Abstract
Estimating forest structural attributes of planted forests plays a key role in managing forest resources, monitoring carbon stocks, and mitigating climate change. High-resolution and low-cost remote-sensing data are increasingly available to measure three-dimensional (3D) canopy structure and model forest structural attributes. In this study, we compared two suites of point cloud metrics and the accuracies of predictive models of forest structural attributes using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data, in a subtropical coastal planted forest of East China. A comparison between UAV-LiDAR and UAV-DAP metrics was performed across plots among different tree species, heights, and stem densities. The results showed that a higher similarity between the UAV-LiDAR and UAV-DAP metrics appeared in the dawn redwood plots with greater height and lower stem density. The comparison between the UAV-LiDAR and DAP metrics showed that the metrics of the upper percentiles (r for dawn redwood = 0.95–0.96, poplar = 0.94–0.95) showed a stronger correlation than the lower percentiles (r = 0.92–0.93, 0.90–0.92), whereas the metrics of upper canopy return density (r = 0.21–0.24, 0.14–0.15) showed a weaker correlation than those of lower canopy return density (r = 0.32–0.68, 0.31–0.52). The Weibull α parameter indicated a higher correlation (r = 0.70–0.72) than that of the Weibull β parameter (r = 0.07–0.60) for both dawn redwood and poplar plots. The accuracies of UAV-LiDAR (adjusted (Adj)R2 = 0.58–0.91, relative root-mean-square error (rRMSE) = 9.03%–24.29%) predicted forest structural attributes were higher than UAV-DAP (Adj-R2 = 0.52–0.83, rRMSE = 12.20%–25.84%). In addition, by comparing the forest structural attributes between UAV-LiDAR and UAV-DAP predictive models, the greatest difference was found for volume (ΔAdj-R2 = 0.09, ΔrRMSE = 4.20%), whereas the lowest difference was for basal area (ΔAdj-R2 = 0.03, ΔrRMSE = 0.86%). This study proved that the UAV-DAP data are useful and comparable to LiDAR for forest inventory and sustainable forest management in planted forests, by providing accurate estimations of forest structural attributes.
Highlights
Planted forests cover approximately 7.3% (290 million ha) of global forests, and they increased steadily by over 105 million ha since the 1990s [1]
We used unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) and digital aerial photogrammetry (DAP) data to acquire two suites of point clouds, and compared the performance and similarity of point cloud-based metrics, as well as the accuracies of forest structural attributes predicted by the metrics, in a subtropical planted forest of east China
Since DAP data can characterize forest upper canopy structure at a lower cost and have the potential to provide 3D point clouds as with LiDAR, the comparison of UAV-LiDAR and DAP metrics was performed across plots among different conditions to provide deeper assessments of the planted forest
Summary
Planted forests cover approximately 7.3% (290 million ha) of global forests, and they increased steadily by over 105 million ha since the 1990s [1]. The development of planted forests can effectively increase the supply of wood, benefit the production of fiber, and enhance forest carbon storage [2,3], as well as maintain biodiversity and mitigate climate change [3,4]. Acquiring forest information and accurately estimating planted forest structural attributes are critical for sustainable. There are increasing requirements for enhancing management in planted forest, traditional forest inventory methods have limited capacities in the objectivity and consistency of tree measurements due to manual operations [6,7]. Remote sensing technologies have the ability to provide accurate and spatially updated information for forest inventories to characterize forest vertical structure and measure forest structural attributes [8–11]. Enhanced forest inventory (EFI) refers to a forest inventory that is based on traditional field inventory data and advanced remote sensing technologies to monitor forest resource information [12]
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