Ana Alice Rodrigues Dantas Almeida , Rafael Lopes Mendonça , Natalia Maria Mendes Silva , Adriano Rolim da Paz
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引用次数: 0
Abstract
Digital elevation models obtained from LiDAR surveys typically have a few meters or sub-meter resolution. DEM-derived products in such a fine resolution may not be desired for several circumstances, such as matching the resolution with other spatial datasets, preparing input data for hydrological models, and reducing the computational cost. This leads to DEM coarsening for further river network extraction. An alternative could be to derive the river flow paths in the original DEM resolution and use this information to obtain the coarser river networks (a procedure known as flow directions upscaling). This approach is the macroscale hydrology benchmark for deriving river networks with spatial resolution on the order of a few kilometers or even larger, based on the available DEM with tenths or hundreds of meters resolution. However, no study has applied this procedure for the change of scale involving fine-resolution LiDAR DEM. This research evaluated for the first time in literature a flow direction upscaling algorithm for deriving relatively coarse-resolution (30, 100, and 200m) river networks from very fine-resolution (1 m) flow paths obtained from LiDAR DEM. Two river basins of contrasting characteristics located in Northeast Brazil are studied. Results were evaluated through visual inspection, percentage within buffer (PWB) metrics, and river length comparison. It is shown that using an upscaling algorithm improves the ability of the coarse network to preserve river networks’ spatial patterns across multiple scale changes. Considering both basins, PWB ranged from 80% to 100% (average of 97%) for the upscaling procedure, while the DEM resampling resulted in PWB between 40% and 100% (average of 85%). A flow direction upscaling algorithm already used for macroscale hydrology proved helpful for the LiDAR-related shift in scale, outperforming the DEM resampling. Increasing the scale change augments the difference in performance between them, making the upscaling procedure more recommended. In addition, such an upscaling procedure provided drainage networks in the 100-m and 200-m resolutions with higher quality than the one obtained in the 30-m resolution directly from a globally available DEM.
通过激光雷达勘测获得的数字高程模型通常只有几米或几米以下的分辨率。在一些情况下,例如与其他空间数据集的分辨率相匹配、为水文模型准备输入数据以及降低计算成本,可能并不需要如此精细分辨率的 DEM 衍生产品。这就导致在进一步提取河网时需要对 DEM 进行粗略处理。另一种方法是在原始 DEM 分辨率中提取河流流向,并利用这些信息获得更粗的河网(这一过程被称为流向放大)。这种方法是宏观水文学的基准,可根据现有的十分之一或数百米分辨率的 DEM 得出空间分辨率为几公里甚至更大的河网。然而,还没有研究将这一程序用于涉及精细分辨率 LiDAR DEM 的尺度变化。本研究首次在文献中评估了一种流向升级算法,该算法可根据从激光雷达 DEM 中获取的极高分辨率(1 米)流径推导出相对较粗分辨率(30、100 和 200 米)的河网。研究对象是位于巴西东北部的两个特点截然不同的河流流域。通过目测、缓冲区内百分比(PWB)度量和河流长度比较对结果进行了评估。结果表明,使用上标算法可以提高粗网络在多种尺度变化中保持河网空间模式的能力。考虑到两个流域的情况,上规模程序的 PWB 在 80% 至 100% 之间(平均为 97%),而 DEM 重采样的 PWB 在 40% 至 100% 之间(平均为 85%)。事实证明,已经用于宏观水文的流向放大算法有助于解决与激光雷达相关的尺度变化问题,其效果优于 DEM 重采样。随着尺度变化的增加,两者之间的性能差异也在扩大,因此更推荐使用上标程序。此外,与直接从全球可用的 DEM 中获得的 30 米分辨率的排水网络相比,这种升级程序提供的 100 米和 200 米分辨率的排水网络质量更高。
期刊介绍:
Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.