![]() ![]() The Brazilian Amazon has the largest rainforest area, but also has significant deforestation rates. Among different types of forests, tropical forests store about half of all forest carbon in the world and play particularly critical roles in atmospheric carbon sequestration. These uncertainties in ACD estimation using MODIS data make it difficult to examine annual ACD dynamics of degradation and growth, however this method can be used to examine the deforestation-induced ACD loss.įorests, which cover approximately 30% of the Earth’s land surface, produce about 75% of the terrestrial gross primary production and contain 80% of total plant biomass, thereby playing important roles in the global carbon cycle and global climate changes. The mixed pixel problem in MODIS data is a major factor in ACD overestimation, while cloud contamination and data saturation are major factors in ACD underestimation. The evaluation of modeling results indicated that ACD can be successfully estimated with a coefficient of determination of 0.67 and root mean square error of 4.18 kg C/m 2 using RF based on spectral indices. The LR and RF methods were used to develop ACD models, in which the samples extracted from LiDAR-estimated ACD were used as dependent variables and MODIS-derived variables were used as independent variables. in the same study area, was used to map ACD distribution in the 23 sites. The ACD estimation model, which was developed by Longo et al. Airborne LiDAR data at 23 sites across the Brazilian Amazon were collected and used to calculate ACD. This research aimed to explore an approach for estimating aboveground carbon density (ACD) in the Brazilian Amazon through integration of MODIS (moderate resolution imaging spectroradiometer) and a limited number of light detection and ranging (Lidar) data samples using linear regression (LR) and random forest (RF) algorithms, respectively. Timely updates of carbon stock distribution are needed to better understand the impacts of deforestation and degradation on forest carbon stock dynamics.
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