{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T11:29:29Z","timestamp":1774610969577,"version":"3.50.1"},"reference-count":82,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T00:00:00Z","timestamp":1557360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Portuguese Foundation for Science and Technology","award":["SFRH\/BD\/115497\/2016"],"award-info":[{"award-number":["SFRH\/BD\/115497\/2016"]}]},{"name":"Portuguese Foundation for Science and Technology","award":["SFRH\/BD\/115497\/2018"],"award-info":[{"award-number":["SFRH\/BD\/115497\/2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use\/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may affect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the official Portuguese LULC map (Carta de Uso e Ocupa\u00e7\u00e3o do Solo, COS) from 1995, 2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized difference vegetation index and normalized difference water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was efficient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.<\/jats:p>","DOI":"10.3390\/rs11091104","type":"journal-article","created":{"date-parts":[[2019,5,9]],"date-time":"2019-05-09T08:19:59Z","timestamp":1557389999000},"page":"1104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":124,"title":["Long-Term Satellite Image Time-Series for Land Use\/Land Cover Change Detection Using Refined Open Source Data in a Rural Region"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6858-4522","authenticated-orcid":false,"given":"Cl\u00e1udia M.","family":"Viana","sequence":"first","affiliation":[{"name":"Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edm\u00e9e Marques, 1600-276 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7201-0548","authenticated-orcid":false,"given":"In\u00eas","family":"Gir\u00e3o","sequence":"additional","affiliation":[{"name":"Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edm\u00e9e Marques, 1600-276 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7228-6330","authenticated-orcid":false,"given":"Jorge","family":"Rocha","sequence":"additional","affiliation":[{"name":"Centre for Geographical Studies, Institute of Geography and Spatial Planning, Universidade de Lisboa, Rua Branca Edm\u00e9e Marques, 1600-276 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A review of large area monitoring of land cover change using Landsat data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the archive: How free data has enabled the science and monitoring promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal MODIS NDVI data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2014.11.015","article-title":"Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia","volume":"158","author":"Schmidt","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.rse.2014.11.024","article-title":"Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.rse.2007.07.002","article-title":"Contribution of multispectral and multitemporal information from MODIS images to land cover classification","volume":"112","author":"Caetano","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"1","article-title":"Land use intensity trajectories on Amazonian pastures derived from Landsat time series","volume":"41","author":"Rufin","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1007\/s10980-005-5238-8","article-title":"Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics","volume":"20","author":"Seto","year":"2005","journal-title":"Landsc. Ecol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Phiri, D., and Morgenroth, J. (2017). Developments in Landsat land cover classification methods: A review. Remote Sens., 9.","DOI":"10.3390\/rs9090967"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1080\/22797254.2017.1299557","article-title":"Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images","volume":"50","author":"Raczko","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1109\/TGRS.2011.2179050","article-title":"Satellite Image Time Series Analysis Under Time Warping","volume":"50","author":"Petitjean","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1109\/LGRS.2013.2288358","article-title":"Efficient Satellite Image Time Series Analysis Under Time Warping","volume":"11","author":"Petitjean","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q. (2016). Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sens., 8.","DOI":"10.3390\/rs8010019"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3729","DOI":"10.1109\/JSTARS.2016.2517118","article-title":"A Time\u2014Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping","volume":"9","author":"Maus","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2307\/3235884","article-title":"Measuring phenological variability from satellite imagery","volume":"5","author":"Reed","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/S0034-4257(02)00135-9","article-title":"Monitoring vegetation phenology using MODIS","volume":"84","author":"Zhang","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v088.i05","article-title":"dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R","volume":"88","author":"Maus","year":"2019","journal-title":"J. Stat. Softw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","unstructured":"Csillik, O., and Belgiu, M. (2017, January 9\u201312). Cropland mapping from Sentinel-2 time series data using object-based image analysis. Proceedings of the 20th AGILE International Conference on Geographic Information Science, Wageningen, The Netherlands."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","first-page":"18671","article-title":"Satellite Imagery Land Cover Classification using K-Means Clustering Algorithm Computer Vision for Environmental Information Extraction","volume":"63","author":"Usman","year":"2013","journal-title":"Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1210","DOI":"10.1080\/2150704X.2017.1375610","article-title":"Active learning for training sample selection in remote sensing image classification using spatial information","volume":"8","author":"Lu","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"16024","DOI":"10.3390\/rs71215819","article-title":"Automatic labelling and selection of training samples for high-resolution remote sensing image classification over urban areas","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","unstructured":"EEA (2000). Down to Earth: Soil Degradation and Sustainable Development in Europe. A Challenge for the 21st Century, European Environment Agency (EEA)."},{"key":"ref_29","unstructured":"FAO (2010). The State of Food Insecurity in the World: Addressing Food Insecurity in Protracted Crises, FAO."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.agee.2005.12.007","article-title":"Effects of changes in agricultural land-use on landscape structure and arable weed vegetation over the last 50 years","volume":"115","author":"Baessler","year":"2006","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Noszczyk, T., Rutkowska, A., and Hernik, J. (2019). Exploring the land use changes in Eastern Poland: Statistics-based modeling. Hum. Ecol. Risk Assess., 1\u201328.","DOI":"10.1080\/10807039.2018.1506254"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Allen, H., Simonson, W., Parham, E., Santos, E.d.B.E., and Hotham, P. (2018). Satellite remote sensing of land cover change in a mixed agro-silvo-pastoral landscape in the Alentejo, Portugal. Int. J. Remote Sens., 1\u201321.","DOI":"10.1080\/01431161.2018.1440095"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1300\/J064v07n04_03","article-title":"Agrosilvopastoral Systems: A Practical Approach Toward Sustainable Agriculture","volume":"7","author":"Russo","year":"1996","journal-title":"J. Sustain. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0169-2046(93)90081-N","article-title":"Threatened landscape in Alentejo, Portugal: The \u2018montado\u2019 and other \u2018agro-silvo-pastoral\u2019 systems","volume":"24","author":"Correia","year":"1993","journal-title":"Landsc. Urban Plan."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s10457-014-9757-7","article-title":"Assessment of environment, land management, and spatial variables on recent changes in montado land cover in southern Portugal","volume":"90","author":"Godinho","year":"2016","journal-title":"Agrofor. Syst."},{"key":"ref_36","first-page":"553","article-title":"Trimmed k-means: An attempt to robustify quantizers","volume":"25","author":"Gordaliza","year":"1997","journal-title":"Ann. Stat."},{"key":"ref_37","unstructured":"Viana, C.M., Gir\u00e3o, I., and Rocha, J. (2019, January 17\u201320). Training samples from open data for satellite imagery classification: Using K-means clustering algorithm. Proceedings of the 22nd AGILE Conference on Geo-Information Science, Limassol, Cyprus."},{"key":"ref_38","unstructured":"Instituto Portugu\u00eas do Mar e da Atmosfera (IPMA) (2019, January 10). Iberian Climate Atlas. Available online: http:\/\/www.ipma.pt\/resources.www\/docs_pontuais\/ocorrencias\/2011\/atlas_clima_iberico.pdf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1011a","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free Access to Landsat Imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1007\/s12518-015-0162-3","article-title":"Assessment of the impact of Landsat 7 Scan Line Corrector data gaps on Sungai Pulai Estuary seagrass mapping","volume":"7","author":"Hossain","year":"2015","journal-title":"Appl. Geomat."