Gis Sem 6

Technological Advances using Remote Sensing and GIS in Forestry Sector of India In spite of an impressive history of scientific forest management in India, the high forest type diversity and vastness of forest resources have posed challenging gaps in forestry database. However, with the advent of Remote Sensing (RS) and Geographical Information System (GIS) hopes of bridging these gaps has arisen. The paper provides an overview on how enhanced measurement of natural resources, facilitated by RS and GIS, may prove invaluable for forest management, with emphasis on developments made during the last one decade. The paper also highlights the pivotal role of Forest Survey of India (FSI) in this respect. Forest resources database needs to be set, run and updated at both divisional (basic unit of forest administration) and national levels in India. At the divisional level the forest officers are guided by working plan, the document that describes the division’s profile and prescribes action for future two decades. It needs periodic updating with all the relevant information of a division. At the national level an assessment of forest and tree cover is required for ascertaining the ecological, economic and even social value the green cover carries. In 1980s, after National Remote Sensing Agency (NRSA) displayed the potential of forest cover mapping and assessment, FSI successfully built capacity for highly accurate RS based forest cover assessment. FSI was subsequently mandated to make biennial forest cover assessment with remote sensing. With improvement in scale of interpretation and resolution of imagery, FSI has made significant progress in forest cover mapping and assessment. In addition to forest cover mapping, remote sensing technology has been extensively used of late to prepare classified Tree Outside Forest (TOF) maps depicting block, linear and scattered patches of trees groups up to 0.1 ha. RS combined with GIS has also been applied in a number of projects, particularly in the preparation of working plans (case study of Mizoram), national forest type mapping, forest fire mapping, etc. Such spatial information generated on maps is of immense value to the planner and policy makers at the district / state and national level.The Global Positioning System (GPS) technology is now being used extensively for field inventory. GPS in conjunction with RS and GIS has been used by FSI for implementation of suitable sampling design to carry out forest inventory in inaccessible areas also.One of the key areas for dissemination of knowledge in the field of RS, GIS and GPS has been training. FSI has been building the capacities of forestry personnel over the year in the field of remote sensing, GIS and GPS applications is forest resource management. FSI also helps SFDs set up GIS cells, which in conjunction with FSI-trained personnel, further facilitate dissemination of these modern technologies in each state.In the final part, the paper seeks to delineate the areas of focus at national and state levels so that duplication of effort and resources is averted. The paper also suggests concrete steps that may be taken for further effective use in forestry of the technological advancements in geoinformatic techniques.

Application of Remote Sensing and GIS for forest fire susceptibility mapping using likelihood ratio model ABSTRACT This paper presents capability of remote sensing and Geographical Information Systems (GIS) to evaluate forest fire susceptibility. Forest Fire locations were identified in the study area from historical hotspots data from year 2000 to 2005 using AVHRR NOAA 12 and NOAA 16 satellite images. Various other supported data such as soil map, topographic data, and agro climate was collected and created using GIS. These data were constructed into a spatial database using GIS. The factors that influence to fire occurrence, such as fuel type and Normalized Differential Vegetation Index (NDVI) were extracted from classified Landsat-7 ETM imagery. Slope and aspect of topography, were derived from topographic database. Soil type was extracted from soil database and dry month code from agroclimate data. Forest fire susceptibility was analyzed using the forest fire occurrence factors by likelihood ratio method. The results of the analysis were verified using forest fire location data. The validation results show satisfactory agreement the susceptibility map and the existing data on forest fire location. The GIS was used to analyze the vast amount efficiently, and statistical programs were used to maintain the specificity and accuracy. The result can be used for early warning, fire suppression resources planning and

allocation. 1.0 Introduction Fire has been identified as one of the major threats causing the loss of forests in several states in Malaysia. According to Forestry Department of Peninsular Malaysia (JPSM) and Forest Research Institute Malaysia (FRIM) statistics show that during the last 7 years (between 1992 and 1998), more than 1600 ha of peat swamp forests of Peninsular Malaysia have been destroyed by fire (Wan Ahmad, 2002). In the event of a prolonged spell without rain, and a lowering of the water table in the peat swamp forest, the organic layers becomes completely dry and is very prone to fire. Fires in these peat swamp forest create much more smoke per hectare than other types of forest fires and are difficult to extinguish. Therefore, the understanding of the areas at risk to fire needs to be closer concentration in peat swamp forests. A precise evaluation of forest fire problems and decision on solutions can only be satisfactory when a fire hazard zone mapping is available (Jaiswal et al, 2002). Geospatial technology, including Remote Sensing and Geographic Information Systems (GIS), provides the information and the tools necessary to develop a forest fire susceptibility map in order to identify, classify and map fire hazard area. Before, during and after disaster, the accurate sharing of information is important. Making the information available via the world-wide web, people can share information to assess the situation and make decisions. In this study we want to develop and produce a forest fire hazard model and map for Sungai Karang and Raja Muda Forest Reserve, Selangor (Peat Swamp Forest) using frequency ratio, which is a statistical model. 2.0 Study Area The study area is located approximately between Upper Left (3º 23’ 53.6”E and 101º 3’ 36.3”N) and Lower Right (3º 45’ 18.05”E and 101º 30’ 55.33”N). The area located within the Kuala Selangor District, northern part Selangor. The landuse at the study area is mainly peat swamp forest, plantation forest, inland forest, scrub, grassland and ex-mining area. The landform of the area ranges from very flat terrain, especially for the peat swamp forest, ex mining, grassland and scrub area, to quite hilly area for the natural forest ranging between 0- 420 meter above sea level. Based on Malaysian Meteorological Services Department, the temperature of northern part of Selangor is between 29º C to 32º C and mean relative humidity of 65% to 70%. The highest temperature is between April to June while the relative humidity is lowest in June, July and September. The rainfall about 58.6mm to 240mm per month was recorded in the study area (Tanjung Karang weather station provided by Malaysian Meteorological Services Department). There is a high potential of danger of fire in the dry season especially in the peat swamp forest and plantation forest. Most of fires are caused by human activities, either due to carelessness or burning activities in crop plantations. On 1995 and 1999, fire was occurred in the peat swamp forest area within the study area. Figure 1 shows Raja Muda Musa and Sg Karang Reserve Forest, Selangor.

