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M.Tech Final Seminar
- 1. SCHOOL OF WATERRESOURCES INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) -Markov Model –A Geo-information Based Approach M.Tech Thesis Presentation Supervisor DR. M. D. BEHERA Presented By Mr. SANTOSH NAVNATH BORATE Date: 04-05-2010 08WM6002
- 2. Outline of Presentation • Introduction • Aim and Objectives • Study Area • Methodology • Model Description • Results and Discussions • Watershed Management Plan • Conclusions
- 3. Introduction Watershed Dynamics Agricultural Settlement Land Uses Industrial Development Artificial Structures Watershed Resources Wetlands Forests Land Covers Bare soils Natural streams, Lakes
- 4. Drivers affecting LULC A) Biophysical Drivers B) Socio-economic Drivers 1. Altitude 1. Urban Sprawl 2. Slope 2. Population Density 3. Soil Type 3. Road Network 4. LU/LC classes 4. Socioeconomic Environment a) Wetlands Policies b) Forest 5. Residential development c) Shrubs 6. Industrial Structure d) Agriculture 7. Public Sector Policies e) Urban Area 8. Literacy 5. Extreme Events 9. GDP a) Flood b) Forest Fire 6. Drainage Network 7. Meteorological a) Rainfall b) Runoff
- 5. Impact of changein watershed Dynamics Changes in land use and land cover- feedback system Patchiness in forest- due to agriculture Deterioration of water quality- water usage Shortage of water resources- spatial patterns of LU Biodiversity loss- due to loss in forest, wetland etc.
- 6. Need ofWatershed Modelling Improper LU practices Drivers complex interaction Geo-information based Approach Remote Sensing- gives spatial and temporal data GIS- integrate spatial and non spatial data
- 7. Aim and Objectives Aim: To model and analyze the watershed dynamics using Cellular Automata (CA) -Markov Model and predict the change for next 10 years Objectives: To generate land use / land cover database with uniform classification scheme for 1972, 1990, 1999 and 2004 using satellite data To create database on demographic, socioeconomic, Infrastructure, etc parameters Analysis of socioeconomic and biophysical drivers impact on watershed dynamics To derive the Transition Area matrix and suitability images based on classification To generate scenarios for projecting future watershed dynamics scenarios using CA- Markov Model To prepare Management Plan to minimize change in watershed dynamics
- 8. Study Area- ChoudwarWatershed River basin map of India • Drainage Area = 195 sq.km • Latitude- 20 29’33 to 20 40’21 N •Longitude- 85 44’59.33 to 85 54’16.62 E •Growing Industrial Area Mahanadi River Basin
- 9. Problems of ChoudwarWatershed Transformation -wetland is transferring in to Agriculture -Unavailability of water
- 10. Land Use LandCover (LULC) Dynamics LULC Distribution for year 1972, 1990, 1999 and 2004 1972 1990 1999 2004 Land use and Land Cover Area Area Area Area Area Area Area Area Categories (ha) (%) (ha) (%) (ha) (%) (ha) (%) Agriculture 3055 15.35 4500.0 22.82 8194 41.57 8878 44.93 Settlement 422 2.12 549.73 2.79 575.9 2.92 738.6 3.74 Forest 11608 58.35 108182 54.86 8624 43.76 8098 40.98 Wetland 1043 5.24 693.17 3.52 430 2.18 160.9 0.81 Marshy Land 1578 7.93 1427.2 7.24 331.3 1.68 313.3 1.59 Fallow and Barren Land 1749 8.79 1354.5 6.87 1124 5.70 1119 5.66 Water 442 2.22 377.29 1.91 430.9 2.19 451 2.28
- 11. Methodology Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004 Data download and Layer stack Geo-referencing and Reprojection Area extraction Multi-temporal image Classification of the satellite data Classification Population Drainage Network Slope Road and Rail Network Preparing Ancillary Data Settlement Residential Land Use Distance from Road Distance Development Land Cover and Rail Network Statistics Calculation of LU/LC area statistics for different classes (for different periods) TAM and Suitability Images Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability Images by MCE Simulation Run CA- Markov model in IDRISI- Andes by giving -1) Basis land Cover Image , 2) TAM and 3) Suitability Image as inputs Analysis Analysis of drivers responsible for watershed change Prediction Predict future watershed dynamics for 2014 from the obtained trend Management Preparation of management plan to minimize change in watershed dynamics Plan
