Sentinel-1A雷达遥感数据模型快速识别洪灾后血吸虫病传播潜在风险区研究

doi:10.12140/j.issn.1000-7423.2020.04.003

中国寄生虫学与寄生虫病杂志 ›› 2020, Vol. 38 ›› Issue (4): 417-422.doi: 10.12140/j.issn.1000-7423.2020.04.003

Sentinel-1A雷达遥感数据模型快速识别洪灾后血吸虫病传播潜在风险区研究

夏尚¹^,², 薛靖波¹^,², 高风华³, 吕山¹^,², 许静¹^,², 张世清³, 李石柱¹^,²^,^*()

1 中国疾病预防控制中心寄生虫病预防控制所,国家热带病研究中心,世界卫生组织热带病合作中心,科技部热带病国际联合研究中心,卫生部寄生虫病原与媒介生物学重点实验室,上海 200025
2 上海交通大学医学院-国家热带病研究中心全球健康学院,上海 200025
3 安徽省血吸虫病防治研究所,合肥 230061

收稿日期:2020-08-10 出版日期:2020-08-30 发布日期:2020-09-09
通讯作者: 李石柱
作者简介:夏尚（1985-）,男,博士,副研究员,主要从事流行病与卫生统计研究。E-mail: sxia@nipd.chinacdc.cn
基金资助:
上海市卫生健康委科技研究项目(20174Y0188);上海市卫生健康委科技研究项目(20194Y0359)

Sentinel-1A radar remote sensing-based modeling for quick identification of potential risk areas of schistosomiasis transmission after flood

XIA Shang¹^,², XUE Jing-bo¹^,², GAO Feng-hua³, LV Shan¹^,², XU Jing¹^,², ZHANG Shi-qing³, LI Shi-zhu¹^,²^,^*()

1 National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention; Chinese Center for Tropical Diseases Research; WHO Collaborating Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology; Key Laboratory of Parasite and Vector Biology, Ministry of Health
2 School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
3 Anhui Institute of Schisomiasis Control and Research, Hefei 230061, China

Received:2020-08-10 Online:2020-08-30 Published:2020-09-09
Contact: LI Shi-zhu
Supported by:
Research Projects of Shanghai Municipal Health Commission(20174Y0188);Research Projects of Shanghai Municipal Health Commission(20194Y0359)

摘要/Abstract

摘要：

目的运用雷达遥感影像数据提取水体信息的方法,快速有效地识别因洪水导致钉螺扩散的血吸虫病传播潜在风险区域。方法采集安徽省当涂县2020年5月15日汛前和7月16日洪水高峰期间两景哨兵一号（Sentinel-1A）卫星合成孔径雷达（SAR）影像数据,采用双极阈值分割方法,构建洪水淹没区域提取模型。收集安徽省2016年钉螺调查数据,建立当涂县钉螺分布空间数据库,随机选取75%和25%钉螺分布数据模型训练数据集和验证数据集,采用归一化植被指数阈值判别方法,构建钉螺孳生点生态环境特征提取模型,识别洪灾后血吸虫病传播潜在风险区域。结果当涂县汛前水体面积约为228.07 km²,洪水高峰期水体面积约为259.26 km²,洪灾导致的水体面积扩大约13.67%。判别洪灾后钉螺潜在扩散地主要分布在当涂县原有钉螺环境相毗邻的江心乡、石臼湖西岸湖阳、大陇、塘南和乌溪乡等地,以及河道相连的内陆田地等区域。对比验证集钉螺实际分布与模型分析结果,模型准确率为82%。结论基于Sentinel-1A雷达影像提取水体信息并用于识别洪灾后血吸虫病传播潜在风险区域是可行的。洪灾后钉螺在水淹区域存在扩散孳生风险,需在重点区域开展有针对性的监测和防控工作。

关键词: 血吸虫病, 洪涝灾害, 雷达遥感影像, 风险分析

Abstract:

