Abstract
This study applies two empirical approaches such as logistic regression, and artificial network (ANN) to combine Conversion of Land Use and its Effects (CLUE-s) model to predict urban sprawl in the Paochiao watershed in Taipei County, Taiwan. The current investigation projected land-use dynamics for the next twenty years using demand prediction models such as the Markov chain and cellular automata model. Accordingly, the empirical models integrating with CLUE-s then simulated land-use patterns for future periods based on the predicted demands.
Results show that land-use patterns predicted by the model tended to fragment and intersperse future patterns. The predicted land-use patterns have significant impact on urban sprawl in the Paochiao watershed. The study is an effective means of enhancing land-use monitoring and managing urbanized watersheds.
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References
Turner, M.G., Gardner, R.H., O’Neill, R.V.: Landscape Ecology in Theory and Practice: Pattern and Process, pp. 108–109. Springer, New York (2001)
Kumar, S., Stohlgren, T.J., Chong, G.W.: Spatial heterogeneity influences native and nonnative plant species richness. Ecol. 87(12), 3186–3199 (2006)
Verburg, P.H., van Eck, J.R.R., de Nijs, T.C.M.: Determinants of land-use change patterns in the Netherlands. Environ. Plan. B – Plan. Design 31(1), 125–150 (2004)
Mcgarigal, K., Marks, B.J.: FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure. Reference Manual, Forest Science Department, Orgon State University, Corvallis, Oregon, 38–53 (1994)
Herold, M., Goldstein, N.C., Clarke, K.C.: The spatiotemporal form of urban growth: measurement, analysis and modeling. Remote Sens. Environ. 86(3), 286–302 (2003)
Lin, Y.P., Lin, Y.B., Wang, Y.T., Hong, N.M.: Monitoring and predicting land-use changes and the hydrology of the urbanized Paochiao watershed in Taiwan using remote sensing data, urban growth models and a hydrological model. Sensors 8(1), 658–680 (2008)
Jongman, R.H.G.: Landscape ecology in land use planning. In: Wiens, J.A., Moss, M.R. (eds.) Issue in Landscape Ecology. International Association for Landscape Ecology, Guelph, Ontario, pp. 112–118 (1999)
Corry, R.C., Nassauer, J.I.: Limitations of using landscape pattern indices to evaluate the ecological consequences of alternative plans and designs. Landscape Urban Plan. 72, 265–280 (2005)
Agarwal, C., Green, G.M., Grove, J.M., Evans, T.P., Schweik, C.M.: A Review and Assessment of Land-use Change Models: Dynamics of Space, Time, and Human Choice. US Department of Agriculture, Forest Service, Northeastern Research Station, 12–27 (2002)
Verburg, P.H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., Sharifah Mastura, S.A.: Modeling the spatial dynamics of regional land-use: the CLUES model. Environ. Manage. 30(3), 391–405 (2002)
Castella, J.C., Verburg, P.H.: Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam. Ecol. Modl. 202(3-4), 410–420 (2007)
Pijanowski, B.C., Brown, D.G., Shellito, B.A., Manik, G.A.: Using neural networks and GIS to forecast land-use changes: a Land Transformation Model. Computers, Environment and Urban Systems 26, 553–575 (2002)
Dai, E., Wu, S.H., Shi, W.Z.: Modeling change-pattern-value dynamics on land-use: an integrated GIS and Artificial Neural Networks approach. Environmental Assessment 36(4), 576–591 (2005)
Pijanowski, B.C., Pithadia, S., Shellito, B.A.: Calibrating a neural network-based urban change model for two metropolitan areas of the Upper Midwest of the United States. International Journal of Geographical Information Science 19(2), 197–215 (2005)
Bourne, L.S.: Physical adjustment processes and land use succession: a review and central city example. Econ. Geogr. 47, 1–15 (1971)
Bell, E.J.: Markov analysis of land use change: an application of stochastic processes to remotely sensed data. Soc. Econ. Plan. Sci. 8, 311–316 (1974)
Robinson, V.B.: Information theory and sequences of land use: an application. The Prof. Geogr. 30, 174–179 (1978)
Jahan, S.: The determination of stability and similarity of Markovian land use change processes: a theoretical and empirical analysis. Soc. Econ. Plan. Sci. 20, 243–251 (1986)
Muller, R.M., Middleton, J.: A Markov model of land-use change dynamics in the Niagara region, Ontario, Canada. Landscape Ecol. 9, 151–157 (1994)
Brown, D.G., Pijanowski, B.C., Duh, J.D.: Modeling the relationships between land use and land cover on private lands in the Upper Midwest. J. Environ. Manage. 59(4), 247–263 (2000)
Weng, Q.: Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modeling. Journal of Environmental Management 64, 273–284 (2002)
Wu, Q., Li, H.Q., Wang, R.S.: Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape Urban Plan. 78(4), 322–333 (2006)
Clarke, K.C., Gaydos, L.J.: Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Fransisco and Washington/Baltimore. Int. J. Geogr. Inf. Sci. 12(7), 699–714 (1998)
Billingsley, P.: Statistical methods in Markov chains. Ann. Math. Stat. 32(1), 12–40 (1961)
Lin, Y.P., Chu, H.J., Wu, C.F., Verburg, P.H.: Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling - a case study. Int. J. Geogr. Inf. Sci. (revised)
Leitão, A.B., Miller, J., Ahern, J., McGarigal, K.: Measuring Landscapes: A Planner’s Handbook. Island Press, Washington D.C (2006)
Elkie, P.C., Rempel, R.S., Carr, A.P.: Patch Analyst User Manual: A Tool for Quantifying Landscape Structure. NWST Technical Manual TM- 002. Ontario (1999)
Clarke, K.C.: Land Transition Modeling With Deltatrons. In: Proceedings of the Land Use Modeling Conference (1997)
Verburg, P.H., Kok, K., Pontius Jr., R.G., Veldkamp, A.: Modeling Land-Use and Land-Cover Change. In: Lambin, E.F., Geist, H.J. (eds.) Land-Use and Land-Cover Change: Local Processes and Global Impacts. The IGBP Series, Springer, Heidelberg (2006)
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Chu, HJ., Lin, YP., Wu, CF. (2010). Forecasting Space-Time Land Use Change in the Paochiao Watershed of Taiwan Using Demand Estimation and Empirical Simulation Approaches. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B.O. (eds) Computational Science and Its Applications – ICCSA 2010. ICCSA 2010. Lecture Notes in Computer Science, vol 6016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12156-2_9
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