{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T19:04:51Z","timestamp":1774638291002,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T00:00:00Z","timestamp":1528934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People's Republic of China","doi-asserted-by":"publisher","award":["2017YFB0503700, 2016YFB0501403"],"award-info":[{"award-number":["2017YFB0503700, 2016YFB0501403"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Carbon sink estimation and ecological assessment of forests require accurate forest type mapping. The traditional survey method is time consuming and labor intensive, and the remote sensing method with high-resolution, multi-spectral commercial satellite images has high cost and low availability. In this study, we explore and evaluate the potential of freely-available multi-source imagery to identify forest types with an object-based random forest algorithm. These datasets included Sentinel-2A (S2), Sentinel-1A (S1) in dual polarization, one-arc-second Shuttle Radar Topographic Mission Digital Elevation (DEM) and multi-temporal Landsat-8 images (L8). We tested seven different sets of explanatory variables for classifying eight forest types in Wuhan, China. The results indicate that single-sensor (S2) or single-day data (L8) cannot obtain satisfactory results; the overall accuracy was 54.31% and 50.00%, respectively. Compared with the classification using only Sentinel-2 data, the overall accuracy increased by approximately 15.23% and 22.51%, respectively, by adding DEM and multi-temporal Landsat-8 imagery. The highest accuracy (82.78%) was achieved with fused imagery, the terrain and multi-temporal data contributing the most to forest type identification. These encouraging results demonstrate that freely-accessible multi-source remotely-sensed data have tremendous potential in forest type identification, which can effectively support monitoring and management of forest ecological resources at regional or global scales.<\/jats:p>","DOI":"10.3390\/rs10060946","type":"journal-article","created":{"date-parts":[[2018,6,14]],"date-time":"2018-06-14T11:06:06Z","timestamp":1528974366000},"page":"946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Yanan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}]},{"given":"Weishu","family":"Gong","sequence":"additional","affiliation":[{"name":"Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA"}]},{"given":"Xiangyun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]},{"given":"Jianya","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/452031a","article-title":"Climate change for the masses","volume":"452","author":"Reay","year":"2008","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/35102500","article-title":"Recent patterns and mechanisms of carbon exchange by terrestrial ecosystems","volume":"414","author":"Schimel","year":"2001","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1126\/science.199.4325.141","article-title":"The biota and the world carbon budget","volume":"199","author":"Woodwell","year":"1978","journal-title":"Science"},{"key":"ref_4","unstructured":"Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K. 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