Breaking ground: Automated disturbance detection with Landsat time series captures rapid refugee settlement establishment and growth in North Uganda

Since 2015, Uganda has welcomed over 700,000 refugees from South Sudan, Democratic Republic of the Congo, Burundi, and other East African nations, and currently hosts over 1.4 million refugees with 92% of that population living in UNHCR-managed settlements. Despite refugee settlements being essential spaces for physical protection and humanitarian aid distribution and reception, the sheer rate of refugee influx and settlement growth has introduced uncertainties around site planning, aid delivery, food security, and landscape change. For example, there is little publicly available information on settlement establishment, growth, or changes in land use/land cover for the vast majority of UNHCR-managed settlements in Uganda and around the world. To address this shortcoming, this research characterizes the spatial and temporal patterns of refugee settlement landscape dynamics using the case study of the Pagirinya Refugee Settlement in Northern Uganda, Landsat and Sentinel-2 satellite image time series, and BFAST, an automated land cover disturbance detection algorithm. To delineate the extent of the settlement and surrounding disturbance, a refugee settlement boundary was generated using a 2018 Landsat NDVI composite, which included land disturbed by settlement establishment and subsequent growth. Landsat time series data were sampled within this boundary to parametrize a BFAST model to detect settlement disturbance, which was deployed over 351 Landsat images from 2005 to 2018. This approach yielded sub-monthly land cover disturbances from 2016 to 2017 with an accuracy of 87.5% resulting from the rapid (within one month) settlement establishment, road construction, the spread of dwellings and other built-up infrastructure throughout the settlement, as well as the conversion of natural grassland to small-scale agriculture within the first six months after refugee settlement began. These results were generated using open-access data and open-source algorithms to pave the way for developing a near real-time satellite image-based settlement monitoring framework, which would aid refugee response and evaluation efforts that are central to Uganda's refugee hosting and settlement plans, as well as implementation of the Global Compact on Refugees.