Mapping Forest Types in China with 10 m Resolution Based on Spectral-Spatial-Temporal Features
文献类型：期刊论文
| 作者 | Cheng, Kai4; Wang, Juanle1,2,3,4 ; Yan, Xinrong2,4
|
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2021-03-01 |
| 卷号 | 13期号:5页码:21 |
| 关键词 | forest type spectral– spatial– temporal features image composition random forest algorithm China |
| DOI | 10.3390/rs13050973 |
| 通讯作者 | Wang, Juanle(wangjl@igsnrr.ac.cn) |
| 英文摘要 | The comprehensive application of spectral, spatial, and temporal (SST) features derived from remote sensing images is a significant technique for classifying and mapping forest types. Facing limitations in the availability of detailed forest type identification processes for large regions, a forest type classification framework based on SST features was developed in this study. The advantages of Sentinel-2 and Landsat series imagery were used to extract SST forest type classification features, using red-edge bands, a gray-level co-occurrence matrix, and harmonic analysis, with the assistance of the Google Earth Engine platform. Considering four representative Chinese geographic regions-middle and high latitudes, complex mountainous areas, cloudy and rainy areas, and the N-S climate transition zone-our method was proven to be effective, with overall classification accuracies > 85%. The scheme to assess the importance of SST features for forest classification in various regions was designed using the Gini criterion in the random forest algorithm and revealed that spectral features were more effective in classifying forest types with complex compositions. Temporal features were found to be favorable for identifying forest types with obvious evergreen and deciduous growth patterns, while spatial features produced better classification results for forest types with different spatial structures, such as needle- or broad-leaved forests. The findings of this study can provide a reference for feature selection in remote sensing forest type classification processes, and identifying forest types in this way could provide support for the accurate and sustainable management of forest resources. |
| 资助项目 | Strategic Priority Research Program (class A) of the Chinese Academy of Sciences[XDA19040501] ; 13th Five-Year Informatization Plan of the Chinese Academy of Sciences[XXH13505-07] ; Construction Project of the China Knowledge Center for Engineering Sciences and Technology[CKCEST-2020-2-4] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000628513300001 |
| 出版者 | MDPI |
| 资助机构 | Strategic Priority Research Program (class A) of the Chinese Academy of Sciences ; 13th Five-Year Informatization Plan of the Chinese Academy of Sciences ; Construction Project of the China Knowledge Center for Engineering Sciences and Technology |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/162112]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Juanle |
| 作者单位 | 1.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.China Pakistan Earth Sci Res Ctr, Islamabad 45320, Pakistan 4.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cheng, Kai,Wang, Juanle,Yan, Xinrong. Mapping Forest Types in China with 10 m Resolution Based on Spectral-Spatial-Temporal Features[J]. REMOTE SENSING,2021,13(5):21. |
| APA | Cheng, Kai,Wang, Juanle,&Yan, Xinrong.(2021).Mapping Forest Types in China with 10 m Resolution Based on Spectral-Spatial-Temporal Features.REMOTE SENSING,13(5),21. |
| MLA | Cheng, Kai,et al."Mapping Forest Types in China with 10 m Resolution Based on Spectral-Spatial-Temporal Features".REMOTE SENSING 13.5(2021):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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