Gassilloud, M. | bioRxiv Science

bioRxiv2026

Tracking 3D forest density dynamics in a mixed temperate forestusing occlusion-aware voxel transmittance and consistent multitemporalUAV-LiDAR

Forest structure and density dynamics drive ecosystem processes including carbon sequestration, microclimate regulation, and photosynthetic capacity. While LiDAR can capture these properties, high-frequency monitoring remains constrained by scarce multi-temporal datasets, inconsistent acquisitions, and occlusion biases confounding true structural change with methodological artifacts. This study investigates spatio-temporal forest structure dynamics through voxel transmittance in a European mixed temperate forest. We acquired an extensive UAV LiDAR dataset of 38 flights with identical sensor settings over more than two years. With a custom ray-tracing framework, we estimated voxel transmittance and mapped occlusion at 0.25m resolution. Multi-temporal comparability was ensured through consistency masking and leaf-off baseline initialization of empty space. Voxel transmittance and attenuation dynamics were analyzed across three areas of interest, covering a mixed plot, a beech and a Douglas fir patch. The results revealed distinct phenological signatures: European beech showed high-amplitude seasonal variation (70% attenuation volume reduction from summer to winter) with rapid spring foliation and stable winter baselines, whereas Douglas fir exhibited 12.7% variance with delayed growth onset and no stable winter baseline. Inter-annual comparisons indicated increasing blocking biomass with upward vertical shifts in transmittance profiles from tree growth and crown expansion. Two-dimensional attenuation mapping enabled the detection of individual tree dynamics and discrete structural disturbances. We demonstrate that consistent high-frequency UAV LiDAR acquisitions enable forest morphology monitoring through relative voxel transmittance. Our occlusion-aware framework differentiates structural changes from sampling biases, providing insights into forest dynamics at high spatio-temporal resolution.

ecology↗

bioRxiv2024

deadtrees.earth - An Open-Access and Interactive Database for Centimeter-Scale Aerial Imagery to Uncover Global Tree Mortality Dynamics

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, not standardized and not spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map tree mortality over time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Drones provide a cost-effective source of training data by capturing high-resolution orthophotos of tree mortality events at sub-centimeter resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than a thousand centimeter-resolution orthophotos, covering already more than 300,000 ha, of which more than 58,000 ha are fully annotated. This community-sourced and rigorously curated dataset shall serve as a foundation for a global initiative to gather comprehensive reference data. In concert with Earth observation data and machine learning it will serve to uncover tree mortality patterns from local to global scales. This will provide the foundation to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. Thus, the open and interactive nature of deadtrees.earth together with the collective effort of the community is meant to continuously increase our capacity to uncover and understand tree mortality patterns.

ecology↗