Abstract Recently, there has been a significant increase in efforts to better inventory and manage important ecosystems across Canada using advanced remote sensing techniques. In this study, we improved the method and results of our first-generation Canadian wetland inventory map at 10-m resolution. Iin order to increase wetland classification accuracy, the main contributions of this new study are adding more training data to the classification process and training Random Forest (RF) models on the Google Earth Engine (GEE) platform within the boundaries of ecozones rather than provinces. A considerable effort has been devoted to data collection, preparation, standardization of datasets for each ecozone. The data cleaning reveals a data gap in several Northern ecozones. Accordingly, high-resolution optical data, from Worldview-2 and Pleiades, were acquired to delineate wetland training data based on visual interpretation in those regions. By using this well-distributed training data, this second generation wetland inventory map represents an improvement of 7% compared to the first generation map. Accuracy varied from 76% to 91% in different ecozones depending on available resources. Furthermore, the results of RF variable importance, which was carried out for each ecozone, demonstrate that and NDVI extracted from Sentinel-1 and Sentinel-2 data, respectively, were the most important features for wetland mapping.

中文翻译：

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使用 Google Earth Engine 的 10 米分辨率的第二代加拿大湿地清单地图

摘要 最近，使用先进的遥感技术更好地清查和管理加拿大重要生态系统的努力有了显着增加。在这项研究中，我们改进了分辨率为 10 米的第一代加拿大湿地清单地图的方法和结果。为了提高湿地分类的准确性，这项新研究的主要贡献是在分类过程中添加更多的训练数据，并在生态区而不是省份的边界内在谷歌地球引擎 (GEE) 平台上训练随机森林 (RF) 模型。为每个生态区的数据收集、准备和标准化付出了相当大的努力。数据清理揭示了几个北部生态区的数据差距。因此，来自 Worldview-2 和 Pleiades 的高分辨率光学数据，获得这些区域的视觉解释以描绘湿地训练数据。通过使用这种分布良好的训练数据，第二代湿地清单地图与第一代地图相比提高了 7%。根据可用资源的不同，不同生态区的准确度从 76% 到 91% 不等。此外，对每个生态区进行的 RF 变量重要性结果表明，分别从 Sentinel-1 和 Sentinel-2 数据中提取的 NDVI 是湿地制图的最重要特征。根据可用资源的不同，不同生态区的准确度从 76% 到 91% 不等。此外，对每个生态区进行的 RF 变量重要性结果表明，分别从 Sentinel-1 和 Sentinel-2 数据中提取的 NDVI 是湿地制图的最重要特征。根据可用资源的不同，不同生态区的准确度从 76% 到 91% 不等。此外，对每个生态区进行的 RF 变量重要性结果表明，分别从 Sentinel-1 和 Sentinel-2 数据中提取的 NDVI 是湿地制图的最重要特征。