文献类型：期刊文献

英文题名：Using time-series imagery and 3DLSTM model to classify individual tree species

作者：Chen, Caiyan[1,2,3] Jing, Linhai[2,7] Li, Hui[1] Tang, Yunwei[2] Chen, Fulong[1,2,4,5] Tan, Bingxiang[6]

第一作者：Chen, Caiyan

通信作者：Jing, LH[1]

机构：[1]Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing, Peoples R China;[2]Int Res Ctr Big Data Sustainable Dev Goals, Beijing, Peoples R China;[3]Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing, Peoples R China;[4]Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang, Peoples R China;[5]Jiangxi Normal Univ, Sch Geog & Environm, Nanchang, Peoples R China;[6]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing, Peoples R China;[7]9 Dengzhuang South Rd, Beijing, Peoples R China

年份：2024

卷号：17

期号：1

外文期刊名：INTERNATIONAL JOURNAL OF DIGITAL EARTH

收录：;EI(收录号：20240615503855);Scopus(收录号：2-s2.0-85183881165);WOS:【SCI-EXPANDED(收录号:WOS:001154183500001)】；

基金：This work was supported by the National Key R & D Program of China under Grant [number 2021YFB3900503]; the National Natural Science Foundation of China under Grant [number 41972308]; the Jiangxi Provincial Technology Innovation Guidance Program (National Science and Technology Award Reserve Project Cultivation Program)under Grant [number 20212AEI91006]; and the Second Tibetan Plateau Scientific Expedition and Research underGrant [number 2019QZKK0806].

语种：英文

外文关键词：Remote sensing; individual tree species classification; time-series imagery; deep learning

摘要：Classification of individual tree species (ITS) is critical for fine-scale forest surveys. However, it is difficult to obtain the complete and high-precision data needed for ITS classification in large areas. Lower spatial resolution time-series imagery is more accessible than other types of imagery and contains rich phenological information. In this study, after delineating individual tree crowns using a 0.2-m unmanned aerial vehicle (UAV) image, we used 3-m time-series imagery and a new 3DLSTM model to identify ITS at the Gaofeng Forest Farm in Guangxi Province, China. The 3DLSTM ITS classification model combines three-dimensional convolutional neural network (3D CNN) and long short-term memory (LSTM) models; thus, spatial, multiband, and time-series information can be extracted simultaneously to identify ITS more accurately. In this study, when only 3-m Planet time-series imagery was used for classification, the 3DLSTM model offered an ITS classification accuracy of 92.68%, outperforming two ITS classifiers (DenseNet or AlexNet model) based on one individual image. Moreover, the 3DLSTM model was better at identifying broad-leaved tree species than other deep-learning models. The experimental results proved that ITS classification could be significantly improved using only 3DLSTM and time-series images, offering the possibility of classifying large-scale ITS at a low cost.