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Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks by Du, Ling; McCarty, Gregory W.; Zhang, Xin; Lang, Megan W.; Vanderhoof, Melanie K.; Li, Xia; Huang, Chengquan; Lee, Sangchul; Zou, Zhenhua is a Environmental Science article available to read on EtoBox.

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The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and their 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 U-Net semantic segmentation network to map forested wetland inundation in the Delmarva area by integrating leaf-off WorldView-3 (WV3) multispectral data with fine spatial resolution light detection and ranging (lidar) intensity and topographic data, including a digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation labels generated from lidar intensity were used for model training and validation. The wetland inundation map results were also validated using field data, 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 re

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Author

Du, Ling; McCarty, Gregory W.; Zhang, Xin; Lang, Megan W.; Vanderhoof, Melanie K.; Li, Xia; Huang, Chengquan; Lee, Sangchul; Zou, Zhenhua

Publisher

MDPI AG; Multidisciplinary Digital Publishing Institute (MDPI); Basel: MDPI, 2009- (ISSN 2072-4292)

Published

2020

Language

EN

Field

Environmental Science (Physical Sciences)