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

February 15, 2020

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.

Citation Information

Publication Year 2020
Title Mapping forested wetland inundation in the Delmarva Peninsula, USA: Use of deep learning model
DOI 10.3390/rs12040644
Authors Ling Du, Greg W. McCarty, Xinhow Zhang, Megan W. Lang, Melanie K. Vanderhoof, Xian-Dan Lin, Chengquan Huang, Sangchul Lee, Zhenhua Zou
Publication Type Article
Publication Subtype Journal Article
Series Title Remote Sensing
Series Number
Index ID 70209565
Record Source USGS Publications Warehouse
USGS Organization Geosciences and Environmental Change Science Center