Effects of point density on interpretability of lidar-derived forest structure metrics in two temperate forests

Abstract

Three-dimensional forest structure plays an important role in processes such as biomass accumulation and fire spread and provides wildlife with habitat and foraging spaces. Advances in lidar mapping have improved forest structure quantification at local to global scales. However, point cloud density may have effects on estimates of forest structure variables that are not well understood and may vary by forest structural type (e.g. closed vs open canopy). In this study we investigated the effects of lidar point cloud density on forest structure parametrization in an open canopy pine-dominated forest at Assateague Island National Seashore (AINS) and a closed-canopy mixed hardwood temperate forest at the Keweenaw Research Center (KRC) using uncrewed aerial system (UAS)-based lidar. We decimated high point density (> 1000 points m-2) lidar data to between 1 and 175 points m-2and analyzed 26 forest structure metrics using Tukey's method, reliability ratio, and correlation analyses. Effects of point density on forest structure parameters were often site-dependent, as anticipated. At AINS, maximum (zmax) and mode (zmode) height significantly differed for point densities less than 10 pts m-2and 25 points m-2, respectively, while at KRC, the thresholds were 75 points m-2for zmax and 50 points m-2for zmode. Reliability ratio of zmax, height skewness, height quantiles, and the coefficient of variation of mean leaf area density (LAD) also varied dependent on point density at AINS. At both sites, metrics related LAD varied significantly (p < 0.001) at all but the highest point densities, and the reliability ratio for zmode, kurtosis of height distribution and mean horizontal coefficient of variation of LAD varied across point densities without any clear pattern. Point density mainly affected correlations between LAD-derived structural metrics and other metrics (e.g., as point density increased, Shannon diversity of LAD changed from being positively to negatively correlated to zmax). This study demonstrates how point density differentially affects lidar-derived forest structure parameters in diverse forest types. Scientists must understand these effects to interpret and compare forest structure attributes derived from different lidars.

Weighted Differential Gradient Method for Filling Pits in Light Detection and Ranging (LiDAR) Canopy Height Model

“…In addition, when the LiDAR point cloud is converted to raster data via interpolation, some points with similar x and y coordinates but with multiple elevation values can form data pits [22]. On the other hand, the density of the point cloud is another factor as well [23,24]. Pitted pixels compromise CHM accuracy, disrupt canopy surface integrity, hinder visual canopy recognition, and impact the precision of single-tree canopy extraction and crown width estimation, leading to errors in treetop extraction [25].…”