Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.2172/1054709 |
报告编号 | INL/EXT-12-26969 |
来源ID | OSTI ID: 1054709 |
Detailed Hydrographic Feature Extraction from High-Resolution LiDAR Data | |
Danny L. Anderson | |
2012-05-01 | |
出版年 | 2012 |
语种 | 英语 |
国家 | 美国 |
领域 | 地球科学 |
英文摘要 | 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 âmDnâ, 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 mDn method yielded the smallest LRMSE. |
英文关键词 | Feature Extraction Hydrology LiDAR Light Detection and Ranging Stream Channel Delineation |
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来源平台 | US Department of Energy (DOE) |
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文献类型 | 科技报告 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/5636 |
专题 | 地球科学 |
推荐引用方式 GB/T 7714 | Danny L. Anderson. Detailed Hydrographic Feature Extraction from High-Resolution LiDAR Data,2012. |
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