{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T20:15:08Z","timestamp":1779135308547,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2013,5,31]],"date-time":"2013-05-31T00:00:00Z","timestamp":1369958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We developed and evaluated a new approach for mapping rubber plantations and natural forests in one of Southeast Asia\u2019s biodiversity hot spots, Xishuangbanna in China. We used a one-year annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and short-wave infrared (SWIR) reflectance data to develop phenological metrics. These phenological metrics were used to classify rubber plantations and forests with the Random Forest classification algorithm. We evaluated which key phenological characteristics were important to discriminate rubber plantations and natural forests by estimating the influence of each metric on the classification accuracy. As a benchmark, we compared the best classification with a classification based on the full, fitted time series data. Overall classification accuracies derived from EVI and SWIR time series alone were 64.4% and 67.9%, respectively. Combining the phenological metrics from EVI and SWIR time series improved the accuracy to 73.5%. Using the full, smoothed time series data instead of metrics derived from the time series improved the overall accuracy only slightly (1.3%), indicating that the phenological metrics were sufficient to explain the seasonal changes captured by the MODIS time series. The results demonstrate a promising utility of phenological metrics for mapping and monitoring rubber expansion with MODIS.<\/jats:p>","DOI":"10.3390\/rs5062795","type":"journal-article","created":{"date-parts":[[2013,5,31]],"date-time":"2013-05-31T11:20:36Z","timestamp":1369999236000},"page":"2795-2812","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":105,"title":["Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series"],"prefix":"10.3390","volume":"5","author":[{"given":"Cornelius","family":"Senf","sequence":"first","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dirk","family":"Pflugmacher","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6576-8377","authenticated-orcid":false,"given":"Sebastian","family":"Van der Linden","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Hostert","sequence":"additional","affiliation":[{"name":"Geography Department, Humboldt-Universit\u00e4t zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2013,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20666","DOI":"10.1073\/pnas.0704119104","article-title":"The emergence of land change science for global environmental change and sustainability","volume":"104","author":"Turner","year":"2007","journal-title":"Proc. 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