Modelling of the biodiversity of tropical forests in China based on unmanned aerial vehicle multispectral and light detection and ranging data

作者：Xi Peng, Zhichao Chen, Yongfu Chen, Qiao Chen, Haodong Liu, Juan Wang, Huayu Li · 发表于：International Journal of Remote Sensing · 年份：2021 · DOI：10.1080/01431161.2021.1954714 · 被引用次数：11 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Wildlife Ecology and Conservation

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 pred...