{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:35:55Z","timestamp":1774528555243,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The temporal non-stationarity of land use and cover change (LUCC) processes is one of the main sources of uncertainty that may influence the calibration and the validation of spatial path-dependent LUCC models. In relation to that, this research aims to investigate the influence of the temporal non-stationarity of land change on urban growth modeling accuracy based on an empirical approach that uses past LUCC. Accordingly, the urban development in Rennes Metropolitan (France) was simulated using fifteen past calibration intervals which are set from six training dates. The study used Idrisi\u2019s Cellular Automata-Markov model (CA-Markov) which is an inductive pattern-based LUCC software package. The land demand for the simulation year was estimated using the Markov Chain method. Model validation was carried out by assessing the quantity of change, allocation, and spatial patterns accuracy. The quantity disagreement was analyzed by taking into consideration the temporal non-stationarity of change rate over the calibration and the prediction intervals, the model ability to reproduce the past amount of change in the future, and the time duration of the prediction interval. The results show that the calibration interval significantly influenced the amount and the spatial allocation of the estimated change. In addition to that, the spatial allocation of change using CA-Markov depended highly on the basis land cover image rather than the observed transition during the calibration period. Therefore, this study provides useful insights on the role of the training dates in the simulation of non-stationary LUCC.<\/jats:p>","DOI":"10.3390\/rs13030468","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T02:58:21Z","timestamp":1611889101000},"page":"468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["The Influence of the Calibration Interval on Simulating Non-Stationary Urban Growth Dynamic Using CA-Markov Model"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6753-5331","authenticated-orcid":false,"given":"Rahim","family":"Aguejdad","sequence":"first","affiliation":[{"name":"French Institute of Pondicherry, Puducherry 605001, India"},{"name":"UMR T\u00e9l\u00e9d\u00e9tection Environnement Territoire et Information Spatiale, Maison de la T\u00e9l\u00e9d\u00e9tection, 34090 Montpellier, France"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/978-1-4020-2562-4_23","article-title":"Modeling Land Use and Land Cover Change","volume":"Volume 6","author":"Brown","year":"2004","journal-title":"Land Change Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.uclim.2014.03.004","article-title":"Adapting cities to climate change: A systemic modelling approach","volume":"10","author":"Masson","year":"2014","journal-title":"Urban Clim."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.envsoft.2016.04.011","article-title":"Methods for translating narrative scenarios into quantitative assessments of land use change","volume":"82","author":"Mallampalli","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2016.09.010","article-title":"Combining narratives and modelling approaches to simulate fine scale and long-term urban growth scenarios for climate adaptation","volume":"86","author":"Houet","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_5","first-page":"214","article-title":"Comparison of simulation models in terms of quantity and allocation of land change","volume":"69","author":"Pontius","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1080\/10807039.2018.1468994","article-title":"A review of approaches to land use changes modelling","volume":"25","author":"Noszczyk","year":"2019","journal-title":"Hum. Ecol. Risk Assess. Int. J."},{"key":"ref_7","unstructured":"Agarwal, C., Green, G.L., Grove, M., Evans, T., and Schweik, C. (2000). A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time and Human Choice, U.S. Department of Agriculture, Forest Service, Northeastern Research Station. General Technical Report NE-297."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.12942\/lrlr-2009-2","article-title":"Simulation Models on Human-Nature Interactions in Urban Landscapes: A Review Including Spatial Economics, System Dynamics, Cellular Automata and Agent-Based Approaches","volume":"3","author":"Haase","year":"2009","journal-title":"Living Rev. Landsc. Res."},{"key":"ref_9","first-page":"555","article-title":"Urban Growth Prediction: A Review of Computational Models and Human Perceptions","volume":"4","author":"Triantakonstantis","year":"2012","journal-title":"J. Geogr. Inf. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.envsoft.2016.04.017","article-title":"A review of current calibration and validation practices in land-change modeling","volume":"82","author":"Bregt","year":"2016","journal-title":"Environ. Model. Softw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.cosust.2013.07.012","article-title":"Opportunities to improve impact, integration, and evaluation of land change models","volume":"5","author":"Brown","year":"2013","journal-title":"Curr. Opin. Environ. Sustain."