Abstract
Research Highlights: This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenology, ground determination method, seedling size, and more. Background and Objectives: Regeneration success in disturbed forests involves costly ground surveys of tree seedlings exceeding a minimum height. Here we assess the accuracy with which conifer seedling height can be estimated using drones, and how height errors translate into counting errors in stocking surveys. Materials and Methods: We compared height estimates derived from DIPCs of different GSD (0.35 cm, 0.75 cm, and 3 cm), phenological state (leaf-on and leaf-off), and ground determination method (based on either the DIPC itself or an ancillary digital terrain model). Each set of height estimates came from data acquired in up to three linear disturbances in the boreal forest of Alberta, Canada, and included 22 to 189 surveyed seedlings, which were split into two height strata to assess two survey scenarios. Results: The best result (root mean square error (RMSE) = 24 cm; bias = −11 cm; R2 = 0.63; n = 48) was achieved for seedlings >30 cm with 0.35 cm GSD in leaf-off conditions and ground elevation from the DIPC. The second-best result had the same GSD and ground method but was leaf-on and not significantly different from the first. Results for seedlings ≤30 cm were unreliable (nil R2). Height estimates derived from manual softcopy interpretation were similar to the corresponding DIPC results. Height estimation errors hardly affected seedling counting errors (best balance was 8% omission and 6% commission). Accuracy and correlation were stronger at finer GSDs and improved with seedling size. Conclusions: Millimetric (GSD <1 cm) DIPC can be used for estimating the height of individual conifer seedlings taller than 30 cm.
Highlights
The boreal forest of Alberta, Canada, is a busy landscape where widespread industrial activity fragments and negatively affects the habitat of threatened species such as woodland caribou (Rangifer tarandus caribou)
Millimetric (GSD
DIPC, Seedlings ≤30 cm the results for small seedlings yielded slightly better root mean square error (RMSE) and median absolute error (MEDAE) than those
Summary
The boreal forest of Alberta, Canada, is a busy landscape where widespread industrial activity (oil and gas, mining, and forestry) fragments and negatively affects the habitat of threatened species such as woodland caribou (Rangifer tarandus caribou). Forests 2020, 11, 924 tree species, and the second determines whether the surveyed area meets the establishment targets in terms of overall stocking and spatial distribution of those seedlings. Both surveys have height thresholds below which a seedling does not count. We consider two scenarios: the survival assessment survey, where the seedlings are very small, and the establishment survey, where most seedlings will exceed 30 cm height. We focus on conifer seedlings because they are preferred over broadleaf tree species for restoring mixedwood boreal forests, since they usually represent the late-stage succession path of these forests. If drones ( known in the literature as unmanned aerial vehicles or remotely piloted aircraft systems, UAV or RPAS respectively) could be used to detect seedlings and count those exceeding the height requirement, this would greatly facilitate the surveying of regenerating linear disturbances quickly and efficiently
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