Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes
Big Data and Multiple Methods for Mapping Small Reservoirs: Comparing Accuracies for Applications in Agricultural Landscapes
复制标题
DOI：
10.3390/rs9121307
复制
发表时间：
2017-12
期刊：
影响因子：
--
通讯作者：
S. Jones;A. Fremier;F. DeClerck;D. Smedley;A. Pieck;M. Mulligan
中科院分区：
文献类型：
--
作者：
S. Jones;A. Fremier;F. DeClerck;D. Smedley;A. Pieck;M. Mulligan
水库在整个旱季是否有水对于避免季节性干旱农业景观中的晚季作物歉收至关重要。全球范围内，特别是小型（<1 Mm3）水库的位置、水量和时间动态记录很少，因此很难确定水库可用水量的地理和年内差距。然而，小型水库最容易干涸，并且常常为最贫困的农民服务。以西非跨界沃尔特河流域（约 413,000 平方公里）作为案例研究，我们提出了一种新方法来绘制水库图并量化 Landsat 得出的水库面积估计的不确定性，该方法可以轻松应用于全球任何地方。我们应用我们的方法来比较从全球地表水月度水历史 (GSW) 数据集导出的库区的准确性，与使用归一化水差指数 (NDWI)、带 6 的修正 NDWI (MNDWI1) 和带 7 的修正 NDWI (MNDWI2) 对 Landsat 8 OLI 图像上的地表水进行分类时导出的库区的准确性。我们量化了水库规模估计的面积精度如何随水分类方法、水库特性和环境背景而变化，并评估了使用不确定的水库面积估计来监测农业背景下的水库动态的选项和局限性。结果显示，对于我们研究地点的 272 个水库面积范围为 0.09 至 72 公顷的样本，根据 GSW 数据得出的水库面积估计值比 MNDWI1 得出的估计值准确度低 19%。陆地卫星估算的准确性随着水库规模和周长面积比的增加而提高，而准确性可能会随着地表植被的增加而下降。我们表明，GSW 得出的水库面积估计可以为当前水库容量和较大水库的季节性动态提供上限。数据差距和不确定性使得 GSW 衍生的水库范围不适合监测面积小于 5.1 公顷（约 49,759 立方米）的水库（沃尔特流域有 674 个水库（56%）），也不适合监测大多数小型水库的季节性波动，限制了其在农业规划中的效用。这项研究是第一个测试新可用 GSW 数据集的实用性和局限性的研究之一，并就在什么条件下可以可靠地使用该数据集和其他基于 Landsat 的地表水图来监测水库资源提供指导。
Whether or not reservoirs contain water throughout the dry season is critical to avoiding late season crop failure in seasonally-arid agricultural landscapes. Locations, volumes, and temporal dynamics, particularly of small (<1 Mm3) reservoirs are poorly documented globally, thus making it difficult to identify geographic and intra-annual gaps in reservoir water availability. Yet, small reservoirs are the most vulnerable to drying out and often service the poorest of farmers. Using the transboundary Volta River Basin (~413,000 sq km) in West Africa as a case study, we present a novel method to map reservoirs and quantify the uncertainty of Landsat derived reservoir area estimates, which can be readily applied anywhere in the globe. We applied our method to compare the accuracy of reservoir areas that are derived from the Global Surface Water Monthly Water History (GSW) dataset to those that are derived when surface water is classified on Landsat 8 OLI imagery using the Normalised Difference Water Index (NDWI), Modified NDWI with band 6 (MNDWI1), and Modified NDWI with band 7 (MNDWI2). We quantified how the areal accuracies of reservoir size estimates vary with the water classification method, reservoir properties, and environmental context, and assessed the options and limitations of using uncertain reservoir area estimates to monitor reservoir dynamics in an agricultural context. Results show that reservoir area estimates that are derived from the GSW data are 19% less accurate for our study site than MNDWI1 derived estimates, for a sample of 272 reservoir extents of 0.09 to 72 ha. The accuracy of Landsat-derived estimates improves with reservoir size and perimeter-area ratio, while accuracy may decline as surface vegetation increases. We show that GSW derived reservoir area estimates can provide an upper limit for current reservoir capacity and seasonal dynamics of larger reservoirs. Data gaps and uncertainties make GSW derived reservoir extents unsuitable for monitoring reservoirs that are smaller than 5.1 ha (holding ~49,759 m3), which constitute 674 (56%) reservoirs in the Volta basin, or monitoring seasonal fluctuations of most small reservoirs, limiting its utility for agricultural planning. This study is one of the first to test the utility and limitations of the newly available GSW dataset and provides guidance on the conditions under which this, and other Landsat-based surface water maps, can be reliably used to monitor reservoir resources.