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Poster Presentation

  1. 1. Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -Markov Model –A Geoinformation Based Approach SANTOSH .N. BORATE Under the Guidance of Prof. M D BEHERA SCHOOL OF WATER RESOURCES IIT KHARAGPUR, KHARAGPUR-721 302 INTRODUCTION Cellular Automata (CA) Watersheds are very crucial as they provide water that meets the different water demand ranging from  Spatial component is incorporated drinking, irrigation, industry, power generation etc. The effects of change in watershed dynamics are leading to a series of  Powerful tool for Dynamic modelling environmental problems, such as deterioration of water quality, biodiversity loss, extinction of aquatic species, alternation of  Each row represents a single time step of the automaton’s evolution. river flows, shortage of water resources, and so on. St+1 = f (St,N,T) where St+1 = State at time t+1 St = State at time t For sustaining development of watershed there is need of Watershed Modeling which implies the proper use of all land, water N = Neighbourhood and natural resources of a watershed for optimum production with minimum hazard to eco-system and natural resources. T = Transition Rule Modelling of watershed dynamics helps to policymaker and decision maker in making the policies and taking the decisions for CA-Markov model operation optimum utilization and sustainable development and management of resources in watershed respectively. CA-Markov model is used for predicting the land cover changes. This is a holistic approach that integrates the Markov and A Remote sensing technique and GIS tool is used taking and process the images of watershed of different time periods. CA- CA models in which Markov Model gives the Transition Area Matrix (Area expected to change) while Cellular Automata Markov model approach is used that integrates the Markov and CA models with the use of a multicriteria decision-making gives the Suitability maps for each class. These both the outputs are used as inputs in CA-Markov Model 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 1972 1990 1999 2004 • To project future watershed dynamics scenarios using CA-Markov Model • To give the plan of measures for minimize the future watershed dynamics change Fig1. Unsupervised Classification for different time periods STUDY AREA Area (ha) Year Water Wet land Marshy land Dense forest Open forest Settle-ment Agricul-ture River basin map of India 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 • Drainage Area = 195 1999 790.88 550.46 1120.17 5709.63 5186.21 1160.62 5403.49 • latitude- 20 29 33.39 to 20 40 21.09 N 2004 472.68 281.88 818.91 5296.32 4763.25 1382.85 6532.56 •Longitude- 85 44 59.33 to 85 54 16.62 E •Growing Industrial Area Table 1: Shows the year wise area of different classes in watershed Spatial Layer Generation of Socioeconomic, Infrastructure parameters a) Spatial layer of Soil classes b) Spatial layer of Land Use c) Spatial layer of Road d) Spatial layer of slope in watershed Land Cover of the watershed network, Drainage Network Mahanadi River Basin Land Use/Land Cover Change trends: DATA AND METHODOLOGY 8000 7000 6000 6000 5000 Data download Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004 Marshy Land and Layer stack 4000 4000 Settlement Dense Forest Area (ha) 3000 Georeferencing and Open Forest Area (ha) 2000 Reprojection 2000 Agriculture Wetland 1000 Area 0 0 extraction Multitemporal 1960 1980 2000 2020 1960 1980 2000 2020 image Classification of the satellite data Classification Year Year Preparing Road network Drainage Network Soil Type Altitude Decreasing Trend of LULC Ancillary Data Increasing Trend of LULC Industrial Structure Population Density Urban Sprawl Slope 1200 Statistics Calculation of LU/LC area statistics (for different periods) 1000 Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability Images by MCE Area (ha) 800 TAM and Suitability Images 600 Water Run CA- Markov model in IDRISI- Andes by giving -1) Basis land Cover Image, 2) TAM and 3) 400 Fallow Land Simulation Suitability Image as inputs 200 Analysis Analysis of drivers responsible for watershed change 0 Prediction Predict Watershed Dynamics for future 10-Years from the obtained trend 1970 1980 1990 2000 2010 Year Markov Chain Analysis (MCA) CONCLUSION On the basis of observed data between time periods, MCA computes the probability that a cell will change from one land use type (state) to another within a specified period of time. From the table and graphs it is observed that Dense forest, open forest, wet lands, Marshy Land are drastically are The probability of moving from one state to another state is called a transition probability. From which exact area decreasing and transformed in to other classes. expected to be change is calculated. 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 where P = Markov transition probability matrix management problems including land use and landscape changes in watershed P i, j = the land type of the first and second time ACKNOWLEDGEMENTS period I express my sincere gratitude to Prof. M D Behera for his proper and timely guidance through out the period of work. Pij = the probability from land type i to land type j 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.