Remote Sensing Primciples - Gj

WHAT IS REMOTE SENSING? `REMOTE SENSING IS THE SCIENCE OF MAKING INFERENCES ABOUT OBJECTS FROM MEASUREMENTS, MADE AT A DISTANCE, WITHOUT COMING INTO PHYSICAL CONTACT WITH THE OBJECTS UNDER STUDY.’ `REMOTE SENSING MEANS SENSING OF THE EARTH’S SURFACE FROM SPACE BY MAKING USE OF THE PROPERTIES OF ELECTROMAGNETIC WAVE EMITTED, REFLECTED OR DIFFRACTED BY THE SENSED OBJECTS, FOR THE PURPOSE OF IMPROVING NATURAL RESOURCE MANAGEMENT, LAND USE AND THE PROTECTION OF THE ENVIRONMENT.’

Remote Sensing is recognizing yellow papaya is ripe

Visual Perception of Remote Sensing Platform Sensor

Green : Raw

Interpreter

Yellow : Ripe !

PRESENT YIELD LEVELS

LAND USE PATTERN

PRESENT AREA UNDER CROPS

INCREASE LAND UNDER CROPS

IMPROVE YIELDS

ARABLE WASTELAND

INCREASE FOOD GRAIN SUPPLY

DEVELOPMENT/ CONSERVATION

BUFFER STOCK

IMPORT OF

FOODGRAIN

FOOD GRAIN REQUIREMENT

WORLD PRICES

USE OF HIGH YIELD VARIETY

AGRICULTURAL PRACTICES

SOURCES OF IRRIGATION

ADDITIONAL SOURCES

IRRIGATION AREA INCREASE

IMPROVE AGRICULTURE INPUTS AGRICULTURAL PRACTICES

FERTILISERS FOR YIELDS

INFORMATION REQUIREMENT SCENARIO FOR INCREASING FOOD GRAIN SUPPLY

WHY IMAGING FROM SPACE? SYNOPTIC COVERAGE 800 km

10 km 100 m 10 m 1.6 m 4.5 km 11.3 km

35.7 km

357 km 1956 km

Polar Orbit

WHY IMAGING FROM SPACE? GLOBAL COVERAGE & REPEAT OBSERVATIONS 750 4

600 3

450 300 150 2

1

14

13

12

11

10

9

8

7

6

Latitude

00 150 15

Orbit Number

300 5

450 600 750

2820 km

Sensor

Platform (satellite)

Data transmission

Ground Truth/ Accuracy Check Base Map

Decision criteria/ Visual

Photo products Thermal emission

Fluorescence

Cloud

Earth Surface

Tables Thematic map

Reports

Funds availability/ Other Database Digital

Reflection

(absorption/scattering/emission)

Atmosphere

Sun

Data Analysis

GIS

Digital products

Decision Making

Data products

Data reception & recording

Periodic Monitoring using RS

Monitoring

Discussions with beneficiaries Implementation at field level

SCHEMATICS SHOWING REMOTE SENSING SYSTEM FOR RESOURCE MANAGEMENT.

REMOTE SENSING PASSIVE

IMAGING CAMERA TV MSMR

ACTIVE

SOUNDING

VTPR

IMAGING

SOUNDING

SLAR

LIDAR

SAR

ENERGY SOURCE FOR PASSIVE SENSING REFLECTION OF SOLAR RADIATION& EMISSION

Radiant exitance (W m-2 µm-1)

Spectral distribution at the top of the atmosphere for solar irradiance and earth’s emission. Sun T ~6000°K, Earth T ~300°K

The radiant exitance from sun and earth follows Planck’s equation

Mλ =

4

10

1000 Sun 100 Earth

λmax =

[

C1

λ5 eC

2

λT

2898 T

]

−1

µm

10 1

0.1

0.5

1

10

For earth, λmax ~9.5 µm

Wavelength (µm)

10.5 – 12.5 µ is used for THERMAL IMAGING to avoid the ozone absorption at ~9.5 µm

At MICROWAVE frequency C2/ λT <<1 Mλ ∝ εT → BRIGHTNESS TEMPERATURE

ATMOSPHERIC WINDOWS

0.4-1.3, 1.5-1.8,

Absorption by

molecules

UV

Electronic Transitions (O3,O2)

Vibrational Transitions (H2O,CO2)

VISIBLE

Far IR-MW

MW

4.2-5.0,

Rotational Transitions (H2O)

Forbidden Transitions (O2)

7--- 15, 1cm-30cm

INFRARED

1.0

4

5

S

5

10

H2O

C

H2O

O2

O2

CO2

O3

3

H2O

1.5 2

CO2

CO2 H2O

0.5µm

H2O

0

H2O H2O

100

THERMAL IR

REFLECTED IR

RADIO

MICROWAVE X

O3

ATMOSPHERIC TRANSMISSION (%)

Ionization Dissociation (O,N2,O2,O3)

µm 3.0-3.6,

IR

UV-VIS-NIR

UV

2.2-2.6,

10 15 20µ 0.1 cm WAVELENGTH

0.5

1.0

L

• REFLECTANCE SPECTRA 80

R E F L E C T A N C E (%)

SILTY

60

CLAY SOIL

VEGETATION 40

MUCK SOIL

20

WATER (Shallow/Deep) 0

0.4

0.8

1.2

1.6

2.0

2.4

2.8

3.6

Wave length (µm)