- 12. Data Required Period Satellite and data type Resolution (m) Path Row 1972 Landsat, MSS 79 150 46 Acquired Satellite 1990 Landsat, TM 30 140 46 Data 1999 Landsat, ETM+ 30 140 46 2004 Landsat, TM 30 140 46 Sl. Data Type Date of Production Source Socioeconomic Census of India 1 Population 1971, 1981, 1991, 2002 data Bhubaneswar Statistical Handbook 2 Residential Development 1971, 1981, 1991, 2002 data Statistical Handbook 3 Industrial development 1991, 2001, 2004, 2007 data 4 Road Network 2001 NRIS 5 Railway Network 2001 NRIS Biophysical Total Area under Winter Statistical Handbook 6 1991, 2001, 2004 Parameters Crops data Sl. Data Type Date of Production Source 1 Drainage Network 2001 NRIS 2 Slope 2001 NRIS
- 13. 1990 1972 1990 Legend road rail network Agriculture Land use Land Cover Settlement 2004 Classification Forest wetland Marshyland Fallow and Barren Land Water Body
- 14. Accuracy Assessment Accuracy Assessment of classified LULC of years 1972, 1990, 1999 and 2004. Class Name 1972 1990 1999 2004 Producers Users Producers Users Producers Users Producers Users Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy Accuracy Water Body 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Wetland 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Marshy land 100.0 75.0 100.0 75.0 100.0 100.0 100.0 100.0 Forest 96.4 93.1 89.7 96.3 87.5 91.3 91.7 91.7 Settlement 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Agriculture 80.0 100.0 90.9 90.9 94.7 85.7 95.7 95.7 Fallow and Barren land 75.0 75.0 100.0 75.0 50.0 100.0 75.0 75.0 Overall Classification Accuracy and Overall Kappa Statistics 1972 1990 1999 2004 Overall Classification Accuracy (%) 92 92 90 92.31 Overall Kappa Statistics 0.8725 0.8723 0.8377 0.8931
- 15. Trends Population trend linefrom 1972 to 2004 Area under winter crops trend line from 1972 to 2004
- 16. Correlation between differentfactors No of Total Area Number of Population Settlement Agriculture House under Winter Industries and Forest hold Crops Mining’s 1 0.89 0.91 - - - -0.99 Population Settlement 0.89 1 0.89 0.94 Agriculture 0.91 0.87 1 - 0.95 0.97 - No of House - 0.94 - 1 hold Total Area - - 0.95 - 1 - - under winter crops Number of 0.97 Industries - - - - 1 - and Mining’s Forest - - -0.99 - - - 1
- 17. Markov Chain Analysis(MCA) 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. The probability of moving from one state to another state is called a transition probability. Let set of states, S = { S1,S2, ……., Sn}. Transition Probability Matrix 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 Transition Area Matrix: is produced by multiplication of each column in Transition Probability Matrix (P) by no. of pixels of corresponding class in later image
- 18. Transition Probability Matrixof for prediction of LULC in year 2004 Marshy Fallow and Water Agriculture Settlement Forest Wetland land Barren Land Body Agriculture 0.7765 0.0328 0.0781 0.0066 0.0344 0.0715 0 Settlement 0.3302 0.5473 0.0631 0.0035 0.0142 0.0417 0 Forest 0.223 0.016 0.7199 0.0027 0.0079 0.0305 0 Wetland 0.4068 0 0.0095 0.5483 0.0144 0 0.021 Marshy land 0.6715 0.0158 0.1074 0.0227 0.1718 0.0015 0.0093 Fallow and Barren Land 0.2049 0.0341 0.1998 0.0026 0.001 0.4945 0.0632 Water Body 0.0234 0.0005 0 0.0285 0.0072 0.1979 0.7425 Transition Area Matrix of for prediction of LULC in year 2004 . Marshy Fallow and Water Agriculture Settlement Forest Wetland land Barren Land Body Agriculture 67984 2875 6842 581 3010 6264 0 Settlement 2092 3466 399 22 90 264 0 Forest 21976 1576 70953 269 781 3005 100 Wetland 1930 0 45 2602 68 0 34 Marshy land 2450 58 392 83 627 5 779 Fallow and Barren Land 2523 419 2460 32 12 6090 3527 Water Body 111 2 0 135 34 940 3527
- 19. Cellular Automata (CA)Model Spatial component is incorporated Powerful tool for Dynamic modelling St+1 = f (St, N, T) where St+1 = State at time t+1 St = State at time t N = Neighbourhood T = Transition Rule • Transition Rules Heart of Cellular Automata Each cell’s evolution is affected by its own state and the state of its immediate neighbours to the left and right. Fig. Von Neumann’s Neighbor and Moore’s Neighbor
