Comparing Eight Computing Algorithms and Four Consensus Methods to Analyze Relationship between Land Use Pattern and Driving Forces — Oak Academic Publishing
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Comparing Eight Computing Algorithms and Four Consensus Methods to Analyze Relationship between Land Use Pattern and Driving Forces
Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
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Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
,
Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
,
Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
,
Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
,
Zhangjiajie City Environmental Monitoring Center Station, Zhangjiajie, China
,
Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
,
Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
,
Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
1 Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
2 Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
3 Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
4 Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
5 Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
6 Zhangjiajie City Environmental Monitoring Center Station, Zhangjiajie, China
7 Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
8 Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
9 Key Laboratory of Agro-Ecological Processes in Subtropical Region and Changsha Research Station for Agricultural & Environmental Monitoring, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
Although many computing algorithms have been developed to analyze the relationship between land use pattern and driving forces (RLPDF), little has been done to assess and reduce the uncertainty of predictions. In this study, we investigated RLPDF based on 1990, 2005 and 2012 datasets at two spatial scales using eight state-of-the-art single computing algorithms and four consensus methods in Jinjing rive catchment in Hunan Province, China. At the entire catchment scale, the mean AUC values were between 0.715 (ANN) and 0.948 (RF) for the single-algorithms, and from 0.764 to 0.962 for the consensus methods. At the subcatchment scale, the mean AUC values between 0.624 (CTA) and 0.972 (RF) for the single-algorithms, and from 0.758 to 0.979 for the consensus methods. At the subcatchment scale, the mean AUC values were between 0.624 (CTA) and 0.972 (RF) for the single-algorithms, and from 0.758 to 0.979 for the consensus methods. The result suggested that among the eight single computing algorithms, RF performed the best overall for woodland and paddy field; consensus method showed higher predictive performance for woodland and paddy field models than the single computing algorithms. We compared the simulation results of the best - and worst-performing algorithms for the entire catchment in 2012, and found that approximately 72.5% of woodland and 72.4% of paddy field had probabilities of occurrence of less than 0.1, and 3.6% of woodland and 14.5% of paddy field had probabilities of occurrence of more than 0.5. In other words, the simulation errors associated with using different computing algorithms can be up to 14.5% if a probability level of 0.5 is set as the threshold. The results of this study showed that the choice of modeling approaches can greatly affect the accuracy of RLPDF prediction. The computing algorithms for specific RLPDF tasks in specific regions have to be localized and optimized.
KeywordsLand Use PatternSpatial ScalesConsensus MethodsComputing Algorithms
Verburg, P.H., Soepboer, W., Limpiada, R., Espaldon, V., Mastura, S. and Veldkamp, A. (2002) Modeling the Spatial Dynamics of Regional Land Use: The CLUE-S Model. Environmental Management, 30, 391-405. https://doi.org/10.1007/s00267-002-2630-x
Verburg, P.H., Schot, P.P., Dijst, M.J. and Veldkamp, A. (2004) Land Use Change Modeling: Current Practice and Research Priorities. GeoJournal, 61, 309-324. https://doi.org/10.1007/s10708-004-4946-y
Li, X. (2011) Emergence of Bottom-Up Models as a tool for Landscape Simulation and Planning. Landscape and Urban Planning, 100, 393-395. https://doi.org/10.1016/j.landurbplan.2010.11.016
Li, X. and Yeh, A.G.O. (2004) Data Mining of Cellular Automata’s Transition Rules. International Journal of Geographical Information Science, 18, 723-744.
Wu, F. and Webster, C.J. (1998) Simulation of Land Development through the Integration of Cellular Automata and Multi-Criteria Evaluation. Environmental Management B: Planning and Design, 25, 103-126. https://doi.org/10.1068/b250103
Manel, S., Dias, J.M. and Ormerod, S.J. (1999) Comparing Discriminant Analysis, Neural Networks and Logistic Regression for Predicting Species Distributions: A Case Study with a Himalayan River Bird. Ecological Modelling, 120, 337-347. https://doi.org/10.1016/S0304-3800(99)00113-1
Thuiller, W., Lafourcade, B., Engler, R. and Araujio, M.B. (2009) BIOMOD—A Platform for Ensemble Forecasting of Species Distributions. Ecography, 32, 369-373. https://doi.org/10.1111/j.1600-0587.2008.05742.x
White, R. and Engelen, G. (1993) Cellular Automata and Fractal Urban Form: A Cellular Modeling Approach to the Evolution of Urban Land Use Patterns. Environmental Management A, 25, 1175-1199. https://doi.org/10.1068/a251175
Li, X. and Yeh, A.G.O. (2002) Neural-Network-Based Cellular Automata for Simulating Multiple Land Use Changes Using GIS. International Journal of Geographical Information Science, 16, 323-343. https://doi.org/10.1080/13658810210137004