Figure 1: Sg Karang and Raja Muda Musa Forest Reserve, Selangor

3.0 Data using GIS and Remote Sensing Accurate detection of the location of hotspots is very important for probabilistic forest fire susceptibility analysis. Recent advances in remote sensing, GIS and computer technologies provided an opportunity to assess and monitor the land cover changes in a near real time basis. NOAA AVHRR satellite data with a spatial resolution of 1.1 km at nadir was found to be extremely useful for national-scale assessment and monitoring of major land cover types (Giri & Shrestha, 1996). Historical forest fire data were collected from satellite remote sensing NOAA AVHRR 12 and NOAA 14 sensors for last 5 years. To assemble a database to assess the surface area and number of hotspots in the study area, a total of 112 hotspots were mapped in a mapped area of 616 km2. The imagery from Landsat-7 ETM of path 157 and row 058 acquired on 21 September 2001 was used in this study. The spatial resolution for Landsat-7 ETM was 30 meter x 30 meter. Fuel map were extracted from satellite imagery. GIS data and ancillary data consist of biophysical and socio-economic variable is based on 1: 25,000 scale. Contour, administrative boundaries, water resources, settlement, transportation infrastructure are based on the topographic map from Survey Department (JUPEM). Forest fire reports have been collected from Forest Department Peninsular Malaysia (JPSM). Hotspots prone areas, fire occurrence map, peat swamp map and soil maps have been acquired and digitized. Socio-economic data such as population data and socio-economic data were obtained from Statistical Department. Meteorological data such as temperature and relative humidity and Fire Danger Rating System (FDRS) map were obtained from Malaysian Meteorological Services Department. Image processing was carried out using ERDAS Imagine 8.7 and PCI Geomatica 9.0. To apply the probabilistic method, a spatial database that considers forest fire-related factors was designed and constructed. These data are available in Malaysia either as paper or as digital maps. The spatial database constructed is shown in Table 1. There were six factors that were considered in calculating the probability, and the factors were extracted from the constructed spatial database. The factors were transformed into a vector-type spatial database using the GIS, and forest fire-related factors were extracted using the database. A digital elevation model (DEM) was created first from the topographic database. Contour and survey base points that had elevation values from the 1:25,000-scale topographic maps were extracted, and a DEM was constructed with a resolution of 20 m. Using this DEM, the slope angle and slope aspect were calculated. The soil map is obtained from a 1:100,000-scale soil map. Landsat-7 ETM, 30 meter x 30 meter resolution was used for extracting fuel map in the Sg Karang and Raja Muda Forest Reserve, Selangor. Multi-parametric analyses or overlays was

carried out using GIS which severity zones and the prioritize was based on frequency ratio approach. Land use and land cover data was classified using a Landsat-7 ETM image employing an unsupervised classification method and topographic map. The fuel type has been classified into ten classes, such as peat swamp forest, mangrove, inland forest, rubber plantation, grassland, oil palm plantation, paddy, shrub, cleared land and unclassified (water bodies and urban) were extracted for fuel type mapping. Finally, the NDVI map was obtained from Landsat-7 ETM satellite images. The NDVI value was calculated using the formula NDVI = (IR – R) / (IR + R), where IR value is the infrared portion of the electromagnetic spectrum, and R-value is the red portion of the electromagnetic spectrum. The NDVI value denotes areas of vegetation in an image. The factors were converted to a raster grid with 30 m × 30 m cells for application of the frequency ratio model. The area grid was 2,418 rows by 1,490 columns (i.e., total number is 3033610) and 112 cells had forest fire occurrences. 4.0 Methodology 4.1 Frequency ratio model and its application Frequency ratio approaches are based on the observed relationships between distribution of hotspot and each hotspot-related factor, to reveal the correlation between hotspot locations and the factors in the study area. Using the frequency ratio model, the spatial relationships between hotspot-occurrence location and each factors contributing hotspot occurrence were derived. The frequency is calculated from analysis of the relation between hotspot and the attributing factors. Therefore, the frequency ratios of each factor’s type or range were calculated from their relationship with hotspot events as shown in Table 2. In the relation analysis, the ratio is that of the area where hotspots occurred to the total area, so that a value of 1 is an average value. If the value is greater than 1, it means a higher correlation, and value lower than 1 means lower correlation. To calculate the Forest Fire Susceptibility Index (FFSI), each factor’s frequency ratio values were summed to the training area as in equation (1). The hotspot susceptibility value represents the relative susceptible to forest fire occurrence. So the greater the value, the higher the susceptible to forest fire occurrence and the lower the value, the lower the susceptible to forest fire occurrence. FFSI = Fr1 + Fr2 + …… + Frn (1) (FFSI: Forest Fire Susceptibility Index; Fr: Rating of each factors’ type or range) The forest fire susceptibility map was made using the FFSI values and for interpretation is shown in Figure 2. This study consists of development of Forest Fire Susceptible Map. Figure 2 shows flowchart of methodology.

