文献类型：期刊文献

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

作者：Peng, Xi[1,2,3] Chen, Zhichao[2] Chen, Yongfu[1,3] Chen, Qiao[1,3] Liu, Haodong[1,3] Wang, Juan[1,3,4] Li, Huayu[1,3,4]

第一作者：Peng, Xi

通信作者：Chen, Q[1]|[a0005e604c56678d970cd]陈巧;

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Sichuan Agr Univ, Coll Forestry, Chengdu, Sichuan, Peoples R China;[3]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing, Peoples R China;[4]Southwest Forestry Univ, Coll Forestry, Kunming, Yunnan, Peoples R China

年份：2021

卷号：42

期号：23

起止页码：8858-8877

外文期刊名：INTERNATIONAL JOURNAL OF REMOTE SENSING

收录：;EI(收录号：20213510825063);Scopus(收录号：2-s2.0-85113537742);WOS:【SCI-EXPANDED(收录号:WOS:000688331600001)】；

基金：This work was supported by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF (No. CAFBB2017ZB004) and the Fundamental Research Funds for the Central Nonprofit Research Institution of CAF (No. CAFYBB2020GC006).

语种：英文

外文关键词：Aircraft detection - Antennas - Biodiversity - Conservation - Forecasting - Forestry - Learning algorithms - Light weight vehicles - Machine learning - Remote sensing - Textures - Tropics - Turing machines - Unmanned aerial vehicles (UAV)

摘要：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 alpha-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 alpha-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 alpha-diversity. The coefficient of determination (R-2) 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 (R-2) 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 alpha-diversity index can be improved by integrating different remote sensing data. Additionally, the most important multispectral metrics used to predict alpha-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 alpha-diversity index of Hainan tropical forest on a fine scale. UAV data may help local biodiversity workers to identify vulnerable areas.