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Detailed hydrographic feature extraction from high-resolution LIDAR data.

机译：从高分辨率LIDAR数据中提取详细的水文特征。

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摘要

Detailed hydrographic feature extraction from high-resolution light detection and ranging (LiDAR) data is investigated. Methods for quantitatively evaluating and comparing such extractions are presented, including the use of sinuosity and longitudinal root-mean-square-error (LRMSE). These metrics are then used to quantitatively compare stream networks in two studies. The first study examines the effect of raster cell size on watershed boundaries and stream networks delineated from LiDAR-derived digital elevation models (DEMs). The study confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes generally yielded better stream network delineations, based on sinuosity and LRMSE. The second study demonstrates a new method of delineating a stream directly from LiDAR point clouds, without the intermediate step of deriving a DEM. Direct use of LiDAR point clouds could improve efficiency and accuracy of hydrographic feature extractions. The direct delineation method developed herein and termed "mDn", is an extension of the D8 method that has been used for several decades with gridded raster data. The method divides the region around a starting point into sectors, using the LiDAR data points within each sector to determine an average slope, and selecting the sector with the greatest downward slope to determine the direction of flow. An mDn delineation was compared with a traditional grid-based delineation, using TauDEM, and other readily available, common stream data sets. Although, the TauDEM delineation yielded a sinuosity that more closely matches the reference, the mDn delineation yielded a sinuosity that was higher than either the TauDEM method or the existing published stream delineations. Furthermore, stream delineation using the mD n method yielded the smallest LRMSE.
机译：研究了从高分辨率光检测和测距（LiDAR）数据中提取的详细水文特征。提出了定量评估和比较此类提取的方法，包括使用弯曲度和纵向均方根误差（LRMSE）。然后，将这些指标用于在两项研究中定量比较流网络。第一项研究检查了栅格像元大小对分流边界和从LiDAR衍生的数字高程模型（DEM）描绘的河流网络的影响。该研究证实，随着LiDAR数据分辨率的大大提高，基于正弦和LRMSE，较小的像元大小通常会产生更好的流网络描述。第二项研究演示了一种直接从LiDAR点云描绘流的新方法，而无需得出DEM的中间步骤。直接使用LiDAR点云可以提高水文特征提取的效率和准确性。本文开发的直接定界方法（称为“ mDn”）是D8方法的扩展，该方法已与栅格化栅格数据一起使用了数十年。该方法将围绕起点的区域划分为多个扇区，使用每个扇区内的LiDAR数据点来确定平均斜率，然后选择具有最大向下斜率的扇区来确定流向。使用TauDEM和其他易于获得的通用流数据集，将mDn轮廓与传统的基于网格的轮廓进行了比较。尽管TauDEM划定产生的曲线与参考更加接近，但mDn划定产生的曲线比TauDEM方法或现有已发布的流划定都高。此外，使用mD n方法进行流描述得出了最小的LRMSE。

著录项

  • 作者

    Anderson, Danny L.;

  • 作者单位

    Idaho State University.;

  • 授予单位 Idaho State University.;
  • 学科 Hydrology.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 129 p.
  • 总页数 129
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 宗教史、宗教地理;
  • 关键词

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