Welcome To the presentation on   Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Growth Dynamics ...
Study Area
Changing Patterns of Dhaka City and its Population Source: GIS division, Bangladesh Centre for Advanced Studies
Problem Identification
Problem Identification
Research Hypothesis What is absent in Dhaka City? CITY PLANNING
Objectives of the Research <ul><li>To  investigate the characteristics of urban land cover changes  (1989-2009) </li></ul>...
Methodology of the Research Data Collection Problem Identification Research Objectives Supervised Classification Selection...
Methodology of the Research Change Detection Simulated Map (2009) Model Calibration Model Validation Predicted Map (2019) ...
Landsat Satellite Images Path 137 Row 44 Map Projection: UTM-46 N Datum: WGS 84  Pixel Size: 30 meters
Image Classification Supervised Classification Land Cover Type Description Builtup Area Residential, Commercial and Indust...
Base Maps
Accuracy Assessment
Accuracy Assessment Base Year Overall Accuracy Kappa Statistics 1989 85.20% 0.8054 1999 86.80% 0.8294 2009 91.60% 0.8821
Change Detection Increasing Decreasing
Markov Chain Analysis A Markov chain is a discrete random process with the property that  the next state depends only on t...
Stochastic Markov Model (St_Markov) Transition Areas Matrix (Cell) Transition Matrix (Probability)  Builtup Area Water Bod...
Conditional Probability Images
Stochastic Markov Model (St_Markov)
Cellular Automata (CA) A cellular automaton is a cellular entity that independently varies its  new state based on its pre...
Cellular Automata Markov Model (CA_Markov) <ul><li>CA_Markov combines: </li></ul><ul><li>Markov Chain </li></ul><ul><li>Ce...
Preparing Suitability Images The basic assumption for preparing suitability images:  a pixel closer to an existing land co...
Suitability Images
3×3 CA Contiguity Filter Cellular Automata Markov Model (CA_Markov) Markov Transition Area Matrix Suitability Images 0 1 0...
Artificial Neural Network
Multi Layer Perceptron Markov Model (MLP_Markov)
Driving Variables (Inputs)
Multi Layer Perceptron Markov Model (MLP_Markov) Builtup Area Water Body Vegetation Low Land Fallow Land Builtup Area 0.78...
Model Validation Map Comparison : Simulated Map (2009) vs. Base Map (2009) St_Markov (2009) CA_Markov (2009) MLP_Markov (2...
MLP_Markov Model Base Map 1999 Base Map 2009 Future Prediction (2019)
Outcome of the Research The outcome of this research will help the Decision Makers and Urban Planners to make  DHAKA  city...
Thank You All QUESTIONS ?
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Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Growth Dynamics: "A Case Study of Dhaka, Bangladesh"

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This is my final Mater thesis presentation. The thesis defense was held on March' 07, 2011 at 15:30 in the seminar room of Universitat Jaume I (UJI), Castellón, Spain.

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Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Growth Dynamics: "A Case Study of Dhaka, Bangladesh"

