Abstract
Biomass is a key biophysical parameter used to estimate carbon storage and forest productivity. Spatially-explicit estimation of biomass provides invaluable information for carbon stock calculation and scientific forest management. Nevertheless, there still exists large uncertainty concerning the relationship between biomass and influential factors. In this study, aboveground biomass (AGB) was estimated using the random forest algorithm based on remote sensing imagery (Landsat) and field data for three regions with different topographic conditions in Zhejiang Province, China. AGB distribution and change combined with stratified terrain classifications were analyzed to investigate the relations between AGB and topography conditions. The results indicated that AGB in three regions increased from 2010 to 2015 and the magnitude of growth varied with elevation, slope, and aspect. In the basin region, slope had a greater influence on AGB, and we attributed this negative AGB-elevation relationship to ecological forest construction. In the mountain area, terrain features, especially elevation, showed significant relations with AGB. Moreover, AGB and its growth showed positive relations with elevation and slope. In the island region, slope also played a relatively more important role in explaining the relationship. These results demonstrate that AGB varies with terrain conditions and its change is a consequence of interactions between the natural environment and anthropogenic behavior, implying that biomass retrieval based on Landsat imagery could provide considerable important information related to regional heterogeneity investigations.
Highlights
Biomass is an important biophysical parameter used to understand carbon dynamics on the background of global climate change, and the spatiotemporal estimation of biomass will provide invaluable information for carbon calculation and scientific forest management [1,2]
All the samples were used to predict the aboveground biomass (AGB) as a previous study had found that using all samples, when compared to a smaller sample size, was propitious to biomass estimation [5]
The objective of this study was to reveal the relationship between AGB/change and topography factors, while the spatiotemporal characteristics of AGB should be explained by the combination of natural condition and anthropogenic behavior
Summary
Biomass is an important biophysical parameter used to understand carbon dynamics on the background of global climate change, and the spatiotemporal estimation of biomass will provide invaluable information for carbon calculation and scientific forest management [1,2]. In the past few decades, remote sensing has been increasingly used to estimate aboveground biomass because of its macroscopical, nondestructive, and efficiency advantages compared to time- and space-limited field survey methods [3,4]. Spatiotemporal biomass retrieval based on remote sensing has been increasingly implemented, because when compared to single-period biomass distribution, it provides more details. Multiple potential features can be derived from the existing Landsat imagery, including multispectral bands, vegetation indices, texture bands, and time-sequence data, which provide abundant information for biomass retrieval [9,10,11]
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