Aboveground biomass estimates from UAV LiDAR improved via contextual learning in a Norway spruce forest

Abstract

Forest structure analyses and biomass prediction systems are key tools for advancing forest trait-based ecology and ecosystem stewardship. The combination of near-field remote sensing techniques---e.g., Unmanned Aerial Vehicles (UAV) and Light Detection and Ranging (LiDAR) systems---with machine-learning methods enhances the accuracy of forest structure analyses and above ground-biomass~(AGB) estimates. In this study, we utilized a UAV-LiDAR system to map the 3D architecture of a monoculture Norway spruce forest in Davos, Switzerland, where a field-based inventory served as ground truth data. The objectives of this effort were (i) to gain insights into variation and gradients of structural traits (i.e., tree height) and (ii) to evaluate whether this knowledge of community structure may prove useful as contextual information to improve predictions of AGB at the individual tree level. To investigate the local association of structural traits, we segmented the point cloud data scene into individual trees and treated tree height as the morphological variable of interest. We then used local indicators of spatial association to determine the extent of significant local context, and defined tree neighborhoods within the forest. For the task of AGB regression, we obtained results of several feature-based regression methods (i.e., AdaBoost, Lasso and Random Forest) and evaluated these based on nested cross-validation.We applied this approach to two separate tree data sets within the same site, one being clustered and continuous, the other discontinuous and scattered in separate sampling plots. In both cases, we found evidence of enhanced AGB prediction performance in context-aware regressions, indicating that gradients in morphological tree traits across the ecosystem proxy for unveiled ecological information that influence tree growth, which can be leveraged to enhance predictions of AGB.