Figure 2: Flowchart of methodology

5.0 Conclusion and Discussion In the present study, frequency analysis method was applied for the forest fire susceptibility mapping for Sungai Karang and Raja Muda Forest reserve. The validations results show that the frequency ratio model has predication accuracy of 3.52%. Here, the authors can conclude that the results of frequency ratio model had shown the best prediction accuracy in forest fire susceptibility mapping. The frequency ratio model is simple, the process of input, calculation and output can be readily understood. The large amount of data can be processed in the GIS environment quickly and easily. Moreover, it is hard to process the large amount of data in the statistical package. Recently, forest fire susceptibility mapping has shown a great deal of importance for haze detection and fire prevention in forest area. The results shown in this paper can help the concerned authorities for forest fire management and mitigation. However, one must be careful while using the models for specific mitigation. This is because of the scale of the analysis where other forest fire related factors need to be considered. Therefore, the models used in the study are valid for awareness so that necessary prevention measures can be taken during the time of forest fire. In this paper, Forest fire susceptibility map was developed to determine the level of severity of forest fire hazard zones in terms of mapping susceptibility to fire by assessing the relative importance between fire factors and the location of fire ignition

Application of Satellite Based Remote Sensing for Monitoring and Mapping of India’s Forest and Tree Cover Introduction Forests are ecological as well as socio-economic resource. These have to be managed judiciously not only for environmental protection and other services but also for various products and industrial raw materials. Considering the crucial role forests play in the country’s ecological stability and economic development, the current National Forest Policy (1988) in India aims at maintaining a minimum of 33 percent of country’s geographical area under forest and tree cover.

This requires periodic monitoring of the forest cover of the country for effective planning and sustainable development. Forest Survey of India (FSI), an organization under the Ministry of Environment & Forests (Government of India) has been mandated to monitor and map country’s forest cover on a biennial basis. Consequently, FSI has been carrying out assessment of forest cover in the country using satellite based remote sensing data and has been publishing its findings in the State of Forest Report (SFR) every two years. Its first assessment of forest cover made in 1987 was published as SFR 1987 and the latest i.e., the eighth one as SFR 2001. With the improvement in satellite data resolution and adoption of digital image processing by FSI, it has been possible to assess forest cover patches as small as 1ha. However, there exists a significant tree cover wealth outside conventional forest areas, most of which is less than 1 ha in extent. These include small patches of trees in plantations and woodlands, or scattered trees on farms, homesteads and urban areas, or trees along linear features such as roads, canals, bunds etc. and constitute significant area. In a study done in Haryana State by FSI in 1997, it was found that growing stock of trees outside forest was approximately seven times than that from natural forests. Trees outside forests (TOF) are therefore considered an alternative but significant source of fuel, fodder, timber and environmental services to the local people. In 2001 assessment, FSI assessed tree cover (less than 1ha in extent) in the country using a stratified sampling and field inventory, and estimated it to be 2.48% of country’s geographical area. Thereafter, FSI has developed a methodology based on high-resolution satellite data for mapping and stratification of TOF leading to an improved sampling design for field inventory. In the present paper, methodologies of forest cover and tree cover assessments as used by the FSI are discussed. Forest Cover Assessment: Till recently, FSI was using mostly visual interpretation of satellite data on 1:250,000 scale for assessment of forest cover. However, in its latest assessment i.e., 2001 assessment, it used digital interpretation of satellite data on 1:50,000 scale for mapping and monitoring forest cover. The present methodology uses Digital Image Processing software and involves the following steps: Acquisition of satellite data: The digital data of IRS-1C and 1D LISS III is acquired from NRSA in CD.. India is covered in about 340 scenes, of IRS 1C and 1D. One scene covers an area of about 20000 km2, having an overlap of about 10% with adjoining scenes. While procuring the data, care is taken to ensure that it is cloud free (with not more than 10% cloud cover) and therefore data pertaining to the period from October-December is preferred. Geometric Rectification of raw data: After downloading the data into computer, rectification is carried out in each image to provide Latitude and Longitude information into raw satellite scene using raster based geometric corrections. Rectification carried out in geographic projection is reprojected in shape of polygonal projection and the scene is geo-coded with using SOI toposheets. Mosaicing of rectified scenes: Different scenes, which are already rectified, may have to be merged together to get one combined FCC (False Colour Composite). FCC of sheet is extracted from mosaiced scene in a chosen area of interest. Image is displayed in three bands 3, 2, 1. Masking of non-forest areas is done separately to extract forest areas on the basis of ground knowledge, cover map of previous cycles and on the basis of information available through SOI toposheets in the area of interest. Classification of forest cover using NDVI: Interactive method of display is used for assigning threshold values for each class (open, dense and scrub) on the basis of the ground knowledge to highlight forest/vegetated areas. Density class of forest cover and colour is accordingly allocated. Survey of India toposheets is used for delineating boundaries of each district and classified map of forest cover is generated.

