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(STEP)","award":["42071312"],"award-info":[{"award-number":["42071312"]}]},{"name":"Tibetan Plateau Scientific Expedition and Research (STEP)","award":["42171291"],"award-info":[{"award-number":["42171291"]}]},{"name":"Tibetan Plateau Scientific Expedition and Research (STEP)","award":["41972308"],"award-info":[{"award-number":["41972308"]}]},{"name":"Tibetan Plateau Scientific Expedition and Research (STEP)","award":["2019QZKK0806"],"award-info":[{"award-number":["2019QZKK0806"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and efficient individual tree species (ITS) classification is the basis of fine forest resource management. It is a challenge to classify individual tree species in dense forests using remote sensing imagery. In order to solve this problem, a new ITS classification method was proposed in this study, in which a hierarchical convolutional neural network (H-CNN) model and multi-temporal high-resolution Google Earth images were employed. In an experiment conducted in a forest park in Beijing, China, GE images of several significant phenological phases of broad-leaved forests, namely, before and after the mushrooming period, the growth period, and the wilting period, were selected, and ITS classifications based on these images along with several typical CNN models and the H-CNN model were conducted. In the experiment, the classification accuracy of the multitemporal images was higher by 7.08\u201312.09% than those of the single-temporal images, and the H-CNN model offered an OA accuracy 2.66\u20133.72% higher than individual CNN models, demonstrating that multitemporal images rich in the phenological features of individual tree species, together with a hierarchical CNN model, can effectively improve ITS classification.<\/jats:p>","DOI":"10.3390\/rs14205124","type":"journal-article","created":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T01:44:13Z","timestamp":1665711853000},"page":"5124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Individual Tree Species Classification Based on a Hierarchical Convolutional Neural Network and Multitemporal Google Earth Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhonglu","family":"Lei","sequence":"first","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3565-1773","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Resources, China University of Geosciences, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhai","family":"Jing","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2257-8749","authenticated-orcid":false,"given":"Yunwei","family":"Tang","sequence":"additional","affiliation":[{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongkun","family":"Wang","sequence":"additional","affiliation":[{"name":"Xueqin College, Chinese University of Hong Kong, Shenzhen 518172, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107744","DOI":"10.1016\/j.agrformet.2019.107744","article-title":"Tree species classification in a temperate mixed forest using a combination of imaging spectroscopy and airborne laser scanning","volume":"279","author":"Torabzadeh","year":"2019","journal-title":"Agric. 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