Impact of Refugee Camps on Their Environment A Case Study Using Multi-Temporal SAR Data

Abstract

Monitoring the environment is a key task of remote sensing in particular in areas whose access is difficult or dangerous or where dense cloud cover obscures optical information. This study proposes an assessment of landscape changes related to large refugee camps, where information about environmental conditions is needed by both humanitarian organizations and regional administrations. Our intention is to provide a robust workflow which is applicable for an operational use. The study area is located in Western Kenya hosts a total number of 350.000 people. Images of ERS-2 and Sentinel-1 are used for the assessment of land degradation in a semi-arid savannah between 1997 and 2014. We expect a relationship between the existence of the refugee camps and the degradation of surrounding landscapes. For this purpose we present an approach which objectively reveals developments in natural resources based on six land-use / land cover classes integrating their relative importance for the ecosystem given by expert-based weights. An index of Natural Resource Depletion (NRD) is calculated using a Random Forest algorithm in order to classify a time series of SAR images and their textures at different spatial scales (r² = 0.71). Especially large-scale textures turned out to contribute to the classification. Or results showed a continuous increase of bare soil areas within a radius of five kilometers around the refugee camps and a total decrease of natural resources by 11.8% in the study area. Although the produced NRD maps reveal hot spots of landscape change for selected periods, a clear pattern of land degradation could not be identified and an evident interrelation between the expansion of the camp and the decrease of natural resources has still to be provided. The proposed approach is applicable to images of other radar sensors as well, such as Sentinel-1 of the European Space Agency which currently collects a multitude of scenes in high spatial resolution. It is therefore suitable for an operational use for the monitoring of land degradation around refugee camps.