文献类型：期刊文献

英文题名：Status, advancements and prospects of deep learning methods applied in forest studies

作者：Yun, Ting[1,2] Li, Jian[1] Ma, Lingfei[3] Zhou, Ji[4] Wang, Ruisheng[5] Eichhorn, Markus P.[6] Zhang, Huaiqing[7]

第一作者：Yun, Ting

通信作者：Zhang, HQ[1]

机构：[1]Nanjing Forestry Univ, Coll Informat Sci & Technol & Artificial Intellige, Nanjing 210037, Peoples R China;[2]Nanjing Forestry Univ, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China;[3]Cent Univ Finance & Econ, Sch Stat & Math, Beijing 102206, Peoples R China;[4]Natl Inst Agr Bot NIAB, Cambridge Crop Res, Cambridge CB3 0LE, England;[5]Univ Calgary, Dept Geomat Engn, Calgary, AB T2N 1N4, Canada;[6]Univ Coll Cork, Sch Biol Earth & Environm Sci, Cork T23N73K, Ireland;[7]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2024

卷号：131

外文期刊名：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

收录：;Scopus(收录号：2-s2.0-85194836520);WOS:【SCI-EXPANDED(收录号:WOS:001255275300001)】；

基金：This study was financially supported by the National Natural Science Foundation of China (grant numbers 32371876, 32271877, and 42101451) , the Natural Science Foundation of Jiangsu Province (BK20221337) , China, the Jiangsu Provincial Agricultural Science and Technology Independent Innovation Fund Project (CX(22)3048) and the Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China (KLSMNRG202208) . Moreover, we are indebted to Professor Cao Lin of Nanjing Forestry University for his guidance and support during the writing of this review.

语种：英文

外文关键词：Deep learning network; Forest application; Remote sensing; Point cloud; Satellite imagery; Aerial photography

摘要：Deep learning, which has exhibited considerable potential and effectiveness in forest resource assessment, is vital for comprehending and managing forest resources and ecosystems. However, extensive assessment of forest resources is highly challenging due to the complex and varied nature of forest types sourced from diverse remote sensing platforms, which include images, point clouds, and fusion data. To facilitate further study, we systematically review the current status, applications and prospects of deep learning technologies for different types of forest remote sensing data. After considering more than two hundred forest-related papers published over the past decade, we introduce sensors and devices for forest data acquisition, classify deep learning methods based on their data processing methods and operational principles, and categorize diverse instances of these methods with various forest applications. Moreover, we summarize available datasets related primarily to forest data and examine the global geographic distribution of the relevant literature. Comprehensive insights into the advantages and limitations of each method are described, offering a forward-looking perspective on the trend of applying deep learning technology to forest research. In this paper, we aim to provide an overview of the current trends and challenges of deep learning techniques applied to forest research, creating a comprehensive picture for use as a reference by both academia and industry professionals.