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USGS2025

Assessing the topographic distribution of legacy soil phosphorus in agricultural fields of the Delmarva Peninsula, Mid-Atlantic Coastal Plain, USA

Phosphorus (P) management remains a challenge in agricultural watersheds. The Choptank River Conservation Effects Assessment Project watershed, located in Maryland and Delaware and draining to the Chesapeake Bay, contains legacy soil P from historical dairy and poultry manure applications. These practices elevated soil P beyond crop needs, contributing to persistent P export to aquatic ecosystems. We assessed spatial P distribution and analyzed GIS (Geographic Information Systems)-derived landscape features driving legacy P movement on a farm (47 ha). We hypothesized that P accumulates in drained lowlands and depressional areas due to gravity-driven processes that accelerate P-enriched water to receiving waters via overland flow. In collaboration with the US Department of Agriculture Legacy P project, we collected 105 soil samples (0- to 5-cm and 5- to 15-cm depths) and 14 ditch sediment samples across five topographic openness classes from a farm with >100 years of dairy manure application. Average Mehlich-III P concentrations were 218 and 179 mg kg −1 at 0- to 5-cm and 5- to 15-cm depths, respectively, with legacy areas defined by P content > 100 mg kg −1 . Soil P and clay particle size were positively correlated ( r = 0.42, p < 0.05), increased as landscape openness decreased, and were negatively correlated with topographic openness (ranging from −0.2 to −0.4, p < 0.05), indicating accumulation of P and clay in low-lying areas. These patterns suggest that historical field-level managements have primarily shaped P distribution, while hydrologic and landscape properties further influence its redistribution via transport pathways and drainage. These findings support the development of landscape models to map critical source areas in low-relief watersheds and guide targeted mitigation in high-risk P export zones.

Maryland↗

USGS2020

Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model

The Delmarva Peninsula in the eastern United States is dominated by thousands of small, forested depressional wetlands that are highly sensitive to climate change and climate variability but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art deep semantic segmentation network to map forested wetland inundation in the Delmarva region by integrating leaf-off Worldview-3 (WV3) multispectral data with fine resolution light detection and ranging (lidar) intensity and topographic data, including digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation maps generated from lidar intensity were used for model calibration and validation. The wetland inundation map results were also validated by field polygons and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% against field data and high overlap with lidar mapped inundation. The integration of topographic metrics in deep learning model can improve classification accuracy in depressional wetlands. This study highlights the great potential of deep learning models to map wetland inundation through use of high resolution optical and lidar remote sensing datasets.

Maryland↗