Abstract. The main objective in our study was to derive an accurate wetland inventory of the Dınàgà Wek’èhodì region, Northwest Territories, while also enhancing our previously established wetland mapping workflow. Our methods used multidate optical and radar satellite imagery and fused these data with ArcticDEM topographic variables. Additionally, few studies to date have assessed the ArcticDEM for wetland mapping; our research helps fill this critical gap in the literature. A machine-learning, object-based approach was employed to classify the fused data stacks and included both mean and standard deviation image-object feature extractions. In this study, 18 random forest models were tested, each including various sensor inputs and feature extractions. The highest accuracy was achieved using a fusion of optical, radar, and ArcticDEM data and included both the mean and standard deviation of image objects (88.17% overall accuracy and kappa 0.858). Vegetated wetlands had producer accuracies ranging from 74% to 86%, whereas open water was 92%. Feature importance rankings indicated that 16 of the top 20 variables were derived from optical data, three from radar, and one from the ArcticDEM. The results of our study will be used to assist governments and other interested parties in advancing conservation initiatives for this significant high-latitude region.

中文翻译：

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使用多日期和多传感器地球观测数据进行高纬度湿地制图：西北地区的案例研究

摘要。我们研究的主要目标是获得西北地区 Dınàgà Wek'èhodì 地区的准确湿地清单，同时增强我们之前建立的湿地制图工作流程。我们的方法使用多日期光学和雷达卫星图像，并将这些数据与 ArcticDEM 地形变量融合。此外，迄今为止，很少有研究评估 ArcticDEM 以进行湿地测绘。我们的研究有助于填补文献中的这一关键空白。采用机器学习、基于对象的方法对融合数据堆栈进行分类，并包括均值和标准差图像对象特征提取。在这项研究中，测试了 18 个随机森林模型，每个模型都包括各种传感器输入和特征提取。使用光学、雷达、和ArcticDEM 数据，包括图像对象的均值和标准差（88.17% 的整体准确度和 kappa 0.858）。植被湿地的生产者准确率从 74% 到 86% 不等，而开放水域为 92%。特征重要性排名表明，前 20 个变量中有 16 个来自光学数据，3 个来自雷达，1 个来自 ArcticDEM。我们的研究结果将用于协助政府和其他相关方推进这一重要高纬度地区的保护举措。一个来自 ArcticDEM。我们的研究结果将用于协助政府和其他相关方推进这一重要高纬度地区的保护举措。一个来自 ArcticDEM。我们的研究结果将用于协助政府和其他相关方推进这一重要高纬度地区的保护举措。