Estimating forest structure at five tropical forested sites using lidar point cloud data

Tropical forests are fundamental components in the global carbon cycle and are threatened by deforestation and climate change. Because of their importance in carbon dynamics, understanding the structural architecture of these forests is vital. Airborne lidar data provides a unique opportunity to examine not only the height of these forests, which is often used to estimate biomass, but also the crown geometry and vertical profile of the canopy. These structural attributes inform temporal and spatial apsects of carbon dynamics providing insight into the past disturbances and growth of forests.

We examined airborne lidar point cloud data from five sites in the Brazilian Amazon collected during the years 2012 to 2014. We generated both digital elevation maps, canopy height models (CHM), and vertical vegetation profiles (VVP) in our analysis. We analyzed the CHM using crown delineation with an iterative maximum finding routine to find the tops of canopies, local maxima to determine edges of crowns, and two parameters that control termination of crown edges. We also ran textural analysis methods on the CHM and VVP. Using multiple linear regression models and boosted regression trees we estimated forest structural parameters including biomass, stem density, basal area, width and depth of crowns and stem size distribution. Structural attributes estimated from lidar point cloud data can improve our understanding of the carbon dynamics of tropical forests on a landscape level and regional level.