Flow chart of methodology of dynamic forest cover mapping using remote sensing is shown in figure-1

Figure1- Flow chart of Forest cover mapping using remote sensing

The output includes forest cover maps on 1:50,000 scale. These maps show forest cover in three classes- (i) Dense forest, having canopy density of more than 40%, (ii) Open Forests with canopy density between 10-40% and (iii) Scrub which are forest areas having less than 10% canopy density. These maps are also generated for district and States/Union Territories by overlaying the respective District/State/UT administrative boundary. Area under forest cover at District/State/country level is then assessed. Change maps are also prepared to depict changes taking place under different land cover classes. In its latest assessment of 2001, taking advantage of advancements in remote sensing and improvement in digital interpretation qualities, FSI has provided a much more comprehensive status of forest cover in the country than in the previous assessments. Some of the new features incorporated in this assessment are: 





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For the first time FSI has interpreted the satellite data of the entire country digitally. In earlier estimates, interpretation has been largely visual. Digital interpretation has the advantage of overcoming subjectivity prevalent in visual method. Due to absorption of digital image processing technique, it has been possible for FSI to interpret the data on 1:50,000 scale. This has resulted in providing more realistic information on forest cover as areas having forest cover down to 1 ha could be delineated while in earlier assessments, forest cover down to 25 ha could only be delineated. Similarly blanks down to 1 ha within forested areas can be separated. The entire exercise has resulted in new base-line information on forest cover. As perennial woody vegetation (including bamboos, palms, coconut, apple, mango etc.) has been treated as tree and thus all lands with tree crops, such as agro-forestry plantations, fruit orchards, tea and coffee estates with trees etc., have been included in forest cover. Mangrove cover has been classified into dense and open mangrove cover. The area of mangrove cover so assessed has been merged in the respective area figures of dense and open forest cover.

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A classification is not complete unless its accuracy is assessed. For the first time an independent and systematic assessment of accuracy of satellite data interpretation was made. An error matrix was generated by comparing classified forest cover with the actual forest cover on the ground at 3,608 locations spread throughout the country. High resolution PAN data was used as proxy for ground verification. The overall accuracy of forest cover classification was found to be 95.9%. Though forest cover in areas as less as 1 ha in extent could be assessed using satellite data, significant tree cover exists in patches of less than 1 ha and in linear shapes along roads, canals, etc. and scattered trees that can not be assessed using remote sensing. An attempt is made for the first time to assess such tree cover using ground inventory method.

The abstract of forest cover assessment 2001 is given in Table 1. Table 1: Forest Cover as per 2001 assessment Area (km2)

Class

Percent of Geographic Area

Forest Cover a) Dense

416,809

12.68

b) Open

258,729

7.87

Total Forest Cover*

675,538

20.55

Non-forest Scrub

47,318

1.44

Total Non-forest**

2,611,725

79.45

3,287,263

100.00

Total Geographic Area

*includes 4,482 km under mangroves (0.14 percent of country’s geographic area) **includes scrub 2

Forest Cover Assessment 2001

Figure 2- Forest cover in India

Tree Cover Assessment: TOF is assessed in rural as well as urban areas, although the greater part exists in rural areas. Initially, conventional field method was used for TOF assessment in rural areas. The state or a group of districts is considered as the study area. Since this area is fairly large there is every possibility of heterogeneity of the study variable i.e. growing stock. TOF being planted along with agricultural crops is likely to be influenced by the Agro-ecological variables. Therefore, study area is stratified according to agro-ecological zones (AEZ), which has already been demarcated by other agencies. Districts, in India, are the basic planning and administrative units, which influence the TOF and therefore, is considered for further stratification of AEZs. Villages are treated as sampling units. Optimum number of sample villages is selected randomly from different districts proportionate to the TOF area of the same. Complete enumeration of all the trees with diameter of 10 cm and above at breast height in the randomly selected villages in each district is carried

out. Data is collected on pre-designed formats following prepared instructions for fieldwork and collected data is processed following appropriate formula. The above-mentioned methodology was providing accurate estimates but was very time consuming. It was not able to provide precise information at district level, which is the basic unit for economic planning. Methodology Using Remote Sensing Data To do away with these constraints many alternatives were tried and finally a methodology based on digital image processing and GIS analysis using multi spectral and panchromatic data for mapping of trees outside forests (TOF) was devised. The remote sensing data is used to provide stratification of the TOF resources, which is utilized to increase the precision and is time effective. In addition, sometimes the objectives of TOF resource assessment may require spatial distribution of resources on maps along with several other features. This objective can also be appropriately tackled by this methodology. High-resolution satellite imageries provide information even up to identification of a single tree but these are cost prohibitive. The IRS LISS III data, which is multi spectral, and has a resolution of 23.5 m ×23.5 m, provide information on vegetation cover. There are techniques available through which tree vegetated land can be segregated from agriculture land if the tree vegetated patch is about one ha and more. However, LISS data cannot be used for smaller patches or scattered trees. The IRS PAN data, which is monochromatic, having resolution of 5.8 m × 5.8 m can identify a tree vegetated land even less than 0.1 ha. Therefore, both LISS III and PAN imageries are used for stratification of TOF resources on the basis of geometrical formation of trees i.e. block plantation (group of trees), linear plantation and scattered trees.

