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 WuTRANSCRIPT
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
0
200
400
600
800
1 2 3 Sampling Case
NP
0
100
200
300
1 2 3 Sampling Case
MPS
(ha
)
0
500000
1000000
1500000
1 2 3 Sampling Case
TE
(m
)
0
2
4
6
8
1 2 3 Sampling Case
MSI
05
10
1 2 3 4 5 6 7 8
Sampling Case
AW
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
Observed
ANN-CLUE-s
CLUE-s
Observed
ANN-CLUE-s
CLUE-s
Observed
ANN-CLUE-s
CLUE-s
Observed
N P
MPS
(ha)
M SI
ANN-CLUE-s based on two kinds of demands Markov demand SLEUTH (Cellular automata) demand
The demand each land use category in 2000~2020
0
1000
2000
3000
4000
5000
6000
7000
8000
2000 2005 2010 2015 2020Year
Dem
and(
ha)
0
1000
2000
3000
4000
5000
6000
7000
8000
2000 2005 2010 2015 2020Year
Dem
and(
ha)
01000
200030004000
50006000
70008000
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
Year
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
150
250
350
450
2000 2005 2010 2015 2020Year
NP
Demand from SLEUTH model Demand from Markov model
200000
300000
400000
500000
2000 2005 2010 2015 2020Year
TE(m
)
Demand from SLEUTH model Demand from Markov model
80
120
160
200
2000 2005 2010 2015 2020Year
MN
N(m
)
MNN: Mean Nearest Neighbor
150
250
350
450
2000 2005 2010 2015 2020
Demand from SLEUTH model Demand from Markov model
150
250
350
450
2000 2005 2010 2015 2020
Demand from SLEUTH model Demand from Markov modelMarkov demand
SLEUTH demand
The landscape matrices in cultivated land
100
150
200
250
300
2000 2005 2010 2015 2020Year
NP
Demand from SLEUTH model Demand from Markov model
100000
150000
200000
250000
300000
2000 2005 2010 2015 2020Year
TE(m
)
Demand from SLEUTH model Demand from Markov model
120
140
160
180
2000 2005 2010 2015 2020Year
MN
N(m
)
150
250
350
450
2000 2005 2010 2015 2020
Demand from SLEUTH model Demand from Markov model
150
250
350
450
2000 2005 2010 2015 2020
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