Objective To use the image data of radar remote sensing for extracting water body information to perform rapid and effective identification of schistosomiasis transmission risk areas due to spreading of snails resulted from flooding.Methods Binary map-sets of Sentinel-1A satellite synthetic aperture radar （SAR） image data for Dangtu County of Anhui Province on May 15, 2020 before the flood and on July 16 at flooding peak period, were collected. A bipolar threshold segmentation method was used to estimate the area covered by flood. The data of snail survey in Anhui Province in 2016 were collected to generate a snail spacial distribution database of Dangtu County. The areas with 75% and 25% snail distribution were randomly selected as the training and verifying map-sets. The threshold discrimination method of normalized vegetation index was applied to extract eco-environmental characteristics of snail habitats and to identify schistosomiasis transmission risk areas after a flood.Results Of Dangtu County, the water body surface area before flood was about 228.07 km², and 259.26 km² in the flood peak period, showing an area expansion of about 13.6%. Based on the identified potential spreading areas of snail habitants after flooding were mainly distributed in where adjacent to the previously recorded snail-infested areas including Jiangxin township, Huyang in the west bank of Shijiu Lake, Dalong, Tangnan and Wuxi townships, as well as in the inland fields connecting rivers. The contrast of the snail actual distribution on the verification map-set with the model analysis indicated an accuracy rate of 82%.Conclusion It is feasible to identify schistosomiasis transmission risk areas after flooding using Sentinel-1A satellite SAR image data for extraction of water body information. There is a risk of spread of snail breeding in flooded areas, thus, it is imperative to strengthen surveillance and control that relevant to the target areas.

Key words: Schistosomiasis, Flood disaster, Radar remote sensing images, Risk analysis

中图分类号:

R532.21

夏尚, 薛靖波, 高风华, 吕山, 许静, 张世清, 李石柱. Sentinel-1A雷达遥感数据模型快速识别洪灾后血吸虫病传播潜在风险区研究[J]. 中国寄生虫学与寄生虫病杂志, 2020, 38(4): 417-422.

XIA Shang, XUE Jing-bo, GAO Feng-hua, LV Shan, XU Jing, ZHANG Shi-qing, LI Shi-zhu. Sentinel-1A radar remote sensing-based modeling for quick identification of potential risk areas of schistosomiasis transmission after flood[J]. Chinese Journal of Parasitology and Parasitic Diseases, 2020, 38(4): 417-422.

