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On the basis of observed data between time periods, MCA computes the probability that a cell will change from oneland use type (state) to another within a specified period of time.
The probability of moving from one state to another state is called a transition probability. From which exact areaexpected to be change is calculated.
Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -Markov Model –A Geoinformation Based ApproachSANTOSH .N. BORATEUnder the Guidance of
Prof. M D BEHERASCHOOL OF WATER RESOURCES
IIT KHARAGPUR, KHARAGPUR-721 302
INTRODUCTION
Watersheds are very crucial as they provide water that meets the different water demand ranging from
drinking, irrigation, industry, power generation etc. The effects of change in watershed dynamics are leading to a series of
environmental problems, such as deterioration of water quality, biodiversity loss, extinction of aquatic species, alternation of
river flows, shortage of water resources, and so on.
For sustaining development of watershed there is need of Watershed Modeling which implies the proper use of all land, water
and natural resources of a watershed for optimum production with minimum hazard to eco-system and natural resources.
Modelling of watershed dynamics helps to policymaker and decision maker in making the policies and taking the decisions for
optimum utilization and sustainable development and management of resources in watershed respectively.
A Remote sensing technique and GIS tool is used taking and process the images of watershed of different time periods. CA-
Markov model approach is used that integrates the Markov and CA models with the use of a multicriteria decision-making
technique is used in predicting the future watershed resources information.
OBJECTIVES
The objective of the study to model and analyze the watershed dynamics change using Cellular Automata (CA) -Markov
Model and predict scenarios for next 10 years . The specific objectives are:
• To generate land use / land cover database with uniform classification scheme using satellite data
• To create database on demographic, socioeconomic, Infrastructure parameters
• Analysis of indicators and drivers and their impact on watershed dynamics
• To derive the Transition Area matrix and suitability images based on classification
• To project future watershed dynamics scenarios using CA-Markov Model
• To give the plan of measures for minimize the future watershed dynamics change
STUDY AREA
River basin map of India
• Drainage Area = 195 sq.km• latitude- 20 29 33.39 to 20 40 21.09 N•Longitude- 85 44 59.33 to 85 54 16.62 E•Growing Industrial Area
Mahanadi River Basin
DATA AND METHODOLOGY
Classification of the satellite data
Drainage Network Soil Type Altitude
Population Density
Road network
Calculation of LU/LC area statistics (for different periods)
Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability Images by MCE
Industrial StructureUrban Sprawl Slope
Run CA- Markov model in IDRISI- Andes by giving -1) Basis land Cover Image, 2) TAM and 3) Suitability Image as inputs
Analysis of drivers responsible for watershed change
Predict Watershed Dynamics for future 10-Years from the obtained trend
Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004
CA-Markov model operation
CA-Markov model is used for predicting the land cover changes. This is a holistic approach that integrates the Markov and
CA models in which Markov Model gives the Transition Area Matrix (Area expected to change) while Cellular Automata
gives the Suitability maps for each class. These both the outputs are used as inputs in CA-Markov Model
Markov Chain Analysis (MCA)
Table 1: Shows the year wise area of different classes in watershed
1972 1990 1999 2004
Fig1. Unsupervised Classification for different time periods
Year
Area (ha)
Water Wet land Marshy land Dense forest Open forest Settle-ment Agricul-ture
1972 493.52 1013.03 2922.47 7329.09 5405.68 432.766 1926.33
1990 507.08 959.023 1574.05 6353.09 5725.06 653.049 3963.78
1999 790.88 550.46 1120.17 5709.63 5186.21 1160.62 5403.49
2004 472.68 281.88 818.91 5296.32 4763.25 1382.85 6532.56
a) Spatial layer of Soil classes in watershed
Spatial Layer Generation of Socioeconomic, Infrastructure parameters
b) Spatial layer of Land Use Land Cover of the watershed
c) Spatial layer of Road network, Drainage
Network
d) Spatial layer of slope
0
2000
4000
6000
8000
1960 1980 2000 2020
Marshy Land
Dense Forest
Open Forest
Wetland0
1000200030004000500060007000
1960 1980 2000 2020
Settlement
Agriculture
0
200
400
600
800
1000
1200
1970 1980 1990 2000 2010
Water
Fallow Land
Decreasing Trend of LULCIncreasing Trend of LULC
Land Use/Land Cover Change trends:
Area (ha)
Year
Area (ha)
Year
Area (ha)
Year
CONCLUSION
ACKNOWLEDGEMENTS
From the table and graphs it is observed that Dense forest, open forest, wet lands, Marshy Land are drastically are
decreasing and transformed in to other classes.
While the Settlement and Agriculture classes are drastically increasing which obtains the area from above four
classes which are decreasing in area.
A combined use of RS/GIS technology, therefore, can be invaluable to address a wide variety of resource
management problems including land use and landscape changes in watershed
I express my sincere gratitude to Prof. M D Behera for his proper and timely guidance through out the period of work.
I am thankful to Prof. S N Panda and JRF and SRF in SAL (Spatial Analytical Lab) of CORAL Department
for their help and support.
Data download and Layer stack
Georeferencing and Reprojection
Area extraction
Multitemporalimage
Classification
Preparing Ancillary Data
Statistics
TAM and Suitability Images
Simulation
Analysis
Prediction
Cellular Automata (CA)
Spatial component is incorporated
Powerful tool for Dynamic modelling
Each row represents a single time step of the automaton’s evolution.
St+1 = f (St,N,T) where St+1 = State at time t+1
St = State at time t
N = Neighbourhood
T = Transition Rule
where P = Markov transition probability matrix
P i, j = the land type of the first and second time
period
Pij = the probability from land type i to land type j