基于国产静止高分卫星GF4-MSS数据的浒苔灾害监测研究

doi:10.14088/j.cnki.issn0439-8114.2022.23.038

湖北农业科学 ›› 2022, Vol. 61 ›› Issue (23): 190-196.doi: 10.14088/j.cnki.issn0439-8114.2022.23.038

基于国产静止高分卫星GF4-MSS数据的浒苔灾害监测研究

董京铭¹, 石轩硕², 张银意¹, 郝玲¹, 马晨晨¹

1.连云港市气象局,江苏连云港 222000;
2.南京信息工程大学海洋科学学院,南京 210044

收稿日期:2022-02-16 出版日期:2022-12-10 发布日期:2023-01-27
通讯作者: 张银意（1971-）,男,江苏泰州人,高级工程师,主要从事专业气象服务研究,（电子信箱）13812337096@139.com。
作者简介:董京铭（1989-）,男,江苏连云港人,工程师,硕士,主要从事应用气象研究,（电话）18961341686（电子信箱）792156709@qq.com。
基金资助:
江苏省气象局面上项目（KM202210）; 连云港市市局自立课题（LG202001）

Study on Ulva prolifera disaster monitoring based on domestic geostationary satellite GF4-MSS data

DONG Jing-ming¹, SHI Xuan-shuo², ZHANG Yin-yi¹, HAO ling¹, MA Chen-chen¹

1. Lianyungang Meteorological Bureau, Lianyungang 222000,Jiangsu,China;
2. School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2022-02-16 Online:2022-12-10 Published:2023-01-27

摘要/Abstract

摘要： 基于国产静止卫星高分四号（GF4）MSS（Multi Spectral Scanner）传感器大气层顶反射率数据,采用缨帽变换得到的绿度指数实现了对中国近海浒苔（Ulva prolifera）灾害的准确监测。该算法无需大气校正和云掩膜环节,容易操作实施,同时可有效排除云像素干扰。将绿度指数应用到2019年多幅GF4-MSS遥感图像上,有效分析了浒苔范围的动态变化,为国产高分卫星系列监测浒苔灾害提供了新的技术支撑,同时推广国产光学卫星资料的使用率。

关键词: 浒苔（Ulva prolifera）, 灾害监测, 高分四号, 绿度指数

Abstract: Based on the top-of-atmosphere reflectance of the domestic geostationary satellite GF4-MSS （Multi-spectral Scanner） sensor, the greenness index obtained by tasseled cap transformation analysis was used to realize the accurate monitoring of Ulva prolifera disaster. The algorithm did not need atmospheric correction and cloud mask, which was easy to operate and implement, and could effectively eliminate cloud pixel interference. The greenness index was applied to multiple GF4-MSS remote sensing images in 2019 to effectively analyze the dynamic changes of Ulva prolifera bloom range, which provided new technical support for the monitoring of Ulva prolifera disasters by domestic high-resolution satellite series, and promoted the utilization rate of domestic optical satellite data.

Key words: Ulva prolifera, disaster monitoring, satellite Gaofen 4, greenness index

中图分类号:

董京铭, 石轩硕, 张银意, 郝玲, 马晨晨. 基于国产静止高分卫星GF4-MSS数据的浒苔灾害监测研究[J]. 湖北农业科学, 2022, 61(23): 190-196.

DONG Jing-ming, SHI Xuan-shuo, ZHANG Yin-yi, HAO ling, MA Chen-chen. Study on Ulva prolifera disaster monitoring based on domestic geostationary satellite GF4-MSS data[J]. HUBEI AGRICULTURAL SCIENCES, 2022, 61(23): 190-196.

