# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 4 Discussion  
In its history, Lima has experienced many flooding events. In the last few years many cartographic mapping efforts have been conducted to identify the areas affected by these events. These mapping exercises were made in-situ, with field inspections, or with the aid of satellite and aerial optical images (e.g., [28]). The studies published by [33; 34] were the first to approach the use of SAR data, and aimed  
Figure 18: Map of flooding risk classification at basin scale over Lima, based on the combination of satellite SAR evidence and natural (Figure 7) and anthropogenic factors (Figure 13) of flooding hazard, vulnerability and exposed elements. to highlight areas affected by landslides and flooded in the city of Chosica, located 60 km from Lima in the mid-upper part of the Rimac River basin. Based on the current state-of-the-art on these events (see also Section 1), our study is the first that attempts the hydraulic risk mapping for the urban areas of the Rimac River basin, with the use of satellite SAR data. The risk mapping obtained as a result of the change detection methodology has demonstrated the ability of this approach to extract valuable information relating to the changes that have developed in the territory over time. This methodology has been very useful in analyzing changes due to flooding and other types of processes (here called \"No Flooding\", NF), especially in an area like the Rimac River basin that is characterized by sudden geomorphological changes (from one year to the next), due to the severity of geodynamic phenomena occurring along the Rimac River tributaries. RGB color composites and ratio maps mutually complemented in enhancing the areas of the basin where changes have occurred, through patterns of decrease vs. increase of the radar backscatter. As recalled in Section 1, from an analytical point of view, this is the practical advantage of this change detection approach compared to other more sophisticated methods. To assess the accuracy of this approach in detecting changes due to flooding, we have compared our results with the map product that was generated by the Copernicus Emergency Management Service (EMS) during the Risk and Recovery Activation EMSN-038 for flood delineation and damage mapping of the flooding event that happened in mid-March 2017 [44]. The activation was under the Framework Service Contract No. 259811 (\"Emergency Management Service - Risk and Recovery Mapping\") and, alongside optical satellite images, the same Sentinel-1 ascending (17/03/2017) and descending (18/03/2017 and 24/03/2017) mode images were used for flood detection. The mapping team reached similar conclusions as in our study, i.e., that the traditional approach based on detecting the decrease of radar backscattering and combined thresholding/region-growing could not work, given the characteristics of the urban environment in Lima, as well as the character, timeline and extent of the flood event [44]. For this reason, a change detection approach was employed for the EMS activation, using the shortest available temporal baselines (i.e., 12 days for both tracks). Figure 19 allows the spatial comparison between our ratio map (Figure 20a) and the EMSN-038 potential maximum flood extent (Figure 19b) in the area of Carapongo/Santa Maria de Huachipa and Cajamarquilla that were the first and second largest areas to be inundated, respectively. The two mapping products provide different, but complementary and interlinked information: while the EMS map focuses on those areas at potential maximum flood, our ratio map documents all the SAR backscatter changes captured in the cross-event pairs of the analyzed interval and, as such, shows many more changes. From the geospatial intersection between the two products, it comes out that the higher concentrations of SAR change patterns found for the 2017 flood event, and interpreted as due to inundation from the Rimac and Huaycolor rivers, fall where the EMS spatial mapping delineated the most affected areas.