Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

作     者：Ruiliang Pu 

作者机构：School of GeosciencesUniversity of South Florida4202 E.Fowler Ave.NES 107TampaFL 33620USA 

出 版 物：《Journal of Remote Sensing》 (国际遥感学报（英文）)

年 卷 期：2021年第2021卷第1期

页      面：211-236页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by the University of South Florida USA 

主　　题：directions LiDAR Remote 

摘      要：Timely and accurate information on tree species(TS)is crucial for developing strategies for sustainable management and conservation of artificial and natural *** the last four decades,advances in remote sensing technologies have made TS classification *** many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature,it is necessary to conduct an updated review on the status,trends,potentials,and challenges and to recommend future *** review will provide an overview on various optical and light detection and ranging(LiDAR)sensors;present and assess current various techniques/methods for,and a general trend of method development in,TS classification;and identify limitations and recommend future *** this review,several concluding remarks were *** include the following:(1)A large group of studies on the topic were using high-resolution satellite,airborne multi-/hyperspectral imagery,and airborne LiDAR data.(2)A trend of“multiplemethod development for the topic was observed.(3)Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy.(4)Recently,unmanned aerial vehicle-(UAV-)based sensors have caught the interest of researchers and practitioners for the topic-related research and *** addition,three future directions were recommended,including refining the three categories of“multiplemethods,developing novel data fusion algorithms or processing chains,and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.