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

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Beniamino Murgante

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

Introduction ,[object Object],[object Object]

The objective ,[object Object],[object Object]

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

Artificial neural network (ANN) Input: Driving factors Output: Land use probability

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

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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 ,[object Object],[object Object],[object Object]

Suggestion ,[object Object],[object Object]

Markov process ,[object Object],[object Object]

Cellular automata ,[object Object],[object Object]

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

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

5. Study area Land use map in 2000

6. Method

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. Artificial neural network (ANN) Input: Driving factors Output: Land use probability

9. Results

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.

12. The demand each land use category in 2000~2020 (a) Markov demand (b) SLEUTH demand

15. The landscape matrices in built-up land MNN : Mean Nearest Neighbor Markov demand SLEUTH demand

16. The landscape matrices in cultivated land Markov demand SLEUTH demand

17.

18.

19.

20.

21.

22.

Hinweis der Redaktion

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

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

Hinweis der Redaktion