Mid-Term Seminar

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Basics of Cellular Automata (CA)-Markov Model for Land Use land Cover modelling

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Mid-Term Seminar

  1. 1. Modelling and Analyzing the Watershed Dynamics using Cellular Automata (CA) - Markov Model –A Geoinformation Based Approach Mid-Semester Seminar 15-10- 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 - Cellular Automata(CA) - Cellular automata-MCA in IDRISI- Andes • Work Done • Work to be done • Conclusion
  3. 3. Introduction Introduction Definition • Watershed, Land Use/ Land Cover Review of Need of Watershed • In order to maintain equilibrium Literature Modelling between surrounding environment and climate 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 Acknowledge- ment
  4. 4. Review of Literature  Research Papers Introduction Anuj Kumar Singh (2003) conducted study of LULCC with Cellular Automata(CA) which has advantage, that it incorporate the spatial component. Suggest that Review of Cellular Automata(CA) Model is highly depend on Spatial variables taken in to Literature consideration . More variables can increase the accuracy of the model. Aim and Daniel G. Brown(2004) Introduced the different type of models for LULCC Objectives Modeling in relation to the purpose of the model, avaibility of data , drivers responsible for LULCC. Study Area Methodology Antonius B. Wijanarto(2006) Described Markov Change Detection is one application of change detection that can be used to predict future changes based Model on the rates of past change. The method is based on probability that a given piece description of land will change from one mutually exclusive state to another. These probabilities are generated from past changes and then applied to predict future Work Done change. Work to be Thomas HOUET, Laurence HUBERT-MOY(2006) Cellular automata (CA), that done provide a powerful tool for the dynamic modeling of land use changes, is a common method to take spatial interactions into account. They have been Acknowledge- implemented in land use models that are able to simulate multiple land use ment types.
  5. 5. Review of Literature continue…… Soe W. Myint and Le Wang(2006) This study demonstrates the integration of Markov chain analysis and Cellular Automata (CA) model to predict the Land Use Introduction Land Cover Change of Norman in 2000 using multicriteria decision making Review of approach. This study used the post-classification change detection approach to Literature identify the land use land cover change in Norman, Oklahoma, between September 1979 and July 1989 using Landsat Multispectral Scanner (MSS) and Aim and Thematic Map (TM) images. Objectives Huiping Liu (2008) Research shows that Land use/land cover change detection Study Area using multi-temporal images by means of remote sensing and ration research of model of urban expansion by GIS are good means of research of urban expansion. Methodology Model  BOOKS description 1. Introduction to probability. - Charles M. Grinstead, J. Laurie Snell Work Done 2. Probability and statistics for Engineers and Scientists. - Ronald E. Walpole Work to be 3. Markov Chains Gibbs Fields, Monte Carlo Simulation and Queues. done - J.E. Marrsden 4. Introduction to Geographic Information System(GIS). Acknowledge- -Kang-tsung Chang 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  To derive the Transition Area matrix and suitability images based on Model description classification Work Done  Analysis of indicators and drivers and their impact on watershed dynamics Work to be done  Projecting future watershed dynamics scenarios using CA-Markov Model Acknowledge- ment
  7. 7. STUDY AREA River basin map of India Introduction • Drainage Area = 195 sq.km • latitude- 20 29 33.39 to 20 40 21.09 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 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 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 Acknowledge- ment Resolution 30m GLCF – Global Land Cover Facility
  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 Acknowledge- ment
  11. 11. METHODOLOGY
  12. 12. METHODOLOGY Toposheet 1945 MSS 1972 TM 1990 ETM+ 1999 TM 2004 Data download and Layer stack Georeferencing and Reprojection Area extraction Multitemporal image Classification of the satellite data Classification Preparing Road network Drainage Network Soil Type Altitude Ancillary Data Industrial Urban Sprawl Slope Population Structure Density Statistics Calculation of LU/LC area statistics for different classes (for different periods) TAM and Obtain Transition Area Matrix (TAM) by Markov Chain Analysis and Suitability Suitability Images Images by MCE Run CA- Markov model in IDRISI- Andes by giving -1) Basis land Cover Image , Simulation 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 IDRISI Software Markov Chain Analysis Review of Literature Cellular Automata (CA) Aim and CA-Markov Model in IDRISI Andes Objectives Input files- 1) Basis land Cover Image , 2) Transition Area Matrix Study Area 3) Suitability Image from MCE Methodology Model description Work Done Work to be done Acknowledge- ment
  14. 14. Work Done Introduction Review of Literature a) Research papers Review of Literature b) Books Aim and Formulation of Methodology Objectives Analysis of parameters which to be consider Study Area Acquisition, Georeferencing, Reprojection of Remote Sensing Data Methodology Collection of data like DEM data, road network, drainage Model network, LULCC, Population, Rainfall etc. description Extraction of Study Area. Work Done Unsupervised Classification of reprojected images Introduction with Geoinfomatics software's ERDAS IMAGINE 9.1, Work to be done ArcGIS 9.1 , IDRISI Andes. Acknowledge- ment