},{"key":"ref_41","unstructured":"DGT (2019, January 10). Especifica\u00e7\u00f5es T\u00e9cnicas da Carta de Uso e Ocupa\u00e7\u00e3o do Solo (COS) de Portugal Continental para 1995, 2007, 2010 e 2015; Lisboa. Available online: http:\/\/mapas.dgterritorio.pt\/atom-dgt\/pdf-cous\/COS2015\/ET-COS-1995-2007-2010-2015.pdf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1080\/0143116031000116435","article-title":"Land cover classification at a regional scale in Iberia: Separability in a multi-temporal and multi-spectral data set of satellite images","volume":"25","author":"Lobo","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1080\/01431160410001685009","article-title":"NAO influence on NDVI trends in the Iberian peninsula (1982\u20132000)","volume":"25","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","first-page":"309","article-title":"Monitoring vegetation systems in the great plains with ERTS","volume":"1","author":"Rouse","year":"1973","journal-title":"Third Earth Resour. Technol. Satell. Symp."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1080\/014311600210380","article-title":"Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data","volume":"21","author":"Azzali","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2006.09.003","article-title":"Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery","volume":"106","author":"Yuan","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/S0034-4257(96)00067-3","article-title":"NDWI\u2014A normalized difference water index for remote sensing of vegetation liquid water from space","volume":"58","author":"Gao","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/S0034-4257(02)00036-6","article-title":"Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications","volume":"82","author":"Ceccato","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Viana, C.M., Encalada, L., and Rocha, J. (2019). The value of OpenStreetMap Historical Contributions as a Source of Sampling Data for Multi-temporal Land Use\/Cover Maps. Isprs Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030116"},{"key":"ref_51","unstructured":"Camacho Olmedo, M.T., Paegelow, M., Mas, J.F., and Escobar, F. (2018). The Influence of Scale in LULC Modeling. A Comparison Between Two Different LULC Maps (SIOSE and CORINE). Geomatic Simulations and Scenarios for Modelling LUCC. A Review and Comparison of Modelling Techniques, Springer."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fritz, H., Garc\u00eda-Escudero, L.A., and Mayo-Iscar, A. (2012). tclust: An R Package for a Trimming Approach to Cluster Analysis. J. Stat. Softw., 47.","DOI":"10.18637\/jss.v047.i12"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","article-title":"Dynamic Programming Algorithm Optimization for Spoken Word Recognition","volume":"26","author":"Sakoe","year":"1978","journal-title":"IEEE Trans. Acoust."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ramezan, C.A., Warner, T.A., and Maxwell, A.E. (2019). Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification. Remote Sens., 11.","DOI":"10.3390\/rs11020185"},{"key":"ref_56","unstructured":"R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The Measurement of Observer Agreement for Categorical Data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Jokar Arsanjani, J., Mooney, P., Zipf, A., and Schauss, A. (2015). Quality Assessment of the Contributed Land Use Information from OpenStreetMap Versus Authoritative Datasets, Springer.","DOI":"10.1007\/978-3-319-14280-7_3"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1109\/TGRS.2006.874140","article-title":"Automatic spectral rule-based preliminary mapping of calibrated landsat TM and ETM+ images","volume":"44","author":"Baraldi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10457-014-9769-3","article-title":"A remote sensing-based approach to estimating montado canopy density using the FCD model: A contribution to identifying HNV farmlands in southern Portugal","volume":"90","author":"Godinho","year":"2016","journal-title":"Agrofor. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"949","DOI":"10.3390\/rs5020949","article-title":"Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs","volume":"5","author":"Atzberger","year":"2013","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ross, C., Fildes, S., and Millington, A. (2017). Land-Use and Land-Cover Change in the P\u00e1ramo of South-Central Ecuador, 1979\u20132014. Land, 6.","DOI":"10.3390\/land6030046"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.3390\/rs5041549","article-title":"Testing the temporal ability of landsat imagery and precision agriculture technology to provide high resolution historical estimates of wheat yield at the farm scale","volume":"5","author":"Lyle","year":"2013","journal-title":"Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2184","DOI":"10.3390\/rs5052184","article-title":"Forecasting regional sugarcane yield based on