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Open AccessArticle

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

1
Department of Forest Ecology, The Silva Tarouca Research Institute, 60200 Brno, Czech Republic
2
Faculty of Forestry and Wood Technology, Mendel University in Brno, 61300 Brno, Czech Republic
3
Department of Ecology and Evolutionary Biology, Brown University, Providence, RI 02912, USA
4
Institute at Brown for Environment and Society, Brown University, Providence, RI 02912, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(19), 3260; https://doi.org/10.3390/rs12193260
Received: 29 August 2020 / Revised: 26 September 2020 / Accepted: 2 October 2020 / Published: 7 October 2020
(This article belongs to the Special Issue 3D Forest Structure Observation)
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. View Full-Text
Keywords: aboveground biomass; carbon; laser scanning; object-based; population; remote sensing aboveground biomass; carbon; laser scanning; object-based; population; remote sensing
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Krůček, M.; Král, K.; Cushman, K.; Missarov, A.; Kellner, J.R. Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees. Remote Sens. 2020, 12, 3260.

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