Semester End Seminar

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Parameterisation of drivers responsible for changes in Land use Land Cover of watershed in Mahanadi River

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Semester End Seminar

  1. 1. Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) - Markov Model –A Geoinformation Based Approach Semester End Seminar 19-11- 2009 Prepared by SANTOSH BORATE 08WM6002 Under the guidance of DR. M. D. BEHERA SCHOOL OF WATER RESORCES INDIAN INSTITUTE OF TECHNOLOGY KHARAGPUR
  2. 2. CONTENTS • Introduction • Review of Literature • Aim and Objectives • Study Area • Methodology • Model Description - Markov Chain Analysis (MCA) - Cellular Automata(CA) - CA-Markov model in IDRISI- Andes • Work Done • Work to be done • Conclusion • Acknowledgement
  3. 3. Introduction Introduction Definition • Watershed, Land Use/ Land Cover Review of Need of Watershed • Implies the proper use of all land, water Literature Modelling and natural resources of a watershed Aim and Objectives Image classification • Prerequisite for Land Use Land Cover Study Area Change (LULCC) detection Methodology • Understand relationships & interactions Model Change detection with human & natural phenomena to description better management Work Done Use of advanced • Remote sensing & GIS tools provides Work to be synoptic coverage & repeatability thus is done spatial technology tools cost effective Conclusion Acknowledge- ment
  4. 4. Review of Literature  Research Papers Introduction Gautam (2006) done the watershed modelling for Kundapallam watershed using remote sensing and GIS by considering the main causes like changing of land use from Review of forest into pasture, agriculture and urban, as a result of population growth and general Literature scarcity, use of the wood as a source of heat and energy in economically poor area, also general degradation of forests caused by industrial growth, Environmental Aim and pollution, and an increase of consumption. Objectives Alemayehu et al. (2009) assessed the impact of watershed management on land use Study Area and land cover dynamics in Eastern Tigray (Ethiopia) and determined the land use and cover dynamics that it has induced. Methodology Daniel G. Brown(2004) Introduced the different type of models for LULCC Modeling in Model relation to the purpose of the model, avaibility of data , drivers responsible for LULCC. description Soe W. Myint and Le Wang(2006) This study demonstrates the integration of Markov Work Done chain analysis and Cellular Automata (CA) model to predict the Land Use Land Cover Change of Norman in 2000 using multicriteria decision making approach. This study Work to be used the post-classification change detection approach to identify the land use land done cover change in Norman, Oklahoma, between September 1979 and July 1989 using Landsat Multispectral Scanner (MSS) and Thematic Map (TM) images. Conclusion Acknowledge- ment
  5. 5. Review of Literature continue…… Fan et al. (2008) conducted the study of detecting the temporal and spatial change in Introduction between1998 to 2003 and then predicted land use and land cover in Core corridor of Pearl River Delta (China) by using Markov and Cellular Automata (CA) model. Review of Literature  BOOKS Aim and 1. Introduction to probability. Objectives - Charles M. Grinstead, J. Laurie Snell 2. Probability and statistics for Engineers and Scientists. Study Area - Ronald E. Walpole 3. Markov Chains Gibbs Fields, Monte Carlo Simulation and Queues. Methodology - J.E. Marrsden 4. Introduction to Geographic Information System(GIS). Model description -Kang-tsung Chang Work Done Work to be done Conclusion Acknowledge- ment
  6. 6. Aim and Objectives AIM Introduction To Model and Analyze the Watershed Dynamics using Cellular Automata (CA) -Markov Model and predict the change for next 10 years. Review of Literature OBJECTIVES Aim and  To generate land use / land cover database with uniform classification Objectives scheme for 1972, 1990, 1999 and 2004 using satellite data Study Area To create database on demographic, socioeconomic, Infrastructure parameters Methodology Analysis of indicators and drivers and their impact on watershed dynamics Model description To derive the Transition Area matrix and suitability images based on Work Done classification Work to be To project future watershed dynamics scenarios using CA-Markov Model done To give the plan of measures for minimize the future watershed dynamics Conclusion change Acknowledge- ment
