A Novel Desert Vegetation Extraction and Shadow Separation Method Based on Visible Light Images from Unmanned Aerial Vehicles
文献类型：期刊论文
| 作者 | Lu, Yuefeng4,5; Song, Zhenqi; Li, Yuqing; An, Zhichao; Zhao, Lan; Zan, Guosheng3; Lu, Miao2 |
| 刊名 | SUSTAINABILITY
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 15期号:4 |
| ISSN号 | 2071-1050 |
| 关键词 | land desertification UAV visible remote sensing imagery vegetation index shadow texture color space |
| DOI | 10.3390/su15042954 |
| 文献子类 | Article |
| 英文摘要 | Owing to factors such as climate change and human activities, ecological and environmental problems of land desertification have emerged in many regions around the world, among which the problem of land desertification in northwestern China is particularly serious. To grasp the trend of land desertification and the degree of natural vegetation degradation in northwest China is a basic prerequisite for managing the fragile ecological environment there. Visible light remote sensing images taken by a UAV can monitor the vegetation cover in desert areas on a large scale and with high time efficiency. However, as there are many low shrubs in desert areas, the shadows cast by them are darker, and the traditional RGB color-space-based vegetation index is affected by the shadow texture when extracting vegetation, so it is difficult to achieve high accuracy. For this reason, this paper proposes the Lab color-space-based vegetation index L2AVI (L-a-a vegetation index) to solve this problem. The EXG (excess green index), NGRDI (normalized green-red difference index), VDVI (visible band difference vegetation index), MGRVI (modified green-red vegetation index), and RGBVI (red-green-blue vegetation index) constructed based on RGB color space were used as control experiments in the three selected study areas. The results show that, although the extraction accuracies of the vegetation indices constructed based on RGB color space all reach more than 70%, these vegetation indices are all affected by the shadow texture to different degrees, and there are many problems of misdetection and omission. However, the accuracy of the L2AVI index can reach 99.20%, 99.73%, and 99.69%, respectively, avoiding the problem of omission due to vegetation shading and having a high extraction accuracy. Therefore, the L2AVI index can provide technical support and a decision basis for the protection and control of land desertification in northwest China. |
| WOS关键词 | IDENTIFICATION ; INDEXES |
| WOS研究方向 | Science & Technology - Other Topics ; Environmental Sciences & Ecology |
| 出版者 | MDPI |
| WOS记录号 | WOS:000942058800001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190319]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China 2.Natl Forestry & Grassland Adm, Acad Forestry Inventory & Planning, Beijing, Peoples R China 3.Hunan Univ Sci & Technol, Hunan Prov Key Lab Geoinformat Engn Surveying Mapp, Xiangtan 411201, 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.Shandong Univ Technol, Sch Civil & Architectural Engn, Zibo 255049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Lu, Yuefeng,Song, Zhenqi,Li, Yuqing,et al. A Novel Desert Vegetation Extraction and Shadow Separation Method Based on Visible Light Images from Unmanned Aerial Vehicles[J]. SUSTAINABILITY,2023,15(4). |
| APA | Lu, Yuefeng.,Song, Zhenqi.,Li, Yuqing.,An, Zhichao.,Zhao, Lan.,...&Lu, Miao.(2023).A Novel Desert Vegetation Extraction and Shadow Separation Method Based on Visible Light Images from Unmanned Aerial Vehicles.SUSTAINABILITY,15(4). |
| MLA | Lu, Yuefeng,et al."A Novel Desert Vegetation Extraction and Shadow Separation Method Based on Visible Light Images from Unmanned Aerial Vehicles".SUSTAINABILITY 15.4(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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