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.envsoft.2013.09.010","article-title":"Inductive pattern-based land use\/cover change models: A comparison of four software packages","volume":"51","author":"Mas","year":"2014","journal-title":"Environ. Model. Softw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/13658810903279008","article-title":"Propagating error in land-cover-change analyses: Impact of temporal dependence under increased thematic complexity","volume":"24","author":"Burnicki","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.landurbplan.2010.03.001","article-title":"Cellular automata models for the simulation of real-world urban processes: A review and analysis","volume":"96","author":"Miranda","year":"2010","journal-title":"Landsc. Urban Plan."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Camacho Olmedo, M.T., Paegelow, M., and Mas, J.F. (2018). LUCC Modeling Approaches to Calibration. Geomatic Approaches for Modeling Land Change Scenarios, Springer International Publishing AG.. Chapter 2.","DOI":"10.1007\/978-3-319-60801-3"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.compenvurbsys.2013.03.006","article-title":"Measuring the neighbourhood effect to calibrate land use models","volume":"41","author":"Naus","year":"2013","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1080\/13658816.2013.831868","article-title":"Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy","volume":"28","author":"Chen","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.envsoft.2012.09.011","article-title":"Characterizing performance of environmental models","volume":"40","author":"Bennett","year":"2013","journal-title":"Environ. Model. Softw."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pickard, B., Gray, J., and Meentemeyer, R. (2017). Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models. Land, 6.","DOI":"10.3390\/land6030052"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s10666-017-9564-4","article-title":"Spatial validation of land-use change models using multiple assessment techniques: A case study of transition potential models","volume":"22","author":"Aguejdad","year":"2017","journal-title":"Environ. Model. Assess."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Aguejdad, R., Doukari, O., Houet, T., Avner, P., and Vigui\u00e9, V. (2016). Mod\u00e9lisation prospective de l\u2019\u00e9talement urbain: Apports et limites des mod\u00e8les de spatialisation. Application aux mod\u00e8les SLEUTH, LCM et NEDUM-2D. Cybergeo Eur. J. Geogr., 782.","DOI":"10.4000\/cybergeo.27668"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1146\/annurev.energy.28.050302.105459","article-title":"Dynamics of land-use and land-cover change in tropical regions","volume":"28","author":"Lambin","year":"2003","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1080\/13658816.2013.770517","article-title":"Evaluating drivers of land-use change and transition potential models in a complex landscape in Southern Mexico","volume":"27","author":"Kolb","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.envsoft.2015.02.013","article-title":"Detecting systemic change in a land use system by Bayesian data assimilation","volume":"75","author":"Verstegen","year":"2015","journal-title":"Environ. Model. Softw."},{"key":"ref_25","unstructured":"Maguire, D., Batty, M., and Goodchild, M. (2005). Transition Potential Modeling for Land Cover Change. GIS, Spatial Analysis and Modeling, ESRI Press."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.envsoft.2011.09.011","article-title":"Comparing two approaches to land use\/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest","volume":"29","author":"Mas","year":"2012","journal-title":"Environ. Model. Softw."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Paegelow, M., Camacho Olmedo, M.T., Mas, J.F., and Houet, T. (2014). Benchmarking of LUCC modelling tools by various validation techniques and error analysis. Cybergeo Eur. J. Geogr., 701.","DOI":"10.4000\/cybergeo.26610"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1007\/s10980-004-0245-8","article-title":"Driving forces for landscape change\u2014Current and new directions","volume":"19","author":"Hersperger","year":"2004","journal-title":"Landsc. Ecol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1080\/13658816.2014.970190","article-title":"How much past to see the future: A computational study in calibrating urban cellular automata","volume":"29","author":"Blecic","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s10666-008-9154-6","article-title":"Performance Evaluation of the SLEUTH Model in the Shenyang Metropolitan Area of Northeastern China","volume":"14","author":"Wu","year":"2009","journal-title":"Environ. Model. Assess."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1016\/j.ecolmodel.2011.01.017","article-title":"Revisiting Kappa to account for change in the accuracy assessment of land-use change models","volume":"222","author":"Bregt","year":"2011","journal-title":"Ecol. Model."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1080\/13658816.2013.792344","article-title":"Measuring the temporal instability of land change using the Flow matrix","volume":"27","author":"Runfola","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1080\/13658816.2019.1591416","article-title":"Modeling spatially non-stationary land use\/cover change in the lower Connecticut River Basin by combining geographically weighted logistic regression and the CA-Markov model","volume":"33","author":"Wang","year":"2019","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.gloenvcha.2014.06.003","article-title":"Regime shifts limit the predictability of land-system change","volume":"28","author":"Sun","year":"2014","journal-title":"Glob. Environ. Chang."