参考文献

[1] XING Q, GUO R, WU L, et al.High-resolution satellite observations of a new hazard of golden tides caused by floating Sargassum in winter in the Yellow Sea[J]. IEEE geoscience and remote sensing letters, 2017, 14(10): 1815-1819.
[2] 邱亚会, 卢剑波. 浒苔遥感监测研究进展[J]. 生态学报, 2015, 35(15): 4977-4985.
[3] QI L,HU C,WANG M, et al.Floating algae blooms in the East China Sea[J]. Geophysical research letters,2017,44:11501-11509.
[4] 周健, 孙福新, 高翔, 等. 浒苔绿潮灾害经济损失评估模型初探[J]. 自然灾害学报, 2018, 27(2): 184-190.
[5] 张海龙,孙德勇,李俊生, 等. 基于GF1-WFV和 HJ-CCD数据的我国近海绿潮遥感监测算法研究[J]. 光学学报,2016,36(6):36-44.
[6] 张广宗, 吴孟泉, 孙晓, 等. 南黄海浒苔漂移轨迹年际变化规律及驱动因素[J]. 海洋与湖沼, 2018, 49(5): 1084-1093.
[7] HU C.A novel ocean color index to detect floating algae in the global oceans[J]. Remote sensing of environment, 2009, 113(10): 2118-2129.
[8] SHI W, WANG M.Green macroalgae blooms in the Yellow Sea during the spring and summer of 2008[J]. Journal of geophysical research, 2009, 114: 1-10.
[9] XING Q, HU C.Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique[J]. Remote sensing of environment, 2016, 178: 113-126.
[10] SON Y B, MIN J E, RYU J H.Detecting massive green algae (Ulva prolifera) blooms in the Yellow Sea and East China Sea using Geostationary Ocean Color Imager (GOCI) data[J]. Ocean science journal, 2012, 47(3): 359-375.
[11] 赵尊强, 陈鑫, 刘慧芳, 等. 基于改进FCN网络的GOCI绿潮分类研究[J]. 软件导刊, 2020, 19(3): 75-79.
[12] QIU Z, LI Z, BILAL M, et al.Automatic method to monitor floating macroalgae blooms based on multilayer perceptron:Case study of Yellow Sea using GOCI images[J]. Optics express,2018, 26(21): 26810-26829.
[13] XIAO Y F, LIU R J, KIM K, et al.A random forest-based algorithm to distinguish Ulva prolifera and Sargassum from multispectral satellite images[J]. IEEE transactions on geoscience and remote sensing, 2021, 60: 1-15.
[14] 陈莹, 孙德勇, 张海龙, 等. 结合GOCI数据的黄海绿潮遥感监测及漂移轨迹研究[J]. 光学学报, 2020, 40(3): 7-19.
[15] ZHANG H, YUAN Y, XU Y, et al.Remote sensing method for detecting green tide using HJ-CCD top-of-atmosphere reflectance[J]. International journal of applied earth observation and geoinformation, 2021, 102: 102371.
[16] 万剑华, 苏婧, 盛辉, 等. 利用静止轨道卫星开展绿潮业务化监测的可行性研究[J]. 激光生物学报,2018,27(2):155-160.
[17] 朱大奎. 江苏海洋环境与沿海经济发展[J]. 水资源保护,2003(6):19-21.
[18] 赵英时. 遥感应用分析原理与方法[M]. 北京: 科学出版社, 2003.
[19] HU C, HE M X.Origin and offshore extent of floating algae in olympic sailing area[J]. Eos, transactions American geophysical union, 2008, 89(33): 302-303.
[20] KAUTH R J, THOMAS G S.The tasselled cap——a graphic description of the spectral-temporal development of agricultural crops as seen by landsat[A].Proceedings, symposium on machine processing of remotely sensed data[C]. West Lafayette:Purdue University,1976. 41-51.
[21] ZHANG H, QIU Z, DEVRED E, et al.A simple and effective method for monitoring floating green macroalgae blooms: A case study in the Yellow Sea[J]. Optics express, 2019, 27(4): 4528-4548.

基于国产静止高分卫星GF4-MSS数据的浒苔灾害监测研究

Study on Ulva prolifera disaster monitoring based on domestic geostationary satellite GF4-MSS data

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价