Wu, F. (2002) Calibration of Stochastic Cellular Automata: The Application to Rural-Urban Land Conversions. International Journal of Geographical Information Science, 16, 795-818. https://doi.org/10.1080/13658810210157769
Elith, J., Graham, C.H., Anderson, R.P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R.J., Huettmann, F., Leathwick, J.R., Lehmann, A., Li, J., Lohmann, L.G., Loiselle, B.A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J.Mc.C., Peterson, A.T., Phillips, S.J., Richardson, K.S., Scachetti-Pereira, R., Schapire, R.E., Soberón, J., Williams, S., Wisz, M.S. and Zimmermann, N.E. (2006) Novel Methods Improve Prediction of Species’ Distributions from Occurrence Data. Ecography, 29, 129-151. https://doi.org/10.1111/j.2006.0906-7590.04596.x
Heikkinen, R.K., Luoto, M., Ajaujo, M.B., Virkkala, R., Thuillar, W. and Sykes, M.T. (2006) Methods and Uncertainties in Bioclimatic Envelope Modeling under Climate Change. Progress in Physical Geography, 30, 751-777. https://doi.org/10.1177/0309133306071957
Guisan, A., Overton, J.M.C., Aspinall, R., Hastie, T., Lehmann, A., Ferrier, S. and Austin, M. (2006) Making Better Biogeographical Predictions of Species’ Distributions. Journal of Applied Ecology, 43, 386-392. https://doi.org/10.1111/j.1365-2664.2006.01164.x
Araújo, M.B. and New, M. (2007) Ensemble Forecasting of Species Distributions. TRENDS in Ecology and Evolution, 22, 42-47. https://doi.org/10.1016/j.tree.2006.09.010
Marmion, M., Parviainen, M., Luoto, M., Heikkinen, R.K. and Thuiller, W. (2009) Evaluation of Consensus Methods in Predictive Species Distribution Modeling. Diversity and Distributions, 15, 59-69. https://doi.org/10.1111/j.1472-4642.2008.00491.x
Araújo, M.B., Whittaker, R.J., Ladle, R.J. and Erhard, M. (2005) Reducing Uncertainty in Projections of Extinction Risk from Climate Change. Global Ecology and Biogeography, 14, 529-538. https://doi.org/10.1111/j.1466-822X.2005.00182.x
Thuiller, W., Araújo, M. and Lavorel, S. (2003) Generalized Models vs. Classification Tree Analysis: Predicting Spatial Distributions of Plant Species at Different Scales. Journal of Vegetation Science, 14, 669-680. https://doi.org/10.1111/j.1654-1103.2003.tb02199.x
Steiner, F., Blair, J., Mcsherry, L., Guhathakurta, S., Marruffo, J. and Holm, M. (2000) A Watershed at a Watershed: The Potential for Environmentally Sensitive Area Protection in the Upper San Pedro Drainage Basin (Mexico and USA). Landscape and Urban Planning, 49, 129-148. https://doi.org/10.1016/S0169-2046(00)00062-1
Liu, Y.B., Nishiyama, S. and Kusaka, T. (2003) Examining Landscape Dynamics at a Watershed Scale Using Landsat TM Imagery for Detection of Wintering Hooded Crane Decline in Yashairo, Japan. Environmental Management, 31, 365-376. https://doi.org/10.1007/s00267-002-2785-5
Turner, II B.L., Skole, D.L., Sanderson, S., Fischer, G., Fresco, L.O. and Leemans, R. (1995) Land-Use and Land-Cover Change: Science/Research Plan. IGBP Report No. 35, HDP Report No. 7, Stockholm and Geneva.
Zou, K.H., O’Malley, A.J. and Mauri, L. (2007) Receiver-Operating Characteristic Analysis for Evaluating Diagnostic Tests and Predictive Models. Circulation, 115, 654-657. https://doi.org/10.1161/CIRCULATIONAHA.105.594929
Swets, J.A. (1988) Measuring the Accuracy of Diagnostic Systems. Science, 240, 1285-1293. https://doi.org/10.1126/science.3287615
Moisen, G.G. and Frescino, T.S. (2002) Comparing Five Modeling Techniques for Predicting Forest Characteristics. Ecological Modelling, 157, 209-225. https://doi.org/10.1016/S0304-3800(02)00197-7
Cressie, N., Calder, C.A., Clark, J.S., Hoef, J.M.V. and Wikle, C.K. (2009) Accounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling. Ecological Applications, 19, 553-570. https://doi.org/10.1890/07-0744.1
De Veaux, R.D., Psichogios, D.C. and Ungar, L.H. (1993) A Comparison of Two Nonparametric Estimation Schemes: MARS and Neural Networks. Computers and Chemical Engineering, 17, 819-837. https://doi.org/10.1016/0098-1354(93)80066-V
Liu, C.R., Berry, P.M., Dawson, T.P. and Pearson, R.G. (2005) Selecting Thresholds of Occurrence in the Prediction of Species Distributions. Ecography, 28, 385-393. https://doi.org/10.1111/j.0906-7590.2005.03957.x
Li, X.H., Tian, H.D., Wang, Y., Li, R.Q., Song, Z.M., Zhang, F.C., Xu, M. and Li, D.M. (2012) Vulnerability of 208 Endemic or Endangered Species in China to the Effects of Climate Change. Regional Environmental Change, 13, 843-852. https://doi.org/10.1007/s10113-012-0344-z
Deng, X.Z., Su, H.B. and Zhan, J.Y. (2008) Integration of Multiple Data Sources to Simulate the Dynamics of Land Systems. Sensors, 8, 620-634. https://doi.org/10.3390/s8020620
Liu, X.L., Li, Y., Shen, J.L., Fu, X.Q., Xiao, R.L. and Wu, J.S. (2014) Landscape Pattern Changes at a Catchment Scale: A Case Study in the Upper Jinjing River Catchment in Subtropical Central China from 1933 to 2005. Landscape and Ecological Engineering, 10, 263-276. https://doi.org/10.1007/s11355-013-0221-z
Hadayeghi, A., Shalaby, A. and Persaud, B. (2009) Development of Planning Level Transportation Safety Tools Using Geographically Weighted Poisson Regression. Accident Analysis and Prevention, 42, 676-688. https://doi.org/10.1016/j.aap.2009.10.016
Bolker, B.M., Brooks, M.E., Clark, C.J., Geange, S.W., Poulsen, J.R., Stevens, H.H. and White, J.S. (2009) Generalized Linear Mixed Models: A Practical Guide for Ecology and Evolution. Trends in Ecology and Evolution, 24, 127-135. https://doi.org/10.1016/j.tree.2008.10.008