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 (≥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.
To create the landslide activity map, we implemented and tested the procedure to fully utilise the 6-day repeatability of the Sentinel-1 constellation in three pilot areas in Slovenia for the ...observation period from 2017 to 2021. The interferometric processing of the Sentinel-1 images was carried out with ENVI SARScape, while the interpretation of the persistent scatterers InSAR data was done in three steps. In the first step, a preliminary interpretation of the landslide areas was performed by integrating the PS InSAR data into a GIS environment with information that could be relevant to explain the movement patterns of the PS InSAR points. In the second step, a field validation was performed to check the PS InSAR in the field and record the potential damage to the objects indicating the slope mass movements. In the third step, the deformations were identified, and areas of significant movement were determined, consisting of clusters of at least 3 persistent scatterers (PS) with a maximum spacing of 10 m. The landslide activity map was created based on the landslide areas categorised into four classes based on the geotechnical analyses, yearly velocity data obtained by PS InSAR, and validation of annual velocity data obtained by in-situ and GNSS monitoring and field observation. A total of 21 polygons with different landslide activities were identified in three study areas. The overall methodology will help stakeholders in the early mapping and monitoring of landslides to increase the urban resilience.
PDF