Deciphering climate-induced displacement in Somalia: A remote sensing perspective

intensity images is [35]: PðRÞ ¼ Gð2LÞgLRL 1 G2ðLÞðg þ RÞ2L ð5Þ Where γ is the true change of the radar cross sections σ0, and L is the number of looks. The probability of detection Pde given a threshold T, L and γ is: PdeðR > TÞ ¼ Z 1 PðRÞdR ð6Þ It is important to notice that P depends on the number of looks L. To illustrate the impact of L on the detector performance let us compute the Pde for the detection of a 3 dB change (γ = 0.5) with T = 0.4. For L = 4 (Sentinel-1) we have: Pde = 0.58, for L = 50 (Lee filter) Pde = 0.86. These figures point out the importance of speckle filtering for the ratio detector. In our imple- mentation the threshold T is determined by supervised learning, i.e., by estimating the ratio in a known area of no change, as it will be described in Thresholding section. This approach minimises the misclassification of arid land as flooded due to similar back- scatter of desert surfaces and flooded areas in arid regions like Somalia. Apart from common thresholding approaches were tested (e.g., unimodal thresholding approaches such as T-point and maximum deviation), a change detection approach was employed based on a ratio between pre-flood and post-flood imagery. This change detection strategy is designed to mini- mise the misclassification of arid landscapes as inundated terrain a frequent challenge due to the comparable backscatter characteristics of desert and water surfaces in arid zones such as Somalia. The current method’s reliance on VV polarisation is rooted in established efficacy,

yet it is recognised that incorporating target decomposition features, which utilise the compre- hensive data from all polarisations, could substantially mitigate this issue. Such an advanced technique promises a refined analysis, leveraging the full spectrum of polarimetric data to dis- tinguish between dry land and aquatic environments with greater precision. The thresholds in this study were calculated by supervised inspection of the imagery. The optimal threshold was calculated for a flooded pixel (PF) and for a non-flooded pixel (PNF). A coefficient fc was calcu- lated based on several trial-and-error tests and also confirmed by findings in [36]. The μ and σ are the mean and standard deviation of the no-change area selected in the ratio image R: PF � m½R� þ fc∗s½R� ð Þ PNF < m½R� þ fcs½R� ð Þ ð7Þ Where PF is the flooded pixel; PNF is the non-flooded pixel; fc is a coefficient factor found based on several iterations; μ = mean R and σ = the R standard deviation over the stable area. Next, ancillary masks were generated based on NASADEM. A threshold was set to generate a binary slope mask. Slopes � 3˚ were considered to be prone to flooding and assigned a pixel value of 1, while slopes >3˚ are considered not likely to flood, assigned a pixel value of 0 and were removed from further analysis. This step was crucial in eliminating any pixels that may have exhibited a change in backscatter due to the angle of signal return from a higher elevation such as hills. Areas with an elevation � 200 m were also masked and deemed unlikely to be

flooded. The slope and elevation threshold were selected based on the values used in the litera- ture (e.g., [34, 37]). Finally, a further terrain filter was applied to remove areas that can be considered unlikely to flood based on the Height Above Nearest Drainage (HAND) [38]. A binary HAND mask was generated where pixels with HAND values �15 m were considered not flood-prone while pixels with HAND < 15 m were considered likely to be flooded. Post-processing of the result- ing Sentinel-1 flood map was performed to remove isolated clusters of pixels < 50 pixels in PLOS ONE Deciphering Climate-induced Displacement in Somalia: A Remote Sensing Perspective PLOS ONE | https://doi.org/10.1371/journal.pone.0304202 August 9, 2024 11 / 27