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An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel

机译：尼日利亚萨赫尔水资源管理水资源管理水资源检测方法的评价

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Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.
机译：水是稀缺，但萨赫尔的基本资源。将景观的雨季夜间池塘和湖泊带到依靠这些资源的小农农民和牧民的生计，以灌溉作物和水合牛。这些水体的远程位置和分散性质限制了典型的监测方法，例如仪表;幸运的是，遥感在这些隔离区域中提供了一种快速且经常衡量地表水范围的方法。存在数十种操作方法来利用遥感来识别水上水平，然而，它们在半干旱萨赫尔识别地表水时的性能并未被良好地记录，并且对该地区的这些方法的局限性不太了解。在这里，我们评估了两个全局动态表面水数据集，这是一个用于分类表面水范围的十五个光谱指标，以及三种简单的决策树方法，专门创建了半干旱环境中的地表水。我们发现现有的全球表面水数据集有效地减少了误报，但大大低估了对当地生计至关重要的较小，更浑水体的存在和程度，这是监测水可用性的重要限制。当在不同区域和季节评估时，十五个光谱指数中的三个展示了高精度和阈值稳定性。这三个简单的决策树方法具有混合性能，只有一个具有整体精度，与最佳性能的光谱索引相比。我们发现，虽然全球表面水数据集可能适合于全球规模的分析，但其他方法校准到当地环境可能会提供更好的性能，以便进行更局部的水监测需求。

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