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.
Highlights
Precise and unambiguous forest type identification is essential when evaluating forest ecological systems for environmental management practices
The spatial distribution of heterogeneous mixed forest such as the Soft Broad-Leaved (SOBL) and CONI forest types was more likely to be located on the low-elevation slopes and flat areas near communities
A hierarchical classification approach based on the object-oriented Random Forest (RF) algorithm was conducted to identify forest types using the Sentinel-1A, Sentinel-2A, Digital Elevation Model (DEM) and multi-temporal Landsat-8 images
Summary
Precise and unambiguous forest type identification is essential when evaluating forest ecological systems for environmental management practices. As the biggest biological resource reservoir, forests play an irreplaceable role in ecosystem management for climate change abatement, environmental improvement and ecological security [3,4,5]. A common issue in climate change mitigation is to reduce forest damage and increase forest resources [6], which can be measured by the precise estimation of carbon storage based on accurate mapping of forest types. Remote sensing technology can be used to obtain forest information from areas with rough terrain or that are difficult to reach, complementing traditional methods while at the same time reducing the need for fieldwork. It is necessary to explore the potential of multi-source remote sensing data to obtain explicit and detailed information of forest types
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