time integral and spatial aggregation of MODIS NDVI","volume":"5","author":"Mulianga","year":"2013","journal-title":"Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"539","DOI":"10.3390\/rs5020539","article-title":"Remote sensing based yield estimation in a stochastic framework\u2014Case study of durum wheat in Tunisia","volume":"5","author":"Meroni","year":"2013","journal-title":"Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1080\/17538947.2016.1168489","article-title":"Mapping rice-fallow cropland areas for short-season grain legumes intensification in South Asia using MODIS 250\u2005m time-series data","volume":"9","author":"Gumma","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"8858","DOI":"10.3390\/rs70708858","article-title":"Mapping Flooded Rice Paddies Using Time Series of MODIS Imagery in the Krishna River Basin, India","volume":"7","author":"Teluguntla","year":"2015","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Radwan, T.M., Blackburn, G.A., Whyatt, J.D., and Atkinson, P.M. (2019). Dramatic Loss of Agricultural Land Due to Urban Expansion Threatens Food Security in the Nile Delta, Egypt. Remote Sens., 11.","DOI":"10.3390\/rs11030332"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Shi, K., Chen, Y., Yu, B., Xu, T., Li, L., Huang, C., Liu, R., Chen, Z., and Wu, J. (2016). Urban expansion and agricultural land loss in China: A multiscale perspective. Sustainability, 8.","DOI":"10.3390\/su8080790"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1111\/gcb.12714","article-title":"Gross changes in reconstructions of historic land cover\/use for Europe between 1900 and 2010","volume":"21","author":"Fuchs","year":"2015","journal-title":"Glob. Chang. Biol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.apgeog.2008.02.001","article-title":"Land-cover and land-use change in a Mediterranean landscape: A spatial analysis of driving forces integrating biophysical and human factors","volume":"28","author":"Serra","year":"2008","journal-title":"Appl. Geogr."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Gutman, G., and Radeloff, V. (2017). Overview of changes in land cover and land use in Eastern Europe. Land-Cover and Land-Use Changes in Eastern Europe after the Collapse of the Soviet Union in 1991, Springer.","DOI":"10.1007\/978-3-319-42638-9"},{"key":"ref_74","unstructured":"Simoes, R.E.O., Pletsch, M.A.J.S., Santos, L.A., C\u00e2mara, G., and Maus, V. (2019, January 01). Satellite Multisensor Spatiotemporal Analysis: A TWDTW Preview Approach. Available online: https:\/\/proceedings.science\/sbsr\/papers\/satellite-multisensor-spatiotemporal-analysis--a-twdtw-preview-approach?lang=pt-br."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.14358\/PERS.74.10.1201","article-title":"Mapping Selective Logging in Mixed Deciduous Forest: A Comparison of Machine Learning Algorithms","volume":"74","author":"Lippitt","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Brodley, C.E., and Friedl, M.A. (1999). Identifying Mislabeled Training Data. J. Artif. Intell. Res., 131\u2013167.","DOI":"10.1613\/jair.606"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Meneses, B.M., Reis, E., Vale, M.J., and Reis, R. (2018). Modelling the Land Use and Land cover changes in Portugal: A multi-scale and multi-temporal approach. Finisterra, 53.","DOI":"10.18055\/Finis12258"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.rse.2014.10.018","article-title":"Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery","volume":"156","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1080\/01431161003698245","article-title":"A comparative evaluation of spectral vegetation indices for the estimation of biophysical characteristics of mediterranean semi-deciduous shrub communities","volume":"32","author":"Palmeirim","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Mansourian, A., Pilesj\u00f6, P., Harrie, L., and von Lammeren, R. (2018). Spatiotemporal analysis and scenario simulation of agricultural land use land cover using GIS and a Markov chain model. Geospatial Technologies for All: Short Papers, Posters and Poster Abstracts of the 21th AGILE Conference on Geographic Information Science, Lund University.","DOI":"10.1007\/978-3-319-78208-9"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0034-4257(02)00048-2","article-title":"A generalized soil-adjusted vegetation index","volume":"82","author":"Gilabert","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:50:22Z","timestamp":1760187022000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/9\/1104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,9]]},"references-count":82,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11091104"],"URL":"https:\/\/doi.org\/10.3390\/rs11091104","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,9]]}}}