图/表 6

图1

图2

图3

图4

图5

图6

参考文献 21

[1]	Xu J, Lv S, Cao CL, et al. Progress and challenges of schistosomiasis elimination in China[J]. Chin J Schisto Control, 2018,30(6):605-609. (in Chinese)
	( 许静, 吕山, 曹淳力, 等. 我国血吸虫病消除工作进展及面临的挑战[J]. 中国血吸虫病防治杂志, 2018,30(6):605-609.)
[2]	Lv S, Xu J, Cao CL, et al. China fighting against schistosomiasis for 70 years: progress and experience[J]. Chin J Parasitol Parasit Dis, 2019,37(5):514-519. (in Chinese)
	( 吕山, 许静, 曹淳力, 等. 我国血吸虫病防治70年历程与经验[J]. 中国寄生虫学与寄生虫病杂志, 2019,37(5):514-519)
[3]	Cao CL, Li SZ, Zhou XN. Impact of schistosomiasis transmission by catastrophic flood damage and emergency response in China[J]. Chin J Schisto Control, 2016,28(6):618-623. (in Chinese)
	( 曹淳力, 李石柱, 周晓农. 特大洪涝灾害对我国血吸虫病传播的影响及应急处置[J]. 中国血吸虫病防治杂志, 2016,28(6):618-623.)
[4]	Yi XM, Wang ZL, Wang QZ, et al. The long-term effects of flood on the prevalence of schistosomiasis[J]. Trop Dis Parasitol, 2010,8(4):192-194. (in Chinese)
	( 尹晓梅, 王珍丽, 汪奇志, 等. 长江特大洪水溃堤洲滩灾后血吸虫病疫情监测[J]. 热带病与寄生虫学, 2010,8(4):192-194.)
[5]	Ge JH, Zhang SQ, Wang TP, et al. Effects of flood on the prevalence of schistosomiasis in Anhui Province in 1998[J]. Trop Dis Parasitol, 2004,2(3):131-134. (in Chinese)
	( 葛继华, 张世清, 汪天平, 等. 1998年特大洪水对安徽省血吸虫病流行的影响[J]. 热带病与寄生虫学, 2004,2(3):131-134.)
[6]	Zhang SQ, Ge JH, Wang TP, et al. Analysis on the epidemic situation of schistosomiasis at Fengyi marshland after dam break by flood in 1998[J]. Pract Prev Med, 2001,8(5):323-324. (in Chinese)
	( 张世清, 葛继华, 汪天平, 等. 凤仪洲1998年洪水溃堤后血吸虫病疫情分析[J]. 实用预防医学, 2001,8(5):323-324.)
[7]	Wang ZL, Gao J, Tao B, et al. Preliminary study on applying high resolution CBERS images to identify Oncomelania snail habitats in lake and marshland regions[J]. Chin J Schisto Control, 2012,22(6):640-644. (in Chinese)
	( 王增亮, 高杰, 陶波, 等. 应用高分辨率中巴资源卫星遥感图像探测湖沼地区钉螺孳生地的初步研究[J]. 中国血吸虫病防治杂志, 2012,22(6):640-644.)
[8]	Zhou YB. Strengthening the research and application of spatial epidemiology to pro-mote precise prevention and control of parasitic diseases in China[J]. Chin J Schisto Control, 2019,31(4):356-357. (in Chinese)
	( 周艺彪. 加强空间流行病学研究与应用助推我国寄生虫病精准防控[J]. 中国血吸虫病防治杂志, 2019,31(4):356-357)
[9]	Liu D, Yang YY, Zhou YD, et al. High spectral resolution lidar for atmosphere remote sensing: a review[J]. Infr Las Eng, 2015,44(9):7-18. (in Chinese)
	( 刘东, 杨甬英, 周雨迪, 等. 大气遥感高光谱分辨率激光雷达研究进展[J]. 红外与激光工程, 2015,44(9):7-18.)
[10]	Xiao X, Zhang L. Research on comparison and selection methods of remote sensing data sources for geographic national conditions monitoring[J]. Bull Sur Map, 2019(8):116-120. (in Chinese)
	( 肖昶, 张莉. 地理国情监测遥感数据源比选方法研究[J]. 测绘通报, 2019(8):116-120.)
[11]	Wu P. Analysis on the endemic situation of schistosomiasis from 2006 to 2012 in Dangtu County, Anhui Province[J]. Trop Dis Parasitol, 2014,12(2):94-95. (in Chinese)
