.G. (Robert, dr.ir.)

INTRODUCTION This module addresses the issues of how to design and implement collaborative geo-information systems on the internet. These systems should be capable of handling standards-based spatial data and spatial functions for the integration of geo-information from spatial data infrastructures, remote and in-situ sensing, crowdsourcing and volunteering, etc. Modern technologies support the creation of web-based infrastructures around a variety of formal information (e.g., from mapping agencies) and informal information (e.g., from social media).

LEARNING OUTCOMES At the end of the module the student should be able to:  Explain the purpose of collaborative geo-information systems on the internet and its components;  Provide examples of crowdsourcing applications;  Compare different applications and user scenarios for Spatial Data Infrastructures (SDIs);  Understand the concept of semantic modelling and explain its role in crowdsourcing and citizen science;  Reason about user requirements and identify the minimal infrastructure for user types;  Design and create rich internet applications which perform like desktop applications but run in a standard web browser;  Apply services to external geodata sources in which data and processing functionality are loosely coupled;  Analyse a case study and reason what type of services are needed and how they should interact with one another;  Identify current shortcomings of collaborative systems/SDI and web technology and be able to identify future trends.

CONTENT You will get hands-on experience with both basic and advanced geo-services for information discovery, retrieval, processing and visualization. This will also involve tutorials and self-study work on service integration and consumption, interoperability, semantic modelling and messaging techniques using XML, RDF, etc. In a group project the students will construct their own infrastructure components. We will embark upon different scenarios of crowdsourcing geo-information.

PREREQUISITES  

Modules 1-11, ITC Master's programme; The knowledge gained in GFM module 9-10: is advantageous, but is not strictly necessary.

Students from other specialisations are explicitly invited to join, but should be prepared to brush up their knowledge using one or two available tutorials.

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RECOMMENDED KNOWLEDGE A working knowledge of geodata structures and on retrieving information from the web is recommended.

COMPULSORY TEXTBOOK(S)   

Reader with self-study materials; Various on-line documents in BB, including slides; Online manuals of the software that is used.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

12

Supervised practicals

20

Unsupervised practicals

24

Individual assignment

16

Group assignment

24

Self study

40

Examination

8

Excursion

0

Fieldwork

0

ASSESSMENT Students execute the module both indivually and in groups: they study the materials together and conduct a group project. A written test is also part of the assessment. The main components are:  Reading assignment (report) 15%  Mini-Research (presentation/discussion) 20%  Infrastructure building (website & presentation/discussion) 30%  Written exam 35% Type of marking for all parts: 1-10

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BLOCK 3: RESEARCH PROFILE

SPATIO-TEMPORAL MODELLING AND ANALYTICS Module

13

Module code

M18-GIP-103

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Zurita Milla, R. (Raul, dr.)

INTRODUCTION Developments in information and communication technologies coupled with an ever-increasing access to data from a wide variety of sensors have resulted in what is known as the data deluge. Despite its enormous potential, most of this data remains underused because the design of effective information extraction workflows remains a challenging task. Overcoming this challenge requires innovative ways of using (geo-)information processing tools that allow analyzing and modelling large and heterogenous collections of geospatial data. In this module, we present various geocomputational approaches that can help to extract actionable geoinformation and/or to improving our understanding of systems and processes with a strong spatiotemporal character. Special attention will be paid to data mining and machine learning methods and to the use of modern and scalable big data approaches (including cloud-based solutions).

LEARNING OUTCOMES At the end of this module the student should be able to:  Discuss the main phases of data analysis;  Discuss advantages and limitations of using data-driven modeling approaches;  Explain to peers the fundaments and usefulness of building geocomputational workflows;  Choose and apply appropriate geocomputational methods for a particular spatio-temporal problem; Organize and conduct the analysis and modeling phases required by a simple spatio-temporal project.

CONTENT This is a project-based module where several real-life challenging problems will be offered to the students who will then explore and try to solve them from a spatio-temporal perspective. Each project will be handled by a group of 2-4 students. Along with the project work, students will be offered lectures on fundamentals of spatio-temporal analysis and modelling and will get a chance to test both basic and advanced methods and techniques in their projects. For this, we will rely on GIS software, use programming languages (e.g. Python), and cloud and high-performance computing approaches. Projects will be drawn from a variety of application areas. The topics covered by the module include:  The data analysis workflow;  Spatio-temporal data mining and machine learning;  Geocomputational workflows. Research skills will also be put into practice in this module. Students will look for relevant literature and will identify their concrete research questions before analyzing and modelling their spatio-temporal data. Students will also report about their findings and will peer review the work of other groups. These aspects will be covered by means of group work, guided discussions, written reports and oral presentations.

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PREREQUISITES Modules 1-11, ITC Master's programme. The knowledge gained in the GFM modules 6 "Spatial data modelling and processing" and 5-10 "programming skills" is advantageous, but not strictly necessary. Hence, students from other specialisations are explicitly invited to join this module.

RECOMMENDED KNOWLEDGE (Basic) Programming skills are recommended.

COMPULSORY TEXTBOOK(S) There is no compulsory textbook for this module.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

26

Supervised practicals

28

Unsupervised practicals

12

Individual assignment

6

Group assignment

32

Self study

36

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT The assessment is based on two main tests:  An analytical project report (group assessment), 75% of the final mark;  A technical peer-review of the work done by another group (individual assessment), 25% of the final mark. Type of marking: 1-10

100

BLOCK 3: RESEARCH PROFILE

SPATIAL-TEMPORAL MODELS FOR FOOD AND WATER SECURITY STUDIES Module

13

Module code

M18-NRS-103

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Venus, V. (Valentijn, MSc)

INTRODUCTION Remote sensing and GIS are important tools to provide input to the spatial assessment of Food and Water Security. Such an assessment is important to both rainfed as irrigated agricultural systems and can be done at regional, national and continental scales. While a holistic study of food and water security requires many disciplinary inputs, at ITC's research and education three main fields are covered:   

Mapping of agro-ecosystems: mapping and characterization of crop production systems and area estimation (inputs for monitoring, modelling and planning); Monitoring agro-ecosystems: detecting past land cover and use changes, and assessing present land cover and crop conditions as for example affected by drought (early warning); Modelling agro-ecosystems: early prediction, quantified estimation of moisture conditions, canopy cover, biomass and yield, plus estimation of future impacts by anticipated climate change.

This module will cover the last bullet point and will present the most contemporary modelling approaches that source from satellite imagery to estimate quantitatively the performance of studied agro-ecosystems as future performances after anticipated climate change. Assessments range from seasonal to interannual and from point to spatial and temporal. An earlier module titled "RS/GIS analysis methods to support Food and Water Security studies" focuses on the first two bullet points. The two modules gradually change focus from characterization to the use of prepared maps for monitoring and finally modelling. Future research aspects concern (amongst others):  Combined use of indices, generated by optical, radar and thermal sensors and crop growth models to directly and quantitatively assess crop growth, standing biomass and harvestable yield;  Impact of climate change on crop performance and yield stability for identification of crop management issues, needed modifications or alternatives. In practice, gained knowledge serves (amongst others):  Operational use of satellite data and development of tailor-made prediction systems for food security and stress monitoring, e.g. 'Improving/constructing Satellite-based Land and Ecosystem Monitoring Systems for an International Network for Food and Environmental Intelligence', and 'Promotion Programs on Satellite-based Earth Observation Technologies';  Generate specific agricultural development support, like 'micro-insurance schemes', where the use of RS-based indices to model/assess risks and loss probabilities for formulating insurance contracts are developed (left-tailed quantitative anomaly assessment).

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LEARNING OUTCOMES The participant will be able to use multi and hyper-temporal imagery and indices, with exogenous (secondary data) and/or field survey data, plus prepared maps with legends, to:  Use simple to advanced (dynamic) crop growth models for yield estimation, change assessments, and spatial suitability assessments (watershed level);  Assess impacts on performance (biomass, yields) due to anticipated climate changes (scenarios), and to retrieve the required climate data and future scenarios (past, present, and future weather conditions);  Use RS-data to "force" or correct crop growth models and estimate improved (actual) crop yields; includes (i) Soil-Leaf-Canopy (SLC; SCOPE) RS-data inversion techniques to estimate e.g. LAI as a forcing variable, and (ii) the heat-balance (ETa) assimilation approach.

