Toward Aboveground Biomass Estimation with RADAR, Lidar and Optical Remote Sensing Data in Southern Mexico

Abstract:

Information on the spatial distribution of aboveground biomass (AGB) over large areas is needed (1) for understanding and managing the processes involved in the carbon cycle, and (2) supporting international policies for climate change mitigation and adaption. Using remote sensing techniques it is possible to provide spatially explicit information of AGB from local to global scales. In this work we present the first results on the use of multi-sensor remote sensing data to estimate AGB over three test sites in southern Mexico. In order to develop a set of AGB retrieval algorithms, we firstly compared different SAR parameters (e.g. multi-polarized backscatter intensities and interferometric coherence) obtained from ALOS PALSAR sensor and Landsat imagery with field-based AGB estimates using empirical regressions and analyzed the relationships between them. The next steps of the work will be development of a two‑stage up‑scaling approach: firstly, to enlarge the cal/val data, we propose to estimate AGB along airborne LiDAR (from G-LiHT sensor) transects using field-based AGB and LiDAR height metrics. With LiDAR-based AGB we will then calibrate SAR parameters in a non‑parametric model (e.g., randomForest) to create AGB maps over the study areas. An overall aim of the study is the analysis of capabilities and limitations of SAR data for AGB mapping and the investigation of the potential synergistic use of SAR, LiDAR and optical systems.The proposed monitoring tool will facilitate quantitative estimations in loss of carbon storage and support the selection of terrestrial (e.g. tropical dry forests, shrublands) sites for conservation priorities with high value for the national carbon budget.