Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data

Peilin Song; Xiaomei Zheng; Yingying Li; Kangyu Zhang; Jingfeng Huang; Hongmei Li; Huijuan Zhang; Li Liu; Chuanwen Wei; Lamin R Mansaray; Duzhi Wang; Xiumei Wang

doi:10.1016/j.scitotenv.2020.137519

Estimating reed loss caused by Locusta migratoria manilensis using UAV-based hyperspectral data

Sci Total Environ. 2020 Jun 1:719:137519. doi: 10.1016/j.scitotenv.2020.137519. Epub 2020 Feb 22.

Authors

Peilin Song¹, Xiaomei Zheng², Yingying Li³, Kangyu Zhang⁴, Jingfeng Huang⁵, Hongmei Li⁶, Huijuan Zhang⁷, Li Liu⁸, Chuanwen Wei⁹, Lamin R Mansaray¹⁰, Duzhi Wang¹¹, Xiumei Wang¹²

Affiliations

¹ Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, The Chinese Academy of Sciences, Beijing 100101, China; Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China. Electronic address: songpl@igsnrr.ac.cn.
² Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China. Electronic address: 21614120@zju.edu.cn.
³ Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China. Electronic address: lyy0102@zju.edu.cn.
⁴ Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China; Nanjing University of Information Science and Technology. Electronic address: zhangkangyu@nuist.edu.cn.
⁵ Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China. Electronic address: hjf@zju.edu.cn.
⁶ Chinese Ministry of Agriculture - CABI Joint Laboratory for Biosafety, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China; CABI, Zhongguancun Nandajie 12, Internal Post Box 56, Beijing 10080, China. Electronic address: h.li@cabi.org.
⁷ Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China. Electronic address: 21714120@zju.edu.cn.
⁸ Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China. Electronic address: ll21714122@outlook.com.
⁹ Institute of Applied Remote Sensing and Information Technology, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Agricultural Remote Sensing and Information Systems, Zhejiang University, Hangzhou 310058, China; Chinese Academy of Meteorological Sciences. Electronic address: weicw@cma.gov.cn.
¹⁰ Department of Agro-meteorology and Geo-informatics, Magbosi Land, Water and Environment Research Centre (MLWERC), Sierra Leone Agricultural Research Institute (SLARI), Freetown PMB 1313, Sierra Leone.
¹¹ Dongying Kenli District Agriculture Bureau, Kenli 257500, China.
¹² Department of Environmental Science and Engineering, School of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China.

PMID: 32120114
DOI: 10.1016/j.scitotenv.2020.137519

Abstract

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 vehicle (UAV) over a simulated Locusta migratoria manilensis damage experiment on a reed (Phragmites australis) 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 SD_r) 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/m² and 26.1 g/m² 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.

Keywords: Hyperspectral measurement; Locust damage monitoring; Loss estimation model; Reed; Unmanned aerial vehicle (UAV).

MeSH terms

Animals
China
Crops, Agricultural
Locusta migratoria*
Plant Leaves
Poaceae