CONTENT Week 1: Day 1: Principles and types of empirical and dynamic Crop Growth Models(CGM), their relationship with RS-data, the state of the art of present day CGM-applications, early prediction logic, data requirements, accuracy, and scales. Intro to the small individual assignment. Day 2-3: weather data: sources, principles, use, predictions; present climate change scenarios (expectations); downscaling climate predictions. Day 4-5: Weather impact assessment at watershed level (area based) on the hydrological cycle and soil moisture. Week 2: Day 6-7: quantitative yield and yield variability assessments, and assess impacts of climate change on crop productivity and variability (point-based, GRID-based). Day 8-10: Soil-Leaf-Canopy (SLC; SCOPE) RS-data (Modis) inversion techniques (ProSail) to estimate time-series of LAI; temporal LAI-cleaning and interpolation through temperature-sum formulae. Week 3: Day 11: instantaneous ETa assessment based on the canopy heat balance. Day- 2: Forcing method to use daily LAI and Eta estimates in a CGM to estimate end-of season yields or daily actual biomass. Day 13-15: Small individual assignment: implement, using required tools, a processing chain of selected spatial-temporal data to generate relevant Food and Water security information (to be submitted; graded exercise). Advice: initiate your project well in time.

PREREQUISITES Skills in RS and GIS (e.g. Core Modules of ITC Master's programme). Participation in Module 12 "Remote sensing/geographic information system analysis methods to support food and water security studies" is a requirement.

RECOMMENDED KNOWLEDGE Background in systems analysis for resources management.

102

BLOCK 3: RESEARCH PROFILE

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

25

Supervised practicals

20

Unsupervised practicals

15

Individual assignment

24

Group assignment

40

Self study

16

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT Individual assignment. Type of marking: 1-10

103

BLOCK 3: RESEARCH PROFILE

STRATEGIC ENVIRONMENTAL ASSESSMENT (SEA) AND ENVIRONMENTAL IMPACT ASSESSMENT (EIA) APPLYING SYSTEMS ANALYSIS AND SPATIAL DECISION SUPPORT TOOLS Module

13

Module code

M18-NRS-104

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Looijen, J.M. (Joan, drs.)

INTRODUCTION Decision making in a complex world: the request for (training in) SEA is growing rapidly worldwide and techniques to visually illustrate and assess the implications of spatial decisions are much in demand. Ad hoc and often uncontrolled development initiatives can have undesired social, economic and ecological consequences. Rapid population growth, pollution, climate change, the exposure to hazards and disasters, and the loss of biodiversity and ecosystem services require effective assessment tools to assist sustainable planning and decision making. Environmental Impact Assessment (EIA) and Strategic Environmental Assessment (SEA) are basically procedures to support this process. EIA is a systematic procedure established to evaluate the impacts of proposed projects. Although by now EIA is acknowledged and legally embedded in most countries, practice has shown that EIA often occurs too late in the planning process. Since the nineties SEA for policies, plans and programmes evolved. The key principles of SEA and EIA are the involvement of relevant stakeholders, a transparent and adaptive planning process, consideration of alternatives, and using the best possible information for decision and policy making. EIA and SEA therefore improve both the (spatial) planning process and the information used in this process. In this module, you will explore how GIS and remote sensing, models and spatial decision support systems can be used to help to identify and structure the problem(s), generate and compare possible solutions, and monitor and evaluate the proposed activities. This module provides a unique opportunity to integrate a multidisciplinary assessment of spatial policies, plans and projects. Hands-on experience with real EIA and SEA projects will be a major part of the module.

LEARNING OUTCOMES In this module you will work with a set of modern techniques and tools to provide geo-information as a basis for environmental assessment of policies, plans or projects. You will learn the basic principles, procedures and steps in EIA and SEA and their interaction with the planning process. You will acknowledge the importance of stakeholder involvement and use dynamic land use modelling and methods to assess and value ecosystem services. You will develop and assess alternatives and scenarios using indicators and metrics, e.g. for integrated impact assessment, bio-energy production and Green Accounting. You will apply spatial decision support tools for site selection, environmental sensitivity and vulnerability assessment, and ecosystem-based risk reduction. You will analyse the potential application of remote sensing and GIS in the environmental assessment process and demonstrate the use of costbenefit analysis and economic valuation for different environmental assessments. In the final project you will be dealing with a typical application within the field of environmental assessment for spatial planning.

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BLOCK 3: RESEARCH PROFILE

CONTENT         

EIA and SEA: concepts, principles, process and interaction with the planning process; Stakeholder involvement: Participatory GIS and community-based modelling; Alternatives: development and analysis of alternatives and scenarios; Environmental assessment methods and techniques: application of GIS, indicators and metrics; Spatial Decision Support tools in EA: spatial multi-criteria evaluation for site selection and vulnerability analysis; dynamic land use modelling; Ecosystem services, green accounting, biodiversity and bio-fuel modelling; Integration of hazard and risk in EA: vulnerability and risk assessment, mitigation & adaptation, risk zoning, ecosystem-based risk reduction; Cost-benefit analysis and economic valuation for different applications; Final project dealing with a typical application within the field of environmental assessment for spatial planning.

The module will be 'problem-driven', based on learning by doing. In the last week several real-life based case studies from different disciplines will be offered to gain hands-on experience with SEA and EIA. You may also work on a case study and data set of your work or interest.

PREREQUISITES Basics of GIS, remote sensing and modelling as covered in ITC Master's programme modules 1-11.

RECOMMENDED KNOWLEDGE Although students may have diverse backgrounds, you should share practical experience of, or have an affinity with, the application of EIA and SEA within a spatial planning context. You may be a professional involved in development planning, or working in a governmental or non-governmental organization. You can be a practitioner, reviewer, consultant, expert, a student or professional working in the field of environment.

COMPULSORY TEXTBOOK(S) Recommended as background reading is the e-book on 'Strategic environmental assessment in action', by Riki Therivel. Earthscan, London, 2004. During the module use will be made of hand outs, power point and multi-media presentations, exercises, videos, web-links, hands-on case studies, digital data sets, computer assisted analysis, a study tour and multi-disciplinary project work.

105

BLOCK 3: RESEARCH PROFILE

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

26

Supervised practicals

34

Unsupervised practicals

10

Individual assignment

12

Group assignment

30

Self study

24

Examination

0

Excursion

8

Fieldwork

0

ASSESSMENT  

Individual assignment (30%) Group assessment (70%)

Type of marking: 1-10

106

BLOCK 3: RESEARCH PROFILE

PARTICIPATORY MAPPING AND THE USE OF LOCAL SPATIAL KNOWLEDGE Module

13

Module code

M18-PGM-103

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Verplanke, J.J. (Jeroen, drs.)

INTRODUCTION Participatory mapping and participatory GIS (PGIS/PPGIS) are established practices in participatory spatial planning and management. It includes actual spatial information techniques, tools, products and outputs that are appropriate to a participatory approach and are for use by mixed groups of professionals and nonprofessionals in a wide range of application domains. The module is about participatory mapping practices which apply a variety of information acquisition, analysis and synthesis tools, to elicit and include all forms of spatial information. As the participatory mapping tools and approaches discussed in this module are suitable to a wide range of application domains the module is suitable for students in all applied fields of study. In this module students therefore get the opportunity to develop individually (if applicable) a participatory research approach tailored for inclusion in their research proposals or which could be useful for their professional careers.

LEARNING OUTCOMES After completing this course, students can:  Translate geo-information issues into the context of participatory approaches;  Review the concepts and importance of local and indigenous spatial knowledge;  Contrast different approaches to acquiring citizen's spatial information and assess their applicability in a variety of contexts;  Assess participatory spatial planning and community-based management of resources in the light of stakeholder interests;  Devise a strategy for and execute a participatory (local-level) spatial data acquisition using participatory mapping tools and applications;  Evaluate the role of different approaches to citizen's spatial information in (personal) research objectives and relate the consequences of the involved method to ethics.

CONTENT In the field of participatory mapping there are some exciting research issues, made more complex and challenging by the inseparability of theory and practice in participatory research topics. Whether the approaches will be discussed in terms of Urban Growth, Food Security, Land Administration, or Disaster Risk Management, this advanced course focuses on the following issues:  The origins and applications of participatory mapping;  Data collection through social media and innovative tools;  Investigating the spatial knowledge in represented in cognitive maps, especially of local or indigenous spatial knowledge;  Handling the complex ethical issues of participation in spatial planning;  Exploring the new research fields of e-participation and VGI (volunteered geographical information);  Assessing the applicability of an array of new technologies such as mobile mapping and multimedia.

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BLOCK 3: RESEARCH PROFILE

PREREQUISITES Affinity with participatory approaches in a local development context.

COMPULSORY TEXTBOOK(S) Participatory Learning and Action 54: Mapping for Change: Practice, Technologies and Communication (IIED, 2005); available online: http://pubs.iied.org/pdfs/14507IIED.pdf

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

24

Supervised practicals

8

Unsupervised practicals

8

Individual assignment

32

Group assignment

32

Self study

32

Examination

2

Excursion

0

Fieldwork

6

ASSESSMENT Group project (50%, marking 1-10) Individual project (50%, marking 1-10)

108

BLOCK 3: RESEARCH PROFILE

LAND GOVERNANCE Module

13

Module code

M18-PGM-104

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Todorovski, D. (Dimo, dr.)

