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DR. M.S. NATHAWAT PROFESSOR AND HEAD, REMOTE SENSING DEPARTMENT PROFESSOR AND HEAD, REMOTE SENSING DEPARTMENT BIRLA INSTITUTE OF TECHNOLOGY, MESRA Arunima.

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Presentation on theme: "DR. M.S. NATHAWAT PROFESSOR AND HEAD, REMOTE SENSING DEPARTMENT PROFESSOR AND HEAD, REMOTE SENSING DEPARTMENT BIRLA INSTITUTE OF TECHNOLOGY, MESRA Arunima."— Presentation transcript:

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

2  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.

3  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 Collateral Data 1.Census Data 2.Climate Data 3.Soil Data 1.Census Data 2.Climate Data 3.Soil Data Thematic layers Classification SOI Toposheets Multi-spectral and Multi- temporal satellite imagery Date Processing Georeferencing LULC Map LULC Map Field Data 1.Vegetal degradation scenario 2.Irrigation scenario 3.Water erosion scenario 4.Salinization scenario 5.Mining scenario 6.Other manmade and natural scenario 1.Vegetal degradation scenario 2.Irrigation scenario 3.Water erosion scenario 4.Salinization scenario 5.Mining scenario 6.Other manmade and natural scenario NDVI Slope DEM Class Integration and deriving DVRI Identifying vulnerable areas Risk categorization Multicriteria based Geo-statistical analysis Using Membership Function

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

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

7 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 t 1 and t 2, 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 t max and t min are the minimum and maximum value that t could take. -(x-  ) 2 /2  2  (  )=1/A  e t2t2 t1t1 dx -(x-  ) 2 /2  2 t max A=  e t min dx where A is given by; er  (t 2 -  ) 2 / √ 2  - er  (t 1 -  ) 2 / √ 2  er  (t max -  ) 2 / √ 2  - er  (t min -  ) 2 / √ 2   (  ; t 1,t 2 ) =

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

9 PROBABILITY CLASS VALUES N D V I

10 LEGEND NDVI VERY HIGH LOW MODERATE VERY LOW HIGH

11 CLASS VALUES PROBABILITY SLOPE 7

12 VERY HIGH LOW MODERATE VERY LOW HIGH LEGEND TERRAIN INDEX

13 CLASS VALUES PROBABILITY LITERACY 20

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

15 CLASS VALUES PROBABILITY POPULATION DENSITY 80

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

17 LEGEND LCAP VERY HIGH LOW MODERATE VERY LOW HIGH

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

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

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

21 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

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

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.

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