Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt
文献类型：期刊论文
| 作者 | Zhang, Junyao1,2; Yao, Yonghui1; Suo, Nandongzhu1,2 |
| 刊名 | PLOS ONE
 |
| 出版日期 | 2020-08-25 |
| 卷号 | 15期号:8页码:25 |
| ISSN号 | 1932-6203 |
| DOI | 10.1371/journal.pone.0238165 |
| 通讯作者 | Yao, Yonghui(yaoyh@lreis.ac.cn) |
| 英文摘要 | Vegetation mapping is of considerable significance to both geoscience and mountain ecology, and the improved resolution of remote sensing images makes it possible to map vegetation at a finer scale. While the automatic classification of vegetation has gradually become a research hotspot, real-time and rapid collection of samples has become a bottleneck. How to achieve fine-scale classification and automatic sample selection at the same time needs further study. Stratified sampling based on appropriate prior knowledge is an effective sampling method for geospatial objects. Therefore, based on the idea of stratified sampling, this paper used the following three steps to realize the automatic selection of representative samples and classification of fine-scale mountain vegetation: 1) using Mountain Altitudinal Belt (MAB) distribution information to stratify the study area into multiple vegetation belts; 2) selecting and correcting samples through iterative clustering at each belt automatically; 3) using RF (Random Forest) classifier with strong robustness to achieve automatic classification. The average sample accuracy of nine vegetation formations was 0.933, and the total accuracy of the classification result was 92.2%, with the kappa coefficient of 0.910. The results showed that this method could automatically select high-quality samples and obtain a high-accuracy vegetation map. Compared with the traditional vegetation mapping method, this method greatly improved the efficiency, which is of great significance for the fine-scale mountain vegetation mapping in large-scale areas. |
| WOS关键词 | REMOTE-SENSING IMAGES ; LAND-COVER ; TAIBAI MOUNTAIN ; POPULATION-STRUCTURE ; RANDOM FOREST ; AREA ; IDENTIFICATION ; VALIDATION ; VARIANCE ; ACCURACY |
| 资助项目 | National Natural Science Foundation of China[41871350] ; National Natural Science Foundation of China[41571099] |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| WOS记录号 | WOS:000565553400015 |
| 出版者 | PUBLIC LIBRARY SCIENCE |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/157928]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yao, Yonghui |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Skate Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Junyao,Yao, Yonghui,Suo, Nandongzhu. Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt[J]. PLOS ONE,2020,15(8):25. |
| APA | Zhang, Junyao,Yao, Yonghui,&Suo, Nandongzhu.(2020).Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt.PLOS ONE,15(8),25. |
| MLA | Zhang, Junyao,et al."Automatic classification of fine-scale mountain vegetation based on mountain altitudinal belt".PLOS ONE 15.8(2020):25. |
入库方式： OAI收割
来源：地理科学与资源研究所
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