Characterizing heterogeneous forest structure in ponderosa pine forests via UAS-derived structure from motion.

Autor: Hanna L; Department of Forest and Rangeland Stewardship, Colorado State University, 1472 Campus Delivery, Fort Collins, CO, 80523, USA., Tinkham WT; United States Department of Agriculture Forest Service, Rocky Mountain Research Station, 240 W Prospect Rd, Fort Collins, CO, 80526, USA. Wade.Tinkham@usda.gov., Battaglia MA; United States Department of Agriculture Forest Service, Rocky Mountain Research Station, 240 W Prospect Rd, Fort Collins, CO, 80526, USA., Vogeler JC; Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, 80523, USA., Ritter SM; Colorado Forest Restoration Institute, Colorado State University, 1472 Campus Delivery, Fort Collins, CO, 80523, USA., Hoffman CM; Department of Forest and Rangeland Stewardship, Colorado State University, 1472 Campus Delivery, Fort Collins, CO, 80523, USA.
Jazyk: angličtina
Zdroj: Environmental monitoring and assessment [Environ Monit Assess] 2024 May 09; Vol. 196 (6), pp. 530. Date of Electronic Publication: 2024 May 09.
DOI: 10.1007/s10661-024-12703-1
Abstrakt: Increasingly, dry conifer forest restoration has focused on reestablishing horizontal and vertical complexity and ecological functions associated with frequent, low-intensity fires that characterize these systems. However, most forest inventory approaches lack the resolution, extent, or spatial explicitness for describing tree-level spatial aggregation and openings that were characteristic of historical forests. Uncrewed aerial system (UAS) structure from motion (SfM) remote sensing has potential for creating spatially explicit forest inventory data. This study evaluates the accuracy of SfM-estimated tree, clump, and stand structural attributes across 11 ponderosa pine-dominated stands treated with four different silvicultural prescriptions. Specifically, UAS-estimated tree height and diameter-at-breast-height (DBH) and stand-level canopy cover, density, and metrics of individual trees, tree clumps, and canopy openings were compared to forest survey data. Overall, tree detection success was high in all stands (F-scores of 0.64 to 0.89), with average F-scores > 0.81 for all size classes except understory trees (< 5.0 m tall). We observed average height and DBH errors of 0.34 m and - 0.04 cm, respectively. The UAS stand density was overestimated by 53 trees ha -1 (27.9%) on average, with most errors associated with understory trees. Focusing on trees > 5.0 m tall, reduced error to an underestimation of 10 trees ha -1 (5.7%). Mean absolute errors of bole basal area, bole quadratic mean diameter, and canopy cover were 11.4%, 16.6%, and 13.8%, respectively. While no differences were found between stem-mapped and UAS-derived metrics of individual trees, clumps of trees, canopy openings, and inter-clump tree characteristics, the UAS method overestimated crown area in two of the five comparisons. Results indicate that in ponderosa pine forests, UAS can reliably describe large- and small-grained forest structures to effectively inform spatially explicit management objectives.
(© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)
Databáze: MEDLINE
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