{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T22:30:21Z","timestamp":1781735421307,"version":"3.54.5"},"reference-count":65,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T00:00:00Z","timestamp":1548979200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2016YFC0501107"],"award-info":[{"award-number":["2016YFC0501107"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Project of Science and Technology Basic Work of Ministry of Science and Technology of China","award":["2014FY110800"],"award-info":[{"award-number":["2014FY110800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Simultaneously considering the spatial and temporal processes is essential for land cover simulation models. A cellular automaton (CA) usually simulates the spatial conversion of land cover through post-classification comparisons between the beginning and the end of the training period. However, such an approach does not consider the temporal evolution of land cover. As a result, a CA model fails to explain the realistic land cover change. This paper proposes a temporal-dimension-extension CA (TDE-CA) by integrating the temporal evolution of land cover with a CA. In the TDE-CA, the Breaks for Additive Season and Trend (BFAST) monitor algorithm was employed in the temporal evolution simulation module (TESM) to simulate the gradual evolution of land cover, and an optimized random forest CA (optimized RF-CA) was used to simulate the spatial conversion driven by many spatial variables. Subsequently, the Ensemble Kalman Filter (EnKF) was employed to integrate the TESM with the optimized RF-CA. The TDE-CA was then tested in the land cover simulation of Shendong mining area during the period 2005\u20132015. The TDE-CA was compared with a Null model, with its sub-models, and with the traditional CA models, including the Logistic-CA and the MLP-CA (Multilayer Perceptron CA) models. The results show that the TDE-CA is superior to the Null model. Furthermore, the overall accuracy and the Kappa coefficient of the TDE-CA were 79.84% and 71.61%, respectively; compared with the TESM and the optimized RF-CA, the values showed 17.14% and 4.48% improvements in the overall accuracies and 0.2167 and 0.0512 improvements in the Kappa coefficients, respectively. When compared with the Logistic-CA and the MLP-CA, we measured 8.41% and 8.25% improvements in the overall accuracies and 0.0985 and 0.0964 improvements in the Kappa coefficients. These experiments indicate that the TDE-CA not only provides an effective model for the spatiotemporal dynamical simulation of land cover, but also enhances the development of the existing simulation theory.<\/jats:p>","DOI":"10.3390\/rs11030301","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T11:19:58Z","timestamp":1549019998000},"page":"301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Integrating Temporal Evolution with Cellular Automata for Simulating Land Cover Change"],"prefix":"10.3390","volume":"11","author":[{"given":"Cangjiao","family":"Wang","sequence":"first","affiliation":[{"name":"Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Xuzhou 221116, Jiangsu Province, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaogang","family":"Lei","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Ministry of Education for Mine Ecological Restoration, China University of Mining and Technology, Xuzhou 221116, Jiangsu Province, China"},{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9697-9457","authenticated-orcid":false,"given":"Andrew J.","family":"Elmore","sequence":"additional","affiliation":[{"name":"Appalachian Laboratory, University of Maryland Center for Environmental Science, 301 Braddock Rd, Frostburg, MD 21532, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duo","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouguo","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.landurbplan.2017.09.019","article-title":"A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects","volume":"168","author":"Liu","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.scitotenv.2005.09.053","article-title":"A simulation-based interval two-stage stochastic model for agricultural non-point source pollution control through land retirement","volume":"361","author":"Luo","year":"2006","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2016.02.060","article-title":"Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data","volume":"185","author":"Vogelmann","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.jenvman.2018.02.090","article-title":"Characterizing, monitoring, and simulating land cover dynamics using GlobeLand30: A case study from 2000 to 2030","volume":"214","author":"Arsanjani","year":"2018","journal-title":"J. Environ. Manag."},{"key":"ref_5","unstructured":"Brown, D., Band, L.E., Green, K.O., Irwin, E.G., Jain, A., Lambin, E.F., Pontius, R.G., Seto, K.C., Turner, B.L., and Verburg, P.H. (2013). Advancing Land Change Modeling: Opportunities and Research Requirements, The National Research Council Press."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1038\/311419a0","article-title":"Cellular automata as models of complexity","volume":"311","author":"Wolfram","year":"1984","journal-title":"Nature"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Salhab, R., Malham\u00e9, R.P., and Ny, J.L. (2015, January 15\u201318). A Dynamic Game Model of Collective Choice in Multiagent Systems. Proceedings of the IEEE Conference on Decision and Control, Osaka, Japan.","DOI":"10.1109\/CDC.2015.7402913"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shang, C., Fang, H., Chen, J., and Zhang, J. (2017, January 17\u201320). Interacting with multi-agent systems through intention field based shared control methods. Proceedings of the Asian Control Conference, Gold Coast, QLD, Australia.","DOI":"10.1109\/ASCC.2017.8287158"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1111\/1467-8306.9302004","article-title":"Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A review","volume":"93","author":"Parker","year":"2015","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.02.013","article-title":"Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images","volume":"116","author":"Zhang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jiang, X., Lin, M., and Zhao, J. (2011, January 28\u201329). Woodland Cover Change Assessment Using Decision Trees, Support Vector Machines and Artificial Neural Networks Classification Algorithms. Proceedings of the 4th International Conference on Intelligent Computation Technology and Automation, Shenzhen, China.","DOI":"10.1109\/ICICTA.2011.363"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.apgeog.2015.12.001","article-title":"Spatial and temporal dimensions of land use change in cross border region of Luxembourg. Development of a hybrid approach integrating GIS, cellular automata and decision learning tree models","volume":"67","author":"Basse","year":"2016","journal-title":"Appl. