Abstract 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.

中文翻译：

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破土动工：使用 Landsat 时间序列的自动干扰检测捕获了乌干达北部难民定居点的快速建立和增长

摘要 自 2015 年以来，乌干达接待了来自南苏丹、刚果民主共和国、布隆迪和其他东非国家的超过 70 万难民，目前收容了超过 140 万难民，其中 92% 的人口居住在联合国难民署管理的定居点。尽管难民定居点是人身保护和人道主义援助分发和接收的重要场所，但难民涌入和定居点增长的绝对速度给场地规划、援助交付、粮食安全和景观变化带来了不确定性。例如，关于在乌干达和世界各地由难民署管理的绝大多数定居点的定居点建立、增长或土地利用/土地覆盖变化的公开信息很少。为了解决这个缺点，本研究使用乌干达北部 Pagirinya 难民定居点的案例研究、Landsat 和 Sentinel-2 卫星图像时间序列以及 BFAST（一种自动土地覆盖干扰检测算法）来表征难民定居点景观动态的时空模式。为了描绘定居点和周围干扰的范围，使用 2018 年 Landsat NDVI 综合生成了难民定居点边界，其中包括受定居点建立和随后增长干扰的土地。Landsat 时间序列数据在此边界内采样，以参数化 BFAST 模型以检测沉降扰动，该模型在 2005 年至 2018 年部署在 351 幅 Landsat 图像上。该方法产生了 2016 年至 2017 年的亚月土地覆盖扰动，精度为 87。5% 来自定居点的快速（一个月内）建立、道路建设、住宅和其他已建成基础设施在整个定居点的扩展，以及在之后的前六个月内将天然草地转变为小规模农业难民安置开始。这些结果是使用开放访问数据和开源算法生成的，为开发近乎实时的基于卫星图像的定居监测框架铺平了道路，这将有助于对乌干达难民收容和定居至关重要的难民响应和评估工作计划，以及《全球难民契约》的实施。以及在难民安置开始后的头六个月内将天然草地转变为小规模农业。这些结果是使用开放访问数据和开源算法生成的，为开发近乎实时的基于卫星图像的定居监测框架铺平了道路，这将有助于对乌干达难民收容和定居至关重要的难民响应和评估工作计划，以及《全球难民契约》的实施。以及在难民安置开始后的头六个月内将天然草地转变为小规模农业。这些结果是使用开放访问数据和开源算法生成的，为开发近乎实时的基于卫星图像的定居监测框架铺平了道路，这将有助于对乌干达难民收容和定居至关重要的难民响应和评估工作计划，以及《全球难民契约》的实施。