Raw images of IRS IC/D PAN and LISS III data for the period between Oct.-Dec. 2002 are acquired from National Remote Sensing Agency, Hyderabad. Thereafter, the PAN image is geometrically rectified with the help of Survey of India toposheets on 1:50,000 Scale. The LISS III image is then co registered with the rectified PAN images. PAN and LISS III images are fused

using appropriate algorithm. Since mapping of TOF areas is the objective, the boundary of forest area is digitized and masked out. The remaining fused image are classified into settlement, water bodies, burnt areas, tree cover and agriculture area using appropriate classifier viz. Maximum likelihood. This classification enables the interpreter to distinguish between tree cover and other classes on fused image. This classified image is visually analyzed with respect to fused images for editing and refinement for inclusion and omissions. Since a cluster of trees having 0.1 ha area or more is defined as block plantation, pixels are clumped and cluster of pixels having area less than 0.1 ha are eliminated. After editing of the classified image the final classified map is generated which is done by taking the PAN, LISS-III and the fused images. Incorporating these corrections final classified image is prepared having three classes in TOF areas, namely, Block, Linear and Scattered. From the classified TOF map information pertaining to area under Block, Linear, Scattered and water bodies can be calculated. In addition, such areas, which do not support tree vegetation, like rivers and water bodies, snow covered mountains, marshes,etc. which is termed as Culturable Non Forest Area (CNFA)can also be calculated. Such information is very helpful for district level planning. Flow chart of methodology of Tree Cover mapping using remote sensing is shown in Figure-3

Figure 3- Flow chart of methodology of Tree Cover mapping

Sampling Method Besides generation of TOF maps, the information on block, linear and scattered patches can be used to estimate the number of trees and the corresponding volume (species wise) using appropriate sampling design by laying out optimum number of plots randomly selected in every stratum. Since the variability in each stratum is expected to be different demanding different sample and plot sizes, pilot studies were conducted to ascertain these so that the variability of the stratum can be properly addressed. In this pilot study, 0.1 ha, 0.2 ha and 0.3 ha plots were considered for Block Stratum. Similarly, strip of size 10 m × 75 m, 10 m × 100 m, 10 m × 125 m, 10 m × 150 m, 10 m ×175 m & 10 m × 200 m were considered for Linear Stratum. For scattered stratum plot of size 0.5 ha, 1.0 ha, 1.5 ha, 2.0 ha, 2.5 ha and 3.0 ha were considered for non-hilly districts and 0.25 ha, 0.50 ha, 0.75 ha and 1.00 ha were considered for the hilly districts. Twenty concentric plots in each stratum were randomly selected and data were recorded. After analysis it was concluded that optimum plot size for Block, Linear and Scattered stratum are 0.1 ha, 10 ×

125 m strip and 3.0 ha respectively for non-hilly districts and 0.1 ha, 10 × 125 m strip and 0.5 ha for hilly district. It was also concluded through pilot study that the sample sizes for Block, Linear and Scattered stratum are 35, 50 and 50 respectively for non-hilly districts and 35, 50 and 95 for hilly district. Desired number of sample points was randomly generated in each stratum separately and the data on pre decided variables were collected on designed formats, following Manual for Assessment for Trees Outside Forests (FSI, 2003). Thereafter, data processing is carried out following appropriate formulae corresponding to sampling design. The following table indicates the results obtained with regard to stems/ha, total number of stems, volume/ha and total volume of trees outside forests in rural areas of Gurdaspur district of Punjab, India. Likewise, similar results obtained from different districts spread across the country are aggregated to generate national level figures (Table 2). Table 2: District level estimates (Gurdaspur, Punjab, India) Geographical Area

3,551 sq.km.

Urban Area

76.42 sq.km.

Forest Area

368 sq.km.

Water bodies

94.58 sq.km.

CNFA (Rural)

3,013 sq.km.

Stems / ha

18.5

Total Stems

5,563,798 (5.56 M)

Volume / ha

3.5 cu.m

Total Volume

1,054,577 cu.m(1.05 M cu.m)

Accuracy of Classification Any classification is not complete unless and until its accuracy is assessed. For the present study the accuracy of classification was assessed by taking 53 points in block, 65 in linear and 65 in scattered stratum for a particular district. It is recommended that 50 or more points should be located for ground verification in each class. The accuracy of this classification was high as evident from the following confusion matrix of Kapurthala district of Punjab state. Table 3: Confusion Matrix Block Linear Scattered

Row Total

User’s Accuracy (%)

Block

41

0

0

41

100

Linear

0

63

0

63

100

Scattered

12

2

65

79

82

Column Total

53

65

65

183

Producer’s Accuracy (%)

77

97

100

Overall Accuracy = 92 % Conclusion The main objective of Forest survey of India in mapping and monitoring forest and tree cover of the country on a two-year cycle is to know the dynamic changes of forest resources in terms of

quantity and quality over a period of time so that appropriate planning and management interventions can be developed for their conservation and sustainable utilization. Remote Sensing based forest cover mapping and monitoring adopted by FSI has proved to be cost and time effective over traditional forest resource monitoring. The methodology using digital image processing and geographical information system, as explained above can be effectively employed using multi spectral and high-resolution satellite imageries to stratify the TOF resources in such a way that the classification system of TOF resource remains valid. In addition, spatial distribution of TOF resources on maps along with other features will provide information for planning and implementation and utilization of these resources in a sustainable manner. Since, this methodology enables resource-based stratification, it is expected to provide better estimates of TOF resources than the one generated through field survey alone.

