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
Center for Environmental Measurement and Modeling, Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH 45268, USA.
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.
Earth Sci Rev. 2022 Dec;235:1-24. doi: 10.1016/j.earscirev.2022.104230.
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.
源头溪流和内陆湿地发挥着至关重要的功能,支撑着健康的流域和下游水域。然而,科学家和水生资源管理者缺乏对国家和州层面溪流与湿地地理空间数据集以及能够进一步改进这些数据的新兴技术的全面综合。我们对美国现有的联邦和州溪流与湿地地理空间数据集进行了综述,重点关注其空间范围、永久性分类以及当前的局限性。我们还查阅了近期经过同行评审的文献,以寻找可能改进溪流和湿地数据集估计、表示和整合的新兴方法。我们发现,联邦和州数据集在很大程度上依赖于美国地质调查局的国家水文数据集来获取溪流范围和持续时间信息。只有11个州(22%)拥有额外的溪流范围信息,7个州(14%)提供了额外的持续时间信息。同样,联邦和州湿地数据集主要使用美国鱼类和野生动物管理局的国家湿地清单(NWI)地理空间数据集,只有两个州使用非NWI数据集。我们的综合研究表明,基于激光雷达的技术在有限空间范围内推进溪流和湿地测绘方面具有潜力。虽然机器学习技术可能有助于扩大这些基于激光雷达得出的估计,但与预处理和数据工作流程相关的挑战仍然存在。由公共影像和云计算支持的高分辨率商业影像可能会进一步有助于刻画溪流和湿地的时空动态,特别是使用多平台和多时间机器学习方法。整合溪流和湿地动态的模型有限,基于实地的工作必须仍然是开发改进的源头溪流和湿地数据集的关键组成部分。还需要对现有数据库持续提供资金和伙伴关系支持,以加强测绘并为水资源研究和政策决策提供信息。