# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 2 Materials and Methods
### Impact Assessment
#### 2.3.2 Socio-Ecological Flood Impacts Model  
The impact assessment of this study aimed to offer an integrative approach which looked at a diversity of sociological and ecological parameters, namely: exposed population depending on population density, the extent of potential damage in urban areas and the ecological consequences of the storm in key mangrove ecosystems.  
To assess the number of potentially affected populations in Mozambique and Madagascar, population-density data were gathered from the World Population Hub at 1 km resolution (Table 1). As the flood layer derived from Sentinel-1 data was of 10 m resolution, the dataset was reprojected to a 1 km resolution to count the overlapping pixels between the flood model and the number of inhabitants per km\({}^{2}\). A reducer computing an aggregated weighted sum of those input pixels was used to obtain the number of people exposed per region and visualize spatial trends in ROIs. As such, flooded areas with low population density, under 250 inhabitants/km\({}^{2}\), were ascribed to the 'low' category, flooded areas with medium population density, between 250-500 inhabitants/km\({}^{2}\), were ascribed to the'medium' category while flooded areas with high population density, above 500 inhabitants/km\({}^{2}\) were ascribed to the 'high' category. It is important to note, however, that this was prescribed arbitrarily and visualized to emphasize differences in population density per affected flooded area. In reality, in Mozambique for example, the mean population density of the 2020 dataset was of 40 inhabitants/km\({}^{2}\).  
Figure 2: Flood model workflow. Adapted from UN-SPIDER radar-based recommended practice. All code can be found on GitHub.  
Assessment of the affected urban areas in the Mozambique Channel was computed using the ESA World Cover dataset at 10 m resolution (Table 1). The tiles of interest were downloaded from the ESA World Cover viewer, which uses a random forest classification algorithm, then pre-processed in QGIS, tiled, merged and compressed. In Google Earth Engine, the same reducer technique to the population parameter was applied by doing the weighted sum of overlapping pixels between the flood model and urban class, after selecting the band associated with the land-cover class for urban areas (50) [28].  
In order to assess the impact of TC Ana on the mangrove ecosystems, a NDVI was computed using a mosaic of Sentinel-2 data (Equation (1)). This used the red and NIR band, which correspond to bands B4 and B8. Equation (1) is the NDVI used to assess mangrove degradation after cyclone season:  
\[NDVI=\frac{NIR-Red}{NIR+Red} \tag{1}\]  
As previously mentioned, due to the high number of clouds during the period of study (cyclone season), the NDVI was computed using a median composite of images from before the start of the cyclone season vs. after the cyclone season. As such, this NDVI effectively looked at the impacts of all the cyclones that occurred between September 2021 ('before') and June 2022 ('after'). This was then applied to the ROIs and areas of mangrove forests using band 95 for the 'before' image and the 'after' image of the World Cover dataset [28]. Finally, a change analysis was computed whereby the difference between the 'before' and 'after' image was computed by dividing the 'after' image by the 'before' image, much like the flood-model methodology. The resulting difference image shows areas with light pixels displaying high change after the cyclone season and areas with dark pixels displaying consistency and no changes after cyclone season. All code and user instructions can be found on GitHub and is reproduceable in Google Earth Engine (GEE) in JavaScript, using the afore-mentioned open-source datasets.