Disaggregating the Patchwork:

Abstract

Identification and inventory of wetlands are essential components of natural resource management. To be effective in these endeavors, it is critical that the process used to detect and document wetlands be time efficient, accurate, and repeatable as new environmental information becomes available. Approaches dependent on aerial photographic interpretation of land cover by individual human analysts necessitate hours of assessment, introduce human error, and fail to include the best available soils and hydrologic data. The goal of the current study is to apply hierarchical modeling and Bayesian inference to predict the probability of wetland presence as a continuous gradient with the explicit consideration of spatial structure. The presented spatial statistical model can evaluate 100 km ² at a 50 x 50 meter resolution in approximately 50 minutes while simultaneously incorporating ancillary data and accounting for latent spatial processes. Model results demonstrate an ability to consistently capture wetlands identified through aerial interpretation with greater than 90 % accuracy (scaled Brier Score) and to identify wetland extents, ecotones, and hydrologic connections not identified through use of other modeling and mapping techniques. The provided model is reasonably robust to changes in resolution, areal extents between 100 km ² and 300 km ², and region-specific physical conditions.