Oak Ridge Associated Universities supporting U.S. Environmental Protection Agency (EPA), Center for Public Health and Environmental Assessment (CPHEA), 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.
U.S. Environmental Protection Agency (EPA), Center for Public Health and Environmental Assessment (CPHEA), Environmental Pathways Modeling Branch (EPMB), 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.
Sci Total Environ. 2024 Nov 1;949:175058. doi: 10.1016/j.scitotenv.2024.175058. Epub 2024 Jul 29.
Wetland habitats provide critical ecosystem services to the surrounding landscape, including nutrient and pollutant retention, flood mitigation, and carbon storage. Wetland connectivity to water bodies and related ecosystems is critical in habitat sustainability, but there are limited resources for landscape-level wetland planning. Considering the network connectivity of an ecosystem type can derive different benefits to the natural and built environment, as well as human health. The value that wetlands provide, along with incentive programs and conservation goals mandated by the government require new and improved wetland spatial data. Utilizing high quality, publicly available data, this study finds that the amount of land in the United States that could support built or restored wetlands is more than double the area of mapped existing wetlands. This study uses 17 input variables (i.e., features extracted from remotely sensed data and auxiliary datasets) at the 10-m resolution and the National Wetlands Inventory to train a random forest model to identify areas that may support a wetland habitat, or potential wetland areas. Models were calculated for each of 18 two-digit hydrologic units that encompass the conterminous United States, and model overall accuracy ranged from 78.0 % to 89.8 %. The models predicted that 21.1 % of the conterminous United States can be categorized as potential wetland area. Selecting input variables to predict areas with wetland potential, rather than to identify existing wetlands, using the random forest algorithm can be transferred to other locations, scales, and ecosystem types. Visualizing potential wetland areas using input data at the 10-m resolution and enhanced methodology improves previous work, as even slight changes in topography, soils, and landscape features can determine ecosystem connections. This product can be used to better place wetland restoration projects to serve ecosystem- and community-wide health by ensuring ecosystem success and targeting areas that face increased climate change impacts.
湿地生境为周围景观提供了至关重要的生态系统服务,包括养分和污染物的截留、洪水缓解以及碳储存。湿地与水体和相关生态系统的连通性对于生境的可持续性至关重要,但用于景观级湿地规划的资源有限。考虑到生态系统类型的网络连通性可以为自然和建成环境以及人类健康带来不同的益处。湿地提供的价值,以及政府规定的激励计划和保护目标,都需要新的和改进的湿地空间数据。本研究利用高质量的公共可用数据,发现美国有更多土地可以支持已建成或恢复的湿地,是现有湿地地图面积的两倍多。本研究使用了 17 个输入变量(即从遥感数据和辅助数据集提取的特征),分辨率为 10 米,以及国家湿地清单,来训练随机森林模型,以识别可能支持湿地生境或潜在湿地的区域。为涵盖美国大陆的 18 个两位数水文单元中的每一个都计算了模型,模型的整体准确性范围从 78.0%到 89.8%。模型预测,美国大陆的 21.1%可以归类为潜在湿地面积。选择输入变量来预测具有湿地潜力的区域,而不是识别现有湿地,使用随机森林算法可以转移到其他位置、规模和生态系统类型。使用 10 米分辨率的输入数据和增强的方法可视化潜在湿地区域,可以改进以前的工作,因为地形、土壤和景观特征的微小变化都可能决定生态系统的连接。该产品可用于更好地进行湿地恢复项目,通过确保生态系统的成功并针对面临气候变化影响增加的地区,为生态系统和社区的健康服务。