},{"key":"ref_36","unstructured":"Eastman, J.R. (2012). IDRISI Selva Help System, Clark Labs, Clark University. IDRISI Selva Version: 17."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1080\/13658816.2013.831867","article-title":"Interest in intermediate soft-classified maps in land change model validation: Suitability versus transition potential","volume":"27","author":"Paegelow","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolmodel.2014.03.011","article-title":"A land use change model: Integrating landscape pattern indexes and Markov-CA","volume":"283","author":"Yang","year":"2014","journal-title":"Ecol. Model."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3435","DOI":"10.1080\/01431160010006881","article-title":"Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia","volume":"22","author":"Petit","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1007\/s10980-009-9433-x","article-title":"Derivation of a yearly transition probability matrix for land-use dynamics and its applications","volume":"25","author":"Takada","year":"2010","journal-title":"Landsc. Ecol."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/0038-0121(77)90041-6","article-title":"Markov analysis of land use change: Continuous time and stationary processes","volume":"11","author":"Bell","year":"1977","journal-title":"Socio. Econ. Plan. Sci."},{"key":"ref_42","unstructured":"Lambin, E.F. (1994). Modelling Deforestation Processes: A Review, European Commission."},{"key":"ref_43","first-page":"126","article-title":"Application of a Hybrid Cellular Automaton\u2013Markov (CA-Markov) Modeling land use change prediction: A case study of Saddle Creek Drainage Basin, Florida","volume":"1","author":"Subedi","year":"2013","journal-title":"Appl. Ecol. Environ. Sci."},{"key":"ref_44","first-page":"542","article-title":"Validation of CA-markov for simulation of land use and cover change in the Langat Basin, Malaysia","volume":"4","author":"Memarian","year":"2012","journal-title":"J. Geogr. Inf. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Saaty, T.L. (1980). The Analytic Hierarchy Process, McGraw-Hill.","DOI":"10.21236\/ADA214804"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Saaty, T.L., and Vargas, L.G. (2001). Models, Methods, Concepts and Applications of the Analytic Hierarchy Process, Kluwer.","DOI":"10.1007\/978-1-4615-1665-1"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1007\/s10980-010-9519-5","article-title":"Diagnostic tools to evaluate a spatial land change projection along a gradient of an explanatory variable","volume":"25","author":"Chen","year":"2010","journal-title":"Landsc. Ecol."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/00045608.2010.517742","article-title":"Comparison of three maps at multiple resolutions: A case study of land change simulation in Cho Don District, Vietnam","volume":"101","author":"Pontius","year":"2011","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"577","DOI":"10.3390\/ijgi2030577","article-title":"Evaluation of Model Validation Techniques in Land Cover Dynamics","volume":"2","author":"Ahmed","year":"2013","journal-title":"Int. J. Geo. Inf."},{"key":"ref_51","unstructured":"McGarigal, K., Cushman, S.A., Neel, M.C., and Ene, E. (2002). FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps, University of Massachusetts. Computer Software Program Produced by the Authors at the University of Massachusetts."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/S0034-4257(03)00075-0","article-title":"The spatiotemporal form of urban growth: Measurement, analysis and modeling","volume":"86","author":"Herold","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.compenvurbsys.2003.12.001","article-title":"The role of spatial metrics in the analysis and modeling of urban land use change","volume":"29","author":"Herold","year":"2005","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.landurbplan.2012.02.010","article-title":"Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition","volume":"106","author":"Aldwaik","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/978-3-319-52522-8_8","article-title":"Lessons and Challenges in Land Change Modeling Derived from Synthesis of Cross-Case Comparisons","volume":"Volume 19","author":"Behnisch","year":"2018","journal-title":"Trends in Spatial Analysis and Modeling"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1068\/b240247","article-title":"A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area","volume":"24","author":"Clarke","year":"1997","journal-title":"Environ. Plan. B Plan. Des."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/468\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:16:58Z","timestamp":1760159818000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/3\/468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,29]]},"references-count":56,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13030468"],"URL":"https:\/\/doi.org\/10.3390\/rs13030468","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,29]]}}}