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Samardzic-Petrovic, M., Kova\u010devi\u0107, M., Bajat, B., and Dragicevic, S. (2017). Machine Learning Techniques for Modelling Short Term Land-Use Change. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6120387"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1080\/13658816.2011.643803","article-title":"Assimilating process context information of cellular automata into change detection for monitoring land use changes","volume":"26","author":"Li","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.envsoft.2011.09.005","article-title":"Uncovering land-use dynamics driven by human decision-making\u2014A combined model approach using cellular automata and system dynamics","volume":"27\u201328","author":"Lauf","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.compenvurbsys.2008.09.008","article-title":"Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model","volume":"33","author":"Moreno","year":"2009","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1080\/13658816.2014.883079","article-title":"A systematic sensitivity analysis of constrained cellular automata model for urban growth simulation based on different transition rules","volume":"28","author":"Li","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.landurbplan.2018.04.016","article-title":"Delineating multi-scenario urban growth boundaries with a CA-based FLUS model and morphological method","volume":"177","author":"Liang","year":"2018","journal-title":"Landsc. Urban Plan."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1080\/13658816.2010.496370","article-title":"Concepts, methodologies, and tools of an integrated geographical simulation and optimization system","volume":"25","author":"Li","year":"2011","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1068\/a33210","article-title":"Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems","volume":"33","author":"Li","year":"2001","journal-title":"Environ. Plan. A"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1080\/13658816.2013.831097","article-title":"Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata","volume":"28","author":"Liu","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1080\/13658810210157769","article-title":"Calibration of stochastic cellular automata: the application to rural-urban land conversions","volume":"16","author":"Fulong","year":"2002","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5918","DOI":"10.3390\/rs70505918","article-title":"Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models","volume":"7","author":"Ozturk","year":"2015","journal-title":"Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.cageo.2007.08.003","article-title":"Cellular automata for simulating land use changes based on support vector machines","volume":"34","author":"Yang","year":"2006","journal-title":"Comput. Geosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"447","DOI":"10.3390\/ijgi4020447","article-title":"Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model","volume":"4","author":"Kamusoko","year":"2015","journal-title":"ISPRS Int. J. Geo-Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.landurbplan.2016.03.011","article-title":"Capturing the varying effects of driving forces over time for the simulation of urban growth by using survival analysis and cellular automata","volume":"152","author":"Chen","year":"2016","journal-title":"Landsc. Urban Plan."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.landurbplan.2011.04.004","article-title":"Modeling dynamic urban growth using cellular automata and particle swarm optimization rules","volume":"102","author":"Feng","year":"2011","journal-title":"Landsc. Urban Plan."},{"key":"ref_28","first-page":"380","article-title":"The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review","volume":"52","author":"Aburas","year":"2016","journal-title":"Int. J. Appl. Earth. Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.compenvurbsys.2014.05.001","article-title":"Supporting SLEUTH\u2014Enhancing a cellular automaton with support vector machines for urban growth modeling","volume":"49","author":"Rienow","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.buildenv.2011.07.012","article-title":"Combining system dynamics model, GIS and 3D visualization in sustainability assessment of urban residential development","volume":"47","author":"Xu","year":"2012","journal-title":"Build. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.apgeog.2013.01.009","article-title":"Spatiotemporal urbanization processes in the megacity of Mumbai, India: A Markov chains-cellular automata urban growth model","volume":"40","author":"Moghadam","year":"2013","journal-title":"Appl. Geogr."},{"key":"ref_32","unstructured":"Candau, J., Rasmussen, S., and Clarke, K.C. (2000, January 2\u20138). A coupled cellular automaton model for land use\/land cover dynamics. Proceedings of the 4th International Conference on Integrating GIS and Environmental Modeling (GIS\/EM4): Problems, Prospects and Research Needs, Banff, AB, Canada."