# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Hazard and Risk Analysis  
The underlying concept behind this research is that the changes in SAR amplitude that we see in the compared SAR pairs can inform us about changes due to either transformations in the urban environment (e.g., new residential areas; terrain flattening; embankment) that could have contributed to increase or decrease one of the factors composing the risk equation (i.e., Hazard \(\times\) Vulnerability \(\times\) Elements at Risk), or impact of past floods. However, as it is well known in the literature, change detection techniques show where and when the SAR backscatter changed, and if it increased or decreased, but do not tell _per se_ the reason that caused such change [41; 85; 86]. Furthermore, two SAR backscatter change patterns of similar magnitude and shape, either spatially close or distant, may be due to different processes. To this purpose, a process of interpretation of the observed SAR amplitude change patterns is needed. In this regard, the brightness of features in a SAR image depends on the portion of the transmitted energy that is returned to the radar from targets on the surface. The magnitude or intensity of this backscattered energy depends on how the radar signal interacts with the surface, which in turn is a function of several variables or parameters. These parameters include the particular characteristics of the radar system (frequency, polarization, viewing geometry, etc.) as well as the characteristics of the surface (land cover type, topographic relief, etc.). Because many of these characteristics are interrelated, they cannot easily be separated into their individual contributions. The change in the various parameters may have an impact on, and affect the response of other parameters, which together affect the amount of backscatter [87]. SAR amplitude change patterns visible in the RGB composites and ratio colored maps (see Section 2.2.3) were extracted and delimited. This first skimming, however, did not allow us to give an accurate classification of the types of changes detected. For this purpose, a visual comparison with other sources like the optical basemap was necessary to ascertain the kind of changes that occurred. The changes were divided into two groups: \"Flooding\" if they could be attributed to issues of flooding; and \"No flooding\" if the changes were not related to flooding but could influence in some way the local susceptibility to flooding (e.g., motorway construction, landfill-earthworks, new building constructions; see Figure 6). In this regard, such relevance was assessed based on the spatial intersection, in QGIS, between these changes and the factors (either anthropogenic or natural) as represented spatially by the vector and raster ancillary data described in Section 2.2.4 (Table 4). Therefore, for example, if a new residential area was found on alluvial deposits at low altitude, at closer distance from the Rimac River, this change was considered as \"negative\" in the overall risk equation because, at equal hazard and vulnerability conditions, it represents an increase of the local elements at risk. Vice versa, the construction of an embankment to protect residential buildings was considered as \"positive\" because, at equal vulnerability conditions and elements at risk, it contributes to decrease the local hazard to inundation. Furthermore, areas where no changes were found, but the processed SAR pairs showed evidence of flooding during previous events, were considered at risk and this information was translated into the high risk level of the final flooding risk map at basin scale. The spatial integration with the ancillary data was also helpful to remove false positives, i.e., SAR backscatter changes that were not due to flooding.