INTRODUCTION Land remains a highly complex issue, and often forms a cause for conflicts at regional, national, local and personal level in view of its value as an economic resource in relation to social, political, cultural and often religious systems. The failure to adopt, at all levels, appropriate (urban and rural) land policies and land management practices remains a primary cause of inequity and poverty. The consequences often take the form of difficult access to land Information, unawareness of land policies and legal frameworks, ignorance about land transactions and prices, misallocation of land rights, land grabbing and abuse. Many of the general governance principles thus appear highly relevant to the management and administration of land. When in place, this in turn strengthens confidence in governments and public agencies, and has a positive economic impact, also on economic development. The main aim of this advanced module is to provide the students with the broad knowledge, tools and skills to strengthen land governance issues while implementing policy frameworks for sustainable development in developing and emerging countries. The main objectives are to:  Introduce governance issues related to land with adequate knowledge and tools required in building transparent land management and administration systems within urban and rural areas;  Describe various substantive issues and tools whereby land governance and transparency in land management and administration are assessed with a view to preventing and/or fighting corruption;  Demonstrate how ethical dilemmas are identified and how tools are applied to promote good governance to address the problem situation and mitigate undesirable consequences.

LEARNING OUTCOMES At the end of the module the student should be able to:  Understand various international initiatives and relevant tools for promoting good governance;  Explain the relation between land, human rights, governance and e-governance;  Describe relevant land governance issues and apply them in land management and land administration in building trust between public agencies and citizens;  Apply relevant tools for good governance to reduce corruption in the relevant case study environment of Asian and African continents.

CONTENT   

The concept of governance and its principles, e-governance, transparency, corruption and reflection on human rights policies; International initiatives (such as UN, FAO, World Bank, UN-HABITAT, FIG, UT/ITC, etc.), paradigm and vision for land governance and transparency issues; various governance indicators; The broader ethical issues to deal with corruption and enhance transparency; exploring and situating ethical dilemmas using real case studies in developing contexts;

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



Key substantive issues and tools (i.e. assessment of transparency, access to land information, public participation, professional ethics and integrity, and institutional reform) to promote good land governance in the management and administration of land; Exploring possible entry points for the key substantive issues to address the problem situation and mitigate undesirable consequences using transparency tools in real case studies developed by the Asian and African land experts.

COMPULSORY TEXTBOOK(S)  

 

Real case studies developed by Asian and African land experts (link on Blackboard); 2012, FAO/VGGT. Voluntary guidelines on the responsible governance of tenure of land, fisheries and forests in the context of national food security. Rome: FAO. Accessed on 5.01.2017: http://www.fao.org/docrep/016/i2801e/i2801e.pdf 2006, FAO/WB. Good Governance in Land Administration Principles and Good Practices. Accessed on 05.01.2017: http://www.fao.org/3/a-ak375e.pdf 2009, FIG Publication 45. Land Governance in Support of The Millennium Development Goals. Accessed on 05.01.2017: https://www.fig.net/resources/publications/figpub/pub45/figpub45.pdf

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

32

Supervised practicals

10

Unsupervised practicals

30

Individual assignment

20

Group assignment

24

Self study

20

Examination

8

Excursion

0

Fieldwork

0

ASSESSMENT    

Assignment 1 (one) presentation short case study (10%); Assignment 2 (two) presentation longer case study (20%); Assignment 3 (three) presentation from written scientific report paper (20%); Written scientific report paper (50%).

Type of marking: 1-10

110

BLOCK 3: RESEARCH PROFILE

COLLABORATIVE PLANNING AND DECISION SUPPORT SYSTEMS APPLIED IN DECISION ROOMS Module

13

Module code

M18-PGM-105

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Boerboom, L.G.J. (Luc, dr.ir.)

INTRODUCTION Collaborative planning is today's planning practice. New tools and methodologies have been developed to improve the processes and enhance quality of outcomes. New developments in fields such as information technology brought new insights in this field. This module develops the student's conceptual and practical understanding of several advanced methods for collaborative planning and decision support and provides theoretical perspectives and underpinnings to prepare students for:  Development of collaborative planning and/or decision support methods, systems and serious games;  Observation and learning about collaborative planning and decision making processes using methods, systems, games, and decision rooms. The first part of this module addresses concepts of serious gaming and game development. The second part covers spatial scenario development through spatial planning support systems. Of course the difference between gaming and decision aiding is discussed. Finally we address collaborative analysis and decision making regarding scenarios. The module makes use of the facilities available in the ITC group decision room.

LEARNING OUTCOMES Upon completion of the module students should be able to:  Explain and apply general approach to scenario development and analysis;  Explain and apply concepts of serious gaming;  Explain the complexity of the collaborative planning environment;  State the role of disciplinary models in the planning process;  Explain ways of handling uncertainty;  Explain the role of various stakeholders, and the way to consider their views in the planning process;  Develop and apply qualitative/quantitative techniques for policy formulation and scenario development;  Develop and evaluate policy and assess its impacts in various scenarios;  Apply qualitative decision rule-based models for scenario development and analysis;  State the potentialities and limitations of qualitative methods for scenario development and analysis;  Explain the principles of decision-making process and use of spatial planning and decision support systems as well as serious games;  Distinguish between various phases of the decision-making process and their required types of information and support systems;  Understand latest developments in web-based decision support system with examples of web-based collaborative spatial multi-criteria evaluation.

CONTENT 

Planning and decision support systems (definition, components, architecture, and examples); 111

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           

Framework for planning and decision making, with examples of land and water resource issues; Introduction to serious gaming; Dealing with data uncertainty, and future and various stakeholders; Scenario definition, concepts, development and analysis; Model-based scenario development approaches; Quantitative and qualitative methods for scenario development; Integrated models for planning and policy formulation, scenario development, impact assessment and analysis; Introduction to models of the decision-making process and decision support systems; Theory and practice of collaborative spatial decision support (EAST); Application of web-based spatial multicriteria evaluation in collaborative planning and decision making; Group decision making and the required information technology; Application of the above techniques in a serious game (students can select the case according to their background and interests).

RECOMMENDED KNOWLEDGE Basic GIS skills required.

COMPULSORY TEXTBOOK(S) Reader will be provided.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures Supervised practicals Unsupervised practicals Individual assignment Group assignment Self study Examination Excursion Fieldwork

ASSESSMENT Quiz, test and group presentation. Type of marking: 1-10

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SATELLITE DATA FOR INTEGRATED WATER RESOURCE ASSESSMENTS AND MODELLING Module

13

Module code

M18-WRS-103

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Rientjes, T.H.M. (Tom, dr.ing.)

INTRODUCTION Water resource and hydrological assessments are (increasingly) becoming more complex and more data demanding. Traditionally in-situ data is used in modelling but there is wide consensus that use of in-situ data in modelling often leads to inferior assessments by poor system and process representation. This particularly applies to data scarce regions where in-situ data collection relies on observatiobn networks of (very) low density and poor design. To overcome these issues, the use of satellite data is widely advocated since observations are systematically repeated over time (15 min.- days) with wide spatial coverage. Over the past decades use of satellite data has become popular by availability of a wide range of satellite products, however, integration of satellite data is not trivial and also assessments how modelling results are affected is not trivial. This module aims at a broader understanding on satellite datamodel integration but also aims at a basic understanding on reliability and accuracy assessments, but also on constraints and strengths of satellite data applications in water resources and hydrological modelling. Various aspects of satellite data error assessment and propagation, and how errors affects model performance will be discussed as well as aspects of water balance closure when satellite data is used instead of in-situ data.

LEARNING OUTCOMES At the end of this module the student is able to:  Assess reliability and accuracy of satellite data;  Argue on constraints and strengths of data integration in modelling;  Integrate satellite data in modelling;  Evaluate model performance when satellite data is used is stead of in-situ data;  Improve hydrological and water resource and modelling skills;  Assess model performance for simulation and forecasting by use of satellite data;  Understand the various aspects involved to close the water balance at catchment scale. Various levels of data integration and assessments will be discussed and is part of the learning outcomes. A number of satellite applications such as precipitation, evapotranspiration (and possibly soil moisture and snow products) are discussed and also make part of the learning outcomes. A number of applications are available including rainfall-runoff modelling, water resources modelling, flood modelling (and possibly snow melt modelling).

CONTENT Current applications of integrated water resource and hydrological models for system simulation and forecasting often rely on in-situ data. Alternative to such data is the use of satellite data for system and process representation in a distributed and coherent fashion. Satellite products are available for terrain and land use modelling, for rainfall, evapotranspiration and moisture representation and for observing floods. This module addresses various aspects of use and integration of satellite data in integrated water resource assessments and hydrological models. Reliabilty and accuracy assessments of satellite data will

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be shown. Learning is by frontal teaching, practicals, assignments and self-study. Besides examples on use of satellitedata, also use of outputs from climatic models (general circulation moldes, regional circulation models and re-analysis data) will be introduced as principles on use and reliability assessments resemble.

