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

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.

Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data

“…As spaceborne LiDAR data such as ICESat-2 ATLAS and GEDI have improved quality, incorporation of these data into optical sensor data (e.g., Landsat, MODIS) may further improve ACD modeling performance [33][34][35]95]. More research is needed to explore the integration of multiple data sources and advanced modeling approaches such as deep learning [89,96].…”

“…Previous studies have already indicated that the quality of samples used for ACD or biomass modeling is the most important factor inducing high estimation uncertainty [23,84,96]. Most of the time, we cannot do much to improve the quality of reference data, except that we can carefully conduct field measurements of the parameters used for ACD calculation and select proper allometric equations to calculate ACD for specific trees.…”

Mapping Woody Volume of Mediterranean Forests by Using SAR and Machine Learning: A Case Study in Central Italy

“…Thanks to increasing hardware and software resources, artificial neural networks (ANNs) have gained increasing attention in the analysis of complex systems, and therefore in the forest biomass estimation from SAR backscattering [51]. Multitemporal SAR-based AGB estimation by means of ANNs was presented and compared with support vector regression and multivariate linear regression in [49], where it was noted that ANNs show better results at higher AGB values.…”

Using satellite estimates of aboveground biomass to assess carbon stocks in a mixed-management, semi-deciduous tropical forest in the Yucatan Peninsula

“…Multi-sensor approaches can leverage the strengths of these various data sources to improve AGB estimates (Bispo et al, 2020).…”

“…Rodriguez-Veiga et al, 2016;Saatchi et al, 2011;McNicol et al, 2018). High-resolution airborne LiDAR surveys offer the potential to bridge the scale gap between inventory plots and satellite data and enhance the range of training sites over which to calibrate models (Urbazaev et al, 2016;Wulder et al, 2012;Asner et al, 2018;Bispo et al, 2020). LiDAR is particularly powerful as it captures precise information on forest structure without signal saturation in dense tropical forests (Lefsky et al, 1999;Asner et al, 2014).…”