Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data
文献类型：期刊论文
| 作者 | Song, Peilin3,4,5; Zheng, Xiaomei3,5; Li, Yingying3,5; Zhang, Kangyu3,5,6; Huang, Jingfeng3,5; Li, Hongmei8,9; Zhang, Huijuan3,5; Liu, Li3,5; Wei, Chuanwen3,5,10; Mansaray, Lamin R.2 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2020-06-01 |
| 卷号 | 719页码:13 |
| 关键词 | Locust damage monitoring Loss estimation model Unmanned aerial vehicle (UAV) Hypeispectral measurement Reed |
| ISSN号 | 0048-9697 |
| DOI | 10.1016/j.scitotenv.2020.137519 |
| 通讯作者 | Huang, Jingfeng(hjf@zju.edu.cn) |
| 英文摘要 | Locusta migratoria manilensis has caused major damage to vegetation and crops. Quantitative evaluation studies of vegetation loss estimation from locust damage have seldom been found in traditional satellite-remote-sensing-based research due to insufficient temporal-spatial resolution available from most current satellite-based observations. We used remote sensing data acquired from an unmanned aerial vehide (UAV) over a sim-ulated Locusta migratoria manilensis damage experiment on a reed (Phragmites =strolls) canopy in Kenli District, China during July 2017. The experiment was conducted on 72 reed plots, and included three damage duration treatments with each treatment including six locust density levels. To establish the appropriate loss estimation models after locust damage, a hyperspectral imager was mounted on a UAV to collect reed canopy spectra. Loss components of six vegetation indices (RVI, NDVI, SAVI, MSAVI, GNDVI, and IPVI) and two "red edge" parameters (D-r and SDr) were used for constructing the loss estimation models. Results showed that: (1) Among the six selected vegetation indices, loss components of NDVI, MSAVI, and GNDVI were more sensitive to the variation of dry weight loss of reed green leaves and produced smaller estimation errors during the model test process, with RMSEs ranging from 8.8 to 9.1 g/m;. (2) Corresponding model test results based on loss components of the two selected red edge parameters yielded RMSEs of 27.5 g/m(2) and 26.1 g/m(2) for D r and SD r respectively, suggesting an inferior performance of red edge parameters compared with vegetation indices for reed loss estimation. These results demonstrate the great potential of UAV-based loss estimation models for evaluating and quantifying degree of locust damage in an efficient and quantitative manner. The methodology has promise for being transferred to satellite remote sensing data in the future for better monitoring of locust damage of larger geographical areas. (C) 2020 Elsevier B.V. All rights reserved. |
| WOS关键词 | CROP SURFACE MODELS ; WINTER-WHEAT BIOMASS ; VEGETATION INDEXES ; RED EDGE ; REFLECTANCE ; MONITOR ; IMAGES ; YIELD ; BAND ; PLAGUE |
| 资助项目 | National Natural Science Foundation of China[61661136004] ; National Natural Science Foundation of China[41471277] ; external cooperation program of Bureau of International Cooperation, Chinese Academy of Sciences[131211KYSB20150034] ; STFC Newton Agritech Programme[ST/N006712/1] |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000521936300055 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; external cooperation program of Bureau of International Cooperation, Chinese Academy of Sciences ; STFC Newton Agritech Programme |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/133384]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Huang, Jingfeng |
| 作者单位 | 1.Inner Mongolia Univ Technol, Sch Energy & Power Engn, Dept Environm Sci & Engn, Hohhot 010051, Peoples R China 2.SLARI, MLWERC, Dept Agrometeorol & Geoinformat, PMB 1313, Freetown, Sierra Leone 3.Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 5.Zhejiang Univ, Key Lab Agr Remote Sensing & Informat Syst, Hangzhou 310058, Peoples R China 6.Nanjing Univ Informat Sci & Technol, Nanjing, Jiangsu, Peoples R China 7.Dongying Kenli Dist Agr Bur, Kenli 257500, Peoples R China 8.Chinese Acad Agr Sci, Inst Plant Protect, Chinese Minist Agr CABI Joint Lab Biosafety, Beijing 100193, Peoples R China 9.CABI, Nandajie 12 Internal Post Box 56, Beijing 10080, Peoples R China 10.Chinese Acad Meteorol Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Song, Peilin,Zheng, Xiaomei,Li, Yingying,et al. Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2020,719:13. |
| APA | Song, Peilin.,Zheng, Xiaomei.,Li, Yingying.,Zhang, Kangyu.,Huang, Jingfeng.,...&Wang, Xiumei.(2020).Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data.SCIENCE OF THE TOTAL ENVIRONMENT,719,13. |
| MLA | Song, Peilin,et al."Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data".SCIENCE OF THE TOTAL ENVIRONMENT 719(2020):13. |
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。