ABSTRACT The large variety of the semi-automated methods in mapping the surface water bodies and the frequent omission of ponds (< 10,000 m2) from permafrost regions inventories motivates this work. Based on the correlation matrix, several widely used classification methods for mapping the surface water bodies were assessed. Water bodies inventory was generated at a local and landscape scale in the Headwater Area of the Yellow River (HAYR) on a Sentinel-2 satellite image from 23 November 2015. The assessed methods are: spectral water indices, supervised and unsupervised classifiers (k-means, Density Slicing, Maximum Likelihood Classification ˗ MLC), and machine learning algorithms (Random Forest and Support Vector Machines). The MLC method applied on visible and near-infrared (NIR) bands represents the best ratio regarding the accuracy (96%), Kappa coefficient (0.87), and water surface (14.87 km2). However, misclassifications are still present, which requires manual editing. Based on the MLC approach, 651 more water bodies were identified than previous inventories. Ponds are account for up to 93% of the 966 of individual water bodies and contribute to 42% of the total water surface, in the context that were omitted before. This analysis emphasize the importance of method settings for the classifier performance, as well as the ponds abundance and substantial contribution to the total water surface. This inventory will improve the general circulation models and provides baseline information for sustainable management of water resources in the HAYR, one of the core Asian Water Towers.

中文翻译：

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青藏高原东北部黄河源区多年冻土景观热岩溶湖泊和池塘绘图

摘要 绘制地表水体的各种半自动化方法和永久冻土地区清单中经常遗漏池塘（< 10,000 平方米）的情况推动了这项工作。基于相关矩阵，评估了几种广泛使用的地表水体绘图分类方法。水体清单是在 2015 年 11 月 23 日的 Sentinel-2 卫星图像上在黄河源头区 (HAYR) 的局部和景观尺度上生成的。评估方法是：光谱水指数、监督和非监督分类器 (k-指，密度切片、最大似然分类 ˗ MLC）和机器学习算法（随机森林和支持向量机）。应用于可见光和近红外 (NIR) 波段的 MLC 方法代表了关于准确度的最佳比率 (96%)，Kappa 系数 (0.87) 和水面 (14.87 km2)。但是，错误分类仍然存在，需要手动编辑。根据 MLC 方法，确定的水体比以前的清单多 651 个。池塘占 966 个单个水体的 93%，占总水面的 42%，在之前省略的情况下。该分析强调了方法设置对分类器性能以及池塘丰度和对总水面的实质性贡献的重要性。该清单将改进大循环模型，并为亚洲核心水塔之一 HAYR 的水资源可持续管理提供基线信息。这需要手动编辑。根据 MLC 方法，确定的水体比以前的清单多 651 个。池塘占 966 个单个水体的 93%，占总水面的 42%，在之前省略的情况下。该分析强调了方法设置对分类器性能以及池塘丰度和对总水面的实质性贡献的重要性。该清单将改进大循环模型，并为亚洲核心水塔之一 HAYR 的水资源可持续管理提供基线信息。这需要手动编辑。根据 MLC 方法，确定的水体比以前的清单多 651 个。池塘占 966 个单个水体的 93%，占总水面的 42%，在之前省略的情况下。该分析强调了方法设置对分类器性能以及池塘丰度和对总水面的实质性贡献的重要性。该清单将改进大循环模型，并为亚洲核心水塔之一 HAYR 的水资源可持续管理提供基线信息。池塘占 966 个单个水体的 93%，占总水面的 42%，在之前省略的情况下。该分析强调了方法设置对分类器性能以及池塘丰度和对总水面的实质性贡献的重要性。该清单将改进大循环模型，并为亚洲核心水塔之一 HAYR 的水资源可持续管理提供基线信息。池塘占 966 个单个水体的 93%，占总水面的 42%，在之前省略的情况下。该分析强调了方法设置对分类器性能以及池塘丰度和对总水面的实质性贡献的重要性。该清单将改进大循环模型，并为亚洲核心水塔之一 HAYR 的水资源可持续管理提供基线信息。