# Improving Flood Detection and Monitoring through Remote Sensing
###### Abstract  
Flood Detection and Monitoring through Remote Sensing. We show that the  
results demonstrate, among other things, how sediment deposition plays an important part in flood propagation. This is in line with other studies in this direction [3]. In [4], high-resolution LiDAR data (16 points/m\({}^{2}\) on average) acquired from an airborne sensor are analyzed in depth to derive information about the condition and weathering state of levees along a section of river coastlines in Southeastern China. Leveee geometric parameters, such as crown height, waterside, and landslide slopes, and elevation transects are evaluated with respect to model flood heights for several return periods, resulting in scores assigned to each leveee parcel to quantify its robustness against water overtopping. Such detailed scores improve upon previous classifications, mainly based on ground campaigns, which involved single parameters and sparse sampling. The great detail placed in the evaluation testifies the potential of remotely sensed data to extract fine-resolution information over relatively large areas with a fraction of the time-personnel effort necessary to perform field surveys. Technical note [5] illustrates an example of application of three of the most diffused and well-known techniques for separating water and non-water areas in SAR images, namely the manual histogram \"valley emphasis\" thresholding method, [PERSON]'s threshold determination, and K-means clustering. The latter algorithm is run here with two clusters. The three methods are tested on a series of Sentinel-1 images taken during a series of strong rain episodes that occurred between the end of 2018 and the beginning of 2019 over a region in the Philippines. Results are compared visually, as well as in terms of total detected flooded area. Although no ground truth was available to assess the methods' performances more quantitatively, the scatter of the results (relative differences in detected flooded areas reaching as much as 60% between manual and K-means) provides a fair idea of the care that should be placed in choosing algorithms and procedures to detect floodwaters in SAR imagery and the important role of reference data to assess algorithms quantitatively. In [6], a detailed analysis is proposed for an approach to flood vulnerability mapping that makes use of high-resolution, SAR-derived, and model-derived flood hazard maps. Results are assessed by using aerial photos available for three test sites during two flood events in UK. Various combinations of remotely sensed and model information are compared. It was concluded that SAR flood maps improve detection performance especially when surface flooded areas are not necessarily due to river water inundation, while modeled flood maps obtained from gauge data are most useful for areas where the SAR sensor cannot \"see\" due to its side-looking geometry, such as streets in high-density urban centers. Synergetic use of both data types results in detection accuracies of up to 94%, with false positive rates as low as 9%, improving by several percent points performances obtained with the use of SAR data only. As discussed in the paper, although high-resolution SAR data are still mostly not free and open access (with the significant exception of the European Sentinel-1 constellation, and with several other SAR sensors collections foreseen to become available as open access in the next future), their use is crucial for complex sites such as urban areas, especially those characterized by relatively high building densities as in many European countries, where high-resolution digital surface models (DSM) are necessary in order to properly model hydrological dynamics. In such cases, current sensors such as Sentinel-1, despite their unparalleled temporal acquisition frequency that is showing promising results in several applications [7; 8], may still have insufficient spatial resolution, while higher-resolution sensors such as the Italian COSMO-SkyMed and the German TerraSAR-X lack temporal frequency.