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Forecasting Space-time Land Use Change  in the Paochiao Watershed of Taiwan Using Demand Estimation and  Empirical Simulat...
Introduction <ul><li>Land use change can be characterized by the complex interaction of  driving factors  associated with ...
<ul><li>The  C onversion of  L and  U se and its  E ffects (CLUE-s) model was applied to simulate the land use scenarios b...
The objective <ul><li>A rtificial  N eural  N etwork (ANN) directly quantify the nonlinear complex relationship between dr...
Study area Land use map in 2000
Method
1. Markov chain  2. Cellular automata ( SLEUTH:  Clarke et al. , 1998)  maps of driving factors Procedure of CLUE-s  ( C o...
Artificial neural network (ANN)  Input: Driving factors Output: Land use probability
Results
Model validation using  landscape metrics   NP : Number of Patches  MPS : Mean Patch Size  TE : Total Edge  MSI : Mean Sha...
<ul><li>ANN-CLUE-s based on two kinds of demands  </li></ul><ul><ul><li>Markov demand  </li></ul></ul><ul><ul><li>SLEUTH (...
The demand each land use category in 2000~2020 (a)  Markov demand   (b) SLEUTH demand
Spatial land-use distribution simulated using the   Markov  demand  (a) 2005 (b) 2010 (c) 2015 and (d) 2020
Spatial land-use distribution simulated using the  SLEUTH   demand (a) 2005 (b) 2010 (c) 2015 and (d) 2020
The landscape matrices in  built-up land MNN : Mean Nearest Neighbor  Markov demand   SLEUTH demand
The landscape matrices in  cultivated land Markov demand   SLEUTH demand
Conclusion  <ul><li>The current work further combined the  ANN  and  CLUE-s  model for analyzing and predicting the proces...
Suggestion <ul><li>Future studies could apply this method to other case studies.  </li></ul><ul><li>This study will furthe...
<ul><li>Thanks for your attention </li></ul>
 
Markov process <ul><li>A Markov process is a system that can be in one of several states, and can pass from one state to a...
Cellular automata <ul><li>The SLEUTH model is a cellular automaton pattern-extrapolation model that combines urban growth ...
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Forecasting Space Time Land Use Change- Hone-Jay Chu, Yu-Pin Lin, Chen-Fa Wu

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Forecasting Space Time Land Use Change- Hone-Jay Chu, Yu-Pin Lin, Chen-Fa Wu

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  • The CLUE-s is a spatial simulation model which explores changes in land-use patterns within user-specified rules of permissible change and rates of change.
  • Elevation, slope, distance to built-up land, distance to major roads, distance to river, distance to urban planning areas, population density, soil erosion coefficient, and soil drainage
  • Transcript of "Forecasting Space Time Land Use Change- Hone-Jay Chu, Yu-Pin Lin, Chen-Fa Wu"

    1. 1. Forecasting Space-time Land Use Change in the Paochiao Watershed of Taiwan Using Demand Estimation and Empirical Simulation Approaches Hone-Jay Chu, Yu-Pin Lin, Chen-Fa Wu Department of Bioenvironmental Systems Engineering, National Taiwan University
    2. 2. Introduction <ul><li>Land use change can be characterized by the complex interaction of driving factors associated with demand, capacity, and social relations. </li></ul><ul><li>Numerous studies have developed to simulate the pattern of land use changes (Agarwal et al., 2002; Verburg et al., 2002; Castella and Verburg, 2007; Pontius et al., 2008). </li></ul>
    3. 3. <ul><li>The C onversion of L and U se and its E ffects (CLUE-s) model was applied to simulate the land use scenarios based on the probability of the land-use presence evaluated by logistic regression . However, a logistic regression model may hardly explain the non-linear functions in land use data. </li></ul>
    4. 4. The objective <ul><li>A rtificial N eural N etwork (ANN) directly quantify the nonlinear complex relationship between driving variables and land-use changes. </li></ul><ul><li>In the study, the ANN generates probabilities of land-use categories. Then, land-use patterns are simulated by the ANN-CLUE-s model based on ANN probability maps. </li></ul>
    5. 5. Study area Land use map in 2000
    6. 6. Method
    7. 7. 1. Markov chain 2. Cellular automata ( SLEUTH: Clarke et al. , 1998) maps of driving factors Procedure of CLUE-s ( C onversion of L and U se and its E ffects) (Source: Projecting land use changes in the Neotropics T. Wassenaar et al. / Global Environmental Change 17 (2007) 86–104) Time-varying demand each land use ANN
    8. 8. Artificial neural network (ANN) Input: Driving factors Output: Land use probability
    9. 9. Results
    10. 10. Model validation using landscape metrics NP : Number of Patches MPS : Mean Patch Size TE : Total Edge MSI : Mean Shape Index … (Elkie et al, 1999) ANN-CLUE-s CLUE-s Observed ANN-CLUE-s CLUE-s Observed ANN-CLUE-s CLUE-s Observed ANN-CLUE-s CLUE-s Observed NP MPS(ha) MSI
    11. 11. <ul><li>ANN-CLUE-s based on two kinds of demands </li></ul><ul><ul><li>Markov demand </li></ul></ul><ul><ul><li>SLEUTH (Cellular automata) demand </li></ul></ul>
    12. 12. The demand each land use category in 2000~2020 (a) Markov demand (b) SLEUTH demand
    13. 13. Spatial land-use distribution simulated using the Markov demand (a) 2005 (b) 2010 (c) 2015 and (d) 2020
    14. 14. Spatial land-use distribution simulated using the SLEUTH demand (a) 2005 (b) 2010 (c) 2015 and (d) 2020
    15. 15. The landscape matrices in built-up land MNN : Mean Nearest Neighbor Markov demand SLEUTH demand
    16. 16. The landscape matrices in cultivated land Markov demand SLEUTH demand
    17. 17. Conclusion <ul><li>The current work further combined the ANN and CLUE-s model for analyzing and predicting the process of land use change. </li></ul><ul><li>Land use change was projected for the next twenty years using the Markov chain and a cellular automata model (SLEUTH) in each land use category. </li></ul><ul><li>Results show built-up sprawl in the area and its effects on land-use patterns, demonstrating that urban sprawl continued to grow in the watershed study during the years between 2001 and 2020, especially the SLEUTH demand. </li></ul>
    18. 18. Suggestion <ul><li>Future studies could apply this method to other case studies. </li></ul><ul><li>This study will further research the integration of Markov chain and cellular Automata for land-use modeling and hydrological processes associated with land use change. </li></ul>
    19. 19. <ul><li>Thanks for your attention </li></ul>
    20. 21. Markov process <ul><li>A Markov process is a system that can be in one of several states, and can pass from one state to another each time step according to fixed probabilities. </li></ul><ul><li>This study assumes land use change as a finite first-order Markov chain with stationary transition probabilities. </li></ul>
    21. 22. Cellular automata <ul><li>The SLEUTH model is a cellular automaton pattern-extrapolation model that combines urban growth and the land-cover change model for Monte Carlo growth simulations (Clarke et al., 1998). </li></ul><ul><li>Slope, Land cover, Exclusion, Urbanization, Transportation, Hillshade </li></ul>
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