ABSTRACT Wetlands across Canada have been, and continue to be, lost or altered under the influence of both anthropogenic and natural activities. The ability to assess the rate of change to wetland habitats and related spatial pattern dynamics is of importance for effective and meaningful management and protection, particularly under the current context of climate change. The availability of cloud-based geospatial platforms has allowed for the production of wetland maps at scales previously unfeasible due to technical limitations, yet the assessment of changes to wetlands at the level of the wetland class (bog, fen, swamp, and marsh) has yet to be implemented across Canada. Class-level change information is important when considering changes and impacts to wetland functions and services. To demonstrate this possibility, this study assessed 30 years of change to wetlands across the province of Newfoundland using Landsat imagery, spectral indices, and Random Forest classification within the Google Earth Engine (GEE) cloud-computing platform. Overall accuracies were high, ranging from 84.37% to 88.96%. In a comparison of different classifiers, Random Forest produced the highest over accuracy results and allowed for the estimation of variable importance, when compared Classification and Regression Tree (CART) and Minimum Distance (MD). The most important variables include the thermal infrared band (TIR), elevation, the difference vegetation index (DVI), the shortwave infrared bands (SWIR), and the normalized difference vegetation index (NDVI). Change detection analysis shows that bog, followed by swamp and fen, are the most common wetland classes across all time periods generally, and marsh wetlands are the least common wetland classes across all time periods respectively. The analysis also shows a general instability of wetland classes, though this is largely due to conversion from one wetland class to another. Future work may integrate RADAR data and consider weather patterns. The results of this study elucidate for the first time patterns of wetland class change across Newfoundland from 1985 to 2015 and demonstrate the potential of the GEE and Landsat historical imagery to assess change at provincial and national scales.

中文翻译：

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使用 Google Earth Engine 上的时间序列 Landsat 图像对湿地进行大规模变化监测：纽芬兰的案例研究

摘要 在人为和自然活动的影响下，加拿大各地的湿地已经并将继续消失或改变。评估湿地栖息地变化率和相关空间格局动态的能力对于有效和有意义的管理和保护非常重要，尤其是在当前气候变化的背景下。基于云的地理空间平台的可用性允许以以前由于技术限制而无法实现的比例制作湿地地图，但在湿地类别（沼泽、沼泽、沼泽和沼泽）级别对湿地变化的评估已经尚未在加拿大实施。在考虑对湿地功能和服务的变化和影响时，类级别的变化信息很重要。为了证明这种可能性，本研究使用 Google 地球引擎 (GEE) 云计算平台内的 Landsat 图像、光谱指数和随机森林分类评估了纽芬兰省 30 年来的湿地变化。总体准确率很高，范围从 84.37% 到 88.96%。在不同分类器的比较中，随机森林产生了最高的准确度结果，并在与分类和回归树 (CART) 和最小距离 (MD) 进行比较时允许估计变量的重要性。最重要的变量包括热红外波段 (TIR)、高程、差异植被指数 (DVI)、短波红外波段 (SWIR) 和归一化差异植被指数 (NDVI)。变化检测分析表明，沼泽，其次是沼泽和沼泽，是所有时间段中最常见的湿地类别，沼泽湿地分别是所有时间段中最不常见的湿地类别。分析还显示了湿地类别的普遍不稳定性，尽管这主要是由于从一种湿地类别转换为另一种湿地类别。未来的工作可能会整合雷达数据并考虑天气模式。本研究的结果首次阐明了 1985 年至 2015 年纽芬兰的湿地等级变化模式，并展示了 GEE 和 Landsat 历史图像在评估省和国家范围内的变化方面的潜力。虽然这主要是由于从一种湿地类别转换为另一种湿地。未来的工作可能会整合雷达数据并考虑天气模式。本研究的结果首次阐明了 1985 年至 2015 年纽芬兰的湿地等级变化模式，并展示了 GEE 和 Landsat 历史图像在评估省和国家范围内的变化方面的潜力。虽然这主要是由于从一种湿地类别转换为另一种湿地。未来的工作可能会整合雷达数据并考虑天气模式。本研究的结果首次阐明了 1985 年至 2015 年纽芬兰的湿地等级变化模式，并展示了 GEE 和 Landsat 历史图像在评估省和国家范围内的变化方面的潜力。