{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:44:04Z","timestamp":1765485844147,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2015,1,14]],"date-time":"2015-01-14T00:00:00Z","timestamp":1421193600000},"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>Seasonal dynamic land cover maps could provide useful information to ecosystem, water-resource and climate modelers. However, they are rarely mapped more frequent than annually. Here, we propose an approach to map dynamic land cover types with frequently available satellite data. Landsat 8 data acquired from nine dates over Beijing within a one-year period were used to map seasonal land cover dynamics. A two-step procedure was performed for training sample collection to get better results. Sample sets were interpreted for each acquisition date of Landsat 8 image. We used the random forest classifier to realize the mapping. Nine sets of experiments were designed to incorporate different input features and use of spatial temporal information into the dynamic land cover classification. Land cover maps obtained with single-date data in the optical spectral region were used as benchmarks. Texture, NDVI and thermal infrared bands were added as new features for improvements. A Markov random field (MRF) model was applied to maintain the spatio-temporal consistency. Classifications with all features from all images were performed, and an MRF model was also applied to the results estimated with all features. The best overall accuracies achieved for each date ranged from 75.31% to 85.61%.<\/jats:p>","DOI":"10.3390\/rs70100865","type":"journal-article","created":{"date-parts":[[2015,1,14]],"date-time":"2015-01-14T10:33:15Z","timestamp":1421231595000},"page":"865-881","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach"],"prefix":"10.3390","volume":"7","author":[{"given":"Jie","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congcong","family":"Li","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luanyun","family":"Hu","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6253-8437","authenticated-orcid":false,"given":"Huabing","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1513-3765","authenticated-orcid":false,"given":"Peng","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China"},{"name":"Joint Center for Global Change Studies, Beijing 100875, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1674","DOI":"10.1126\/science.1118160","article-title":"The importance of land-cover change in simulating future climates","volume":"310","author":"Feddema","year":"2005","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1038\/nature08823","article-title":"The next generation of scenarios for climate change research and assessment","volume":"463","author":"Moss","year":"2010","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.5194\/bg-9-2145-2012","article-title":"Revisiting land cover observation to address the needs of the climate modeling community","volume":"9","author":"Bontemps","year":"2012","journal-title":"Biogeosciences"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/nclimate1908","article-title":"The role of satellite remote sensing in climate change studies","volume":"3","author":"Yang","year":"2013","journal-title":"Nat. Clim. Change"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0034-4257(02)00078-0","article-title":"Global land cover mapping from MODIS: Algorithms and early results","volume":"83","author":"Friedl","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"Arino, O., Bicheron, P., Achard, F., Latham, J., Witt, R., and Weber, J.L. GlobCover the Most Detailed Portrait of Earth. Available online:http:\/\/www.esa.int\/esapub\/bulletin\/bulletin136\/bul136d_arino.pdf."},{"key":"ref_8","unstructured":"Arino, O., Ramos, J., Kalogirou, V., Defourny, P., and Achard, F. (July, January 28). Globcover 2009. Proceedings of the Living Planet Symposium, Bergen, Norway."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/17538947.2013.786146","article-title":"Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with LiDAR-based estimates of error","volume":"6","author":"Sexton","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2012.10.010","article-title":"Long-term land cover dynamics by multi-temporal classification across the landsat-5 record","volume":"128","author":"Sexton","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11434-013-0045-0","article-title":"Monitoring dynamic changes of global land cover types: Fluctuations of major lakes in China every 8 days during 2000\u20132010","volume":"59","author":"Sun","year":"2014","journal-title":"Chin. Sci. Bull."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10451","DOI":"10.1029\/93JD00324","article-title":"Snow cover model for global climate simulations","volume":"98","author":"Loth","year":"1993","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_21","unstructured":"Rouse, J.W., Haas, R.H., Deering, D.W., Schell, J.A., and Harlan, J.C. (1974). Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, NASA\/GSFC. Type III Final Report."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"Smc3","author":"Haralick","year":"1973","journal-title":"IEEE Syst. Man Cy. Soc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(92)90011-8","article-title":"A comparison of spatial feature-extraction algorithms for land-use classification with SPOT HRV data","volume":"40","author":"Gong","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/01431160701281015","article-title":"The use of texture for image classification of black & white air photographs","volume":"29","author":"Caridade","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"964","DOI":"10.3390\/rs6020964","article-title":"Comparison of classification algorithms and training sample sizes in urban land classification with Landsat thematic mapper imagery","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random forest classification of mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1080\/00045608.2011.596357","article-title":"A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery","volume":"102","author":"Liu","year":"2012","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, J., Zhao, Y., Li, C., Yu, L., Liu, D., and Gong, P. (2014). Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution. ISPRS J. Photogramm. Remote Sens., in press.","DOI":"10.1016\/j.isprsjprs.2014.03.007"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2005.12.012","article-title":"A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery","volume":"101","author":"Liu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1016\/j.rse.2007.10.002","article-title":"Using local transition probability models in markov random fields for forest change detection","volume":"112","author":"Liu","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1111\/j.2517-6161.1986.tb01412.x","article-title":"On the statistical-analysis of dirty pictures","volume":"48","author":"Besag","year":"1986","journal-title":"J. Roy. Stat. Soc. B Met."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/JSTSP.2011.2139193","article-title":"A survey of active learning algorithms for supervised remote sensing image classification","volume":"5","author":"Tuia","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"219","DOI":"10.14358\/PERS.77.3.219","article-title":"Automated methods for measuring DBH and tree heights with a commercial scanning LiDAR","volume":"77","author":"Huang","year":"2011","journal-title":"Photogramm Eng. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1109\/TGRS.2013.2247612","article-title":"Map-MRF approach to Landsat ETM plus SLC-off image classification","volume":"52","author":"Zhu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2885","DOI":"10.1016\/j.csda.2008.09.017","article-title":"Fast estimation of spatially dependent temporal vegetation trends using gaussian markov random fields","volume":"53","author":"Bolin","year":"2009","journal-title":"Comput. Stat. Data Anal."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/1\/865\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:41:38Z","timestamp":1760215298000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/7\/1\/865"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,1,14]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2015,1]]}},"alternative-id":["rs70100865"],"URL":"https:\/\/doi.org\/10.3390\/rs70100865","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2015,1,14]]}}}