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.

中文翻译：

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从Sentinel-1图像中自动检测地表水体，以有效控制蚊虫幼虫

要有效控制蚊虫幼虫，必须要有地表水体图。这项研究的目的是选择一种方法，利用Sentinel-1合成孔径雷达数据对稻田和湿地中的地表水体进行自动和规则的制图。修改并开发了四种用于自动化应用的方法：大津谷底强调算法，基于熵的纹理特征的分类方法，使用K均值无监督分类的方法以及使用Haralick的纹理相似性和模糊特征的方法。规则分类。使用在中马其顿（希腊）地区2018年和2019年蚊子繁殖期间收集的现场数据对结果进行了评估。大津谷强调技术可提供最高的整体精度（0.835）。由于较高的植被密度，夏季初（0.948）的精度高于水稻种植季末的精度。在主要的杀幼虫剂施用期间，进一步评估了使用该方法的结果。发现植被，堆积区，稻田中的浮藻，湿地中的盐皮形成以及水深的存在会影响算法的性能。设计了一个WebGIS平台，用于可视化生产的水图以及与蚊虫幼虫有关的其他数据。和水深被发现会影响算法的性能。设计了一个WebGIS平台，用于可视化生产的水图以及与蚊虫幼虫有关的其他数据。和水深被发现会影响算法的性能。设计了一个WebGIS平台，用于可视化生产的水图以及与蚊虫幼虫有关的其他数据。