Tropical forest degradation is a major contributor to greenhouse gas emissions. Tree height can be used as an important predictor of forest growth, and yield models can provide basic data for forest degradation assessments. As an important parameter of unmanned aerial vehicle-light detection and ranging (UAV-LiDAR), it is not clear how the point cloud density affects the extraction accuracy of tree height in degraded tropical rain forests. To solve this problem, we collected UAV-LiDAR data at a flight altitude of 150 m, and then resampled the UAV-LiDAR data obtained according to the point cloud density percentage resampling method and obtained UAV-LiDAR data for five different point cloud densities, namely, 12, 17, 28, 64, and 108 points/m². On the basis of the resampled LiDAR data, we generated a canopy height model (CHM) to extract the height of Dacrydium pierrei (D. pierrei). The results show that (1) With the increase in the point cloud density, the accuracy of tree height extraction gradually increased, with a maximum accuracy at 108 points/m² (root mean squared error (RMSE)% = 22.78%, bias% = 14.86%). The accuracy (RMSE%) increased by 6.92% as the point cloud density increased from 12 points/m² to 17 points/m², but only increased by 0.99% as the point cloud density increased from 17 points/m² to 108 points/m², indicating that 17 points/m² is a critical point for tree height extraction of D. pierrei. (2) Compared with the results from broad-leaved forests, the accuracy of D. pierrei height extraction from coniferous forest was higher. With the increase in point cloud density, the difference in the accuracy of D. pierrei height between two stands gradually increased. When the point cloud density was 108 points/m², the differences in RMSE% and bas% were 3.55% and 6.22%, respectively. When the point cloud density was 12 points/m², the differences in RMSE% and bias% were 2.71% and 4.69%, respectively. Our research identified the lowest LiDAR data point cloud density required to ensure a certain accuracy in tree height extraction, which will help scholars formulate UAV-LiDAR forest resource survey plans.

Knowledge of forest structure is vital for sustainable forest management decisions. Terrestrial laser scanning cannot describe the canopy trees in a large area, and it is unclear whether unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the ability to capture the forest canopy structural parameters in tropical forests. In this study, we estimated five forest canopy structures (stand density (N), basic area (G), above-ground biomass (AGB), Lorey’s mean height (HL), and under-crown height (hT)) with four modeling algorithms (linear regression (LR), bagged tree (BT), support vector regression (SVR), and random forest (RF)) based on UAV-LiDAR data and 60 sample plot data from tropical forests in Hainan and determined the optimal algorithms for the five canopy structures by comparing the performance of the four algorithms. First, we defined the canopy tree as a tree with a height ≥70% HL. Then, UAV-LiDAR metrics were calculated, and the LiDAR metrics were screened by recursive feature elimination (RFE). Finally, a prediction model of the five forest canopy structural parameters was established by the four algorithms, and the results were compared. The metrics’ screening results show that the most important LiDAR indexes for estimating HL, AGB, and hT are the leaf area index and some height metrics, while the most important indexes for estimating N and G are the kurtosis of heights and the coefficient of variation of height. The relative root mean squared error (rRMSE) of five structure parameters showed the following: when modeling HL, the rRMSEs (10.60%–12.05%) obtained by the four algorithms showed little difference; when N was modeled, BT, RF, and SVR had lower rRMSEs (26.76%–27.44%); when G was modeled, the rRMSEs of RF and SVR (15.37%–15.87%) were lower; when hT was modeled, BT, RF, and SVR had lower rRMSEs (10.24%–11.07%); when AGB was modeled, RF had the lowest rRMSE (26.75%). Our results will help facilitate choosing LiDAR indexes and modeling algorithms for tropical forest resource inventories.

Rapid and accurate monitoring of biodiversity is a major challenge in biodiversity conservation. Obtaining data using unmanned aerial vehicles (UAV) provides a new direction for biodiversity monitoring. However, studies on the relationship between UAV data and biodiversity are limited. In this study, we used a machine learning algorithm to evaluate the effectiveness of UAV-light detection and ranging (LiDAR) and UAV multispectral data for estimating three α-diversity indices in tropical forests located in Hainan, China. We obtained 126 biodiversity-related metrics (68 from multispectral and 58 from LiDAR) based on the UAV data and three α-diversity indices from 62 sample plots at two sites. We used the recursive feature elimination algorithm to filter significant metrics. We found that both multispectral and LiDAR data can be used to predict α-diversity. The coefficient of determination (R²) values of multispectral data (LiDAR data) for the species richness, Shannon index, and Simpson index were 0.69, 0.70, and 0.57 (0.72, 0.63, 0.44), respectively. LiDAR data were more accurate than multispectral data for predicting species richness, whereas multispectral data were more accurate than LiDAR data for predicting the Shannon and Simpson indices. Given the best result obtained with a single datum, the accuracy (R²) of the combination of the two data types for species richness and Shannon and Simpson indices increased by 0.05, 0.05, and 0.06, respectively, indicating that the prediction accuracy of the α-diversity index can be improved by integrating different remote sensing data. Additionally, the most important multispectral metrics used to predict α-diversity were related to vegetation index and texture metrics, whereas the most important LiDAR metrics were related to canopy height characteristics. Our research results indicate that UAV data are effective for predicting the α-diversity index of Hainan tropical forest on a fine scale. UAV data may help local biodiversity workers to identify vulnerable areas.