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Abstract
Surface-water body maps are imperative for effective mosquito larvae control. This study aims to select a method for the automatic and regular mapping of surface-water bodies in rice fields and wetlands using Sentinel-1 synthetic aperture radar data. Four methods were adapted and developed for automated application: the Otsu valley-emphasis algorithm, a classification method based on the textural feature of entropy, a method using K-means unsupervised classification, and a method using the Haralick’s textural feature of dissimilarity and fuzzy-rules classification. The results were assessed using field data collected during the mosquito breeding periods of 2018 and 2019 in the region of Central Macedonia (Greece). The Otsu valley-emphasis technique provides the highest overall accuracy (0.835). The accuracy is higher at the beginning of the summer (0.948) than at the end of the rice-growing season due to higher density of vegetation. Results using this method were further assessed during the main larvicide application period. The presence of vegetation, built-up areas, floating algae in rice-paddies, salt-crust formations in wetlands, and water depth, were found to affect the performance of the algorithm. A WebGIS platform was designed for the visualization of the produced water maps along with other data related to mosquito-larvae presence.
Highlights
Surface-water bodies are important breeding habitats of mosquitoes, surface-water body maps together with other parameters are often used as predictors of mosquito larvae presence.[1]
The results show that the Otsu Valley-emphasis method performs better than the other methods with a mean overall accuracy (OA) of 0.835 and kappa coefficient of 0.588
An OA of 0.915 in rice fields and 0.854 in wetland systems were recorded during the period May–June of the years 2018 and 2019
Summary
Surface-water bodies are important breeding habitats of mosquitoes, surface-water body maps together with other parameters (meteorological and phenological) are often used as predictors of mosquito larvae presence.[1] Regular monitoring of surface-water bodies in near realtime (NRT) is necessary for the timely prevention of outbreaks, such as the Rift Valley Fever[2] or the West Nile Virus (WNV). The latter accounts for high numbers of human cases and fatalities, and for large outbreaks in Greece (during the periods 2010–2014 and 2017–2019, a total of more than 1.200 human cases were identified) and other European countries. The use of unmanned aircraft systems (UAS) for the acquisition of aerial photographs in the areas of interest is considered a widespread practice, but the resources and equipment
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