  7. 7. STUDY AREA River basin map of India Introduction • Drainage Area = 195 sq.km • latitude- 20 29’33 to 20 40’21 N Review of •Longitude- 85 44’59.33 to 85 54’16.62 E Literature •Growing Industrial Area Aim and Objectives Study Area Mahanadi Methodology River Basin Model description Work Done Work to be done Conclusion Acknowledge- ment
  8. 8. Parameters to be considered A) Biophysical Parameters: B) Socio-economic Parameters Introduction Review of 1. Altitude 1. Urban Sprawl Literature 2. Slope 2. Population Density 3. Soil Type 3. Road Network Aim and 4. LU/LC classes 4. Socioeconomic Environment Objectives a) Wetlands Policies Study Area b) Forest 5. Residential development c) Shrubs 6. Industrial Structure Methodology d) Agriculture 7. GDPA e) Urban Area 8. Public Sector Policies Model 5. Extreme Events 9. Literacy description a) Flood b) Forest Fire Work Done 6. Drainage Network Work to be 7. Meteorological done a) Rainfall b) Runoff Conclusion Acknowledge- ment
  9. 9. Acquired Satellite Data Satellite data for time period 1972 – procured from GLCF site Introduction Landsat PATH 150 Review of MSS ROW 46 Literature Resolution 79m Aim and Satellite data for time period 1990 – procured from GLCF site Objectives Landsat PATH 140 Study Area TM ROW 46 Resolution 30m Methodology Satellite data for time period 1999 – procured from GLCF site Model description Landsat PATH 140 ETM+ ROW 46 Work Done Resolution 30m Work to be Satellite data for time period 2004 – procured from GLCF site done Landsat PATH 140 TM ROW 46 Conclusion Resolution 30m Acknowledge- GLCF – Global Land Cover Facility ment
  10. 10. Data Collection Introduction 1. Population Density 2. Land Use Land Cover Review of 3. Soil Map Literature 4. Rainfall Aim and 5. Road Network Objectives 6. Urban Sprawl 7. GDPA Study Area 8. Literacy 9. Residential development Methodology Model description Work Done Work to be done Conclusion Acknowledge- ment
  11. 11. METHODOLOGY
  12. 12. Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004 Data download and Layer stack Georeferencing and Reprojection Area extraction Multitemporal Classification of the satellite data image Classification Road network Drainage Network Soil Type Altitude Preparing Ancillary Data Industrial Population Urban Sprawl Slope Structure Density Statistics Calculation of LU/LC area statistics for different classes (for different periods) TAM and Suitability Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability Images 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 coming 10 years from the obtained trend
  13. 13. CA-Markov Model Description Introduction Markov Chain Analysis Cellular Automata (CA) Review of Literature CA-Markov Model in IDRISI Andes Aim and Input files- 1) Basis land Cover Image , Objectives 2) Transition Area Matrix 3) Suitability Image Study Area Methodology Model description Work Done Work to be done Conclusion Acknowledge- ment
  14. 14. Work Done Introduction Review of Literature Acquisition, Georeferencing, Reprojection of Remote Sensing Data Review of Collection of demographic, socioeconomic, Infrastructure parameters data Literature like DEM data, road network, drainage network, LULCC, Population, Rainfall etc. Aim and Objectives Generation of spatial layers of demographic, socioeconomic and Infrastructure parameters Study Area Generation of database of land use land cover in uniform classification scheme Methodology Analysis of Land Use Land Cover Change Model Introduction with Geo-informatics software's ERDAS IMAGINE 9.1, ArcGIS description 9.1, IDRISI Andes. Work Done Work to be done Conclusion Acknowledge- ment
  15. 15. Work to be done Introduction To develop the criteria for model construction To run CA- Markov model through IDRISI- Andes software Review of Literature Analysis of drivers responsible for land use land cover change in watershed Aim and Objectives To predict the watershed dynamics scenarios for next future 10 years To give the plan of measures for minimize the future watershed Study Area dynamics change Methodology Model description Work done Work to be Done Conclusion Acknowledge- ment
  16. 16. Conclusion Introduction Watershed modeling implies the proper use of all land, water and natural resources of a watershed for optimum production Review of with minimum hazard to eco-system and natural resources. Literature Helps to policymaker and decision maker. Aim and Need of implementation of measure plan Objectives Study Area Methodology Model description Work done Work to be Done Conclusion Acknowledge- ment
