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

understand the impact of environmental threats on the amount of variation of crops. Accurate identification and extraction of different land cover features (e.g., agricultural fields) is impor- tant for assessing distribution across agricultural regions. The Table 5 describe the statistical metric for the accuracy of the land cover model. While the accuracy, precision, and recall on the test set provide an insight into the product’s potential accuracy, a more rigorous accuracy assessment was carried out to obtain a closer esti- mate of the genuine accuracy. Random stratified points were chosen across the entire AOI, and very high-resolution imagery (e.g., Google Earth) was employed to determine the actual ground truth class for each point. The overall accuracy of the aggregated LC maps was esti- mated at 78%, a decline from the generalisation accuracy of 92% derived from the unseen test set. This decreased performance is likely attributable to the substantial amount of new, unseen data introduced to the model. Moreover, this dip in overall accuracy could be mitigated by referencing VHR imagery from Google Earth as ground truth, especially in instances where the Sentinel 2 imagery was indistinct or inconclusive. In response, 17 LC maps were produced corresponding to significant flood events leading to IDPs from 2016 to 2019. The dates for creating LC maps were selected using the first avail- able Sentinel-2 image after for each flood event. LC maps for drought events (see Fig 8) were generated as aggregates for growing seasons; Gu from March to late May and Deyr from the

end of September to the end of December). Eight aggregate LC maps were produced, provid- ing a general representation of Land Cover for each agricultural growing season between 2016 Fig 8. Aggregated land cover map of the study area represent the Deyr growing season generated from Sentinel-2 time-series from 1st October to the end of the December 2018. https://doi.org/10.1371/journal.pone.0304202.g008 Table 5. Precision, recall and F1 scores as calculated on a prediction of a mosaic comprised of single Sentinel-2 images from November 2017. Class Precision Recall F1 Score Support Agricultural Field 59.4% 62.3% 60.8% 61 Forest 47.8% 33.7% 39.5% 98 Shrubland/Grassland 60.3% 80.7% 69.0% 243 Settlement 72.7% 88.9% 80.0% 18 Soil/Sparse Vegetation 79.3% 48.3% 60.1% 151 Water 94.5% 92.9% 93.7% 56 https://doi.org/10.1371/journal.pone.0304202.t005 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 15 / 27