Water Dynamics Analysis in Karst Flood Areas Using Sentinel-1 Time Series

Studying karst water dynamics is challenging because of the often unknown underground flows. Therefore, studies of visible karst waters receive considerable research emphasis. Researchers are turning to various data sources, including remote sensing imagery, to study them. This research paper presents an assessment of a water bodies dataset, automatically detected from Sentinel-1 imagery, for karst flood research. Statistical and visual analyses were conducted to assess the reliability and effectiveness of the dataset. Spearman's correlation coefficients were employed for statistical analysis to determine the degree of correlation between the areas of water bodies dataset and official water level data. Visual analyses involved the creation of heat maps based on the identified water areas, which were then compared to official flood maps, and the preparation of an analysis of historical flood events or results of hydrological and hydraulic modelling. Additionally, vegetation maps were produced to identify areas that lacked detection and complemented the heat maps. Statistical assessment showed a strong correlation (& GE;0.6) between the dataset and official water level data in smaller flood-prone areas with less complex inflow. Visual analyses using heat maps and vegetation maps effectively identified frequently flooded areas but had limitations in areas with dense vegetation. Comparisons with flood maps showed an important value of the dataset as an additional source of information for karst flood studies. This assessment highlights the dataset's potential in combination with other data sources and modelling approaches.

Automated summary

Studying karst water dynamics is challenging due to unknown underground flows. This research paper assesses a water bodies dataset detected from Sentinel-1 imagery for karst flood research. Statistical analysis using Spearman's correlation coefficients and visual analysis utilizing heat maps and vegetation maps were conducted to evaluate the dataset's reliability and effectiveness.