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

doi:10.12140/j.issn.1000-7423.2020.04.003

Abstract

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

CLC Number:

R532.21

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.

Figures/Tables 6

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