},{"key":"ref_33","first-page":"568","article-title":"Integrating the system dynamic and cellular automata models to predict land use and land cover change","volume":"52","author":"Xu","year":"2016","journal-title":"Int. J. Appl. Earth. Obs. Geoinf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1007\/s00477-012-0671-0","article-title":"Modeling urban land use conversion of Daqing City, China: a comparative analysis of \u201ctop-down\u201d and \u201cbottom-up\u201d approaches","volume":"28","author":"Li","year":"2014","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/13658810410001713407","article-title":"Spatio-temporal dynamics in California\u2019s Central Valley: Empirical links to urban theory","volume":"19","author":"Herold","year":"2005","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2016.03.036","article-title":"Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000\u20132014)","volume":"185","author":"Zhu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.rse.2011.10.030","article-title":"Continuous monitoring of forest disturbance using all available Landsat imagery","volume":"122","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.rse.2010.07.008","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr\u2014Temporal segmentation algorithms","volume":"114","author":"Kennedy","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2014.01.011","article-title":"Continuous change detection and classification of land cover using all available Landsat data","volume":"144","author":"Zhu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ding, J., Zhou, J., and Tarokh, V. (2017, January 14\u201316). Optimal prediction of data with unknown abrupt change points. Proceedings of the IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, Canada.","DOI":"10.1109\/GlobalSIP.2017.8309096"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM:a library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM TIST"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1824","DOI":"10.1109\/TGRS.2002.802519","article-title":"Seasonality extraction by function fitting to time-series of satellite sensor data","volume":"40","author":"Jonsson","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.12.016028","article-title":"Semisupervised GDTW kernel-based fuzzy c-means algorithm for mapping vegetation dynamics in mining region using normalized difference vegetation index time series","volume":"12","author":"Jia","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_46","unstructured":"Chen, C., Wang, J., Qin, W., and Dong, X. (2011, January 16\u201318). A new adaptive weight algorithm for salt and pepper noise removal. Proceedings of the 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), Xianning, China."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/S0169-2046(01)00160-8","article-title":"Predicting land-cover and land-use change in the urban fringe: A case in Morelia city, Mexico","volume":"55","author":"Erna","year":"2001","journal-title":"Landsc. Urban Plan."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/s10236-003-0036-9","article-title":"The Ensemble Kalman Filter: theoretical formulation and practical implementation","volume":"53","author":"Evensen","year":"2003","journal-title":"Ocean Dyn."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s00168-007-0138-2","article-title":"Comparing the input, output, and validation maps for several models of land change","volume":"42","author":"Pontius","year":"2008","journal-title":"Ann. Reg. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.ecolmodel.2004.05.010","article-title":"Useful techniques of validation for spatially explicit land-change models","volume":"179","author":"Pontius","year":"2004","journal-title":"Ecol. Model."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2013.02.007","article-title":"Assessing the accuracy of blending Landsat\u2013MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection","volume":"133","author":"Emelyanova","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1109\/TGRS.2006.872081","article-title":"On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance","volume":"44","author":"Gao","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2610","DOI":"10.1016\/j.rse.2010.05.032","article-title":"An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions","volume":"114","author":"Zhu","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Liu, Q., Liu, G., Huang, C., Liu, S., and Zhao, J. (2014, January 13\u201318). A tasseled cap transformation for Landsat 8 OLI TOA reflectance images. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1007\/s10980-008-9296-6","article-title":"Predicting land cover change and avian community responses in rapidly urbanizing environments","volume":"23","author":"Hepinstall","year":"2008","journal-title":"Landsc. Ecol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.ecolmodel.2006.05.036","article-title":"Analysis of pattern\u2013process interactions based on landscape models\u2014Overview, general concepts, and methodological issues","volume":"199","author":"Seppelt","year":"2006","journal-title":"Ecol. Model."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ecolmodel.2013.02.027","article-title":"Combining system dynamics and hybrid particle swarm optimization for land use allocation","volume":"257","author":"Liu","year":"2013","journal-title":"Ecol. Model."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1007\/s12665-015-5122-z","article-title":"Time\u2013space characterization of vegetation in a semiarid mining area using empirical orthogonal function decomposition of MODIS NDVI time series","volume":"75","author":"Lei","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s00267-010-9601-4","article-title":"The Fate of Priority Areas for Conservation in Protected Areas: A Fine-Scale Markov Chain Approach","volume":"47","author":"Tattoni","year":"2011","journal-title":"Environ. Manag."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1080\/00045608.2012.707591","article-title":"FUTURES: Multilevel Simulations of Emerging Urban\u2013Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm","volume":"103","author":"Meentemeyer","year":"2013","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Castagnetti, C., Bertacchini, E., Corsini, A., and Rivola, R. (2014, January 22\u201325). A reliable methodology for monitoring unstable slopes: the multi-platform and multi-sensor approach. Proceedings of the SPIE Remote Sensing, Earth Resources and Environmental Remote Sensing\/GIS Applications, Amsterdam, The Netherlands.","DOI":"10.1117\/12.2067407"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Li, X., Lu, H., Zhou, Y., Hu, T., Liang, L., Liu, X., Hu, G., and Yu, L. (2017). Exploring the performance of spatio-temporal assimilation in an urban cellular automata model. Int. J. Geogr. Inf. Sci., 2195\u20132215.","DOI":"10.1080\/13658816.2017.1357821"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/301\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:30:32Z","timestamp":1760185832000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/3\/301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,1]]},"references-count":65,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["rs11030301"],"URL":"https:\/\/doi.org\/10.3390\/rs11030301","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,1]]}}}