# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) Data Mining
#### 2.2.4 Ancillary Data  
The geospatial ancillary data (either vector or raster) related to topography, geology, permanent and seasonal water bodies, urban footprint, new urban development, roads and infrastructures, population at the district level, allowed us to access environmental and contextual information, gain more understanding and achieve a reliable interpretation of the radar backscatter changes that took place in the territory in 1997-1998 and 2017-2018. The details of the ancillary data are reported in Table 4. Data were handled, intersected and geospatially analyzed to a common coordinate system in the Quantum GIS (QGIS) platform (v. 3.2.3). One Sentinel-2 cloud-free image was used as the optical basemap of the study area, to browse and consult alternatively to the cache-file Google Earth basemap, to find a first visual correspondence with the objects and shapes visible in the SAR images. Figure 6 shows the three key spatial hazard datasets and risk elements that we examined to assess the hydraulic risk in Lima: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Typology** & **Description** & **Format** &
\begin{tabular}{c} **Spatial Resolution** \\ **and/or Scale** \\ \end{tabular} & **Reference Date** & **Source** \\ \hline \multirow{3}{*}{Anthropogenic} & Administrative boundaries & Shupefile & 1:100,000 & - & Geocatmin [72] \\  & Census data & Shupefile & 1:100,000 & 2011 & Geocatmin [73] \\  & (population,households) & & 12 m & 2011 & Global Urban Footprint [74,75] \\  & Urban land cover & Raster & - & - & QGIS desktop v3.2.3 service \\  & Open Street Map & Raster & - & - & Ministerio del \\  & Hydrographic basin & Shupefile & 1:175,000 & - & Ambiente-MINAM [76] \\ \multirow{3}{*}{Natural} & ASTER GDEM v2 & Raster & 1 arcsec & 2011 & USGS-NASA [77] \\  & Topographic contour lines & Shupefile & 1:100,000 & - & GEO_CSP_PERU [78] \\  & Geology & Shupefile & 1:100,000 & - & GEO_CSP_PERU [79] \\  & Global Surface Water & Web Map & - & 1984-2019 & EC JRC/Google [80,81] \\  & Service & Service & - & 2016 & Scihub \\ \multirow{3}{*}{Basemap} & Sentinel-2 image (cloud-free) & Raster & - & - & Google Earth \\  & Google Earth basemap & Html (cache) & - & - & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ancillary data over Lima (Peru) used in the geospatial analysis in the QGIS environment. Acronyms: ASTER - Advanced Spaceborne Thermal Emission and Reflection Radiometer; GDEM - Global DEM. Figure 5: Example of change patterns in the radar backscatter: (**a**,**b**) decrease and (**c**,**d**) increase of the radar backscatter, in (**a**,**c**) RGB composites (R = master; G = slave; B = slave) and in (**b**,**d**) the corresponding signals in the ratio maps (slave/master). * Terrain slope: this is a physical environment factor that contributes to the geological hazard as the predisposition of the territory to being flooded during a flood event depending on its elevation vs. the inundation depth. Initially, given that SRTM was used for the terrain correction of the SAR data (see Section 2.2.2), both versions of the same dataset, i.e., SRTM 1 arc-second (30 m) and 3 arc-seconds (90 m), were tested. However, they were replaced with the ASTER GDEM, given the evidence that the SRTM products were affected by gaps in some areas of the Rimac River basin and, as such, they were discontinuous and lacking information. Using the ASTER GDEM, instead, a raster was obtained, with slope values \(\beta\) ranging between 0\({}^{\circ}\) and 89\({}^{\circ}\), and subsequently reclassified according to the following classes defined by 5-degree wide intervals: A: \(\beta\leq 5^{\circ}\), B: \(5^{\circ}<\beta\leq 10^{\circ}\), C: \(10^{\circ}<\beta\leq 15^{\circ}\), D: \(15^{\circ}<\beta\leq 20^{\circ}\), E: \(\beta>20^{\circ}\), and finally vectorized. Class A, in particular, includes the alluvial terraces as per the classification in [82].