FUZZY LOGIC MODEL FOR DESERTIFICATION VULNERABILITY RISK ASSESSSMENT

1. FUZZY LOGIC MODEL FOR DESERTIFICATION VULNERABILITY RISK ASSESSSMENT Arunima dasgupta JRF, SPACE APPLICATIONS CENTRE, ISRO PH.D STUDENT, BIRLA INSTITUTE OF TECHNOLOGY, MESRA, RANCHI DR. M.S. NATHAWAT PROFESSOR AND HEAD, REMOTE SENSING DEPARTMENT BIRLA INSTITUTE OF TECHNOLOGY, MESRA MR. K L N SASTRY, DR. P S DHINWA, DR. S K PATHAN SPACE APPLICATIONS CENTRE, ISRO, AHMEDABAD

2. KEY LINES • 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 . 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.

3. OBJECTIVES 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.

4. Multi-spectral and Multi-temporal satellite imagery SOI Toposheets Field Data Collateral Data • Vegetal degradation scenario • Irrigation scenario • Water erosion scenario • Salinization scenario • Mining scenario • Other manmade and natural scenario • Census Data • Climate Data • Soil Data Georeferencing Date Processing DEM LULC Map NDVI Slope Thematic layers Classification Class Integration and deriving DVRI METHODOLOGY Using Membership Function Identifying vulnerable areas Multicriteria based Geo-statistical analysis Risk categorization

5. TOPOSHEET CENSUS DATA COLLATERAL DATA SOIL SUBSET BY VECTOR LAYER Socio-Economic Parameters Climate Data NDVI Ground truth SLOPE Classification and Analysis IMAGE Class Integration and deriving DVRI METHODOLOY – GEOSTATISTICAL APPROACH DVRI MAP LCAP LUSE Ground truth

6. Socio-economic Parameter Indices Natural Parameter Indices Population pressure Index Economic Indices Infrastructure-facility-Parameter indices Soil index, Climate Index, Vegetation Index, Land-capability index Classification according to their deviation from mean Deriving membership and Identifying vulnerable areas MODELING APPROACH DV Risk Categories of Natural parameter DV Risk Categories of socio-economic parameter Using Membership Function Multicriteria based Geo-statistical analysis Integrated Multicriteria based analysis DV Risk Index(DVRI)

7. t2 ()=1/A  e dx -(x- )2/22 t1 tmax -(x- )2/22 dx A=  e where A is given by; tmin MEMBERSHIP FUNCTIONS where tmax and tmin are the minimum and maximum value that t could take. Thus the probability of variable t belonging to class c if its value was measured to be  with standard error , is given by; 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; er(t1- )2/√2 er(t2- )2/√2 - (; t1,t2) = er(tmin- )2/√2 er(tmax- )2/√2 -

8. STUDY AREA :BELLARY DISTRICT, KARNATAKA Latitude: 14° 30' to 15°50' North Longitude: 75°40' and 77°11‘ East

9. SOME STUDY N D V I PROBABILITY CLASS VALUES -0.2 0.2

10. NORMALIZED DIFFERENCE VEGETATION INDEX MAP VERY LOW LOW MODERATE LEGEND NDVI HIGH VERY HIGH

11. Cont…. SLOPE PROBABILITY CLASS VALUES 7

12. LEGEND TERRAIN INDEX VERY LOW LOW MODERATE HIGH VERY HIGH TERRAIN INDEX MAP

13. Cont…. LITERACY PROBABILITY CLASS VALUES 20

14. LITERACY INDEX MAP LEGEND LITERACY INDEX VERY LOW LOW MODERATE HIGH VERY HIGH DATA USED: CENSUS 2001

15. Cont…. POPULATION DENSITY PROBABILITY CLASS VALUES 80

16. POPULATION DENSITY MAP LEGEND POPULATION DENSITY LOW MODERATE HIGH TOWN DATA USED: CENSUS 2001

17. LAND CAPABILITY MAP LEGEND LCAP VERY LOW LOW MODERATE HIGH VERY HIGH

18. VILLAGE AMINITY INDEX MAP LEGEND AMINITY INDEX VERY LOW LOW MODERATE HIGH VERY HIGH DATA USED: CENSUS 2001

19. PARAMETERS INTEGRATION 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; SI(VH) = [SE (VH) SQ (VH)] Ū Where, SE = Soil erodability Risk SQ = Soil Quality Risk SE(VH) = [D (VL) P (VL) S (VH)] Ū Where, SE = Soil erodability Risk D = Depth P = Permeability S = Slope Ū NP(VH) = [SE(VH)] [VI(VL) A(VH)] [LCAP(VH)] Ū Ū Ū Where, NP = Natural parameter Risk SE = Soil erodability Risk VI = Vegetation (NDVI) Risk A = Aridity Risk LCAP = land-Utility Index

20. SOIL INDEX MAP Based on the composite Index of: Soil Erodability and Soil Quality

21. FINAL DVRI CATEGORIZATION VLR LEGEND DVRI D V R I S K C A T E G O R I E S OF S O C I O – E C O N O M I C P A R A M E T E R LR <0-1 VHR MR 0.1-0.4 0.41-0.6 HR 0.61-0.75 >0.75

22. LEGEND VULNERABILITY SEVERITY SETTLEMENTS WATERBODY DESERTIFICATION VULNERABILITY RISK INDEX MAP VERY LOW LOW MODERATE HIGH VERY HIGH Based on the Composite Index of all Natural & Socio-Economic – Parameter indices

23. 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. C O N C L U S I O N

24. THANK YOU