PREREQUISITES Preferred are modules 9-10 of the WREM specialisation. Students from NRM and AES sometimes join but some extra study is needed to fully grasp aspects of hydrological modelling.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

28

Supervised practicals

24

Unsupervised practicals

32

Individual assignment

16

Group assignment

24

Self study

12

Examination

8

Excursion

0

Fieldwork

0

ASSESSMENT Assessment of this module will be by individual and group assignments. Weights to the assignments are 50% and 50%. Type of marking: 1-10

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WATER, CLIMATE AND CITIES Module

13

Module code

M18-WRS-104

Period

2 July 2018 - 20 July 2018

EC

5

Module coordinator

Timmermans, W.J. (Wim, dr.ir.)

INTRODUCTION This module will offer a set of methods and techniques for analysis and monitoring of climate and climate change, with applications in climate change impacts and adaptation.

LEARNING OUTCOMES Upon the completion of this module, the students will have:  A better understanding of the physical processes (meteorology) determining the climate, and thus climate change;  A better understanding of the climate adaptation and response, with respect to water-related issues ("climate change impact");  Hands-on experience with respect to (regional) modeling ("techniques");  Advanced knowledge about the implications of climate change and its implications for water resources resulting from various climate change scenarios and climate change response options, including associated synergies;  Experimental knowledge on urban climate observations.

CONTENT Freshwater is indispensable for all forms of life and is needed, in large quantities, in almost all human activities. Climate, freshwater, biophysical and socio-economic systems are interconnected in complex ways, so a change in any one of these induces a change in another. Climate change adds a major pressure to nations that are already confronting the issue of sustainable freshwater use. The challenges related to freshwater are:  Having too much water;  Having too little water;  Having too much pollution. Each of these problems may be exacerbated by climate change. Freshwater-related issues play a pivotal role among the key regional and global vulnerabilities. Therefore, the relationship between climate change and freshwater resources is of primary concern and interest. This module intends to introduce to students relevant processes and tools related to climate and climate change impacts for the spatial and temporal distribution of freshwater resources, at global as well as at regional scales.

PREREQUISITES MSc modules 1-11 in WREM, NRM, AES, GEM, relevant module 12.

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RECOMMENDED KNOWLEDGE Basic knowledge in mathematical and statistical analysis, basic understanding in quantitative Earth Observation, programming skills and image processing skills.

COMPULSORY TEXTBOOK(S) 1. Lecture Notes "Climate Change", WREM specialisation, July 2009; 2. Selection of relevant scientific papers; 3. Module PowerPoint's, as used during the lectures.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

46

Supervised practicals

22

Unsupervised practicals

0

Individual assignment

16

Group assignment

0

Self study

56

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT  

Test 1, written exam: a written test will be held to assess the understanding of the theoretical aspects of this module, including those relevant in practicals and case studies; Test 2, individual assignment: students must report on a case that demonstrates their understanding of the case studies and how they would apply the knowledge in a selected application domain.

Each test accounts for 50% of the mark of the module Type of marking: 1-10 .

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RESEARCH THEMES/MSC RESEARCH PROPOSAL Module

14-15

Module code

P18-EDU-104

Period

30 July 2018 - 7 September 2018

EC

10

Module coordinator

Dopheide, E.J.M. (Emile, drs.)

INTRODUCTION The Faculty ITC Research Programme is formulated as a number of the following interlinked research themes:  4D-Earth  Acquisition and quality of geo-spatial information (ACQUAL);  Forest Agriculture and Environment in the Spatial Sciences (FORAGES);  People, Land and Urban Systems (PLUS);  Spatio-temporal analytics, maps and processing (STAMP);  Water Cycle and Climate (WCC). These research themes and activities of the six scientific departments form the subject framework and organizational structure in which Master's students conduct their individual research. The purpose of modules 14 and 15 is i) to deepen the knowledge and skills of the students within the research activities of the scientific department, and ii) to help the students to define their own MSc Research Proposal. Each scientific department offers a specific research project or a set of specific research support activities (lectures; hands-on sessions; specific survey techniques) during modules 14 and 15. Although the general structure is the same, the content will be specific to the department's research themes and activities. Departments are free to fill this in within the boundaries described in this module description. The research projects or research support activities can be inter-disciplinary. A large part of the modules is spent on finalizing the MSc Research Proposal with support and feedback from staff and peers. At the end of module 15 the MSc Research Proposal will be assessed by a Proposal Assessment Board based on a written proposal, a presentation and an oral defense. The Proposal Assessment Board decides if the proposal is acceptable, as one of the conditions to be admitted to the MSc Research phase (modules 16-23). Through the MSc Research Proposal the students should demonstrate the ability to undertake independent research. The students have to make a choice of the envisaged MSc research topic during Block 2 of the course. This choice for the MSc research topic has to be made before the start of module 11 (exact date will be announced in due time) For more information about the content and scope of the Faculty ITC Research Programme, please visit: http://www.itc.nl/research-themes

LEARNING OUTCOMES Upon completion of these two modules, the student will be able to:

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1. Undertake and reflect upon a specific research project or a set of specific research support activities typical for the research theme in which the student undertakes the research; 2. Critically review MSc Research Proposals from peers; 3. Define ways to tackle a scientific problem and structure research; 4. Place the research project in a wider scientific and societal context; 5. Structure the proposed scientific research to the specifications of the scientific discipline; 6. Meet quality standards and excellence in research; 7. Present scientific information in written English at a standard acceptable to the scientific community. Learning outcomes 1 and 2 refer to module 14 and learning outcomes 3 to 8 to module 15. Please note however; the activities of module 14 and 15 will run parallel and are interrelated.

CONTENT In a plenary session at the start of module 14, the Research Theme will be further introduced. Two main activities run parallel in modules 14 and 15:  A research project or a set of specific research support activities;  Developing the MSc Research Proposal for the individual Thesis with feedback from staff and peers. Research project or a set of specific research support activities The small research project or specific activities to support the research of the domain which are typically part of the student's MSc research gives the students an opportunity to practice part of the research. These activities are considered an important preparation for conducting the individual MSc research in the MSc Research phase, as well as for the student's future professional academic working practice. The projects and research support activities are defined by the scientific departments with a view to catering for a variety of research approaches and interests, as well as the relevance of these to society and the research field. Finalizing MSc Research Proposal The MSc Research Proposal is finalized by the student in mutual agreement with their supervisors, who are appointed in module 11. The MSc Research Proposal should be a logical and ordered exposition of the envisaged research (as introduced in module 11), including data availability, (fieldwork) methods, a flowchart, and time planning. In the last week of module 15, the MSc Research Proposal is presented before a Proposal Assessment Board. The research proposal is assessed based on the written MSc Research proposal, a presentation and an oral defence. Assessment criteria refer to the scientific scope and depth, scientific method, the reporting, the presentation and defence, and the process. At least 70% of the modules will be allocated to self-study. This time is mostly for finalizing the MSc Research Proposal. When presenting the proposal, the student must also show the Proposal Assessment Board that all the required data is available or, if not, that steps (including fieldwork if appropriate) will be taken to acquire these data in time. Likewise, requirements for hardware and/or software should be specified to ensure that these can be made available as required. Acceptance of the proposal is one of the prerequisites for the start of the individual research (modules 1623). The student will draft a supervision plan in consultation with the supervisors. Peer review and critical reflection The students will critically review and comment upon the progress of their peers. This could be related to

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the research project and/or to the development of their MSc Research Proposal, depending on setup of the modules as defined by the specific scientific department. A tutor will be appointed to guide the student or a student group during module 14-15 . The tutors will convene plenary sessions to critically exchange experiences among peers and monitor the progress of all participating students. The student should be able to reflect on what they have learned from the research project or various research support activities and the peer review sessions in relation to their MSc Research Proposal.

PREREQUISITES At the start of module 14 students should have an accepted MSc research topic and have elements of a first draft of their MSc Research Proposal. Students should have discussed this draft with their assigned supervisors. To prepare an acceptable proposal and carry out the subsequent research work, it is necessary to have a sufficient level of knowledge in the chosen research field. Consequently, if the student wants to undertake research in which the focus differs from that of the domain modules followed in Block 2, the student will have to provide satisfactory evidence that the student has the relevant background, knowledge and skills.

RECOMMENDED KNOWLEDGE To be specified by the responsible scientific department.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures Supervised practicals Unsupervised practicals Individual assignment Group assignment Self study Examination Excursion Fieldwork

ASSESSMENT The first two learning outcomes (1 and 2) are assessed through a combination of:  A report on a specific research project or a set of specific research support activities;  A individual written reflection report;  Active participation in group and/or peer review activities. The module 14 assessments result in a "Completed"or "Fail". The student will be informed at the start of the module 14 about the set-up and details of the assessments. The remainder of the learning outcomes (3 to 7) are assessed through the MSc Research proposal. Assessment will be done by a Proposal Assessment Board based on a written proposal, a presentation and an oral defense.

