Comparison of UAV LiDAR and Digital Aerial Photogrammetry Point Clouds for Estimating Forest Structural Attributes in Subtropical Planted Forests

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 (△Adj-R2 = 0.09, △rRMSE = 4.20%), whereas the lowest difference was for basal area (△Adj-R2 = 0.03, △rRMSE = 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.

Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

“…Having high-density point clouds imply the capability to penetrate through the forest canopy effectively and acquire the whole vertical distribution information leading to a higher ability to estimate forest structural attributes accurately. High overlaps and limited shadows, occlusions and shaking of leaves from surrounding trees or the occasional shaking of leaves might have also partially contributed to the low RMSE values we had observed [61]. Using lidar full-waveform data and a normalized cut segmentation approach, [62] showed promising results and that were close to this study, with a relative RMSE of 9% in the best cases.…”

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

“…Considering the forest characteristics and species, Puliti et al [37] suggested that boreal forests are usually considered as a more simple type due to the species composition and the height variations, in contrast with temperate broadleaved forests that are complex due to the irregular height of species. In line with this, Cao et al [84] analysed different tree species, dawn redwood (coniferous) and poplar (broadleaved), revealing that the results obtained with different sensors were more similar in dawn redwood (coniferous) specie. According to the author, this is because dawn redwood (coniferous) has a more regular tree crown shape when compared to poplar (broadleaved) species, which simplifies identification process executed by the algorithm.…”

“…However, it is worth to notice that results are dependent on UAV sensor used (e.g., RGB, LiDAR, CIR), as well as the forest characteristics and species. Taking into account the sensors used in the aforementioned studies, Puliti et al [37] used a UAV-based CIR imagery; Cao et al [84] and Ota et al [85] used a UAV-based RGB imagery; and Cao et al [84] and Guo et al [86] used a UAV LiDAR system. Despite the fact the results obtained are similar, Cao et al [84] refer that the accuracies of models obtained with UAV-LiDAR were higher than those obtained by UAV-RGB.…”

Examining the utility of Simultaneous Localisation and Mapping (SLAM) LiDAR on a low-altitude unmanned aerial vehicle (UAV) across environments of various composition and dynamic complexity

“…insufficient nutrient availability). This is similar to the findings of Cao et al [58] who also reported comparable results between UAV LiDAR and photogrammetry regarding forest inventory. Here, however, UAV LiDAR demonstrated more consistent and significant correlations to the biophysical parameters of the crop and subsequently greater potential in the prediction of harvest outcomes.…”

“…Recently, SfM photogrammetry from UAVs has been compared to LiDAR from groundvehicles [56] and manned aircraft [57] with far fewer studies undertaking such a comparison using UAV LiDAR [58,59], an important factor should spatio-temporal scales be considered as part of the comparison. Unlike previous works, we employ a side-by-side test of the Hovermap LiDAR and MicaSense RedEdge multispectral camera simultaneously mounted to a single UAV platform to acquire a time-series data set from both sensors.…”