PostedFriday, May 24, 2019 at 9:24 AM
Understanding relationships between poverty and environment is crucial for sustainable development and ecological conservation. Annual monitoring of socioeconomic changes using household surveys is prohibitively expensive. Here, we demonstrate that satellite data predicted the poorest households in a landscape in Kenya with 62% accuracy. A multilevel socioecological treatment of satellite data accounting for the complex ways in which households interact with the environment provided better prediction than the standard single-buffer approach. The increasing availability of high-resolution satellite data and volunteered geographic data means this method could be modified and upscaled in the future to help monitor the sustainable development goals.