Automated Change Detection Methods for Satellite Data that can Improve Conservation Implementation

Abstract

A significant limitation in biodiversity conservation has been the effective implementation of laws and regulations that protect species habitats from degradation. Flexible, efficient, and effective monitoring and enforcement methods are needed to help conservation policies realize their full benefit. As remote sensing data become more numerous and accessible, they can be used to identify and quantify land cover changes and habitat loss. However, these data remain underused for systematic conservation monitoring in part because of a lack of simple tools. We adapted and developed two generalized methods that automatically detect land cover changes in a variety of habitat types using free and publicly available data and tools. We evaluated the performance of these algorithms in two ways. First, we tested the algorithms over 100 sites of known change in the United States, finding these approaches were effective (AUC > 0.90) at distinguishing between areas of land cover change and areas of no change. Second, we evaluated algorithm effectiveness by comparing results to manually identified areas of change in four case studies involving imperiled species habitat: oil and gas development in the range of the Greater Sage Grouse; sand mining operations in the range of the dunes sagebrush lizard; loss of Piping Plover coastal habitat in the wake of hurricane Michael (2018); and residential development in beach mouse habitat. The relative performance of each algorithm differed in each habitat type, but both provided effective means of detecting and delineating habitat loss. Our results show how these algorithms can be used to help close the implementation gap of monitoring and enforcement in biodiversity conservation and we provide a free online tool that can be used to run these analyses.