Using LiDAR to detect in-stream woods : a scaled approach

• Main Author: Abalharth, Mahdi Hadi
• Language: English
• Published: University of British Columbia 2013
• Online Access: http://hdl.handle.net/2429/45128

In-stream woods significantly influence watershed hydrology, flow regime, channel morphology and stability, and processes in streams. Consequently, in-stream woods play a major role in the existence and conservation of riparian and aquatic ecosystems. In this thesis, I attempt to detect and quantify LWD in stream channels using a remote sensing method, LiDAR, in conjunction with the traditional fieldwork. To the best of my knowledge, LiDAR-based analysis has not been used to study woods in stream channels. I, initially, attempted to re-apply advanced medical image processing and segmentation techniques on the LiDAR intensity images in order to confine the LiDAR terrain-based analysis to the stream channel networks, optimizing time and computing resources. The results exhibited significant image enhancement and accurate segmentation in certain regions; however, an automatic and a unified framework to delineate the stream channel networks, across different scales and spatial locations, is still required. LiDAR-based analysis demonstrated a more comprehensive solution for detecting in-stream woods in relation to the fieldwork through a high rate of commission and a low rate of omission. The filtered approach predicted the presence of 95% of fieldwork-reported in-stream woods, highlighting a 5% rate of omission, but with 25% rate of commission indicated by the identification of at least 15 new LWD locations that were not initially reported by the field crew. The non-filtered approach identified 87% of field-reported LWD, highlighting a 13% rate of omission and, similar to the filtered approach, a %25 rate of commission. Overall, the non-filtered and the filtered LiDAR showed fairly accurate predictions for in-stream woods’ dimensional measurements (length, width, and height) with respect to the field data. However, the filtered approach showed better dimension estimation of in-stream woods compared to the unfiltered LiDAR. Although a margin of error existed for fieldwork and LiDAR methods, a careful examination of orthophotos showed that LiDAR results were more accurate than the Laser Range Finder (LRF) used in the field.