Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions

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.

Remote Sensing Technology Applications in Forestry and REDD+

“…This Special Issue contains 12 studies that provided insight into new advances in the field of remote sensing for forest management and REDD+. This included developments into (1) algorithm development using satellite data [10][11][12][13][14][15][16]; (2) synthetic aperture radar (SAR) [11,17];…”

“…Chen et al [11] combine texture characteristics and backscatter coefficients of Sentinel-1 with multispectral information derived from Sentinel-2 and traditional field inventory data to develop above-ground biomass (AGB) prediction models using machine learning. Shen et al [12] apply machine learning techniques, using Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) images to monitor the five-year change in AGB over three regions with different topographic conditions in Zhejiang Province, China. Li et al [13] test various statistical frameworks on Landsat-8 OLI data to improve AGB mapping over a subtropical forest in Western Hunan in Central China.…”

“…Random forest (RF) has been the most popular machine learning algorithm to link remote sensing data to forest structural attributes such as AGB [11,12], canopy cover [10] or forest fire susceptibility [15]. Spracklen and Spracklen [14] apply RF to identify old-growth forests using remote sensing data.…”

Modelagem de florestas secundárias de Mata Atlântica utilizando a estatística bayesiana

“…Por exemplo, a inclinação influenciou positivamente a biomassa acima do solo em florestas secundárias na China, até um limite de 45º para algumas áreas (Shen et al, 2018). Enquanto, a diversidade de áreas baixas pode ser menor comparada a áreas de planaltos e encostas .…”

“…O aumento da biomassa acima do solo nas encostas pode ser devido a menor intervenção humana, resultando em uma vegetação melhor preservada (Shen et al, 2018). Por outro lado, relevos com maior ângulo de inclinação e voltados para o sol recebem maiores níveis de radiação (Chuvieco, 2016) e experimentam maiores temperaturas e evaporação da água dos solos .…”