Remote Sensing & LIDAR

This cluster highlights the powerful role of remote sensing, particularly LiDAR (Light Detection and Ranging), in advancing our understanding of forest structure, biomass estimation, and ecosystem dynamics. The selected works leverage cutting-edge remote sensing techniques to monitor forest health, track successional patterns, and improve the resolution of ecological modeling at both regional and global scales.

A key theme across these studies is the integration of airborne or satellite-based LiDAR data with other remote sensing modalities (e.g., hyperspectral or radar), enabling unprecedented insights into vertical and horizontal forest heterogeneity. These innovations help detect subtle structural complexity, assess the impacts of disturbances such as fire or drought, and support large-scale forest inventory and conservation planning. The papers also demonstrate emerging computational tools and benchmarking frameworks for processing LiDAR data, making these techniques more accessible for ecological applications.

Featured Publications

Seeing trees from space: above-ground biomass estimation using airborne LiDAR in Southeast Asian tropical forests
Phua, M.-H.; Ling, Z.-Y.; Coomes, D. A. et al. – iForest – Biogeosciences and Forestry (2023)
DOI: 10.3832/ifor2204-010
This study presents an effective framework for estimating above-ground biomass (AGB) using airborne LiDAR across Southeast Asian tropical forests, highlighting how remote sensing bridges data gaps in under-sampled regions.

Tracking shifts in forest structural complexity after disturbance using airborne LiDAR
Rosen, A.; Fischer, F. J.; Coomes, D. A. et al. – Ecography (2023)
DOI: 10.1111/ecog.07377
Using repeat LiDAR surveys, this paper tracks changes in forest complexity post-disturbance, providing insights into ecosystem recovery processes at landscape scales.

Computational tools for assessing forest recovery after fire: Comparing LiDAR-derived indicators across disturbance gradients
Holcomb, A.; Mathis, S. V.; Coomes, D. A. et al. – Science of Remote Sensing (2023)
DOI: 10.1016/j.srs.2023.100106
This paper develops and validates computational approaches using LiDAR to quantify post-fire recovery, offering scalable methods for disturbance ecology research.

Benchmarking airborne laser scanning tree segmentation algorithms across tropical forest sites
Cao, Y.; Ball, J. G. C.; Coomes, D. A. et al. – International Journal of Applied Earth Observation and Geoinformation (2023)
DOI: 10.1016/j.jag.2023.103490
This benchmark study compares tree segmentation techniques in dense tropical canopies, helping refine methodologies for individual tree detection and forest inventory.

Fire traps in the wet subtropics: New perspectives from remote sensing of forest structure and composition
Chan, A. H. Y.; Coomes, D. A. – Journal of Applied Ecology (2023)
DOI: 10.1111/1365‑2664.14575
By integrating LiDAR and hyperspectral data, this study investigates fire feedbacks in subtropical forests, uncovering novel fire-trapping mechanisms with implications for fire management.