- 20. Cellular Automata(CA) –MCAin IDRISI -Andes • Combines cellular automata and the Markov change land cover prediction. • Adds knowledge of the likely spatial distribution of transitions to Markov change analysis. Input files required- 1) Basis land Cover Image , 2) Transition Area Matrix 3) Suitability Images
- 21. Transition Suitability Maps Transitionsuitability implies the suitability of a cell for a particular land cover. Slope Biophysical Drainage Network drivers Vegetative Cover Drivers Considered Population Growth Residential Socio- Development economic Agricultural Expansion Factors Distances to road and rail Proximate network Factors Distances to town River Course Constraints Existing Settlement Road and rail network
- 22. Factors Slope Population Road Rail Network Settlement Distance Distance
- 23. Weights Applied forDrivers by AHP Land use and land Relative Land use and cover classes Factors Weight Constraints land cover Relative Population 0.1837 River Course classes Factors Weight Constraints Residential Population 0.1031 River Course Development 0.206 Settlement Residential Agriculture settlement Road and rail Wetland Development 0.1078 Settlement Distance 0.5668 network Road and rail slope 0.0435 Slope 0.7891 network Population 0.1617 River Course Population 0.0744 River Course Residential Drainage Development 0.1703 Settlement distance 0.6042 Settlement Road rail network Road and rail Marshy Land Road and rail Settlement distance 0.0908 network Slope 0.2007 network Slope 0.057 Road rail Settlement network distance 0.1207 Distance 0.5202 Population 0.2202 River Course Population 0.1188 River Course Residential Residential Fallow and Development 0.2169 Settlement Development 0.1188 Settlement barren land Settlement Road and rail Forest Road rail network Road and rail Distance 0.494 network distance 0.0678 network Slope 0.0689 Slope 0.3897 Agriculture Population 0.0953 Settlement Settlement Road and rail Distance 0.3049 Water Slope 0.6548 network Drainage distance 0.2499
- 24. Constraints or Limitations Existing Road Rail Network Settlement
- 25.
- 26. CA-Markov Output Predicted Land Use Land cover Actual Land Use Land cover map for year 2004 map for year 2004
- 27. CA-Markov Output Predicted Land Use Land cover map for year 2014
- 28. Management Plan Objectivesconsidered • To construct the small water and soil conservation structures at gullies. • To participate rural peoples and stakeholder for prevent land degradation and watershed management activities. • Improvement of agriculture production. • Use of Remote Sensing and GIS Decision Rules decision rules are formulized for selection of sites for various soil and water conservation structures as per the guidelines given by Integrated Mission for Sustainable Development (IMSD, 1995), Indian National Committee on Hydrology (INCOH) Structures Area Slope Permeability Run-off Land Use Potential Check dam - Gentle to steep Low to Medium Hilly area slope Medium Percolation >40 ha Nearly Level to Medium to Low/Medium Near stream Pond Gentle slope high Irrigation 2 ha Nearly level to Very Low Low/Medium Agriculture Tank Gentle slope
- 29. Management Plan Map of suitable locations for different water conservation structures in watershed
- 30. Conclusions •This research work demonstrates the ability of GIS and Remote Sensing in capturing spatial-temporal dynamics of watershed. •We believe that the study has demonstrated the usefulness of a holistic model that combines Markov and CA models for watershed changes. •The combination of Markov and a simple CA filter was reasonably accurate for projecting future land use land cover, since it produced the overall accuracy of 76.22% which is more than US standard acceptable accuracy 60%. •We can prepare the future watershed management plan on the basis of projected land use land cover of watershed dynamics by CA- Markov Model.
- 31.