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The MSc Research proposal assessment will lead to a "Completed" or "Fail" on the course record for module 15.

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GETTING REAL ABOUT YOUR RESEARCH: TRANSLATING SCIENCE INTO ACTION Module

14-15

Module code

U18-EOS-102

Period

30 July 2018 - 7 September 2018

EC

10

Module coordinator

Stein, A. (Alfred, prof.dr.ir.)

INTRODUCTION Enterpreneurial skills are essential in translating science into action. GIS provides important tools to analyze environmental, urban or agricultural systems. Linking them to spatio-temporal models to use earth observation data can lead to important findings about system dynamics and provides new understanding for effective decision and policy making and setting research into the market. Especially when using modeling and GIS for management and governance purposes we need to be well aware of the quality of the spatial data and analytical methods used. Aspects of importance are:    

The scale of system and models; Representativeness of the data; Decision making and users; Goals of individual projects.

This topic concerns earth observation aspects and spatial data quality. It includes statistical approaches for defining and quantifying uncertainty in spatial data in the context of remotely sensed data. Spatial data infrastructure technology is of importance and includes systems modeling and model analysis. Analysis of model performance also includes uncertainty analysis and model quality assessment. The rationale for this module is to address a particular environmental, agricultural or urban system and establish a business company to address relevant questions . This is linked with the individual research proposals. Central question is to which degree this company can represent such a system.

LEARNING OUTCOMES The module has the following aims: 1. Consider systems in a wide context: from nature to information systems; 2. Translate interdisciplinary group research projects into a single business project; 3. Practice and develop your research skills; 4. Develop a business plan. The module has the following learning outcomes associated with the above aims: To consider systems in the broadest context. At the end, students should be able to: 1. Obtain knowledge on systems theory and dynamic modeling; 2. Equate a system with a business concept; 3. Put individual research topics into a wider concept; 4. Evaluate the use of a GIS for representing the system and know how it can be integrated with individual research projects; 5. Evaluate integrated analytical models and GIS to represent the chosen system as a business case.

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To foster interdisciplinary group research. At the end, students should be able to: 1. Define ways to tackle a scientific problem and structure research into a company; 2. Place research projects in a business context; 3. Work and share knowledge in a (multi-disciplinary) research team. To practice and develop your research skills. At the end, students should be able to: 1. Be aware of the societal benefit of research; 2. Be aware how science and business could meet in individual and combined projects; 3. Present scientific information in written English at a standard acceptable to the scientific community; 4. Reflect critically on your personal role in a business development process. To simulate the MSc thesis process by developing a business plan. At the end, students should be able to: 1. Structure scientific research to specifications of society and business development; 2. Write an MSc research proposal.

CONTENT Students start after the introduction to prepare for the workshop. At day 2, the group project has short presentations (3 minutes) of each individual research plan during a mini-workshop on systems and GIS. This introduces the systems in a practical setting and discusses GIS as a framework for modeling processes in the system. Aspects to consider are collecting data, evaluating, reporting, controlling spatial data quality, understanding the system and the societal needs from the market. Following the workshop, the students form project teams as future companies in which they undertake a group assignment. Each group selects a system of preference and identifies information on the system and data together with supplementary documentation. At day 3, a project brief is formulated, which in 1 - 1.5 pages presents the key elements of the company. Next, the project work starts. Each project team as a company conducts an independent evaluation of the business in a systems-based context. In particular use of GIS as the supporting is addressed. The market is explored and financial statements are made. At day 7, an elevator pitch is scheduled in the second week where each business presents itself briefly. At day 9, the module has a half-day mini workshop where the students present their business plans and discuss with staff acting as potential investors. Students write individual reviews of the plans presented. At day 10, the business cases are handed in.

PREREQUISITES This module is open to all ITC MSc students.

RECOMMENDED KNOWLEDGE Successful completion of modules 1 to13. Some basic background in statistical analysis and systems theory is recommended.

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

4

Supervised practicals

40

Unsupervised practicals

32

Individual assignment

4

Group assignment

8

Self study

200

Examination

0

Excursion

0

Fieldwork

0

ASSESSMENT The assessment of module 14 (completed/fail) consists of:  Project brief (10%)  Elevator pitch (20%)  Business plan (40%)  Final workshop presentation (20%)  Individual peer review (10%) Type of marking: completed/fail The assessment of module 15 consists of Msc Research Proposal assessment. Type of marking: completed/fail

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RESEARCH PREPARATION 4D-EARTH Module

14-15

Module code

U18-ESA-103

Period

30 July 2018 - 7 September 2018

EC

10

Module coordinator

Kerle, N. (Norman, prof.dr.)

INTRODUCTION MSc students in the AES streams 'Natural Hazards and Disaster Risk Management' and 'Engineering Geology' conduct their individual MSc research project in the framework of the 4D-EARTH/DMAN research theme of ITC's ESA department. In these modules 14 and 15 the DMAN research team intends to create a stimulating working environment for the students to develop a high-quality MSc Research Proposal. While students will work on their MSc Research Proposals they will also participate in a number of peer review sessions. These sessions provide the opportunity to discuss and improve their proposals with the support of their peers. In a series of presentations and hands-on sessions the students are challenged to critically consider a number of methodological choices that have to be made regarding (field) data collection and (pre-)processing. At the end of these modules 14 and 15 each student will present his MSc Research Proposal to a Proposal Assessment Board.

LEARNING OUTCOMES At the end of this module, the student is able to present and defend an MSc Research Proposal that meets the ITC standards for MSc Research Proposal development. In order to achieve this main objective the student is able to:  Develop a comprehensive research proposal, building it up in a step-wise manner from the initial research idea;  Provide an overview of research approaches, techniques and tools relevant to the 4D-EARTH/DMAN research theme;  Select and justify an appropriate research method and related techniques and tools for an MSc research project within the 4D-EARTH/DMAN research theme;  Both give and receive constructive and critical feedback on elements of a draft MSc Research Proposal

CONTENT Proposal development and peer review sessions  Individual proposal writing;  Peer review sessions.  Additional lectures on field data acquisition methods, ITC geoscience laboratory tools etc.  Optional lectures and exercises in teh PGM department on topics such as use of questionnaires or conducting of interviews

PREREQUISITES  

ITC's module 11: Research skills; An identified and accepted MSc research topic.

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

6

Supervised practicals

0

Unsupervised practicals

0

Individual assignment

10

Group assignment

12

Self study

260

Examination

0

Excursion

0

Fieldwork

0

ASSESSMENT Assessment module 14:  Active participation during presentations and hands-on sessions (to be approved by lecturing staff);  Active participation during peer review sessions (to be approved by facilitator);  Preparation of peer review reports (each student has to prepare a review report per peer review sessions for one of the fellow students; to be approved by lecturing staff);  Preparation of an individual reflection report (each student writes a report of max. 2 pages in which (s)he reflects on what was learned during the peer review sessions, the presentations and the handson session as input for the proposal development). Type of marking: completed/fail Assessment module 15: MSc Research Proposal assessment Type of marking: completed/fail

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BLOCK 3: RESEARCH PROFILE

GEODATA AND SERVICE PROVISION IN CRISIS SITUATIONS: SUPPORTING UNITED NATIONS PEACEKEEPING OPERATIONS Module

14-15

Module code

U18-GIP-103

Period

30 July 2018 - 7 September 2018

EC

10

Module coordinator

Drakou, E. (Valia, dr.)

INTRODUCTION This module uses the context of United Nations (UN) peacekeeping operations to let students develop a research or project proposal based on their own research interests. The Geospatial Information Section of Department of Field Support of the UN, whilst providing cartographic and geographic support to the UN Secretariat, is also responsible for offering geographic information support to peace keeping and peace building missions around the world. The module aims to introduce the students with the main concepts of peacekeeping and peace building operations by the UN and also familiarize with operational overview and geographic support given to the different UN missions. Throughout the module, operational challenges in geographic information support faced in different deployment phases of the missions are discussed, thus students will be exposed to every day challenges faced by peacekeepers and peace builders around the globe. The challenge will be to develop a convincing proposal that combines the students' skills and interests with the needs of the UN peacekeeping operations.

LEARNING OUTCOMES Upon the completion of this module, students will be able to meet the needs of the UN and the international community by developing the following knowledge and skills:  understand the organizational set-up of UN and geo-related activities occurring in UN Peace Operations environment;  Know the challenges in working in a data-poor environment in an UN Peace Operations environment;  Practice how to plan and operate to support geo-information needs of the UN and the international community in one of the typical phases of UN deployment through a scenario setting;  Develop one or more of the following skills:  Develop a strategic and operational plan at a particular deployment phase;  Gather user requirements and the relevant geo-information for operation;  Design a system architecture which ensures efficient and effective geo-information maintenance;  Integrate relevant geo-information for a specific tactical operation;  Visualize relevant geo-information for a specific client.

