Winter remote sensing images are more suitable for forest mapping in Jiangxi Province
文献类型：期刊论文
| 作者 | Wang, Ruilin2; Wang, Meng2,3; Sun, Xiaofang2; Wang, Junbang1; Li, Guicai4 |
| 刊名 | EUROPEAN JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2023-12-31 |
| 卷号 | 56期号:1页码:13 |
| 关键词 | forest mapping seasonality Sentinel-2 classifiers Google Earth Engine > |
| DOI | 10.1080/22797254.2023.2237655 |
| 通讯作者 | Wang, Meng(wangmeng@qfnu.edu.cn) |
| 英文摘要 | Jiangxi Province boasts the second-highest forest coverage in China. Its forests play a crucial role in providing essential ecosystem services and maintaining the ecological health of the region. High-resolution and high-precision forest mapping are significant in the timely and accurate monitoring of dynamic forest changes to support sustainable forest management. This study used Sentinel-2 images from four seasons in the Google Earth Engine (GEE) platform to map forest distribution. Moreover, the classification results were compared and analyzed using different classification algorithms and feature-variable combinations. Based on the overall accuracy, the optimal image seasonality, feature combinations and classification algorithms were selected, and the forest maps of Jiangxi Province were mapped from 2019 to 2021. The accuracy evaluation showed that the winter image classification results had the highest accuracy (above 0.88). The red edge bands carried by Sentinel-2 could effectively improve the classification accuracy. The Random Forest classifier is the optimal classification algorithm for forest mapping in Jiangxi Province. The forest mapping obtained can be used for ecological health assessment and ecosystem function. The study provides a scientific basis for accurate and timely extraction of forest cover and can serve as a valuable resource for forest management planning and future research. |
| WOS关键词 | GOOGLE EARTH ENGINE ; LAND-COVER CLASSIFICATION ; TIME-SERIES ; ECOSYSTEM SERVICES ; SPATIAL-RESOLUTION ; MACHINE ; URBAN ; SENTINEL-2 ; ALGORITHMS ; MAP |
| 资助项目 | National Natural Science Foundation of China[42071373] ; Natural Science Foundation of Shandong Province, China[ZR2020MD021] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:001036759400001 |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Shandong Province, China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/195722]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Meng |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Natl Ecosyst Sci Data Ctr, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China 2.Qufu Normal Univ, Coll Geog & Tourism, Rizhao, Peoples R China 3.Qufu Normal Univ, Coll Geog & Tourism, Rizhao 276825, Peoples R China 4.China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Ruilin,Wang, Meng,Sun, Xiaofang,et al. Winter remote sensing images are more suitable for forest mapping in Jiangxi Province[J]. EUROPEAN JOURNAL OF REMOTE SENSING,2023,56(1):13. |
| APA | Wang, Ruilin,Wang, Meng,Sun, Xiaofang,Wang, Junbang,&Li, Guicai.(2023).Winter remote sensing images are more suitable for forest mapping in Jiangxi Province.EUROPEAN JOURNAL OF REMOTE SENSING,56(1),13. |
| MLA | Wang, Ruilin,et al."Winter remote sensing images are more suitable for forest mapping in Jiangxi Province".EUROPEAN JOURNAL OF REMOTE SENSING 56.1(2023):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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