The Development of Forest Fire Forecasting System using Internet GIS and Satellite Remote Sensing Abstract The purpose of this study is to develop the most effective method for a forest fire forecasting in small mountain through GIS and Remote Sensing. The study area was Young-chon area including all of the Kyung-sang Province, Korea. GIS DB was constructed based on factors of geographical and natural features those are necessary factors to forecast a forest fire. It was clarified that satellite image and some spatial data is very effective for developing the Graphic User Interface to forecast the forest fire using Internet GIS. In addition, the forest fire hazard area was prevented and managed effectively. Introduction Recently the human lives, fortune and the ecosystem have been deadly threatened by the many cases of forest fire and it's huge size. Even though there is trip of extinguish equipment, the main reason of this large sized forest fire is that there is limitation of traditional method and no more scientific way to predict these disasters. In this paper, we present that the fatal damage by forest fire could be reduced if there are suitable predictions and rapid provision against forest fire using Internet GIS. This Internet GIS modeling is the most perfect way for forest fire forecasting system because forest fire has a movement in both in spatial and temporal. CFFDRS(Canadian Forest Fire Danger Ration System) was developed for a prediction of forest fire in 1987 and GIMS(Geographical Information and Modeling System) was installed for a management of it by Kessell(1990). GIMS could assign a part by telling the shape of forest fire in real time and help the managers of forest fire have best decision against these disasters. In 1993 Gracia and Lee prepared a map for forest fire forecasting in Alberta after evaluating the main danger factors of forest fire. In Korea, Y.H Cheong(1989) studied about predicting the dangerous rate of oxidation and S.Y Lee(1990) found out the relationship between the factor of environment and temperature of forest fire . K.C Lee(1998) constructed the modeling of suitability analysis about forest fire extinguish water tank using GIS . The study purposes of this paper are as follows that the investigation into actual condition of forest fire in Young-chon area was first carried out and secondly constructed in to GIS DB. Danger index of forest fire was computed with the based on topographic and meteorological factor in this area and evaluated the relationship between these factors and forest fire. Finally, the network presentation system of that using Internet GIS, which is the main goal of this paper, was installed. Review on Physical Factors of Forest Fire in Study Area

In the view of season, the number of forest fire increase in the spring and the winter because it is very dry and small amount of precipitation. 3,362 cases took places between 1990~1999 and the damaged area was 13991.43ha. Among them 2,069 cases happened between March and April and 883 cases occured in the winter. The main reasons of forest fire in this area have been composed of accidental fire(44%), ditch burn up(22%), visit tomb accident fire(7%), playing with fire(5%), etc(23%). 49% cases of them took places around 2 p . m ~ 6 p . m, 38% took places 11 a . m ~1 p . m, 9% took places 7 p . m ~ 4 a . m, 4% took places 5 a . m ~ 10 p . m in order. In study area, 84.2% cases of forest fire happened from March. 21 to April 10. The potential factors including aspect, elevation, slope, stream, vegetation, which can have an effect on forest fire were extracted for probability analysis. Aspect is related to the amount of sunshine. In general, the cases of forest fire occur in area of south more than in the area of north because a southern exposure has higher burning point. Actually, more than 40% of forest fire happened in aspect of south area while it doesn't happen in the other area. Therefore, the aspect is really related to forest fire. Comparing with previous forest fire, more than 90% cases of forest fire happened at 100m above the sea level. Most these disasters take places in lower area above the sea level. 65 % cases in entire forest fire occurred in between a slope of zero and a slope of twenty degrees. The rate of forest fire decrease remarkably as slope increases. Stream is regarded as an important role not only to extinguish forest fire but also to provide moisture toward plants. The area far from stream has higher dangerous factors. Especially, the road can be immediate factor to forest fire because there are human beings. Fuel, which is composed of the amount of precipitation, the humidity of air, the direction of the wind and temperature, is very related to season, and time. In study area, the air is exceedingly dry in the winter and much precipitation is in the summer. Also, its north, west, east is consisted in a mountainous area, which is over 900 meters above the sea level. There are open field in its south area and cultivated fruit. The Kum-ho river, which is joined with Nam-chun(Jaho-chun, Gokung-chun) and Bukchun(Sinryung-chun, Gohyun-chon), is flowed in the middle of this study area. The size of this is 919 and 69% in this area is forests and crop fields and 18% is cultivated area. In 1999 the number of a broad-leaved tree was twice than the number of a needle-leaf tree while the number of both was same. By analyzing Landsat TM satellite image data, the classes of trees are consist of 55% of Coniferous, 30% of Deciduous, 15% of Mixture forest.

Figure 1. The location of study area

Materials and Methods In this paper the classes of trees in this area were simply composed of Conifer, Deciduous, Mixed forest, and Agriculture. And the dimensions of damage area, the classification of vegetation and land classification map were found out by Landsat images. The spatial data including topographic map, geologic map and aerial photo was used to make forest fire hazard index GIS DB. ERDAS IMAGINE 8.3 and Unix Arc/Info GIS tool for image processing and spatial analysis are used and Map Object 2.0 and Visual Basic 6.0 for Internet Network are needed. Virtual GIS is applied to realize forest fire hazard index on 3D terrain. Topographies of three areas, which are called Hawsan, Hawnam, Jungang in Young-chon city, are analyzed. More than 60% of forest fire happened in between a slope of zero and a slope of twenty degrees and in aspect of south and southern west. Places of those disasters occurred between 100m and 350m above the sea level and close to road, which is far from river. Table 1. Forest fire Summary

