DR. M.S. NATHAWATDR. M.S. NATHAWAT

PROFESSOR AND HEAD, REMOTE SENSING DEPARTMENTPROFESSOR AND HEAD, REMOTE SENSING DEPARTMENT

BIRLA INSTITUTE OF TECHNOLOGY, MESRABIRLA INSTITUTE OF TECHNOLOGY, MESRA

Arunima dasgupta Arunima dasgupta JRF, SPACE APPLICATIONS CENTRE, ISROJRF, SPACE APPLICATIONS CENTRE, ISRO

PH.D STUDENT, BIRLA INSTITUTE OF TECHNOLOGY, MESRA, RANCHIPH.D STUDENT, BIRLA INSTITUTE OF TECHNOLOGY, MESRA, RANCHI

MR. K L N SASTRY, DR. P S DHINWA, DR. S K MR. K L N SASTRY, DR. P S DHINWA, DR. S K PATHANPATHAN

SPACE APPLICATIONS CENTRE, ISRO, AHMEDABADSPACE APPLICATIONS CENTRE, ISRO, AHMEDABAD

Since the parameters involved in the study are fuzzy in nature and the severity has to be classified by using fuzzy labels like low, medium, high etc., it is felt that it could be more appropriate to use fuzzy calculation.

Desertification refers to land degradation in arid, semi- arid and dry sub-humid areas resulting from various factors, including climatic variations and human activities. Fuzzy Logic is the logic to define the degree to attain a particular value, or to participate in a particular class .

Developing a suitable statistical model using Fuzzy membership function,

Classifying parameters according to their deviation from mean value and evaluating accuracy of their membership in a certain class

Identifying the transitional vulnerable areas,

Obtaining Desertification Vulnerability Risk Index - DVRI ; incorporating all natural and socioeconomic variables, and their combined effect.

Collateral Data

Collateral Data

1. Census Data

2. Climate Data

3. Soil Data

1. Census Data

2. Climate Data

3. Soil Data

Thematic layers

Thematic layers

ClassificationClassification

SOI Toposheet

s

SOI Toposheet

s

Multi-spectral and Multi-temporal satellite

imagery

Multi-spectral and Multi-temporal satellite

imagery

Date Processing

Date Processing

Georeferencing

Georeferencing

LULCMap

LULCMap

Field Data

Field Data

1. Vegetal degradation scenario

2. Irrigation scenario3. Water erosion

scenario4. Salinization

scenario5. Mining scenario6. Other manmade

and natural scenario

1. Vegetal degradation scenario

2. Irrigation scenario3. Water erosion

scenario4. Salinization

scenario5. Mining scenario6. Other manmade

and natural scenario

NDVINDVI

SlopeSlope

DEMDEM

Class Integration and deriving DVRI

Class Integration and deriving DVRI

Identifying vulnerable areas

Risk categorization

Multicriteria based Geo-statistical

analysis

Using Membership

Function

IMAGEIMAGE

TOPOSHEETTOPOSHEET

SUBSET BY SUBSET BY VECTOR LAYERVECTOR LAYER

NDVINDVI

SLOPESLOPE

LCAPLCAP

LUSELUSE

CENSUS DATA

Socio-Economic Parameters

Socio-Economic Parameters

COLLATERAL DATA

Climate Data

Climate Data

SOILSOIL

Classification and Analysis

Classification and Analysis

Class Integration and deriving DVRI

Class Integration and deriving DVRI

Ground truthGround truth

DVRI MAPDVRI MAP

Ground truth

Ground truth

Deriving membership and Identifying vulnerable

areas

Using Membership

Function

Multicriteria based Geo-statistical

analysis

The membership function is a graphical representation of the magnitude of participation of each input. Assuming that the value of a given variable t is measured to be and the error in this measurement is assumed to be Gaussian with zero(0) mean and standard deviation . The objective is to derive the membership functions of classes defined for the variable t as ranges of its value. For example, if t is assigned to a certain class c, if its value ranges between t1 and t2, the probability of t belonging to this class is given by;

Thus the probability of variable t belonging to class c if its value was measured to be with standard error , is given by;

where tmax and tmin are the minimum and maximum value that t could take.

-(x- )2/22()=1/A et2

t1

dx

-(x- )2/22tmax

A= e tmin

dx

where A is given by;

er(t2- )2/√2 -

er(t1- )2/√2er(tmax- )2/√2 -er(tmin- )2/√2

(; t1,t2) =

Latitude: 14° 30' to 15°50' NorthLongitude: 75°40' and 77°11‘ East

PR

OB

AB

ILIT

YCLASS VALUES

N D V I

-0.2 0.2

LEGENDLEGENDNDVI

VERY HIGH

LOW

MODERATE

VERY LOW

HIGH

±

CLASS VALUES

PR

OB

AB

ILIT

Y

SLOPE

7

±

VERY HIGH

LOW

MODERATE

VERY LOW

HIGH

LEGENDLEGENDTERRAIN INDEX

CLASS VALUES

PR

OB

AB

ILIT

Y

LITERACY

20

VERY HIGH

LOW

MODERATE

VERY LOW

HIGH

LEGENDLEGENDLITERACY INDEX

DATA USED: CENSUS 2001

±

CLASS VALUES

PR

OB

AB

ILIT

Y

POPULATION DENSITY

80

MODERATE

LOW

HIGH

LEGENDLEGENDPOPULATION DENSITY

TOWN

DATA USED: CENSUS 2001

±

LEGENDLEGENDLCAP

VERY HIGH

LOW

MODERATE

VERY LOW

HIGH

±

DATA USED: CENSUS 2001

VERY HIGH

LOW

MODERATE

VERY LOW

HIGH

LEGENDLEGENDAMINITY INDEX

±

Let, in case of natural parameter analysis, once the membership grades to the fuzzy variables are evaluated, the risk of desertification would be obtained from the given fuzzy relations criteria, using geospatial analysis techniques. For example, one of the criteria is given as;

Where, NP = Natural parameter RiskSE = Soil erodability RiskVI = Vegetation (NDVI) RiskA = Aridity RiskLCAP = land-Utility Index

NPNP(VH) = [(VH) = [ SESE(VH)] [(VH)] [ VIVI(VL) (VL) AA(VH)] [(VH)] [LCAPLCAP(VH)](VH)]Ū

Where, SE = Soil erodability RiskD = DepthP = PermeabilityS = Slope

SESE(VH) = [(VH) = [D D (VL) (VL) P P (VL) (VL) S S

(VH)] (VH)]

Ū

SISI(VH) = [(VH) = [SE SE (VH) (VH) SQ SQ (VH)] (VH)] Ū Where, SE = Soil erodability Risk

SQ = Soil Quality Risk

Ū

Ū

Ū

LEGEND

SOIL INDEX

VERY LOW

LOW

MODERATE

HIGH

VERY HIGH

Based on the composite Index of:Soil Erodability and Soil Quality

±

D V

R

I S

K

C A

T E

G O

R I

E S

D

V

R I

S K

C

A T

E G

O R

I E

S

OF

O

F

S

O C

I O

– E

C O

N O

M I

C

S O

C I

O –

E C

O N

O M

I C

P

A R

A M

E T

E R

P

A R

A M

E T

E R

VHVHRR

LEGENDLEGENDVULNERABILITY SEVERITY

Based on the Composite Index of allNatural & Socio-Economic – Parameter indices

±VERY HIGH

LOW

MODERATE

VERY LOW

HIGH

SETTLEMENTS

WATERBODY

Gaussian Probability Density function can be used as Membership Function.

Fuzziness is the reality of environment. Hence, in the context of environmental management this approach is appropriate and applicable.