{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:35:11Z","timestamp":1776274511856,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences","award":["Y951150Z2F"],"award-info":[{"award-number":["Y951150Z2F"]}]},{"name":"Science and Technology Major Project of Xinjiang Uygur Autonomous Region","award":["2018A03004"],"award-info":[{"award-number":["2018A03004"]}]},{"name":"National Key R&amp;D Program of China","award":["2019YFC1520800"],"award-info":[{"award-number":["2019YFC1520800"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19030501"],"award-info":[{"award-number":["XDA19030501"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41972308"],"award-info":[{"award-number":["41972308"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.<\/jats:p>","DOI":"10.3390\/rs13010144","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T21:58:36Z","timestamp":1609797516000},"page":"144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Tree Species Classification of Forest Stands Using Multisource Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Haoming","family":"Wan","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2257-8749","authenticated-orcid":false,"given":"Yunwei","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Linhai","family":"Jing","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3565-1773","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3999-1174","authenticated-orcid":false,"given":"Fang","family":"Qiu","sequence":"additional","affiliation":[{"name":"Geospatial Information Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX 75080, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9735-7265","authenticated-orcid":false,"given":"Wenjin","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2020.10.015","article-title":"Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks","volume":"170","author":"Schiefer","year":"2020","journal-title":"ISPRS J. 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