4.8

FRESH SNOW GREEN VEGETATION DARK TONED SOIL LIGHT TONED SOIL CLEAR WATER TURBID WATER

GREEN BAND

RED BAND

NEAR IR

SHORTWAVE IR

(0.52-0.59 µm)

(0.62-0.67 µm)

(0.77-0.86 µm)

(1.55-1.75 µm)

COLOUR FORMATION YELLOW (MINUS BLUE)

RED

GREEN

CYAN (MINUS RED)

MAGENTA (MINUS GREEN)

BLUE

COLOUR COMPOSITES

Natural Colour BLUE+GREEN+RED

False Colour Composite GREEN

BLUE

RED

GREEN

NIR

RED

SIGNATURE Key to feature identification from space imagery depends on the characteristic changes in the properties of the EM spectrum reflected/emitted from the target surface – referred ‘signature’ Signatures could be inferred through: • Spectral Variation • Polarisation Change • Thermal Inertia • Temporal Variation

80 R E F 60 L E C 40 T A N 20 C E (%) 0

SILTY CLAY SOIL VEGETATION

MUCK SOIL WATER (Shallow/Deep) 0.4

0.8

1.2

1.6

2.0

2.4

Wave length (µm)

7000

Signatures are not completely deterministic; they are statistical in nature with a mean and dispersion

Frequency

6000 5000 4000 3000 2000 1000 0 2.79

3.08

3.37

Radiance

3.66

3.95

(mw/cm2/

4.24

4.53

str/µm)

4.82

Band 3 Histogram (.52-.59 micron)

Band 4 Histogram (.77-.86 micron)

Barren

Crop

100 50 0

40 45 50 55 60 65 70 75 80 85 90 95 100105110

Pixel count

Water

150

200 150 100 50 50

50 55 60 65 70 75 80 85 90 95 100 105110 115

Grey Level Values

Scatter plot Band 3 versus Band 4 120

Crop, Barren

Water

Grey Level Values

Scatter plot Band 3 versus Band 4 120

Crop

105

Crop

Barren

105

90

Barren

75 60

Band 4

Band 4

Pixel count

250 200

90 75

Urban

60

Water

45 40

50

60

70

80

Band 3

Water

45 90

100 110

40

50

60

70

80

Band 3

90

100 110

Classification 1 2 3 4

Fallow

Barren Forest

Water

Multispectral image

Wheat

Classified map

Schematics showing generation of thematic map from multispectral image.

INFORMATION CONTENT VS RESOLUTION

A) OCM (360m)

E) 36m (LISS-II)

B) 360m (OCM)

C) 188m (WiFS)

F) 23m (LISS-III)

D) 72m (LISS-I)

G) 5.8m (IRS 1C PAN)

. `A’ is from a scene from IRS Ocean Colour Monitor (OCM). The area in the small square marked (≈ 4km x 4km) is shown in various resolutions from B to G..

APPLICATIONS OF DIFFERENT RESOLUTION

• 1M+ SCALES

• 1:500K SCALES

• 1:250K SCALES

1m

• 1:50K SCALES

• 1:12500 SCALES • 1:2000/4000/1:8000 SCALES

Sampling the Spectrum BGR 400 nm

700

NIR

SWIR 1500

3000

LWIR

MWIR 5000

LOW

Panchromatic: one very wide band

MED

Multispectral: several to tens of bands

HIGH

Hyperspectral: hundreds of narrow bands

14000 nm

MULTISPECTRAL - HYPERSPECTRAL SIGNATURE COMPARISON Multispectral

Resampled to Landsat TM7 Bands

STEREO IMAGING dP2

dP1

Negatives

f

h dP = H−h B

(H-h)

h ~ H

dP B/H

dP - parallax difference between the points B - length of airbase H - flight altitude h

dP2

dP1

True base (Datum)

dP

B

Geometry of a stereoscopic pair of aerial photographs. B is the air base (absolute parallax). dP is the difference in parallax from top and bottom of the object.

SPOT HRS

APPLICATIONS OF REMOTE SENSING FOR EARTH RESOURCES MANAGEMENT TO IDENTIFY THE CATEGORY TO WHICH THE EARTH SURFACE EXPRESSION (MANIFESTED AS DATA) BELONGS, BASED ON SIGNATURE DIFFERENCES. THE SURFACIAL EXPRESSIONS ARE INDICATORS OF CERTAIN RESOURCES, WHICH ARE NOT DIRECTLY OBSERVABLE BY REMOTE SENSING. TO INFER A PARTICULAR PARAMETER OR PHENOMENON (WHICH IS ONLY PARTLY REPRESENTED IN THE DATA) USING SUITABLE MODELLING (YIELD OF A CROP, VOLUME OF TIMBER FROM FOREST, OCEAN CURRENTS, ETC.)

i

Changes in Quilon district (Kerala)

KAKKI RESERVOIR

Landsat 4 MSS (29th Jan 1983)

5

N 0

(1: 250, 000) SCALE (Approx)

KAKKI RESERVOIR

IRS-1D LISS III (28th Feb 2002)

5 Km

GEOGRAPHIC INFORMATION SYSTEM (GIS) ™

TO ARRIVE AT A DECISION WE NEED TO INTEGRATE DATA FROM VARIOUS SOURCES

™

THE DATA SOURCES INCLUDE SPATIAL AND ATTRIBUTE INFORMATION

™

GIS IS A COMPUTER BASED TOOL FOR END TO END PROCESSING FOR A DECISION SUPPORT SYSTEM CAPTURE, STORAGE, ANALYSIS (QUERIES), RETRIEVAL, DISPLAY

Land use slope soil