{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T08:42:33Z","timestamp":1770540153448,"version":"3.49.0"},"reference-count":89,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R &amp; D Program of China","award":["2022YFB3903604"],"award-info":[{"award-number":["2022YFB3903604"]}]},{"name":"National Key R &amp; D Program of China","award":["2-9-2021-045"],"award-info":[{"award-number":["2-9-2021-045"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2022YFB3903604"],"award-info":[{"award-number":["2022YFB3903604"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2-9-2021-045"],"award-info":[{"award-number":["2-9-2021-045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Tree species classification based on multi-source remote sensing data is essential for ecological evaluation, environmental monitoring, and forest management. The optimization of classification features and the performance of classification methods are crucial to tree species classification. This paper proposes Angle-weighted Standard Deviation Elliptic Cross-merge Rate (ASDER) as a separability metric for feature optimization. ASDER uses mutual information to represent the separability metric and avoids the difficulty of differentiation caused by multiple ellipse centers and coordinate origins forming straight lines by angle weighting. In classification method, Multi-head Self-attention Long Short-Term Memory\u2014Full Convolution Network (MALSTM-FCN) is constructed in this paper. MALSTM-FCN enhances the global correlation in time series and improves classification accuracy through a multi-head self-attention mechanism. This paper takes Beijing Olympic Forest Park (after this, referred to as Aosen) as the research area, constructs a tree species classification dataset based on an actual ground survey, and obtains a classification accuracy of 95.20% using the above method. This paper demonstrates the effectiveness of ASDER and MALSTM-FCN by comparing temporal entropy and LSTM-FCN and shows that the method has some practicality for tree species classification.<\/jats:p>","DOI":"10.3390\/rs15071723","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T05:19:24Z","timestamp":1679548764000},"page":"1723","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tree Species Classification Based on ASDER and MALSTM-FCN"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5337-2216","authenticated-orcid":false,"given":"Hongjian","family":"Luo","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-7399","authenticated-orcid":false,"given":"Dongping","family":"Ming","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Lu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]},{"given":"Xiao","family":"Ling","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1080\/01431161.2019.1672904","article-title":"Landslide mapping with remote sensing: Challenges and opportunities","volume":"41","author":"Zhong","year":"2020","journal-title":"Int. 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