	( 吴萍. 2006-2012年安徽当涂县血吸虫病疫情分析[J]. 热带病与寄生虫学, 2014,12(2):94-95.)
[12]	Gao FH, Zhang SQ, Wang TP, et al. Investigation of Oncomelania hupensis snails in Anhui Province in 2016[J]. Chin J Schisto Control, 2018,30(5):19-25. (in Chinese)
	( 高风华, 张世清, 汪天平, 等. 2016年安徽省钉螺调查结果分析[J]. 中国血吸虫病防治杂志, 2018,30(5):19-25.)
[13]	Xia CC, Lu CF, Li S, et al. Maximum entropy model versus remote sensing-based methods for extracting Oncomelania hupensis snail habitats[J]. Chin J Schisto Control, 2017,29(1):12-17. (in Chinese)
	( 夏聪聪, 卢成芳, 李思, 等. 基于最大熵模型和遥感的两种钉螺孳生地提取方法的比较[J]. 中国血吸虫病防治杂志, 2017,29(1):12-17.)
[14]	Wang ZL, Zhu R. Zhang ZJ, et al. Identification of snail habitats in the Poyang Lake region, based on the application of indices on joint normalized difference vegetation and water[J]. Chin J Epidemiol, 2012,33(8):823-827. (in Chinese)
	( 王增亮, 朱蓉, 张志杰, 等. 联合应用归一化水体和植被指数提取鄱阳湖区钉螺孳生地的研究[J]. 中华流行病学杂志, 2012,33(8):823-827.)
[15]	Cao ZG, Wang TP, He JC, et al. Evaluation on the effect of comprehensive control strategy of schistosomiasis with emphasis on infection source control in Anhui province[J]. Chin J Prev Med, 2012,46(2):114-118. (in Chinese)
	( 操治国, 汪天平, 何家昶, 等. 安徽省实施以传染源控制为主的血吸虫病综合防治策略效果评价[J]. 中华预防医学杂志, 2012,46(2):114-118.)
[16]	Gao FH, Zhang SQ, Wang TP, et al. Endemic status of schistosomiasis in Anhui Province in 2018[J]. Trop Dis Parasitol, 2020,18(1):41-45. (in Chinese)
	( 高风华, 张世清, 汪天平, 等. 2018 年安徽省血吸虫病疫情分析[J]. 热带病与寄生虫学, 2020,18(1):41-45.)
[17]	Wang YJ, Li SZ, Jiang QW, et al. Progress of spatial epidemiology applied to prevention and control of schistosomiasis[J]. Chin J Schisto Control, 2019,31(1):53-57. (in Chinese)
	( 王英鉴, 李石柱, 姜庆五, 等. 空间流行病学在血吸虫病防控实践中的应用进展[J]. 中国血吸虫病防治杂志, 2019,31(1):53-57.)
[18]	Xue JB, Zhang LJ, Wang Q, et al. Application of high resolution remote sensing technology in research of schistosomiasis surveillance[J]. Chin J Schisto Control, 2015,27(5):551-554. (in Chinese)
	( 薛靖波, 张利娟, 王强, 等. 高分辨率遥感技术在日本血吸虫病监测中的应用[J]. 中国血吸虫病防治杂志, 2015,27(5):551-554) doi: 10.16250/j.32.1374.2015072
[19]	Jia SC, Xue DJ, Li CR, et al. Study on new method for water area information extraction based on Sentinel-1 data[J]. Yangtze River, 2019,50(2):213-217. (in Chinese)
	( 贾诗超, 薛东剑, 李成绕, 等. 基于Sentinel-1数据的水体信息提取方法研究[J]. 人民长江, 2019,50(2):213-217.)
[20]	Zeng FL, Li L, Wan LH. SAR-based fast flood mapping using Sentinel-1 imagery[J]. Geo World, 2015,22(5):100-103. (in Chinese)
	( 曾玲方, 李霖, 万丽华. 基于Sentinel-1卫星SAR数据的洪水淹没范围快速提取[J]. 地理信息世界, 2015,22(5):100-103.)
[21]	Zhang SQ, Wang TP, Ge JH, et al. Influence on the diffusion of snail by flooding in Anhui Province[J]. Trop Dis Parasitol, 2004,2(2):90-94. (in Chinese)
	( 张世清, 汪天平, 葛继华, 等. 洪涝灾害对安徽省钉螺扩散的影响[J]. 热带病与寄生虫学, 2004,2(2):90-94.)

