As one of the world's largest freshwater ecosystems, the Great Lakes Basin houses thousands of acres of wetlands that support a variety of crucial ecological and environmental functions at the local, regional, and global scales. Monitoring these wetlands is critical to conservation and restoration efforts; however, current methods that rely on field monitoring are labor-intensive, costly, and often outdated. In this article, we present a graphical user interface constructed in Google Earth Engine called the Wetland Extent Tool (WET), which allows semiautomatic wetland classification according to a user-input area of interest and date range. WET conducts multisource, moderate resolution processing utilizing Landsat 8 Operational Land Imager, Sentinel-2 MultiSpectral Instrument, Sentinel-1 C-SAR, and Shuttle Radar Topography Mission (SRTM) datasets to classify wetlands in the entire Great Lakes Basin. We evaluated classification results of wetlands, uplands, and open water from May–September 2019, and tested whether SRTM elevation, slope, or the Dynamic Surface Water Extent produced the most accurate results in each Great Lake Basin in conjunction with optical indices and radar composites. We found that slope produced the most accurate classification in Lake Michigan, Huron, Superior, and Ontario, while elevation performed best in Lake Erie. Classification results averaged 86.2% overall accuracy, 70.0% wetland consumer's accuracy, and 82.7% wetland producer's accuracy across the Great Lakes Basin. WET leverages cloud-computing for multisource processing of moderate resolution remote sensing data, and employs a user interface in Google Earth Engine that wetland managers and conservationists can use to monitor wetland extent in the Great Lakes Basin in near real-time.

中文翻译：

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利用 Google Earth Engine 用户界面，利用光学和雷达地理空间数据集对 10 m 的五大湖盆地进行半自动湿地分类

作为世界上最大的淡水生态系统之一，五大湖盆地拥有数千英亩的湿地，在当地、区域和全球范围内支持各种重要的生态和环境功能。监测这些湿地对于保护和恢复工作至关重要；然而，目前依赖于现场监测的方法是劳动密集型的、成本高昂的，而且往往已经过时。在本文中，我们展示了一个在 Google 地球引擎中构建的图形用户界面，称为湿地范围工具 (WET)，它允许根据用户输入的感兴趣区域和日期范围进行半自动湿地分类。WET 利用 Landsat 8 Operational Land Imager、Sentinel-2 MultiSpectral Instrument、Sentinel-1 C-SAR、和航天飞机雷达地形任务 (SRTM) 数据集对整个五大湖盆地的湿地进行分类。我们评估了 2019 年 5 月至 9 月的湿地、高地和开阔水域的分类结果，并结合光学指数和雷达复合材料测试了 SRTM 高程、坡度或动态地表水范围是否在每个大湖盆地产生了最准确的结果. 我们发现坡度在密歇根湖、休伦湖、苏必利尔和安大略湖产生的分类最准确，而海拔在伊利湖表现最好。整个五大湖盆地的分类结果平均总体准确度为 86.2%，湿地消费者准确度为 70.0%，湿地生产者准确度为 82.7%。WET 利用云计算对中等分辨率遥感数据进行多源处理，