  1. 1. Welcome To the presentation on Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Growth Dynamics A Case Study of Dhaka, Bangladesh BAYES AHMED Date: 07.03.2011
  2. 2. Study Area
  3. 3. Changing Patterns of Dhaka City and its Population Source: GIS division, Bangladesh Centre for Advanced Studies
  4. 4. Problem Identification
  5. 5. Problem Identification
  6. 6. Research Hypothesis What is absent in Dhaka City? CITY PLANNING
  7. 7. Objectives of the Research <ul><li>To investigate the characteristics of urban land cover changes (1989-2009) </li></ul><ul><li>To predict the future urban growth of Dhaka City </li></ul>
  8. 8. Methodology of the Research Data Collection Problem Identification Research Objectives Supervised Classification Selection of Study Area Accuracy Assessment (If Not) (If Accurate) Landsat Satellite Image (2009) Landsat Satellite Image (1999) Landsat Satellite Image (1989) Guide Map (2001) Guide Map (1995) Guide Map (1989)
  9. 9. Methodology of the Research Change Detection Simulated Map (2009) Model Calibration Model Validation Predicted Map (2019) Directions for Future Planning Model Selection (If Accurate) (If Valid) (If Not) Base Map (2009) Base Map (1999) Base Map (1989) (Best Fitted Model) Base Map (2009) Base Map (1999) Base Map (1989) MLP_Markov CA_Markov St_Markov
  10. 10. Landsat Satellite Images Path 137 Row 44 Map Projection: UTM-46 N Datum: WGS 84 Pixel Size: 30 meters
  11. 11. Image Classification Supervised Classification Land Cover Type Description Builtup Area Residential, Commercial and Industrial Areas Water Body River, Lakes, Ponds and Canals Vegetation Trees, Natural Vegetation, Parks and Playgrounds Low Land Wetlands, Marshy Land and Crop Fields Fallow Land Open Space, Bare and Exposed Soils
  12. 12. Base Maps
  13. 13. Accuracy Assessment
  14. 14. Accuracy Assessment Base Year Overall Accuracy Kappa Statistics 1989 85.20% 0.8054 1999 86.80% 0.8294 2009 91.60% 0.8821
  15. 15. Change Detection Increasing Decreasing
  16. 16. Markov Chain Analysis A Markov chain is a discrete random process with the property that the next state depends only on the immediately preceding state(s)
  17. 17. Stochastic Markov Model (St_Markov) Transition Areas Matrix (Cell) Transition Matrix (Probability) Builtup Area Water Body Vegetation Low Land Fallow Land Builtup Area 0.6649 0.0268 0.0533 0.0298 0.2252 Water Body 0.2125 0.1074 0.1030 0.1969 0.3802 Vegetation 0.1675 0.0853 0.3304 0.1173 0.2995 Low Land 0.0766 0.4006 0.0514 0.3446 0.1267 Fallow Land 0.4126 0.0144 0.2603 0.0199 0.2928 Builtup Area Water Body Vegetation Low Land Fallow Land Builtup Area 95476 3845 7658 4278 32332 Water Body 10034 5074 4865 9300 17953 Vegetation 17569 8945 34655 12302 31409 Low Land 4574 23914 3070 20572 7566 Fallow Land 57732 2009 36415 2789 40964
  18. 18. Conditional Probability Images
  19. 19. Stochastic Markov Model (St_Markov)
  20. 20. Cellular Automata (CA) A cellular automaton is a cellular entity that independently varies its new state based on its previous state and that of its immediate neighbors according to a specific rule von Neumann Moore
  21. 21. Cellular Automata Markov Model (CA_Markov) <ul><li>CA_Markov combines: </li></ul><ul><li>Markov Chain </li></ul><ul><li>Cellular Automata (CA) </li></ul><ul><li>Multi-Criteria Evaluation (MCE) </li></ul><ul><li>Multi-Objective Land Allocation (MOLA) </li></ul>
  22. 22. Preparing Suitability Images The basic assumption for preparing suitability images: a pixel closer to an existing land cover type has the higher suitability The images have been standardized to the same continuous suitability scale (0-255) using fuzzy set membership analysis process
  23. 23. Suitability Images
  24. 24. 3×3 CA Contiguity Filter Cellular Automata Markov Model (CA_Markov) Markov Transition Area Matrix Suitability Images 0 1 0 1 1 1 0 1 0
  25. 25. Artificial Neural Network
  26. 26. Multi Layer Perceptron Markov Model (MLP_Markov)
  27. 27. Driving Variables (Inputs)
  28. 28. Multi Layer Perceptron Markov Model (MLP_Markov) Builtup Area Water Body Vegetation Low Land Fallow Land Builtup Area 0.7823 0.0174 0.0347 0.0194 0.1463 Water Body 0.2079 0.1264 0.1008 0.1927 0.3721 Vegetation 0.1529 0.0779 0.3887 0.1071 0.2734 Low Land 0.0695 0.3634 0.0467 0.4054 0.1150 Fallow Land 0.3825 0.0133 0.2413 0.0185 0.3445
  29. 29. Model Validation Map Comparison : Simulated Map (2009) vs. Base Map (2009) St_Markov (2009) CA_Markov (2009) MLP_Markov (2009) Fraction Correct 0.50275 0.72558 0.91982 Kappa 0.29143 0.62578 0.88689
  30. 30. MLP_Markov Model Base Map 1999 Base Map 2009 Future Prediction (2019)
  31. 31. Outcome of the Research The outcome of this research will help the Decision Makers and Urban Planners to make DHAKA city much more : PLANNED and LIVEABLE
  32. 32. Thank You All QUESTIONS ?

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