Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France ...

• Main Authors: Morin, David, Planells, Milena, Mermoz, Stéphane, Mouret, Florian
• Format: Report
• Language: unknown
• Published: arXiv 2023
• Subjects: Machine Learning stat.ML; FOS Computer and information sciences; palsa
• Online Access: https://dx.doi.org/10.48550/arxiv.2310.14662; https://arxiv.org/abs/2310.14662

Mapping forest resources and carbon is important for improving forest management and meeting the objectives of storing carbon and preserving the environment. Spaceborne remote sensing approaches have considerable potential to support forest height monitoring by providing repeated observations at high spatial resolution over large areas. This study uses a machine learning approach that was previously developed to produce local maps of forest parameters (basal area, height, diameter, etc.). The aim of this paper is to present the extension of the approach to much larger scales such as the French national coverage. We used the GEDI Lidar mission as reference height data, and the satellite images from Sentinel-1, Sentinel-2 and ALOS-2 PALSA-2 to estimate forest height and produce a map of France for the year 2020. The height map is then derived into volume and aboveground biomass (AGB) using allometric equations. The validation of the height map with local maps from ALS data shows an accuracy close to the state ...