{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:33:15Z","timestamp":1775147595186,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T00:00:00Z","timestamp":1611878400000},"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":"the Science and Technology Major Project of Xinjiang Uygur Autonomous Region","award":["No.2018A03004"],"award-info":[{"award-number":["No.2018A03004"]}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["No. XDA19030501"],"award-info":[{"award-number":["No. XDA19030501"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41972308"],"award-info":[{"award-number":["No. 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>Tree species surveys are crucial to forest resource management and can provide references for forest protection policy making. The traditional tree species survey in the field is labor-intensive and time-consuming, supporting the practical significance of remote sensing. The availability of high-resolution satellite remote sensing data enable individual tree species (ITS) recognition at low cost. In this study, the potential of the combination of such images and a convolutional neural network (CNN) to recognize ITS was explored. Firstly, individual tree crowns were delineated from a high-spatial resolution WorldView-3 (WV3) image and manually labeled as different tree species. Next, a dataset of the image subsets of the labeled individual tree crowns was built, and several CNN models were trained based on the dataset for ITS recognition. The models were then applied to the WV3 image. The results show that the distribution maps of six ITS offered an overall accuracy of 82.7% and a kappa coefficient of 0.79 based on the modified GoogLeNet, which used the multi-scale convolution kernel to extract features of the tree crown samples and was modified for small-scale samples. The ITS recognition method proposed in this study, with multi-scale individual tree crown delineation, avoids artificial tree crown delineation. Compared with the random forest (RF) and support vector machine (SVM) approaches, this method can automatically extract features and outperform RF and SVM in the classification of six tree species.<\/jats:p>","DOI":"10.3390\/rs13030479","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T09:25:22Z","timestamp":1611912322000},"page":"479","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["A New Individual Tree Species Recognition Method Based on a Convolutional Neural Network and High-Spatial Resolution Remote Sensing Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9921-9621","authenticated-orcid":false,"given":"Shijie","family":"Yan","sequence":"first","affiliation":[{"name":"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"}]},{"given":"Huan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Peking University, Beijing 100871, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1080\/10106049.2016.1178814","article-title":"Carbon sinks and tropical forest biomass estimation: A review on role of remote sensing in aboveground-biomass modelling","volume":"32","author":"Zaki","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bravo, F., LeMay, V., and Jandl, R. 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