Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees

Abstract

We applied a supervised individual-tree segmentation algorithm to ultra-high-density drone lidar in a temperate mountain forest in the southern Czech Republic. We compared the number of trees correctly segmented, stem diameter at breast height (DBH), and tree height from drone-lidar segmentations to field-inventory measurements and segmentations from terrestrial laser scanning (TLS) data acquired within two days of the drone-lidar acquisition. Our analysis detected 51% of the stems >15 cm DBH, and 87% of stems >50 cm DBH. Errors of omission were much more common for smaller trees than for larger ones, and were caused by removal of points prior to segmentation using a low-intensity and morphological filter. Analysis of segmented trees indicates a strong linear relationship between DBH from drone-lidar segmentations and TLS data. The slope of this relationship is 0.93, the intercept is 4.28 cm, and the r2 is 0.98. However, drone lidar and TLS segmentations overestimated DBH for the smallest trees and underestimated DBH for the largest trees in comparison to field data. We evaluate the impact of random error in point locations and variation in footprint size, and demonstrate that random error in point locations is likely to cause an overestimation bias for small-DBH trees. A Random Forest classifier correctly identified broadleaf and needleleaf trees using stem and crown geometric properties with overall accuracy of 85.9%. We used these classifications and DBH estimates from drone-lidar segmentations to apply allometric scaling equations to segmented individual trees. The stand-level aboveground biomass (AGB) estimate using these data is 76% of the value obtained using a traditional field inventory. We demonstrate that 71% of the omitted AGB is due to segmentation errors of omission, and the remaining 29% is due to DBH estimation errors. Our analysis indicates that high-density measurements from low-altitude drone flight can produce DBH estimates for individual trees that are comparable to TLS. These data can be collected rapidly throughout areas large enough to produce landscape-scale estimates. With additional refinement, these estimates could augment or replace manual field inventories, and could support the calibration and validation of current and forthcoming space missions.

ForestGEO: Understanding forest diversity and dynamics through a global observatory network

“…While ground plots have advanced understanding of forest carbon stocks, they cover far <0.01% of forest area (Schimel et al, 2015). Remote-sensing technologies provide the potential to scale up ground-based observations of forest biomass (Avitabile et al, 2016), structure (Yang et al, 2016;Krůček et al, 2020), productivity (Liu et al, 2017), and mortality (Clark et al, 2004) from local-to-global scales, and to be a key solution for estimating global carbon stocks and fluxes, and consequently, forest responses to anthropogenic change (Schimel et al, 2015;Randin et al, 2020). Airborne remote sensing of hyperspectral reflectance can enable mapping of tree functional composition and diversity (Antonarakis et al, 2014;Asner et al, 2017;Durán et al, 2019), and quantification of their responses to anthropogenic impacts (Swinfield et al, 2019).…”

Estimating Tree Diameters from an Autonomous Below-Canopy UAV with Mounted LiDAR

“…Autonomous surveys of forest physical structure could be further improved via the integration of 3D LiDAR, optical cameras, and more sophisticated methods of point cloud analysis [1,3,4,6,7,11,12,15,17,18,21,36]. Chen et al [3] flew a remotely piloted below-canopy UAV with mounted 3D LiDAR in a temperate pine forest and developed novel software for reconstructing tree geometry from 3D point clouds.…”

“…Recent years have seen major advances in the use of cameras [10,11] and LiDAR [12][13][14] for forest scanning, and in the software used to turn the scans into digital models from which forests' physical structures can be measured [1,3,15]. Most UAV-based surveys of forests to date have used above-canopy UAVs [2,10,12,[16][17][18][19][20][21]. These are effective in temperate and boreal forests [2,20,21], where trees are deciduous or foliage is relatively sparse, so that sensors can penetrate through the entire forest profile.…”

“…Most UAV-based surveys of forests to date have used above-canopy UAVs [2,10,12,[16][17][18][19][20][21]. These are effective in temperate and boreal forests [2,20,21], where trees are deciduous or foliage is relatively sparse, so that sensors can penetrate through the entire forest profile. In denser forests, however, Remote Sens.…”

Tree Shape Variability in a Mixed Oak Forest Using Terrestrial Laser Technology: Implications for Mating System Analysis

“…TLS automatically measures the surrounding three-dimensional (3D) space using millions to billions of 3D points. The major advantage of using TLS in measuring tree architecture lies in its capability to document the structure rapidly, automatically, and in millimeter-level detail, supporting high quality field data studies in biology or genetics [18].…”