CONTENT The module will introduce main normative concepts used in the area of UN Peacekeeping Operations and how geographic information can provide additional value to the mandates agreed upon by the international community.

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The module will introduce students with:  Organizational set up of UN Peacekeeping Operations;  Typical phases of deployment;  Typical geographic support given in the different phases of deployment;  Geographic information collection, integration, generation/production, visualization/dissemination and maintenance issues;  Geographic operational strategic planning.

PREREQUISITES Open to all Master students.

RECOMMENDED KNOWLEDGE Basic skills on GIS and Remote Sensing (Core Modules).

COMPULSORY TEXTBOOK(S) Course folder with handouts, PowerPoint files.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

16

Supervised practicals

0

Unsupervised practicals

0

Individual assignment

5

Group assignment

55

Self study

207

Examination

4

Excursion

0

Fieldwork

0

ASSESSMENT Assesment module 14:  Individual report  Group presentation Type of marking: completed/fail Assessment module 15 (completed/fail): MSc Research Proposal assessment Type of marking: completed/fail

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BIOMASS ESTIMATION AND CARBON ASSESSMENT Module

14-15

Module code

U18-NRS-102

Period

30 July 2018 - 7 September 2019

EC

10

Module coordinator

Leeuwen - de Leeuw, L.M. van (Louise, ir.)

INTRODUCTION The research activities of the Natural Resources department theme FORAGES form the subject framework and organizational structure in which Master students from the NRS-related specialisations Natural Resources Management (NRM), Geo-information Science and Earth Observation for Environmental Modelling and Management (GEM) and Geographic Information Systems and Earth Observation for Environmental Modelling and Natural Resource Management (iGEON) conduct their individual research. FORAGES offers the following research topics:  Biodiversity mapping, vegetation change detection and species distribution modelling;  Crop production modelling and monitoring;  Forest biomass estimation and carbon assessment. With the modules 14-15, the NRS department intends to create an optimal environment for the students to develop a sound MSc Research Proposal. While students work individually on the development of their MSc Research Proposals, they also attend key lectures, presentations and hands-on sessions on methods and techniques. In peer review sessions they have the opportunity to discuss and improve their proposals with the support of their peers. However, most of the time will be spent on individual study and proposal development. At the end of module 15 the students present their MSc Research Proposal for the Proposal Assessment Board (PAB). The PAB decides whether or not the student is admitted to the Block 4 of the Master's programme (modules 16 -23).

LEARNING OUTCOMES At the end of module 14-15 the student is able to:  Present and defend an MSc Research Proposal according to the ITC standards for MSc Proposal Development. In order to achieve this main objective the student is able to:  Describe the research methods and techniques relevant for the FORAGES Research domain; Biomass estimation and carbon assessment;  Select appropriate field methods and related techniques in biomass estimation and carbon assessment and test these methods and techniques for applicability in his/her own research;  Give and receive constructive and critical feedback on the different chapters of a draft MSc Research Proposal.

CONTENT The first week will be spent on the individual MSc Research Proposal. Students will be asked to perform a literature review and to conceptualise their own research. The rationale behind this first week of individual work is that the student has time to reflect on her/his own research.

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This reflection will enable the student and her/his supervisors to reach a well-reasoned decision on the focus of the topic and serve as basis for the selection of fieldwork skills, related to natural resources, needed for the individual research of the student. The second and third week will then be spent on acquiring relevant field skills, practical experience with some specific instruments (such as terrestrial Laser scanner) and analysing the field data in a mini research (group work). The last weeks are then reserved for finalizing the research proposal and the proposal defence. The following is an example of what could be expected of this particular research topic: Field data collection about tree species, tree diameter at breast height (DBH), height, crown diameter and canopy cover percentage as input to allometric equations of different tree species in order to estimate biomass and related carbon content of trees. A special practical will be organised about the use of a Terrestrial Lidar instrument for measuring tree parameters and if required a UAV demonstration and data analysis practical can be arranged if such is required for the student's research topic. During the module ample time will be reserved for writing the proposal and discussing it with peers and supervisors. At least two peer reviews with feedback sessions will be organised and regular progress meetings (in groups) scheduled.

PREREQUISITES Successful completion of modules 1 to 13 of the MSc curriculum. To prepare an acceptable MSc Research Proposal and carry out the subsequent research work, it is necessary to have a sufficient level of knowledge in the chosen research theme. Consequently, if a student wants to undertake research in which the focus differs from that of the specialisation modules followed in Block 2, he/she will have to provide satisfactory evidence that he/she has the relevant background, knowledge and skills.

RECOMMENDED KNOWLEDGE Good knowledge of Remote Sensing and GIS

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

6

Supervised practicals

10

Unsupervised practicals

0

Individual assignment

18

Group assignment

40

Self study

190

Examination

1

Excursion

0

Fieldwork

23

ASSESSMENT Module 14: 1. Conceptual diagram or summary report of proposed research; 2. A total of two module 14 peer review reports; 3. Individual written reflection report. Type of marking: completed/fail

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BLOCK 3: RESEARCH PROFILE

Module 15: MSc Research Proposal assessment Type of marking: completed/fail

130

BLOCK 3: RESEARCH PROFILE

CROP PRODUCTION MODELLING AND MONITORING Module

14-15

Module code

U18-NRS-103

Period

30 July 2018 - 7 September 2018

EC

10

Module coordinator

Venus, V. (Valentijn, MSc)

INTRODUCTION The research activities of the Natural Resources department theme FORAGES form the subject framework and organizational structure in which MSc students from the NRS related courses Natural Resources Management (NRM), Geo-information Science and Earth Observation for Environmental Modelling and Management (GEM) and Geographic Information Systems and Earth Observation for Environmental Modelling and Natural Resource Management (iGEON) conduct their individual research. FORAGES offers the following research topics:  Biodiversity mapping, vegetation change detection and species distribution modelling;  Crop production modelling and monitoring;  Forest biomass estimation and carbon assessment. With the modules 14-15, the NRS department intends to create an optimal environment for the students to develop a sound MSc Research Proposal. While students work individually on the development of their MSc Proposals, they also attend key lectures, presentations and hands-on sessions on methods and techniques. In peer review sessions they have the opportunity to discuss and improve their proposals with the support of their peers. However, most of the time will be spent on individual study and proposal development. At the end of module 15 the students present their research proposal for the Proposal Assessment Board (PAB). The PAB decides whether or not the student is admitted to the Block 4 of the Master programme (modules 16 -23).

LEARNING OUTCOMES At the end of module 14-15 the student is able to:  Present and defend an MSc Research Proposal according to the ITC standards for MSc Proposal Development. In order to achieve this main objective the student is able to:  Describe the research methods and techniques relevant for the FORAGES Research domain;  Select appropriate field work methods and related techniques in crop production modelling and monitoring and test these methods and techniques for applicability for their own research;  Give and receive constructive and critical feedback on the different chapters of a draft MSc Research Proposal.

CONTENT The first week will be spent on the individual research proposal. Students will be asked to perform a literature review and to develop a conceptual framework related to their own research. The rationale behind this first week of individual work is that the student has time to reflect on her/his own research.

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Together with the conceptual framework, this reflection will enable the student and her/his supervisors to reach a well-reasoned decision on which fieldwork skills related to natural resources are needed for the individual research of the student. The second and third week will then be spent on acquiring relevant fieldwork skills and analysing the fieldwork data in a mini research (group work). The last weeks are then reserved for finalizing the research proposal and the proposal defence. The following is an example of what could be expected of this particular research topic: Measure the optical aspects of agricultural land use systems, varying from leaf/canopy chemical variables (spectral reflectance, chlorophyll concentration, water availability, etc.) to canopy structural variables (LAI, fAPAR, etc.).

PREREQUISITES Successful completion of modules 1 to 13 of the MSc curriculum. To prepare an acceptable MSc Research Proposal and carry out the subsequent research work, it is necessary to have a sufficient level of knowledge in the chosen research theme. Consequently, if a student wants to undertake research in which the focus differs from that of the specialisation modules followed in Block 2, he will have to provide satisfactory evidence that he has the relevant background, knowledge and skills.

RECOMMENDED KNOWLEDGE Basic understanding of plant functions (photosynthesis, respiration, etc.)

