San Francisco Estuary and Watershed Science
Geospatial Tools for the Large-Scale Monitoring of Wetlands in the San Francisco Estuary: Opportunities and Challenges
- Author(s): Taddeo, Sophie
- Dronova, Iryna
- et al.
Published Web Locationhttps://doi.org/10.15447/sfews.2019v17iss2art2
Significant wetland losses and continuing threats to remnant habitats have motivated extensive restoration efforts in the San Francisco Bay–Delta estuary of California, the largest in the western United States. Consistent monitoring of ecological outcomes from this restoration effort would help managers learn from past projects to improve the design of future endeavors. However, budget constraints and challenging field conditions can limit the scope of current monitoring programs. Geospatial tools and remote sensing data sets could help complement field efforts for a low-cost, longer, and broader monitoring of wetland resources. To understand where geospatial tools could best complement current field monitoring practices, we reviewed the metrics and monitoring methods used by 42 wetland restoration projects implemented in the estuary. Monitoring strategies within our sample of monitoring plans relied predominantly on field surveys to assess key aspects of vegetation recovery while geospatial data sets were used sparingly. Drawing on recent publications that focus on the estuary and other wetland systems, we propose additional geospatial applications to help monitor the progress made toward site-specific and regional goals. These include the use of ecological niche models to target on-the-ground monitoring efforts, the up-scaling of field measurements into regional estimates using remote sensing data, and the analysis of time-series to detect ecosystem shifts. We discuss challenges and limitations to the broad-scale application of remote sensing data in wetland monitoring. These notably include the need to find a venue to store and share computationally intensive data sets, the often cumbersome pre-processing effort needed for long-term analyses, and multiple confounding factors that can obscure the signal of remote sensing data sets.