{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:16:45Z","timestamp":1776277005206,"version":"3.50.1"},"reference-count":90,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T00:00:00Z","timestamp":1606780800000},"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>Indonesia is the world\u2019s fourth largest coffee producer. Coffee plantations cover 1.2 million ha of the country with a production of 500 kg\/ha. However, information regarding the distribution of coffee plantations in Indonesia is limited. This study aimed to assess the accuracy of classification model and determine its important variables for mapping coffee plantations. The model obtained 29 variables which derived from the integration of multi-resolution, multi-temporal, and multi-sensor remote sensing data, namely, pan-sharpened GeoEye-1, multi-temporal Sentinel 2, and DEMNAS. Applying a random forest algorithm (tree = 1000, mtry = all variables, minimum node size: 6), this model achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy of 79.333%, 0.774, 92.000%, and 90.790%, respectively. In addition, 12 most important variables achieved overall accuracy, kappa statistics, producer accuracy, and user accuracy 79.333%, 0.774, 91.333%, and 84.570%, respectively. Our results indicate that random forest algorithm is efficient in mapping coffee plantations in an agroforestry system.<\/jats:p>","DOI":"10.3390\/rs12233933","type":"journal-article","created":{"date-parts":[[2020,12,1]],"date-time":"2020-12-01T13:08:51Z","timestamp":1606828131000},"page":"3933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Mapping the Distribution of Coffee Plantations from Multi-Resolution, Multi-Temporal, and Multi-Sensor Data Using a Random Forest Algorithm"],"prefix":"10.3390","volume":"12","author":[{"given":"Anggun","family":"Tridawati","sequence":"first","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Department of Geodesy and Geomatics, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"},{"name":"Center for Remote Sensing, Institut Teknologi Bandung, Bandung 40132, Indonesia"},{"name":"Department of Geodesy and Geomatics Engineering, Faculty of Engineering, Universitas Lampung, Sumantri Brojonegoro No 1, Bandar Lampung 35141, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ketut","family":"Wikantika","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Department of Geodesy and Geomatics, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"},{"name":"Center for Remote Sensing, Institut Teknologi Bandung, Bandung 40132, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tri Muji","family":"Susantoro","sequence":"additional","affiliation":[{"name":"Research and Development Center for Oil and Gas Technology \u201cLEMIGAS\u201d, Ministry of Energy and Mineral Resources, Jakarta 12230, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agung Budi","family":"Harto","sequence":"additional","affiliation":[{"name":"Remote Sensing and Geographic Information Science Research Group, Department of Geodesy and Geomatics, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia"},{"name":"Center for Remote Sensing, Institut Teknologi Bandung, Bandung 40132, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soni","family":"Darmawan","sequence":"additional","affiliation":[{"name":"Institut Teknologi Nasional Bandung, PHH Mustofa No 23, Bandung 40124, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lissa Fajri","family":"Yayusman","sequence":"additional","affiliation":[{"name":"Center for Remote Sensing, Institut Teknologi Bandung, Bandung 40132, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1716-4063","authenticated-orcid":false,"given":"Mochamad Firman","family":"Ghazali","sequence":"additional","affiliation":[{"name":"Department of Geodesy and Geomatics Engineering, Faculty of Engineering, Universitas Lampung, Sumantri Brojonegoro No 1, Bandar Lampung 35141, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"135","DOI":"10.21082\/p.v14n2.2015.135-150","article-title":"Prospek pengembangan agroforestri berbasis kopi di Indonesia","volume":"14","author":"Supriadi","year":"2016","journal-title":"Perspektif"},{"key":"ref_2","unstructured":"ICO (2019, September 12). 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