Area Date

Temperature Precipitation(mm) Damage(ha) Vegetation

A

1999/4/15 10.43

42.5

5

Conifer

C

1999/3/31 8.55

0

1.5

Conifer

G

1999/3/4

18.5

1.5

Deciduous

4.97

After analyzing above table1, the main factors which could affect forest fire, are needle-leaf trees the aspect of southern west and humidity. Forest fire hazard index could be extracted by using average data acquired from an observation station based on three above factors and presented it in a contour line. In general, predict modeling was used like density transfer, density regression, significance regression, discriminate function analysis, logistic regression. In this research, logistic regression was considered most suitable analysis because it could compute difference of a variable environment between occurrence spot, in addition nonoccurrence spot and applied to undetected area yield probability. Zi = 3.754 + 0.231×(slope) + 0.324×(elevation)+ 0.165×(aspect) + 0.328×(stream) + 0.195×(forest type) + -0.017×(agricultural pattern) + -0.128×(urban) + 0.030×(road)+

0.872×(rainfall)+ 0.652×(sunshine)+0.713×(moisture) Pi = exp(Zi) / (1 + exp(Zi))

Figure 2. Hazard Map on Study Area

Forest fire hazard Index Forecast System using GIS Development of Forecasting System The purpose of this study is to develop the most effective method for a forest fire forecasting through GIS and Remote Sensing. In this study digital map was prepared and expressed numerically which includes factors of geographical and natural features, which are necessary factors to forecast a forest fire hazard index. Fire potential requires collecting baseline vegetation information, daily to weekly monitoring of vegetation condition or vigor daily monitoring of weather conditions, and acquiring risk management information. A computer-based model is to predict wild fire behavior across time and space. The computer model uses fuel type, weather conditions, slope, aspect and elevation to predict the direction, speed, and burn intensity of a wild fire across various landscapes. The model uses Geographic Information System (GIS) technology. The program is responsible for all the complex computations necessary for simulating fire behavior. The model's user-interface is designed so advanced computer skills and GIS knowledge is not required to execute the model. Ease-of-use puts fire behavior prediction into the hands of fire managers where it can be most effectively applied. With fire damage growing every year, fire departments need better planning tools to minimize fire's impact. The model is also a good analysis tool for resource managers. A Graphic User Interface (GUI) allows the user to easily specify and edit the data and parameters necessary to execute each simulation. Forest fire Danger Index Presentation System would be useful to managers, policy makers and scientists interested in mitigating and evaluating the effects of forest fire. Real time forest fire hazard information is offer to public welfare and administration business management.

Figure 3. Procedure of Study Frame

Conclusion In conclusion, the information of forest in the specific area can be easily searched, analyzed and managed through Internet GIS and Remote Sensing. It makes possible for this forest fire forecasting system to predict and to prevent forest fire in effective and scientific because it can assume exact forest fire hazard index in real time and present in proper time. Especially forest fire hazard index was presented in real time integrated with meteorological data through internet web base to forest fire task officer and local citizens without time lagging. It also allows to analyze with spatial modeling and monitoring in the predicted area. Therefore it was clarified that the forecasting system using Web based GIS is prominent for management and prevention of forest fire

Forest Burned Area Mapping by using SPOT Images Gwo-Jern Hwant1 Wen-Fu Chen2

Keywords: remote sensing, forest fire, pseudo color images, aerial photograph, working circle, map overlay, geographic information system. Abstract The objective of this research is to apply remote sensing technique to investigate the methodology of mapping forest fire burned area. SPOT HRV were used to compare with the panoramic aerial photo mapped forest fire burned areas in this research. The areas included YuShan working circle and Hsiu-Ku-Luan working circle in Taiwan. Each was independently

interpreted and the results were compared and analyzed by map overlay utilized geographic information system software Arc/Info. The resulted precision rate of calculated burned interpreted from aerial photograph was 98.1%. It is worthwhile. 1. Technician of, Bureau of Forestry of Taiwan Province, ROC 2. Prof. of National Chung-Hsing Univ. Tai-Chung, Taiwan, ROC Introduction Forest fire affects ecology seriously, it will break even erode the soil. The most important thing we must to do during the forest is being fired are fire fighting and the firing area control. In order to prevent forest fire, Taiwan Forestry Bureau has used various method of fire prevention broadcast and taken fire-prevention workshops to remind and warm the fire fighter of hot-spot location to check the safety during dry season. On the other hand, in order to enhance the forester to learn various fires fighting skill, the Forest Bureau evaluate the work at the end of year. The Forest Bureau was called by the Police Team of Yu-San National Park that a severe forest fire occurred at the connecting zone of the 51st compartment of Yu-San working circle in Yu-San National Park and the 15th compartment of Hsiu-Ku-Luan working circle at noon of 4.1.1998. The flight team was then commanded to take picture above the burning sites. Finally, was judged the fire reached the 51st and 53rd compartment of Yu-San working circle as well as the 15th and 16th compartment of Hsiu-Ku-Luan working circle. The burned area was 287.78 ha. Most of the forest fire burned area was grass land and habitat alpine sassos site and there were only few weed under Pinus Taiwanensis. Science the Remote Sensing Development Planning Committee of Administration of Economic was erected in 1976.8. the research and application of remote sensing technique has started in Taiwan. There are four kinds of resolution in remote sensing multi-spectral bands: spatial, spectral-temporal and radiation etc., and those afford advantageous selection in application, so the remote sensing technique always be used on forest resource surveying and forest fire distribution surveying of large scale. The purpose of this research is to utilize the SPOT images, which were taken on the forest fire burned sites at Ah-Sue-Ku Mountain area in 4. 4. 1998. The results was compared and checked with the aerial photographs of the same sites, which were taken in 4. 3. 1998. According to the accurate areas, which were estimated from the satellite remote sensing, we can help to afford the authority a more correct and quick method on estimating the area of the burned sites. Material and Method 1. Material 1. Study Sites Hsiu-Ku Luan circle is located in Hua-Lien Forest District Office with the area of 70,429.23 ha. The space is very vast and the elevation distributes in a big range. On the high elevation distributes in a big range. On the high elevation site, there grow pure crop of Pine Tree with some Hemlock, Formosan Douglas Fir and grows grass near the ridge of Central Mountain. On the moderate elevation site, the mixed crop of Chamaecyparis Formosensis and Hemlock grow very well. On the low elevation site, there grows hardwood forest only and most of the expensive Cinnamomum Randaiense grows piecemeal at those points that the traffic is not convenient. Yu-San working circle is located in Gia-Yi Forest District Office with the area of 49,647.77 ha. The lowest elevation of this working circle is 250 m at Dah-Pu working circle and the high elevation is 3,952m at the main peak of Yu-San Mountains. It forms a vertical zone

