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 WuDepartment of Bioenvironmental Systems Engineerin

g, National Taiwan University

Introduction Land use change can be characterized b

y the complex interaction of driving factors associated with demand, capacity, and social relations.

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).

The Conversion of Land Use and its Effects (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.

The objective Artificial Neural Network (ANN)

directly quantify the nonlinear complex relationship between driving variables and land-use changes.

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.

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 (Conversion of Land Use and its Effects)

(Source: Projecting land use changes in the NeotropicsT. 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

Results

Model validation using landscape metrics

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MPD Built-up Cultivated land Grassland

Forest Water Bare land

NP: Number of Patches MPS: Mean Patch Size TE: Total Edge MSI: Mean Shape Index … (Elkie et al, 1999)

ANN-CLUE-s

CLUE-s

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ANN-CLUE-s

CLUE-s

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ANN-CLUE-s

CLUE-s

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ANN-CLUE-s

CLUE-s

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ANN-CLUE-s based on two kinds of demands Markov demand SLEUTH (Cellular automata) demand

The demand each land use category in 2000~2020

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Built-up Cultivated land Grassland Forest Water Bare land

(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

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Demand from SLEUTH model Demand from Markov model

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Demand from SLEUTH model Demand from Markov model

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MNN: Mean Nearest Neighbor

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Demand from SLEUTH model Demand from Markov model

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Demand from SLEUTH model Demand from Markov modelMarkov demand

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The landscape matrices in cultivated land

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Demand from SLEUTH model Demand from Markov model

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Demand from SLEUTH model Demand from Markov model

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Demand from SLEUTH model Demand from Markov model

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Demand from SLEUTH model Demand from Markov modelMarkov demand

SLEUTH demand

Conclusion The current work further combined the ANN and CLUE-s

model for analyzing and predicting the process of land use change.

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.

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.

Suggestion

Future studies could apply this method to other case studies.

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.

Thanks for your attention

Markov process

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.

This study assumes land use change as a finite first-order Markov chain with stationary transition probabilities.

Cellular automata

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).

Slope, Land cover, Exclusion, Urbanization, Transportation, Hillshade