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Headwater streams and inland wetlands: Status and advancements of geospatial datasets and maps across the United States.
Christensen, Jay R; Golden, Heather E; Alexander, Laurie C; Pickard, Brian R; Fritz, Ken M; Lane, Charles R; Weber, Marc H; Kwok, Rose M; Keefer, Madeline N.
Afiliação
  • Christensen JR; Center for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USA.
  • Golden HE; Center for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USA.
  • Alexander LC; Center for Public Health and Environmental Assessment, Office of Research and Development, US Environmental Protection Agency, Washington DC 20460 USA Region 10, US Environmental Protection Agency, Portland, OR 97205, USA.
  • Pickard BR; TetraTech Inc, Research Triangle Park, NC 27709 USA.
  • Fritz KM; Center for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USA.
  • Lane CR; Center for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Athens, GA, 30605 USA.
  • Weber MH; Center for Public Health and Environmental Assessment, Office of Research and Development, US Environmental Protection Agency, Corvallis, OR 97333 USA.
  • Kwok RM; Office of Wetlands, Oceans, and Watersheds, Office of Water, US Environmental Protection Agency, Washington, DC 20460, USA.
  • Keefer MN; TetraTech Inc, Research Triangle Park, NC 27709 USA.
Earth Sci Rev ; 235: 1-24, 2022 Dec.
Article em En | MEDLINE | ID: mdl-36970305
ABSTRACT
Headwater streams and inland wetlands provide essential functions that support healthy watersheds and downstream waters. However, scientists and aquatic resource managers lack a comprehensive synthesis of national and state stream and wetland geospatial datasets and emerging technologies that can further improve these data. We conducted a review of existing United States (US) federal and state stream and wetland geospatial datasets, focusing on their spatial extent, permanence classifications, and current limitations. We also examined recent peer-reviewed literature for emerging methods that can potentially improve the estimation, representation, and integration of stream and wetland datasets. We found that federal and state datasets rely heavily on the US Geological Survey's National Hydrography Dataset for stream extent and duration information. Only eleven states (22%) had additional stream extent information and seven states (14%) provided additional duration information. Likewise, federal and state wetland datasets primarily use the US Fish and Wildlife Service's National Wetlands Inventory (NWI) Geospatial Dataset, with only two states using non-NWI datasets. Our synthesis revealed that LiDAR-based technologies hold promise for advancing stream and wetland mapping at limited spatial extents. While machine learning techniques may help to scale-up these LiDAR-derived estimates, challenges related to preprocessing and data workflows remain. High-resolution commercial imagery, supported by public imagery and cloud computing, may further aid characterization of the spatial and temporal dynamics of streams and wetlands, especially using multi-platform and multi-temporal machine learning approaches. Models integrating both stream and wetland dynamics are limited, and field-based efforts must remain a key component in developing improved headwater stream and wetland datasets. Continued financial and partnership support of existing databases is also needed to enhance mapping and inform water resources research and policy decisions.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Earth Sci Rev Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Earth Sci Rev Ano de publicação: 2022 Tipo de documento: Article