Using Machine Learning to Downscale Projected Land Conversion: Application to Cropland Expansion into Endangered Species Habitats

Decisionmakers need to better understand the effects of programs and policies impacting land conversion and associated environmental, economic, and social impacts to improve assessment of the costs and benefits of alternative policies. It is not only the quantity of land conversion taking place, but which land is being converted that determines outcomes for endangered species habitats, biodiversity, water quality, carbon sequestration, and other ecosystem services. However, there is a current lack of tools that can facilitate evaluation of impacts at a sufficiently spatially disaggregated level to adequately evaluate them. In this study, we utilize projections of land conversion from the U.S. Forest Service 2020 Resource Planning Assessment under alternative socioeconomic and climate scenarios. Specifically, we consider the projected change in cropland between 2020-2050 under (1) low emissions forcing, moderate economic growth; and (2) high emissions forcing, high economic growth. We downscale county-level projections to a 30x30 meter raster of cropland expansion for the State of Louisiana using machine learning. We then overlay spatial data on endangered species ranges to assess impacts of land conversion to cropland. This enables us to estimate the area of endangered species’ habitats that would potentially be impacted by cropland expansion. The information generated can inform the design of data collection efforts as well as policies aimed at reducing negative impacts on critical habitats associated with cropland expansion.