修复后的河道形态变化:无人机激光扫描与传统勘测技术对比

Jonathan P. Resop, Coral Hendrix, T. Wynn-Thompson, W. Hession
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引用次数: 0

摘要

在监测修复后的溪流以确定其稳定性、水质和水生栖息地可用性的变化时,准确、精确地测量河道形态非常重要。从业人员通常依靠全站仪等传统测量方法来测量河道指标(如横截面积、宽度、深度和坡度)。然而,这些方法在取样密度粗、实地工作时间长等方面存在局限性。基于无人机的激光雷达或无人机激光扫描(DLS)可提供分辨率更高的点云,具有改善修复后监测工作的潜力。在这项研究中,我们使用全站仪(2010 年和 2021 年)和 DLS(2017 年和 2021 年)对 2010 年进行修复的 Stroubles 溪(美国弗吉尼亚州布莱克斯堡)的 1.3 公里河段进行了两次勘测。最初的修复工程分为三个处理河段:T1(牲畜隔离)、T2(牲畜隔离和河岸处理)和 T3(牲畜隔离、河岸处理和嵌入洪泛区)。从 2021 年 DLS 扫描中提取了横截面河道形态指标,并将其与 2021 年全站仪勘测计算的指标进行了比较。与全站仪相比,DLS 在研究河段上生成的横截面数量是全站仪的 6.5 倍,每个横截面的点数是全站仪的 8.8 倍。两种测量方法得出的指标(如河道宽度(R2 = 0.672)和横截面积(R2 = 0.597))之间具有良好的一致性。为了证明 DLS 相对于传统勘测方法的优势,通过 DLS 数据生成了 0.1 米的数字地形模型 (DTM)。根据无人机激光雷达数据,从 2017 年到 2021 年,处理河段 T3 显示出最大的稳定性,其横截面指标的变化和可变性最小,单位河段长度的侵蚀面积和体积最小。
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Channel Morphology Change after Restoration: Drone Laser Scanning versus Traditional Surveying Techniques
Accurate and precise measures of channel morphology are important when monitoring a stream post-restoration to determine changes in stability, water quality, and aquatic habitat availability. Practitioners often rely on traditional surveying methods such as a total station for measuring channel metrics (e.g., cross-sectional area, width, depth, and slope). However, these methods have limitations in terms of coarse sampling densities and time-intensive field efforts. Drone-based lidar or drone laser scanning (DLS) provides much higher resolution point clouds and has the potential to improve post-restoration monitoring efforts. For this study, a 1.3-km reach of Stroubles Creek (Blacksburg, VA, USA), which underwent a restoration in 2010, was surveyed twice with a total station (2010 and 2021) and twice with DLS (2017 and 2021). The initial restoration was divided into three treatment reaches: T1 (livestock exclusion), T2 (livestock exclusion and bank treatment), and T3 (livestock exclusion, bank treatment, and inset floodplain). Cross-sectional channel morphology metrics were extracted from the 2021 DLS scan and compared to metrics calculated from the 2021 total station survey. DLS produced 6.5 times the number of cross sections over the study reach and 8.8 times the number of points per cross section compared to the total station. There was good agreement between the metrics derived from both surveying methods, such as channel width (R2 = 0.672) and cross-sectional area (R2 = 0.597). As a proof of concept to demonstrate the advantage of DLS over traditional surveying, 0.1 m digital terrain models (DTMs) were generated from the DLS data. Based on the drone lidar data, from 2017 to 2021, treatment reach T3 showed the most stability, in terms of the least change and variability in cross-sectional metrics as well as the least erosion area and volume per length of reach.
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