RivaMap: An automated river analysis and mapping engine
RivaMap: An automated river analysis and mapping engine
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DOI：
10.1016/j.rse.2017.03.044
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发表时间：
2017-12-01
影响因子：
13.5
通讯作者：
Passalacqua, Paola
中科院分区：
文献类型：
--
作者：
Isikdogan, Furkan;Bovik, Alan;Passalacqua, Paola
河流对地球的水循环至关重要，对许多人类社会和生态系统产生了深刻的影响，但目前在全球范围内对它们的监测很差。现场测量站分布稀疏和不均匀，覆盖世界大部分地区，而遥感图像在空间和时间上都是密集的，可以在全球范围内使用。遥感多光谱图像，如陆地卫星任务获取的图像，可用于使用适当的算法对河流进行分析和测量。然而，现有的算法在限制产生结果的覆盖范围以及在短时间内阻止对大范围的河网进行自动化分析方面存在局限性。理想情况下，河流地图应该尽可能“实时”，例如，随着新的地球成像数据的出现，快速和连续地计算出来。为了推动这一问题的进展，我们描述了一个自动河流分析和测绘引擎RivaMap，它能够在短时间内从遥感数据计算大规模水文数据集。RivaMap通过提供工具来划定河流并估计其宽度，从而促进了水资源管理。作为RivaMap的一个实际应用，我们给出了一个大陆尺度的北美河流中心线和宽度的数据集，该数据集是根据Landsat数据自动计算的。我们通过将RivaMap生成的数据与类似的数据集NARWidth以及现场测量进行比较来验证我们的映射引擎。实验结果表明，RivaMap能够高效、准确地从大尺度遥感图像中提取河流。这项研究的结果、软件和计算的示范数据集都是公开的。(C)2017 Elsevier Inc.保留所有权利。
Rivers are essential to the Earth's water cycle and deeply impact many human societies and ecosystems, yet they are currently monitored poorly at the global scale. In-situ gauging stations are distributed sparsely and heterogeneously and do not cover much of the world, whereas remotely sensed images are spatially and temporally dense and available globally. Remotely sensed multispectral images, such as the ones acquired by Landsat missions, are available to enable the analysis and surveying of rivers using suitable algorithms. However, existing algorithms are limited in ways that restrict the coverage of the produced results and that prevent the automated analysis of river networks at large scales over short periods of time. Ideally, river maps should be as "live" as possible, e.g., computed quickly and continuously as new Earth imaging data becomes available. Towards advancing progress on this problem, we describe an automated river analysis and mapping engine, RivaMap, that enables the computation of large-scale hydrography data sets from remotely sensed data in a short period of time. RivaMap facilitates water resource management by providing tools to delineate rivers and to estimate their width. As a practical application of RivaMap, we present a continental-scale centerline and width data set of North American rivers, that is automatically computed on Landsat data. We validate our mapping engine by comparing the RivaMap-generated data to a similar data set, NARWidth, and also to in-situ measurements. Our experimental results show that RivaMap is able to efficiently and accurately extract rivers from remotely sensed images at large scales. The outcomes of this research, the software, and the computed exemplary data set are publicly available. (C) 2017 Elsevier Inc. All rights reserved.