Enhancing vegetation formation classification: Integrating coarse-scale traditional mapping knowledge and advanced machine learning
文献类型：期刊论文
| 作者 | Zhang, Tao5; Li, Baolin3,4; Yuan, Yecheng4; Gao, Xizhang4; Zhou, Ji5; Jiang, Yuhao2; Xu, Jie2; Zhou, Yuyu1 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 923页码:171477 |
| 关键词 | Predictive vegetation mapping Remote sensing Environmental variable Upstream of the Yellow River |
| DOI | 10.1016/j.scitotenv.2024.171477 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Mapping vegetation formation types in large areas is crucial for ecological and environmental studies. However, this is still challenging to distinguish similar vegetation formation types using existing predictive vegetation mapping methods, based on commonly used environmental variables and remote sensing spectral data, especially when there are not enough training samples. To solve this issue, we proposed a predictive vegetation mapping method by integrating an advanced machine learning algorithm and knowledge in an early coarse -scale vegetation map (VMK). First, we implemented classification using the random forest algorithm by integrating the early vegetation map as an auxiliary feature (VMF). Then, we determined the rationality of classified vegetation types and distinguished the confusing types, respectively, based on the knowledge of the spatial distributions and hierarchies of vegetation. Finally, we replaced each recognized unreasonable vegetation type with its corresponding reasonable vegetation type. We implemented the new method in upstream of the Yellow River based on GaoFen-1 satellite images and other environmental variables (i.e., topographical and climate variables). Results showed that the overall accuracy using the VMK method ranged from 67.7 % to 76.8 %, which was 10.9 % to 13.4 % and 3.2 % to 6.6 %, respectively, higher than that of the method without the early vegetation map (NVM) and the VMF method, based on cross -validation with 20 % to 60 % random training samples. The spatial details of the vegetation map using the VMK method were also more reasonable compared to the NVM and VMF methods. These results indicated that the VMK method can distinctly improve the mapping accuracy at the vegetation formation level by integrating knowledge of existing vegetation maps. The proposed method can largely reduce the requirements on the number of field samples, which is especially important for alpine mountains and arctic region, where collecting training samples is more difficult due to the harsh natural environment. |
| WOS关键词 | LAND-USE ; IMAGERY ; MAP ; CROPLAND ; AREA |
| WOS研究方向 | Environmental Sciences & Ecology |
| WOS记录号 | WOS:001221942100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205212]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Li, Baolin |
| 作者单位 | 1.Univ Hong Kong, Dept Geog, Hong Kong 999077, Peoples R China 2.Natl Forestry & Grassland Adm, Acad Forest Inventory & Planning, Beijing 100714, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Tao,Li, Baolin,Yuan, Yecheng,et al. Enhancing vegetation formation classification: Integrating coarse-scale traditional mapping knowledge and advanced machine learning[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2024,923:171477. |
| APA | Zhang, Tao.,Li, Baolin.,Yuan, Yecheng.,Gao, Xizhang.,Zhou, Ji.,...&Zhou, Yuyu.(2024).Enhancing vegetation formation classification: Integrating coarse-scale traditional mapping knowledge and advanced machine learning.SCIENCE OF THE TOTAL ENVIRONMENT,923,171477. |
| MLA | Zhang, Tao,et al."Enhancing vegetation formation classification: Integrating coarse-scale traditional mapping knowledge and advanced machine learning".SCIENCE OF THE TOTAL ENVIRONMENT 923(2024):171477. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。