  15. 15. Work to be done Introduction Prepare the spatial layers of socio-economic parameters considered. Review of Literature Obtain Transition Area Matrix by Markov Chain Analysis and Suitability Images by MCE Aim and Objectives Run CA- Markov model in IDRISI- Andes Analysis of drivers responsible for land use land cover change in Study Area watershed Methodology Predict the watershed dynamics for next future 10 years Model description Work done Work to be Done Acknowledge- ment
  16. 16. 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 Christina Connolly who gave the trial version of IDRISI Software from Clark Lab. Study Area SAL (Spatial Analytical Lab) of CORAL Department and JRF and Methodology SRF in Lab. Model GLCF (Global Land Cover Facility) – RS data download. description SRTM (Shuttle Radar Topography Mission )- DEM data download. Work done NRSC (National Remote Sensing Centre)- LULC data Work to be done Acknowledge- ment
  17. 17. 17
  18. 18. 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, ……., Sr }. 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 Acknowledge- ment
  19. 19. Markov Chain Analysis Example: Forest in 2000 is change into two major classes in 2001, Introduction paddy field and residential; 33 % of forest is changing to residential, Review of while 20 % changing to paddy field. Literature Aim and Forest Objectives Study Area Residential Methodology Paddy Model 2000 2001 description F R P Work Done F .47 .33 .20 P= R PRF PRR PRP transition probability matrix Work to be P PPF PPR PPP done Acknowledge- ment
  20. 20. 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 F R P Aim and F 94 66 40 Objectives A= R ARF ARR ARP Study Area P 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. - Although the transition probabilities may be accurate for a Work to be done particular class as a whole, there is no spatial element to the modeling process. Acknowledge- - Using cellular automata adds a spatial dimension to the model. ment
  21. 21. Cellular Automata (CA) Model Introduction Review of  Spatial component is incorporated Literature  Powerful tool for Dynamic modelling Aim and  Each row represents a single time step Objectives of the automaton’s evolution. Study Area Methodology St+1 = f (St,N,T) where St+1 = State at time t+1 Model description St = State at time t N = Neighbourhood Work Done T = Transition Rule Work to be done Acknowledge- ment
  22. 22. Cellular Automata (CA) Model Introduction Transition Rules  Heart of Cellular Automata Review of  Each cell’s evolution is affected by its own state and the state of its Literature immediate neighbours to the left and right. Aim and Objectives Study Area Methodology Fig. Von Neumann’s Neighbor and Moore’s Neighbor Model description Suitability Maps: Ex- To check the suitability of pixel for Settlement or Agriculture Work Done It depends on various Factors : biophysical and Proximity Factor like altitude, rainfall, distance from road etc Work to be Sc = Su + N……………………(1) done Su = (∑Wi * fi) ……………………………………(2) Acknowledge- ment ∑ Wi
  23. 23. Cellular Automata (CA) Model Classes Settlement Agriculture Introduction Biophysical (Weights) (Weights) Factors Review of Literature Rainfall 4 8 Aim and Objectives Slope 8 2 Altitude 5 1 Study Area Classes Settlement Agriculture Methodology Proximate (Weights) (Weights) Model Factors description Distance From Road 10 6 Work Done Distance From City 5 7 Distance From Industry 3 3 Work to be done Table.1. Allotment of Weights for Settlement and agricultural class Acknowledge- If SSet ≥ SAg ………then state = Settlement ment If SSet ≤ SAg ………then state = Agriculture
  24. 24. 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 landuse change occurs in proximity to existing Model like landuse classes, and not in a wholly random manner. description • In each iteration of the simulation each class will normally Work Done gain land from one or more of the other classes or it may lose some to one or more of the other classes. Work to be done Acknowledge- ment
  25. 25. Conclusion Introduction  Morkov Model does not incorporate the spatial component in modelling Land Use and Land Cover prediction Integration of Markov Review of Literature chain analysis and Cellular Automata (CA) model adds knowledge of the likely spatial distribution of transitions to Markov change analysis. Aim and Objectives  Integration of Markov chain analysis and Cellular Automata (CA) Study Area model to predict the Land Use Land Cover Change is reasonably accurate , since it produces overall accuracy above the 85% when Methodology comparing predicted map to the original satellite image Model description Work Done Work to be done Acknowledge- ment
  26. 26. 1. First (earlier) land cover image 2. Second (Last) land cover image 3. Prefix for output Conditional Probability Image 4. No. of time period between first and last land cover image 5. No. of time period to project forward from second image
  27. 27. Markov Spacelessness Introduction Review of Literature Aim and Objectives Study Area Methodology Model description Work Done Work to be done Acknowledge- ment
  28. 28. 1. Basic Land Cover image 2. Markov Area Transition File 3. Transition Suitability Image Collection 4. Out Put Land Cover Projection 5. No. of Cellular Automata iterations
  29. 29. 1972 1990 1999 2004 Forest Agriculture Settlement Wetland Water Body

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