Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach

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 due to 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. A multi-sensor and machine learning approach was used to 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. The approach combined vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). 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. The study used a random forest (RF) machine-learning approach to select the best remote-sensing variables to quantify the AGB on a large scale. The results showed that the combination of remote-sensing data and machine learning can provide accurate estimates of AGB in the Cerrado biome. The Cerrado is characterized by a mosaic of grasslands, shrublands, and forestlands in varying proportions, depending on the location. Its physiognomies range from campo (grasslands) to the typical Cerrado stricto sensu (trees and shrubs up to 8–10-m-high and with an understory dominated by grass) and the cerradão (forest formations with trees up to a height of 20-m-high). The conversion of the Cerrado biome to different types of land uses is occurring much faster than in the Brazilian Amazon, mainly because the Brazilian livestock and agricultural frontier has been expanding towards the northern parts of the Cerrado over the last decades. The study highlights the importance of monitoring AGB and carbon stocks effectively, and reliable maps are needed for climate change mitigation policies. 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. The challenge here is to take the large latitudinal gradient and the high variation of the vegetation structure into consideration, as well as the paucity of field studies quantifying AGB over different regions of the biome. Different types of sensors, such as satellite-based multispectral imagers and Synthetic Aperture RADAR (SAR) systems, as well as airborne light detection and ranging (LiDAR), have been successfully applied to estimate AGB in the tropics. SAR and LiDAR have been increasingly used to estimate AGB in the last eight years, providing information about the forest structure, which is highly correlated to AGB. The study demonstrates the effectiveness of multi-sensor data synergy in tropical savannas, which has been intensively used to study other biomes.