Conference Agenda

Accurate estimation of forest height is essential to improve the understanding and quantification of forest structure, aboveground biomass (AGB) and carbon dynamics. The integration between spaceborne and airborne remote sensing technologies such as synthetic aperture radar (SAR), airborne laser scanning (ALS) and field data might be the key to reliably assess the contribution of these environments to the carbon cycle and the impacts of human-induced disturbances. Several satellites provide forest height estimates at fine resolution, however, until now, these height estimates have not been systematically validated, especially in the carbon-dense tropical forests and using novel satellite missions.

The ESA BIOMASS mission — the first spaceborne satellite to operate a fully polarimetric SAR P-band — plays a fundamental role in advancing forest height estimation, since its wavelength (~70 cm) allows the radar signal and its backscattering to deeply penetrate the forest canopy. Light detection and ranging (LiDAR) technology in combination with in situ measurements can support overall calibration and validation (Cal/Val) BIOMASS activities related to forest height estimations.

Our goal is to gather and process airborne LiDAR and field data collected over the Brazilian Amazon, creating a reference dataset to validate the BIOMASS forest height product. The LiDAR data was collected by the Brazilian Forest Service over forests under sustainable forest management, enabling pre- (undisturbed) and post-disturbance (logged) assessment. The field data was collected by companies operating in the same areas, including estimates such as geolocation, diameter at breast height, height and stem volume of each tree, related to its corresponding logging annual site.

ALS point cloud data can be processed to generate canopy height models (CHM). The currently automated pipeline performs LiDAR point cloud cleaning, normalization, ground classification and rasterization, producing standardized DTM, DSM, and CHM outputs. Quality assurance products, such as pulse density, scan angle, and laser penetration maps, are also generated to ensure traceability. Field data processing uses the variables from logged trees to measure gaps created after logging and to compare them with ALS tree height estimates. The reference dataset is rasterized.

The validation strategy uses the reference dataset to perform a multi-level spatial comparison by interpolation methods with the BIOMASS height product. Height layers and field data can be co-registered and compared through pixel-based and aggregated statistical analyses. The expected results include the computation of coefficient of determination (R²), root mean square error, and bias, complemented by a regression analysis to identify systematic deviations between datasets. The analysis is an independent validation framework that captures both canopy-level and tree-level variations.

Our results aim to contribute to the ongoing Cal/Val activities of the ESA BIOMASS mission. We strongly believe that ALS data and in situ measurements can provide benchmark information for calibrating and validating overall remote sensing products. We will thus help reduce uncertainties in global carbon stock and flux estimations, particularly those associated with land-use change, forest degradation, and regrowth.