Sentinel-1A雷达遥感数据模型快速识别洪灾后血吸虫病传播潜在风险区研究

Sentinel-1A radar remote sensing-based modeling for quick identification of potential risk areas of schistosomiasis transmission after flood

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 21

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李婕, 文雨松, 李召军. 我国旅游开发对血吸虫病防治的影响[J]. 中国寄生虫学与寄生虫病杂志, 2023, 41(3): 355-360.
[2]	陈琳, 朱继峰, 邱竞帆, 徐志鹏, 张东辉, 陈璐, 何健, 李伟, 杨坤, 季旻珺. 寓全健康理念于血吸虫病防控虚拟仿真项目建设[J]. 中国寄生虫学与寄生虫病杂志, 2023, 41(1): 81-84.
[3]	冯家鑫, 公衍峰, 罗卓韦, 汪伟, 曹淳力, 许静, 李石柱. 我国血吸虫病防治策略的科学基础与“十四五”展望[J]. 中国寄生虫学与寄生虫病杂志, 2022, 40(4): 428-435.
[4]	陈兵, 张国莉, 张高红. 血吸虫病候选疫苗临床研究进展[J]. 中国寄生虫学与寄生虫病杂志, 2022, 40(4): 511-515.
[5]	高元, 胡媛, 曹建平. 免疫细胞对血吸虫病肝纤维化作用的研究进展[J]. 中国寄生虫学与寄生虫病杂志, 2022, 40(1): 88-93.
[6]	郭苏影, 祝红庆, 曹淳力, 邓王平, 鲍子平, 贾铁武, 李银龙, 吕超, 秦志强, 张利娟, 冯婷, 杨帆, 吕山, 许静, 李石柱. 2020年长江中下游地区洪涝灾害后血吸虫病传播风险评估[J]. 中国寄生虫学与寄生虫病杂志, 2021, 39(6): 753-758.
[7]	施亮, 熊春蓉, 刘毛毛, 魏秀参, 张键锋, 王鑫瑶, 王涛, 杭德荣, 羊海涛, 杨坤. 基于深度学习技术的湖北钉螺视觉智能识别模型效能评价[J]. 中国寄生虫学与寄生虫病杂志, 2021, 39(6): 764-770.
[8]	李博, 张聪, 单晓伟, 易佳, 涂珍, 何汇, 唐丽, 朱红, 刘建兵. 湖北省血吸虫病规范化实验室病原学检测能力评估[J]. 中国寄生虫学与寄生虫病杂志, 2021, 39(5): 572-576.
[9]	吴家利, 李博, 刘斯, 涂祖武, 唐丽, 涂珍, 周晓蓉, 孙凌聪, 肖瑛, 朱红. 基于人群抗体水平的湖北省血吸虫病传播风险分析[J]. 中国寄生虫学与寄生虫病杂志, 2021, 39(5): 578-584.
[10]	胡飞, 高祖禄, 袁敏, 李召军, 李宜锋, 刘跃民, 李剑瑛, 谢曙英, 文雨松, 林丹丹. 鄱阳湖区居民血吸虫感染及肝纤维化变化趋势的研究[J]. 中国寄生虫学与寄生虫病杂志, 2021, 39(5): 629-636.
[11]	刘蓉, 张剑锋, 严晓岚, 闻礼永. SF-36和EQ-5D-5L量表在晚期血吸虫病患者生命质量评价中的比较研究[J]. 中国寄生虫学与寄生虫病杂志, 2021, 39(5): 639-645.
[12]	刘蓉, 闻礼永. 晚期血吸虫病基础和临床研究新进展[J]. 中国寄生虫学与寄生虫病杂志, 2021, 39(4): 429-436.
[13]	洪中, 吴铃铃, 王丽萍, 许静. 全球血吸虫病防控进展及面临的挑战[J]. 中国寄生虫学与寄生虫病杂志, 2021, 39(4): 514-519.
[14]	王丽萍, 秦志强, 吕山, 李石柱, 周晓农, 许静. 湄公血吸虫病流行概况及消除面临的挑战[J]. 中国寄生虫学与寄生虫病杂志, 2020, 38(6): 758-763.
[15]	倪庆翔, 阮卫, 姚立农, 倪丹丹, 项方吕. 温州市首例国外输入性埃及血吸虫病病例的实验室检测和流行病学调查分析[J]. 中国寄生虫学与寄生虫病杂志, 2020, 38(5): 667-669.