distribution from Tropical Zone, Warm Zone, Temperate Zone and then Frigid Zone. The constitution of forest species and the forest types are very complex. 2. SPOT Images Data The SPOT HRV multispectral and panchromatic level-10 images with resolution of 12.5m x 12.5m and 6.25m x 6.25m. 3. Land Cover Map: Photographic Base Map (1/5,000 and 1,10,000 scale), Compartment Base Map (1/5,000 scale). 2. Method 1. Delineating with Aerial Photographic Base Map. The Forestry Bureau assigned the burned sites and then we collected the relative map data. Then taken aerial b/w and color photographs were then checked with the stereoscopes and interprets the burned area manually with photographs and finally to delineate the area boundary. After we finished interpretation with aerial photographs we selected the control points. Finally, after mapped the interpreted boundary on the circle photographic base map (1/5,000 scale) we plotted the burned area and calculated the area after digitized it. To those susceptible points we visited the field sited to check them and then remapped the burned area. 2. SPOT Satellite Image Appliance A. A High-Resolution Pseudo Synthetic Images Making. Firstly, We used the SPOT MSS images to resample as RGB with resolution of 6.25m and then transform the RGB to be ISH. Secondly, we combined the panchromatic images to be three bands images and finally retransform ISH to be RGB and we got the high resolution pseudo color synthetic images B. The Burned Boundary Mapping with Image Enhancement We used GPS to assign the burned area of TM2 coordinate and then referred the circle photographic base map to locate the burned area on the satellite images. Secondarily, we enhanced the pseudo color images with linear enhancement method and then smoothed it by operating with 3*3 matrix and removed the noises. Finally, we used IMAGINE software to sharpen the edge with 5*5 matrix to figure out the boundary of the burned area obviously. Results and Discussion 1. Results from Aerial Photographic Interpretation The area of the burned area from the aerial photograph at 4.3.1998 was 287.78 ha. The photograph was shown in Figure 1.

Fig 1. BW Photograph Film of Forest Burned Area

The area of the 53rd compartment of Yu-Shan working circle and the 15th & 16th compartment of Hsiu-Ku-Luan working circle were calculated and was given in table 1. Table 1. The Burned Area of Yu-Shan Working Circle and Hsiu-Ku-Luan Working Circle (unit hectare) Working Circle Comp. of Yu-Shan Comp. of Hsiu-Ku-Luan 15th comp. 16th comp.

Total

Situation

53rd comp.

Serious

7.64

0.03

----

7.67

Moderate

2.85

50.60

28.34

81.79

Light

0.02

47.49

150.81

198.32

Total

10.51

98.12

179.15

287.78

Comp.: compartment

The disaster zone of the 53rd compartment of Yu-Shan working circle and the 15th & 16th compartment of Hsiu-Ku-Luan working circle was shown in figure 2.

Figure 2. The Map of Equal-class of Forest Fire Disaster

2. The Interpreted Results of the Satellite Images Figure 3 showed the SPOT image of the forest fire in 4.4.1998. The area, which was in black color, was the burned area and it was equal to 164.98a. and the white color area was the smoke of forest fire

Figure 3. The interpreted forest fire map from satellite image.

Conclusion •

The technique of aerial phtogrammetry has being broadly used on surveying of forestry and agriculture disaster for it can afford the necessary information to the relative institute and spend less labor and cost. Although the information an be got from both aerial

• • •

phtotogrammetry and satellite images classification and they can help maturely, but in case of emergent disaster the satellite images can afford information more fast. The resulted area was 287.78 ha from the forest fire photograph that was taken in 4.3.1998. The accuracy would be up to the proficient training, experience, the quality and kind of photograph. The calculated burned area 164.9ha was go from SPOT image. The precision rate was 98.1% by comparing to aerial photograph interpretation. The result was worthwhile. It will be a tendency for Taiwan forestry Bureau to integrate the technique of satellite remote sensing, GPS and GIS on forest fire fighting and fire danger assessment.