Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas

TL;DR

This study enhances high biomass forest mapping accuracy by integrating spaceborne LiDAR data with existing AGB maps, effectively reducing saturation issues and improving biomass estimates in tropical forests.

Contribution

It introduces a novel correction factor method using GLAS LiDAR data to improve existing AGB maps, especially at high biomass levels.

Findings

Improved AGB map precision with 7 t/ha increase in accuracy.

RMSE reduced from 81 to 74.1 t/ha, R² increased from 0.62 to 0.71.

High biomass underestimation was mitigated, reaching a maximum of 650 t/ha.

Abstract

Mapping forest AGB (Above Ground Biomass) is of crucial importance to estimate the carbon emissions associated with tropical deforestation. This study proposes a method to overcome the saturation at high AGB values of existing AGB map (Vieilledent's AGB map) by using a map of correction factors generated from GLAS (Geoscience Laser Altimeter System) spaceborne LiDAR data. The Vieilledent's AGB map of Madagascar was established using optical images, with parameters calculated from the SRTM Digital Elevation Model, climatic variables, and field inventories. In the present study, first, GLAS LiDAR data were used to obtain a spatially distributed (GLAS footprints geolocation) estimation of AGB (GLAS AGB) covering Madagascar forested areas, with a density of 0.52 footprint/km 2. Second, the difference between the AGB from the Vieilledent's AGB map and GLAS AGB at each GLAS footprint location was calculated, and additional spatially distributed correction factors were obtained. Third, an ordinary kriging interpolation was thus performed by taking into account the spatial structure of these additional correction factors to provide a continuous correction factor map. Finally, the existing and the correction factor maps were summed to improve the Vieilledent's AGB map. The results showed that the integration of GLAS data improves the precision of Vieilledent's AGB map by approximately 7 t/ha. By integrating GLAS data, the RMSE on AGB estimates decreases from 81 t/ha (R 2 = 0.62) to 74.1 t/ha (R 2 = 0.71). Most importantly, we showed that this approach using LiDAR data avoids underestimating high biomass values (new maximum AGB of 650 t/ha compared to 550 t/ha with the first approach).