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[Rapid identification of Oncomelania hupensis snail habitat in the Poyang Lake region by Geographic Information System (GIS) and Remote Sensing (RS)].

Jiagang Guo, Dandan Lin, Hu GuangHan, An Ning, Hongyun Liu, Lü Shang-Biao, Dong Li, Xiaohua Wu, Rongrong Wang, Minggang Chen, Jiang Zheng, Marcel Tanner

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Abstract

OBJECTIVE: To identify Oncomelania hupensis snail habitats and areas with high transmission potential by GIS/RS. METHODS: Marshland areas near high endemic villages of schistosomiasis in the Poyang Lake region were selected. Corresponding map was digitized and (Landsat 5 TM) image was corrected according to the digital map. The image in dry seasons was calculated by both normalized difference vegetation index (NDVI) and tasseled cap model. RESULTS: Result showed that snails spots were distributed in class 6, 7 and 8. Farther analysis of both NDVI and tasseled cap model showed that the snail habitats were mainly distributed in the areas where NDVI value was more than 110, and in tasseled cap wetness value between -10 to 3 with correction rate 94.93%. CONCLUSION: First step was to use unsupervised classification to define the class 6, 7 and 8 snail habitat environment. Second step was to extract the value by NDVI model, and to define a healthy vegetation as snail suspicious habitat when NDVI value was more than 110. Then the third step was to use tasseled cap wetness model to define the areas as snail habitats which value was between -10 to 3.

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OBJECTIVE: To identify Oncomelania hupensis snail habitats and areas with high transmission potential by GIS/RS. METHODS: Marshland areas near high endemic villages of schistosomiasis in the Poyang Lake region were selected. Corresponding map was digitized and (Landsat 5 TM) image was corrected according to the digital map. The image in dry seasons was calculated by both normalized difference vegetation index (NDVI) and tasseled cap model. RESULTS: Result showed that snails spots were distributed in class 6, 7 and 8. Farther analysis of both NDVI and tasseled cap model showed that the snail habitats were mainly distributed in the areas where NDVI value was more than 110, and in tasseled cap wetness value between -10 to 3 with correction rate 94.93%. CONCLUSION: First step was to use unsupervised classification to define the class 6, 7 and 8 snail habitat environment. Second step was to extract the value by NDVI model, and to define a healthy vegetation as snail suspicious habitat when NDVI value was more than 110. Then the third step was to use tasseled cap wetness model to define the areas as snail habitats which value was between -10 to 3.

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Available abstract

OBJECTIVE: To identify Oncomelania hupensis snail habitats and areas with high transmission potential by GIS/RS. METHODS: Marshland areas near high endemic villages of schistosomiasis in the Poyang Lake region were selected. Corresponding map was digitized and (Landsat 5 TM) image was corrected according to the digital map. The image in dry seasons was calculated by both normalized difference vegetation index (NDVI) and tasseled cap model. RESULTS: Result showed that snails spots were distributed in class 6, 7 and 8. Farther analysis of both NDVI and tasseled cap model showed that the snail habitats were mainly distributed in the areas where NDVI value was more than 110, and in tasseled cap wetness value between -10 to 3 with correction rate 94.93%. CONCLUSION: First step was to use unsupervised classification to define the class 6, 7 and 8 snail habitat environment. Second step was to extract the value by NDVI model, and to define a healthy vegetation as snail suspicious habitat when NDVI value was more than 110. Then the third step was to use tasseled cap wetness model to define the areas as snail habitats which value was between -10 to 3.

Key concepts: Oncomelania hupensis, Normalized Difference Vegetation Index, Oncomelania, Habitat, Snail, Vegetation (pathology), Environmental science, Geography

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[Rapid identification of Oncomelania hupensis snail habitat in the Poyang Lake region by Geographic Information System (GIS) and Remote Sensing (RS)]. — Research Paper | ScholarLens