Mapping Wetland Probabilities: Tools, Models, and Applications
Published:
This webinar focused on wetland mapping in Washington state.
Abstract: Inland wetlands disproportionately contribute to the soil organic (SOC) carbon pool by storing 20-30% of all SOC despite occupying only 5-8% of the land surface. However, difficulty identifying wetland areas under perennial forest canopy increases uncertainty in estimates of SOC stocks across watershed to regional scales. We used a machine learning approach that utilized aerial lidar-derived hydrologic and topographic metrics to characterize the landscape surface and identify areas of potential wetland formation for three study areas in the Pacific Northwest which represent an east-to-west (low-to-high) precipitation gradient. This approach produces a spatially explicit and continuous model of wetland probability as a range from wetland to upland across a landscape. We then collected soil samples and measured SOC stocks along the wetland-to-upland probability gradient and used the probability along with surficial geology corresponding to geomorphology to model SOC stocks.