Abstract
The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1.
Highlights
The tropical savanna in Brazil, known as the Cerrado, is the second-largest biome in South America, covering over 200 million ha or approximately 23% of the Brazilian territory [1]
We used the global 25-m resolution ALOS PALSAR/PALSAR−2 annual mosaics, which are freely available at https://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm. This dataset was pre-processed by the Japanese Aerospace Exploration Agency (JAXA) using L-band SAR images of the backscattering coefficient acquired by the Advanced Land Observing Satellite (ALOS) and Advanced Land Observing Satellite-2 (ALOS-2)
As the light detection and ranging (LiDAR) point clouds are sensitive to the forest structure, we assumed that these plots could express the physical and structural variations of the vegetation of the study site, and we allowed the aboveground biomass (AGB) LiDAR model to extrapolate for values < 19 Mg ha−1
Summary
The tropical savanna in Brazil, known as the Cerrado, is the second-largest biome in South America, covering over 200 million ha or approximately 23% of the Brazilian territory [1]. 3% of the Cerrado is strictly protected by the law within conservation areas [12,14], and the high rates of vegetation loss and degradation have made the Cerrado the second-largest source of carbon emissions in Brazil [15] In this context, it is essential to monitor AGB and carbon stocks effectively, and reliable maps are needed for climate change mitigation policies [16,17,18]. Uncertainties in current vegetation carbon stock estimates over the Cerrado are high, and biomass estimates vary by more than 50 Mg ha−1 within the same area [19,20,21] This demands improvements in the accuracy and spatial resolution to estimate the AGB in this biome. Using a machine-learning approach, we produced the first high-resolution AGB map (30 m) of a Brazilian Cerrado area based on a combination of vegetation inventory plots, airborne LiDAR data, and satellite images (Landsat 8 and ALOS−2/PALSAR-2). We used a RF model and jackknife analyses to analyse the importance of the remote-sensing predictor variables, enabling us to select the best ones to quantify the AGB
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