Using remote sensing products to predict recovery of vegetation across space and time following energy development

Using localized studies to understand how ecosystems recover can create uncertainty in recovery predictions across landscapes. Large archives of remote sensing data offer opportunities for quantifying the spatial and temporal factors influencing recovery at broad scales and predicting recovery. For example, energy production is a widespread and expanding land use among many semi-arid ecosystems of the Western United States dominated by sagebrush (Artemisia spp.), a keystone species providing a variety of ecological services. With remotely-sensed (Landsat) estimates of vegetation cover collected every 2–5 years from southwestern Wyoming, USA, over nearly three decades (1985–2015), we modeled changes in sagebrush cover on 375 former oil and gas well pads in response to weather and site-level conditions. We then used modeled relationships to predict recovery time across the landscape as an indicator of resilience for vegetation after well pad disturbances, where faster recovery indicates a greater capacity to recover when similarly disturbed. We found the rate of change in sagebrush cover generally increased with moisture and temperature, particularly at higher elevations. Rate of change in sagebrush cover also increased and decreased with greater percent sand and larger well pads, respectively. We predicted 21% of the landscape would recover to pre-disturbance conditions within 60 years, whereas other areas may require >100 years for recovery. These predictions and maps could inform future restoration efforts as they reflect resilience. This approach also is applicable to other disturbance types (e.g., fires and vegetation removal treatments) across landscapes, which can further improve conservation efforts by characterizing past conditions and monitoring trends in subsequent years.