  17. 17. Acknowledgement Introduction Prof. S.N Panda gave the guidance on Modelling of watershed. Prof. C Chatterjee guided in selection of watershed Review of Literature Prof. M.D. Behera guided in developing overall methodology and Aim and gave ancillary data. Objectives SAL (Spatial Analytical Lab) of CORAL Department and JRF and SRF in Lab. Study Area GLCF (Global Land Cover Facility) – RS data download. Methodology SRTM (Shuttle Radar Topography Mission )- DEM data download. Model NRSC (National Remote Sensing Centre)- LULC data description Work done Work to be done Conlclusion Acknowledge- ment
  18. 18. 18
  19. 19. Markov Chain Analysis Introduction Subdivide area into a number of cells On the basis of observed data between time periods, MCA Review of Literature computes the probability that a cell will change from one land use type (state) to another within a specified period of time. Aim and Objectives The probability of moving from one state to another state is called a transition probability. Study Area Let set of states, S = { S1,S2, ……., Sn}. Methodology Model description Work Done where P = Markov transition probability matrix P Work to be i, j = the land type of the first and second time period done Pij = the probability from land type i to land type j Conclusion Acknowledge- ment
  20. 20. Markov Chain Analysis Example: Wetland class in 2000 changes into two major classes in Introduction 2004, agriculture class and settlement; 33 % of wetland is changing to Review of agriculture, while 20 % changing to settlement. Literature Aim and Wetland Objectives Study Area Settlement Methodology Agriculture Model 2000 2004 description W A S Work Done W .47 .33 .20 P= A PRF PRR PRP transition probability matrix Work to be S PPF PPR PPP done Conclusion Acknowledge- ment
  21. 21. Markov Chain Analysis Introduction Transition Area Matrix: is produced by multiplication of each column in Transition Probability Matrix (P) by no. of pixels of corresponding class in Review of Literature later image W A S Aim and W 94 66 40 Objectives A= A ARF ARR ARP Study Area S APF APR APP Methodology Disadvantages: Model description Markov analysis does not account the causes of land use change. An even more serious problem of Markov analysis is that it is insensitive Work Done to space: it provides no sense of geography. Work to be done Conclusion Acknowledge- ment
  22. 22. Cellular Automata (CA) Model Introduction  Spatial component is incorporated Review of  Powerful tool for Dynamic modelling Literature St+1 = f (St,N,T) Aim and where St+1 = State at time t+1 Objectives St = State at time t Study Area N = Neighbourhood Methodology T = Transition Rule Model Transition Rules description  Heart of Cellular Automata  Each cell’s evolution is affected by its own state and the state of its Work Done immediate neighbours to the left and right. Work to be done Conclusion Acknowledge- Fig. Von Neumann’s Neighbor and Moore’s Neighbor ment
  23. 23. Cellular Automata(CA) –MCA in IDRISI -Andes Introduction • Combines cellular automata and the Markov change land Review of Literature cover prediction. Aim and • Adds knowledge of the likely spatial distribution of Objectives transitions to Markov change analysis. Study Area • The CA process creates a suitability map for each class based on the factors (Biophysical and Proximate) and Methodology ensuring that land use change occurs in proximity to Model existing like land use classes, and not in a wholly random description manner. Work Done Work to be done Conclusion Acknowledge- ment
  24. 24. Fig. (a)Spatial layer of slope b) Slope aspect
  25. 25. Fig. Spatial layer of Road network, Drainage Network
  26. 26. Fig. Spatial layer of Land Use Land Cover of the watershed
  27. 27. Fig. Spatial layer of Soil classes in watershed
  28. 28. 1972 1990 1999 2004 Dense forest Fig. Unsupervised classification of Land use land cover Open forest Agriculture Wet Marshy Dense Open Fallow Water Body Water land land forest forest settlement agriculture land 1972 493.5231 898.9983 1426.311 8597.823 5276.701 584.82 2266.1775 833.6934 Wetland 1990 507.0877 959.9171 1156.969 7398.054 4156.04 780.7347 3633.84405 661.1715 Settlement 1999 585.6323 823.784 680.5031 8383.313 3478.379 793.1621 4936.611825 311.01053 2004 471.87 687.51 340.74 6539.49 2959.74 1110.33 7338.42 554.4 Marshy land Fallow land
  29. 29. Fig. Land Use Land Cover Trend
  30. 30. Agriculture Settlement Forest Wetland Marshy land Fallow and Barren Land Water Body Legend road rail network Agriculture Settlement Forest wetland Marshyland Fallow and Barren Land Water Body

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