Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

dc.contributor.authorLiu, Yanan
dc.contributor.authorGong, Weishu
dc.contributor.authorHu, Xiangyun
dc.contributor.authorGong, Jianya
dc.date.accessioned2023-11-20T20:43:02Z
dc.date.available2023-11-20T20:43:02Z
dc.date.issued2018-06-14
dc.description.abstractCarbon 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.
dc.description.urihttps://doi.org/10.3390/rs10060946
dc.identifierhttps://doi.org/10.13016/dspace/ss1m-tjv6
dc.identifier.citationLiu, Y.; Gong, W.; Hu, X.; Gong, J. Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data. Remote Sens. 2018, 10, 946.
dc.identifier.urihttp://hdl.handle.net/1903/31459
dc.language.isoen_US
dc.publisherMDPI
dc.relation.isAvailableAtCollege of Behavioral & Social Sciencesen_us
dc.relation.isAvailableAtGeographyen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us
dc.subjectSentinel imagery
dc.subjectDEM
dc.subjectLandsat-8
dc.subjectforest types
dc.subjectrandom forest
dc.subjectmulti-source
dc.titleForest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data
dc.typeArticle
local.equitableAccessSubmissionNo

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