Amanda T. Stahl, Ph.D.

To address environmental problems with sustainable solutions requires interdisciplinary approaches that bridge gaps between scholarly research, conservation policy, and decision-making for land and resource management. My work applies concepts from sustainability science to environmental problem-solving at the interface between ecological research and conservation policy. To inform the coordination of conservation measures at local and large landscape scales, I develop and test innovative approaches to integrative social-ecological analysis with GIS and remotely sensed imagery. My research team developed and continues to host an online User Library for those who would like to learn about using Google Earth Engine for routine environmental monitoring: https://labs.wsu.edu/ecology/research-projects/cbem-user-library/

Some of my current projects involve identifying opportunities to coordinate streamside conservation efforts for a maximum return on investments. This requires researchers, environmental decision-makers and conservation planners to consider not only shorter-term benefits (such as enhancing fish habitat and water quality), but also longer-term, larger-scale benefits associated with continuously vegetated corridors along rivers and streams (such as water supply, flood hazard mitigation, and providing movement corridors for wildlife). These integrative analyses can help to highlight areas of opportunity to coordinate actions by location and potentially leverage funding or policy incentives for local and broader landscape-level conservation outcomes.

I continue to experiment with integrative social-ecological GIS analysis to generated map-based platforms that inform coordinated actions for multiple conservation outcomes at site- to large-landscape scales. I also use drone and satellite remote sensing technologies to develop environmental monitoring methods that provide feedback on conservation measures aiming for both environmental and economic sustainability on working lands. My goal is to promote seamless communication so that big data and scientific understanding can be harnessed to provide more accurate, relevant information for conservation policy and management in near-real-time.