{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:12:41Z","timestamp":1774379561974,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T00:00:00Z","timestamp":1700092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42330507"],"award-info":[{"award-number":["42330507"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101473"],"award-info":[{"award-number":["42101473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071342"],"award-info":[{"award-number":["42071342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["8222052"],"award-info":[{"award-number":["8222052"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["42330507"],"award-info":[{"award-number":["42330507"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["42101473"],"award-info":[{"award-number":["42101473"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["42071342"],"award-info":[{"award-number":["42071342"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["8222052"],"award-info":[{"award-number":["8222052"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Chestnut trees hold a prominent position in China as an economically significant forest species, offering both high economic value and ecological advantages. Identifying the distribution of chestnut forests is of paramount importance for enhancing efficient management practices. Presently, many studies are employing remote sensing imaging methods to monitor tree species. However, in comparison to the common classification of land cover types, the accuracy of tree species identification is relatively lower. This study focuses on accurately mapping the distribution of planted chestnut forests in China, particularly in the Huairou and Miyun regions, which are the main producing areas for Yanshan chestnuts in northeastern Beijing. We utilized the Google Earth Engine (GEE) cloud platform and Sentinel-2 satellite imagery to develop a method based on vegetation phenological features. This method involved identifying three distinct phenological periods of chestnut trees: flowering, fruiting, and dormancy, and extracting relevant spectral, vegetation, and terrain features. With these features, we further established and compared three machine learning algorithms for chestnut species identification: random forest (RF), decision tree (DT), and support vector machine (SVM). Our results indicated that the recognition accuracy of these algorithms ranked in descending order as RF &gt; DT &gt; SVM. We found that combining multiple phenological characteristics significantly improved the accuracy of chestnut forest distribution identification. Using the random forest algorithm and Sentinel-2 phenological features, we achieved an impressive overall accuracy (OA) of 98.78%, a Kappa coefficient of 0.9851, and a user\u2019s accuracy (UA) and producer\u2019s accuracy (PA) of 97.25% and 98.75%, respectively, for chestnut identification. When compared to field surveys and official area statistics, our method exhibited an accuracy rate of 89.59%. The implementation of this method not only offers crucial data support for soil erosion prevention and control studies in Beijing but also serves as a valuable reference for future research endeavors in this field.<\/jats:p>","DOI":"10.3390\/rs15225374","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T01:56:29Z","timestamp":1700099789000},"page":"5374","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Method of Chestnut Forest Identification Based on Time Series and Key Phenology from Sentinel-2"],"prefix":"10.3390","volume":"15","author":[{"given":"Nina","family":"Xiong","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Precise Forestry, Beijing Forestry University, No. 35, Qinghua East Road, Haidian District, Beijing 100083, China"},{"name":"Institute of GIS, RS & GNSS, Beijing Forestry University, No. 35, Qinghua East Road, Haidian District, Beijing 100083, China"},{"name":"Management Research Department, Beijing Municipal Institute of City Management, Jia 48# Shangjialou, Chaoyang District, Beijing 100028, China"},{"name":"Beijing Key Laboratory of Municipal Solid Wastes Testing Analysis and Evaluation, Beijing Municipal Institute of City Management, Jia 48# Shangjialou, Chaoyang District, Beijing 100028, China"}]},{"given":"Hailong","family":"Chen","sequence":"additional","affiliation":[{"name":"PIESAT Information Technology Co., Ltd., Beijing 100195, China"}]},{"given":"Ruiping","family":"Li","sequence":"additional","affiliation":[{"name":"PIESAT Information Technology Co., Ltd., Beijing 100195, China"}]},{"given":"Huimin","family":"Su","sequence":"additional","affiliation":[{"name":"PIESAT Information Technology Co., Ltd., Beijing 100195, China"}]},{"given":"Shouzheng","family":"Dai","sequence":"additional","affiliation":[{"name":"PIESAT Information Technology Co., Ltd., Beijing 100195, China"}]},{"given":"Jia","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Precise Forestry, Beijing Forestry University, No. 35, Qinghua East Road, Haidian District, Beijing 100083, China"},{"name":"Institute of GIS, RS & GNSS, Beijing Forestry University, No. 35, Qinghua East Road, Haidian District, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"ref_1","first-page":"83","article-title":"Current situation and trends of Chinese chestnut industry and market development","volume":"4","author":"Han","year":"2021","journal-title":"China Fruits"},{"key":"ref_2","first-page":"1226","article-title":"Extraction of chestnut forest distribution based on multitemporal remote sensing observations","volume":"35","author":"Chen","year":"2020","journal-title":"Remote Sens. 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