Satellite Imagery-Based Identification of High-Risk Areas of <i>Schistosome</i> Intermediate Snail Hosts Spread after Flood
Snail intermediate host monitoring and control are essential for interrupting the parasitic disease schistosomiasis. Identifying large-scale high-risk areas of snail spread after floods has been greatly facilitated by remote sensing imagery. However, previous studies have usually assumed that all in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/15/3707 |
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| author | Juan Qiu Dongfeng Han Rendong Li Ying Xiao Hong Zhu Jing Xia Jie Jiang Yifei Han Qihui Shao Yi Yan Xiaodong Li |
| author_facet | Juan Qiu Dongfeng Han Rendong Li Ying Xiao Hong Zhu Jing Xia Jie Jiang Yifei Han Qihui Shao Yi Yan Xiaodong Li |
| author_sort | Juan Qiu |
| collection | DOAJ |
| description | Snail intermediate host monitoring and control are essential for interrupting the parasitic disease schistosomiasis. Identifying large-scale high-risk areas of snail spread after floods has been greatly facilitated by remote sensing imagery. However, previous studies have usually assumed that all inundation areas carry snails and may have overestimated snail spread areas. Furthermore, these studies only used a single environmental factor to estimate the snail survival risk probability, failing to analyze multiple variables, to accurately distinguish the snail survival risk in the snail spread areas. This paper proposes a systematic framework for early monitoring of snail diffusion to accurately map snail spread areas from remote sensing imagery and enhance snail survival risk probability estimation based on the snail spread map. In particular, the flooded areas are extracted using the Sentinel-1 Dual-Polarized Water Index based on synthetic aperture radar images to map all-weather flooding areas. These flood maps are used to extract snail spread areas, based on the assumption that only inundation areas that spatially interacted with (i.e., are close to) the previous snail distribution regions before flooding are identified as snail spread areas, in order to reduce the misclassification in snail spread area identification. A multiple logistic regression model is built to analyze how various types of snail-related environmental factors, including the normalized difference vegetation index (NDVI), wetness, river and channel density, and landscape fractal dimension impact snail survival, and estimate its risk probabilities in snail spread area. An experiment was conducted in Jianghan Plain, China, where snails are predominantly linearly distributed along the tributaries and water channels of the middle and lower reaches of the Yangtze River. The proposed method could accurately map floods under clouds, and a total area of 231.5 km<sup>2</sup> was identified as the snail spread area. The snail survival risk probabilities were thus estimated. The proposed method showed a more refined snail spread area and a more reliable degree of snail survival risk compared with those of previous studies. Thus, it is an efficient way to accurately map all-weather snail spread and survival risk probabilities, which is helpful for schistosomiasis interruption. |
| first_indexed | 2024-03-09T12:13:34Z |
| format | Article |
| id | doaj.art-23b4928cef0c426b86822491fb4ab7be |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:13:34Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-23b4928cef0c426b86822491fb4ab7be2023-11-30T22:49:14ZengMDPI AGRemote Sensing2072-42922022-08-011415370710.3390/rs14153707Satellite Imagery-Based Identification of High-Risk Areas of <i>Schistosome</i> Intermediate Snail Hosts Spread after FloodJuan Qiu0Dongfeng Han1Rendong Li2Ying Xiao3Hong Zhu4Jing Xia5Jie Jiang6Yifei Han7Qihui Shao8Yi Yan9Xiaodong Li10Key Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaHubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, ChinaHubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, ChinaHubei Center for Disease Control and Prevention, Hubei Provincial Academy of Preventive Medicine, Wuhan 430079, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaKey Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission, College of Resources and Environmental Science, South-Central Minzu University, Wuhan 430074, ChinaKey Laboratory of Monitoring and Estimate for Environment and Disaster of Hubei Province, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, ChinaSnail intermediate host monitoring and control are essential for interrupting the parasitic disease schistosomiasis. Identifying large-scale high-risk areas of snail spread after floods has been greatly facilitated by remote sensing imagery. However, previous studies have usually assumed that all inundation areas carry snails and may have overestimated snail spread areas. Furthermore, these studies only used a single environmental factor to estimate the snail survival risk probability, failing to analyze multiple variables, to accurately distinguish the snail survival risk in the snail spread areas. This paper proposes a systematic framework for early monitoring of snail diffusion to accurately map snail spread areas from remote sensing imagery and enhance snail survival risk probability estimation based on the snail spread map. In particular, the flooded areas are extracted using the Sentinel-1 Dual-Polarized Water Index based on synthetic aperture radar images to map all-weather flooding areas. These flood maps are used to extract snail spread areas, based on the assumption that only inundation areas that spatially interacted with (i.e., are close to) the previous snail distribution regions before flooding are identified as snail spread areas, in order to reduce the misclassification in snail spread area identification. A multiple logistic regression model is built to analyze how various types of snail-related environmental factors, including the normalized difference vegetation index (NDVI), wetness, river and channel density, and landscape fractal dimension impact snail survival, and estimate its risk probabilities in snail spread area. An experiment was conducted in Jianghan Plain, China, where snails are predominantly linearly distributed along the tributaries and water channels of the middle and lower reaches of the Yangtze River. The proposed method could accurately map floods under clouds, and a total area of 231.5 km<sup>2</sup> was identified as the snail spread area. The snail survival risk probabilities were thus estimated. The proposed method showed a more refined snail spread area and a more reliable degree of snail survival risk compared with those of previous studies. Thus, it is an efficient way to accurately map all-weather snail spread and survival risk probabilities, which is helpful for schistosomiasis interruption.https://www.mdpi.com/2072-4292/14/15/3707satellite imageryschistosomiasisfloodingsnail spreadrisk mapping |
| spellingShingle | Juan Qiu Dongfeng Han Rendong Li Ying Xiao Hong Zhu Jing Xia Jie Jiang Yifei Han Qihui Shao Yi Yan Xiaodong Li Satellite Imagery-Based Identification of High-Risk Areas of <i>Schistosome</i> Intermediate Snail Hosts Spread after Flood Remote Sensing satellite imagery schistosomiasis flooding snail spread risk mapping |
| title | Satellite Imagery-Based Identification of High-Risk Areas of <i>Schistosome</i> Intermediate Snail Hosts Spread after Flood |
| title_full | Satellite Imagery-Based Identification of High-Risk Areas of <i>Schistosome</i> Intermediate Snail Hosts Spread after Flood |
| title_fullStr | Satellite Imagery-Based Identification of High-Risk Areas of <i>Schistosome</i> Intermediate Snail Hosts Spread after Flood |
| title_full_unstemmed | Satellite Imagery-Based Identification of High-Risk Areas of <i>Schistosome</i> Intermediate Snail Hosts Spread after Flood |
| title_short | Satellite Imagery-Based Identification of High-Risk Areas of <i>Schistosome</i> Intermediate Snail Hosts Spread after Flood |
| title_sort | satellite imagery based identification of high risk areas of i schistosome i intermediate snail hosts spread after flood |
| topic | satellite imagery schistosomiasis flooding snail spread risk mapping |
| url | https://www.mdpi.com/2072-4292/14/15/3707 |
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