Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data
dc.contributor.author | Liu, Yanan | |
dc.contributor.author | Gong, Weishu | |
dc.contributor.author | Hu, Xiangyun | |
dc.contributor.author | Gong, Jianya | |
dc.date.accessioned | 2023-11-20T20:43:02Z | |
dc.date.available | 2023-11-20T20:43:02Z | |
dc.date.issued | 2018-06-14 | |
dc.description.abstract | 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. | |
dc.description.uri | https://doi.org/10.3390/rs10060946 | |
dc.identifier | https://doi.org/10.13016/dspace/ss1m-tjv6 | |
dc.identifier.citation | Liu, 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.uri | http://hdl.handle.net/1903/31459 | |
dc.language.iso | en_US | |
dc.publisher | MDPI | |
dc.relation.isAvailableAt | College of Behavioral & Social Sciences | en_us |
dc.relation.isAvailableAt | Geography | en_us |
dc.relation.isAvailableAt | Digital Repository at the University of Maryland | en_us |
dc.relation.isAvailableAt | University of Maryland (College Park, MD) | en_us |
dc.subject | Sentinel imagery | |
dc.subject | DEM | |
dc.subject | Landsat-8 | |
dc.subject | forest types | |
dc.subject | random forest | |
dc.subject | multi-source | |
dc.title | Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data | |
dc.type | Article | |
local.equitableAccessSubmission | No |
Files
Original bundle
1 - 1 of 1