COMPULSORY TEXTBOOK(S) Lecture notes:Approaches and methods for regional production estimation: an (historical) overview; Temporal dynamics in weather and its influence on CO2 assimilation in crops; Textbook: Land-use systems analysis.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

4

Supervised practicals

4

Unsupervised practicals

12

Individual assignment

18

Group assignment

40

Self study

200

Examination

0

Excursion

0

Fieldwork

10

ASSESSMENT Module 14: 1. Conceptual diagram or summary report of proposed research; 2. Module 14 peer review report; 3. Individual written reflection report. Type of marking: completed/fail

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Module 15: MSc Rsearch Proposal assessment Type of marking: completed/fail

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BIODIVERSITY MAPPING, VEGETATION CHANGE DETECTION AND SPECIES DISTRIBUTION Module

14-15

Module code

U18-NRS-104

Period

30 July 2018 - 7 September 2018

EC

10

Module coordinator

Westinga, E. (Eduard, drs.)

INTRODUCTION The research activities of the Natural Resources department theme FORAGES form the subject framework and organizational structure in which Master students from the NRS related specialisations Natural Resources Management (NRM), Geo-information Science and Earth Observation for Environmental Modelling and Management (GEM) and Geographic Information Systems and Earth Observation for Environmental Modelling and Natural Resource Management (iGEON) conduct their individual research. FORAGES offers the following research topics:  Biodiversity mapping, vegetation change detection and species distribution modelling;  Crop production modelling and monitoring;  Forest biomass estimation and carbon assessment. With the modules 14-15, the NRS department intends to create an optimal environment for the students to develop a sound MSc Research Proposal. While students work individually on the development of their MSc Research Proposals, they also attend key lectures, presentations and hands-on sessions on methods and techniques. In peer review sessions they have the opportunity to discuss and improve their proposals with the support of their peers. However, most of the time will be served for individual study and proposal development. At the end of module 15 the students present their research proposal for the Proposal Assessment Board (PAB). The PAB decides whether or not the student is admitted to the Block 4 of the Master's programme (modules 16-23).

LEARNING OUTCOMES At the end of module 14-15 the student is able to:  Present and defend an MSc Research Proposal according to the ITC standards for MSc Proposal Development. In order to achieve this main objective the student is able to:  Describe the research methods and techniques relevant for the FORAGES Research domain biodiversity mapping, vegetation change detection and species distribution modelling;  Select appropriate field work methods and related techniques inbiodiversity mapping, vegetation change detection and species distribution modelling and test these methods and techniques for applicability for their own research;  Give and receive constructive and critical feedback on the different chapters of a draft MSc Research Proposal.

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CONTENT The first week will be spent on the individual research proposal. Students will be asked to perform a literature review and to develop a conceptual framework related to their own research. The rationale behind this first week of individual work is that the student has time to reflect on her/his own research. Together with the conceptual framework, this reflection will enable the student and her/his supervisors to reach a well-reasoned decision on which fieldwork skills related to natural resources are needed for the individual research of the student. The second and third week will then be spent on acquiring relevant fieldwork skills and analysing the fieldwork data in a mini research (group work). The last weeks are then reserved for finalizing the research proposal and the proposal defence. The following is an example of what could be expected of this particular research topic: Vegetation status or change can be mapped based on old aerial photographs and recent satellite images. The most recent remotely sensed data will be verified, based on ground observation. Vegetation cover measurement and estimation techniques will be explained and trained in the field.

PREREQUISITES Successful completion of modules 1 to 13 of the MSc curriculum. To prepare an acceptable MSc Research Proposal and carry out the subsequent research work, it is necessary to have a sufficient level of knowledge in the chosen research theme. Consequently, if a student wants to undertake research in which the focus differs from that of the specialisation modules followed in Block 2, he will have to provide satisfactory evidence that he has the relevant background, knowledge and skills.

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

8

Supervised practicals

16

Unsupervised practicals

0

Individual assignment

11

Group assignment

36

Self study

200

Examination

1

Excursion

0

Fieldwork

16

ASSESSMENT Module 14: 1. Conceptual diagram or summary report of proposed research; 2. Module 14 peer review report; 3. Individual written reflection report. All assessments have to be successfully completed in order to obtain 'completed' for this module. Type of marking: completed/fail Module 15: MSc Research Proposal assessment Type of marking: completed/fail

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PEOPLE, LAND AND URBAN SYSTEMS (PLUS) RESEARCH METHODS AND TECHNIQUES Module

14-15

Module code

U18-PGM-104

Period

30 July 2018 - 7 September 2018

EC

10

Module coordinator

Richter, C. (Christine, dr.)

INTRODUCTION The research activities of PGM Department, People Land and Urban Systems (PLUS), form the subject framework and organizational structure in which Master students from the PGM-related specialisations, Land Administration (LA) and Urban Planning and Management (UPM), conduct their individual research. With the modules 14-15 PLUS Research Methods and Techniques, the PGM department intends to create an optimal environment for the students to develop a sound MSc Research Proposal. While students work individually on the development of their MSc Research Proposals, they also attend key lectures, presentations and hands-on sessions on PLUS methodologies, methods and techniques. In peer review sessions they have the opportunity to discuss and improve their proposals with the support of their peers. However, most of the time will be served for individual study and proposal development. At the end of modules 14-15 PLUS Research Methods and Techniques the students present their MSc Research Proposal to the Proposal Assessment Board (PAB) . The PAB decides whether or not the student is admitted to Block 4 of the Master programme (modules 16-23). For more information on the PLUS research theme please visit: http://www.itc.nl/PLUS

LEARNING OUTCOMES At the end of module 14-15 PLUS the student is able to:  Present and defend an MSc Research Proposal according to the ITC standards for MSc Proposal Development. In order to achieve this main objective for the modules 14-15 PLUS the student is able to:  Discuss research methodologies, methods and techniques relevant for the PLUS Research domain;  Select the appropriate research method and related techniques to carry out coherent research in the PLUS Research domain  Give and receive constructive and critical feedback on the different chapters of a draft MSc Research Proposal.  Write a coherent MSc research proposal

CONTENT Key lectures  Key presentations by the PGM Professors  Introductory lectures for research strategies and methods PLUS Methods and Techniques  Presentations and/or hands-on sessions;

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

Particular research methods and techniques used in PGM research (PLUS): concept operationalization and method integration (specifically case study approach); qualitative and quantitative data collection and analysis, ethical considerations in the use of spatial data and mapping.

Peer review and proposal development • Individual proposal writing; • Peer review sessions (5).

PREREQUISITES   

Module 11; MSc Research topic identified; First draft of MSc Research Proposal, especially literature review related to the research topic

COMPULSORY TEXTBOOK(S)   

Bryman, A. (2016).Social Research Methods.Oxford University Press: chapters 2,3,24; and additional excerpts will be made available on Blackboard Readings for survey methods and statistical analysis:will be made available on Blackboard Required for in-group discussions during the ethics sessions are the following articles:  Kitchin R. 2016 The ethics of smart cities and urban science. Phil. Trans. R. Soc. A 374 : 20160115.http://dx.doi.org/10.1098/rsta.2016.0115 (15 pages)  Robert W. Lake, R. W. (1993) Planning and applied geography: positivism, ethics, and geographic information systems, Prog Hum Geogr; 17; 404 DOI: 10.1177/030913259301700309 (10 pages)  Crampton, J. (1995) The Ethics of GIS, Cartography and Geographic Information Systems, 22 (1). pp. 84-89

[Other reading materials will be available on Blackboard, but are not required reading.]

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ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

20

Supervised practicals

45

Unsupervised practicals

0

Individual assignment

23

Group assignment

0

Self study

200

Examination

0

Excursion

0

Fieldwork

0

ASSESSMENT  







Modules 14-15 PLUS will be assessed in the following way: Active participation in lectures and hands-on sessions (students can miss a maximum of three of the lectures and/or supervised practical sessions. For up to two additional sessions missed an extra graded assignment will be given. All peer-review sessions need to be attended). Peer review reports. Students are required to submit their draft proposals (two days before) and the peer review/feedback reports (one day before) in time for the peer-review sessions; and all peer review sessions need to be attended; Method comparison report: Students have to write a small report of maximum two pages, where they compare at least to data collection methods and respective analysis design in terms of suitability to address their research objective. The MSc Research proposal assessment will lead to a "completed" or "fail" on the course record for module 15.

