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

Authors

Gassilloud, M., Koch, B., Goeritz, A.

Abstract

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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 18 Sep 2026.

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