dr. m.s. nathawat professor and head, remote sensing department professor and head, remote sensing...
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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.