Abstract Shoreline wetlands along Lake Ontario are valuable, multi-functional resources that have historically provided large numbers of important ecosystem goods and services. However, alterations to the lake’s natural hydrologic regime have impacted traditional meadow marsh in the wetlands, resulting in competition and colonization by dense and aggressive Typha angustifolia and Typha x glauca (Cattails). The shift to a Typha-dominated landscape resulted in an array of negative impacts, including increased Typha density, substantial decreases in plant species richness and diversity, and altered habitat and changes in associated ecosystem services. Successful long-term adaptive management of these wetland resources requires timely and accurate monitoring. Historically, wetland landscapes have been surveyed and mapped using field-based surveys and/or photointerpretation. However, given their resource- and cost-intensive nature, these methods are often prohibitively time- and labor-consuming or geographically limited. Other remote sensing applications can provide more rapid and efficient assessments when evaluating wetland change trajectories or analyzing direct and indirect impacts across larger spatial and temporal scales. The primary goal of this study was to develop and describe methodology using U.S. Army Corps of Engineers National Coastal Mapping Program hyperspectral imagery, light detection and ranging data, and high-spatial resolution true-color imagery to provide updated wetland classifications for Lake Ontario coastal wetlands. This study used existing field-collected vegetation survey data (Great Lakes Coastal Wetland Monitoring Program), ancillary imagery, and existing classification information as training data for a supervised classification approach. These data were used along with a generalized wetland schema (classes based on physical and biological gradients: elevation, Typha, meadow marsh, mixed emergent, upland vegetation) to generate wetland classification data with Kappa values near 0.85. Ultimately, these data and methods provide helpful knowledge elements that will allow for more efficient inventorying and monitoring of Great Lake resources, forecasting of resource condition and stability, and adaptive management strategies.

中文翻译：

---

使用美国安大略湖沿岸的机载高光谱图像对香蒲为主的湿地进行分类

摘要 安大略湖沿岸湿地是宝贵的多功能资源，历史上提供了大量重要的生态系统产品和服务。然而，湖泊自然水文状况的改变已经影响了湿地中的传统草甸沼泽，导致密集和侵略性香蒲和青香香蒲（香蒲）的竞争和定殖。向以香蒲为主的景观转变导致了一系列负面影响，包括香蒲密度增加、植物物种丰富度和多样性大幅下降、栖息地改变和相关生态系统服务发生变化。对这些湿地资源进行成功的长期适应性管理需要及时准确的监测。从历史上看，湿地景观已经使用实地调查和/或照片解释进行了调查和绘图。然而，鉴于它们的资源和成本密集型性质，这些方法通常非常耗时费力或受地域限制。在评估湿地变化轨迹或分析更大空间和时间尺度的直接和间接影响时，其他遥感应用可以提供更快速、更有效的评估。本研究的主要目标是开发和描述使用美国陆军工程兵团国家海岸测绘计划高光谱图像、光探测和测距数据以及高空间分辨率真彩色图像的方法，为安大略湖沿海湿地提供更新的湿地分类. 本研究使用现有的现场收集的植被调查数据（五大湖沿海湿地监测计划）、辅助图像和现有的分类信息作为监督分类方法的训练数据。这些数据与广义湿地模式（基于物理和生物梯度的类别：海拔、香蒲、草甸沼泽、混合涌现、高地植被）一起使用，以生成 Kappa 值接近 0.85 的湿地分类数据。最终，这些数据和方法提供了有用的知识元素，可以更有效地盘点和监测大湖资源、预测资源状况和稳定性以及适应性管理策略。和现有的分类信息作为监督分类方法的训练数据。这些数据与广义湿地模式（基于物理和生物梯度的类别：海拔、香蒲、草甸沼泽、混合涌现、高地植被）一起使用，以生成 Kappa 值接近 0.85 的湿地分类数据。最终，这些数据和方法提供了有用的知识元素，可以更有效地盘点和监测大湖资源、预测资源状况和稳定性以及适应性管理策略。和现有的分类信息作为监督分类方法的训练数据。这些数据与广义湿地模式（基于物理和生物梯度的类别：海拔、香蒲、草甸沼泽、混合涌现、高地植被）一起使用，以生成 Kappa 值接近 0.85 的湿地分类数据。最终，这些数据和方法提供了有用的知识元素，可以更有效地盘点和监测大湖资源、预测资源状况和稳定性以及适应性管理策略。