Type of marking for both modules: completed/fail Assessment Matrix: Pass Fail Attendance lectures and No more than three lecture Missed more than three lecture hands-on sessions and/or supervised practical and/or supervised practical sessions missed and did not sessions and/or did not submit submit extra assignments to two extra assignments for up to 2 additional missed sessions; additional missed sessions; did attended all peer-review not attend all peer-review sessions sessions Peer review reports All peer review reports Not all peer review reports submitted submitted and

or

Approved by staff

Not approved by staff

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Method comparison report

 Constructive feedback  In-depth  Critical Approved by staff

 Negative feedback  Superficial  Sloppy Not approved by staff

1. Submitted on time on Lacks one or more of the Blackboard with file correctly points listed in column 2, row 4 named of this table. 2. Main research problem and general objective are clearly stated. 3. Chosen and briefly described at least two data collection methods from module 14 4. Describes the different data acquired through each collection method (primary/secondary; type of respondents; type of study area; sample size, etc. depending on your chosen methods). 5. Describes the type of analysis that would be performed on the data collected through each method. 6. Explains, what are the advantages and disadvantages of each method with respect to the research problem 7. Explains, what are the practical (for example large number of survey; gaining access to respondents, etc.) and ethical considerations pertaining to each chosen method. 8. Explains how or why not, the two methods can be combined to address research problem 9. Coherently written and between 750-900 words in length

Responsible for assessment: 1. Attendance: respective lecturing staff (attendance list); 2. Peer review process: facilitator of the peer review group; 3. Method comparison report: module 14-15 team, pass/fail, including feedback; 4. Research proposal by individual student's thesis supervision committee.

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WATER CYCLE AND CLIMATE Module

14-15

Module code

U18-WRS-105

Period

30 July 2018 - 7 September 2018

EC

10

Module coordinator

Salama, S. (Suhyb, dr.ir.)

INTRODUCTION The purpose of modules 14 and 15 is to:  Deepen the knowledge and skills of students within the research activities of the department;  Help the student to define his own MSc Research Proposal;  Write an MSc Research Proposal and defend this to the Proposal Assessment Board.

LEARNING OUTCOMES 1. Define the objectives of your research, whereby your proposed MSc project has a visible added value; 2. Identify the needed data and design field measurements that meet recognized quality standards; 3. Select/design appropriate method and data analysis for your data that lead to the achievement of your objectives; 4. Present scientific information in English at a standard acceptable to the scientific community; 5. Prepare you to write an MSc Research Proposal and defend this to the Proposal Assessment Board.

CONTENT A project-driven learning approach is selected for this module, whereby the lectures are confined to the first two weeks: Lectures are on Mondays (30 July and 6 August):  Lecture 1: Definition of the research and data collection;  Lecture 2: Describing the method and the analysis. Assignment presentations are on Fridays (3 and 10 August):  Assignments 1 and 2 are presented on 3 August;  Assignment 3 is presented on 10 August. 1. Assignment 1: Added value of the research project; 2. Group Assignment 2: Planning field campaign; 3. Describing the method and performing the analysis. The last four weeks (13 August to 7 September) are reserved for proposal writing.

PREREQUISITES The WREM specialisation modules from 5 to10.

COMPULSORY TEXTBOOK(S) Compulsory reading material will be made available prior to the start of module.

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BLOCK 3: RESEARCH PROFILE

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

5

Supervised practicals

0

Unsupervised practicals

0

Individual assignment

45

Group assignment

30

Self study

200

Examination

8

Excursion

0

Fieldwork

0

ASSESSMENT The assessment of modules 14 and 15 consists of two main parts:  A. Presenting assignments 1, 2 and 3 leading to a "completed" or "fail" for module 14;  B.The MSc Research Proposal is finalized by the student in mutual agreement with his MSc supervisors, appointed in Module 11. In the last week of Module 15, the research proposal is presented before a Proposal Assessment Board, leading to a "completed" or "fail" for module 15.

141

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BLOCK 4: MSC RESEARCH

142

BLOCK 4: MSC RESEARCH

MSC RESEARCH Module

16-23

Module code

P18-EDU-105

Period

10 September 2018 - 8 March 2019

EC

40

Module coordinator

Dopheide, E.J.M. (Emile, drs.)

INTRODUCTION The final stage of the Master's programme is dedicated to the execution of an individual research project. Each student works independently on an approved research topic (see module 14-15) connected to one of the research themes of ITC (see http://www.itc.nl/research-themes ). In this final block of the course, the students further develop their research skills, interact with their fellow students, PhD researchers and staff members and, finally, demonstrate that they have achieved the learning outcomes of the Master's programme by research, on a satisfactory academic level.

LEARNING OUTCOMES The student must be able to:  Address a well-formulated relevant research problem of sufficient scope and depth linked to relevant literature;  Undertake research with a clear and transparent methodology with proper use of concepts, methods and techniques;  Write a well-structured and readable thesis report with a clear layout;  Orally present and defend the research and use proper argumentation in the discussion about the research;  Work in a structured and rather independent way, while making adequate use of the guidance of the supervisor.

CONTENT Based on the pre-proposal handed in before module 11, and the final accepted research proposal prepared in module 15, the student will carry out the planned activities. The students will be provided with guidelines for the thesis early in the course (specifically in module 11). Regular individual progress meetings with the supervisors will be held to monitor the progress on the research and thesis writing, and records of the progress will be kept. The supervisors keep the course director informed about the progress. The activities normally include:  Describe and define a problem statement and research topic and its research margins;  In-depth literature review, including assessment of the usability of literature and previous research;  Collection of relevant online - and archived data;  If appropriate, preparation and execution of fieldwork to collect primary data required for the research;  Data processing and analysis and, if deemed necessary, adjustment of the research plan in consultation with the supervisors (based on sound arguments);  Active participation in seminars and capita selecta of the research theme under which the MSc research resorts;  Mid-term presentation;  Preparation of the final manuscript of the MSc thesis (=hardcopy thesis and digital files with thesis, appendices and full dataset including the original data and results);  A critical review of the quality, use and usefulness of the data and results, as well as the learning process;

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

Oral presentation and defence of the MSc thesis before the Thesis Assessment Board, all in accordance with the relevant paragraphs of the MSc regulations.

During the MSc research phase a number of follow-up lectures of module 11 (Research Skills) will be given to support the MSC research:  Preparing for the midterm and MSc Research exam;  Research quality and thesis assessment;  Structuring results, discussion and conclusions;  Graphic presentation in a MSc thesis.

PREREQUISITES  

Successful completion of Master's programme modules 1-15 Proven ability to undertake independent research through an accepted MSc research proposal in module 15

For details see Education and Examination Regulations and Rules and Regulations of the Faculty ITC Examination Board

ALLOCATED TIME PER TEACHING AND LEARNING METHOD Teaching / learning method

Hours

Lectures

6

Supervised practicals

0

Unsupervised practicals

0

Individual assignment

1144

Group assignment

0

Self study

0

Examination

2

Excursion

0

Fieldwork

0

ASSESSMENT The MSc Research exam consists of the assessment of the Thesis and the oral test that includes a presentation and defence. A Thesis Assessment Board will assess the Thesis and oral test on scientific scope and depth, scientific method, reporting, presentation and defence, and research process, and will decide on the mark.

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BLOCK 4: MSC RESEARCH

THEME: ACQUISITION AND QUALITY OF GEOSPATIAL INFORMATION (ACQUAL) Module

16-23

Module code

U18-EOS-101

Period

10 September 2018 - 8 March 2019

EC

40

Module coordinator

Vosselman, M.G. (George, prof.dr.ir.)

146

BLOCK 4: MSC RESEARCH

THEME: SPATIO-TEMPORAL ANALYTICS, MAPS AND PROCESSING (STAMP) Module

16-23

Module code

U18-GIP-102

Period

10 September 2018 - 8 March 2019

EC

40

Module coordinator

Kraak, M.J. (Menno-Jan, prof.dr.)

147

BLOCK 4: MSC RESEARCH

THEME: 4D-EARTH Module

16-23

Module code

U18-ESA-101

Period

10 September 2018 - 8 March 2019

EC

40

Module coordinator

Jetten, V.G. (Victor, prof.dr.)

148

BLOCK 4: MSC RESEARCH

THEME: FOREST AGRICULTURE AND ENVIRONMENTAL IN THE SPATIAL SCIENCES (FORAGES) Module

16-23

Module code

U18-NRS-101

Period

10 September 2018 - 8 March 2019

EC

40

Module coordinator

Nelson, A.D. (Andy, prof.dr.)

149

BLOCK 4: MSC RESEARCH

THEME: PEOPLE, LAND AND URBAN SYSTEMS (PLUS) Module

16-23

Module code

U18-PGM-102

Period

10 September 2018 - 8 March 2019

EC

40

Module coordinator

Maarseveen, M.F.A.M. van (Martin, prof.dr.ir.)

150

BLOCK 4: MSC RESEARCH

THEME: WATER CYCLE AND CLIMATE (WCC) Module

16-23

Module code

U18-WRS-101

Period

10 September 2018 - 8 March 2019

EC

40

Module coordinator

Su, Z. (Bob, prof.dr.)

151

UNIVERSITY OF TWENTE FACULTY OF GEO-INFORMATION SCIENCE AND EARTH OBSERVATION (ITC) PO Box 217 7500 AE ENSCHEDE The Netherlands T: +31 53 487 4444 F: +31 53 487 4400 E: [email protected] I: www.itc.nl Study guides are also published on ITC’s website, see

www.itc.nl/study