Deriving Tree Size Distributions of Tropical Forests from Lidar

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf-tree matrix derived from allometric relations of trees. Using the leaf-tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha(-1)/normalized RMSE 18.8%/R-2 0.76; 50 ha: 22.8 trees ha(-1)/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha(-1), bias 0.8 m(2) ha(-1)) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

Automated summary

Remote sensing is a crucial tool for monitoring forest changes due to global change and other threats. This study presents a novel methodology to infer tree size distribution from lidar measurements, showing high accuracy at scales above 1 ha. The approach is reliable for forests with specific characteristics like low height, dense canopy, or low tree height heterogeneity.