Abstract
A crevasse is an important surface feature of a glacier. This study aims to detect crevasses from high-density airborne LiDAR point clouds. However, existing methods continue to suffer from the data holes within the crevasse region and the influence of the undulating non-crevasse glacier surfaces. Therefore, a bidirectional analysis method is proposed to robustly extract the crevasses from the point clouds, which utilizes their vertical and horizontal characteristics. First, crevasse points are separated from non-crevasse points using a hybrid-entity method, where the height difference and the nearly vertical characteristic of a crevasse sidewall are considered, to better distinguish the crevasses from the undulating non-crevasse glacier surfaces. Second, the crevasse regions/edges are adaptively delineated by a local statistical analysis method that is based on a novel feature of the Delaunay triangulation mesh of non-crevasse points in the horizontal plane. Last, the pseudo-crevasse points and regions are removed by a cross-analysis method. To test the performance of the proposed method, this study selected airborne LiDAR point clouds from two sites of Alaskan glaciers (i.e., Tyndall Glacier and Seward Glacier) as the experimental datasets. The results were verified by a comparison with the ground truth that was manually delineated. The proposed method achieved acceptable results: the recall, precision, and F 1 scores of both sites exceeded 94.00%. Moreover, a comparative experiment was carried out and the results confirmed that the proposed method achieved superior performance.
Highlights
As a visible surface feature of glacier ice, crevasses form when a yield tensile stress or critical strain rate is reached, and they are frequently distributed on glaciers, ice sheets and ice shelves [1,2]
When the distance of the longest triangle edge (LTE) of a point is larger than tLTE, the point is a crevasse edge point, and the corresponding triangle including the longest edge belongs to the crevasse region
This paper proposed a bidirectional analysis method to obtain three-dimensional glacier crevasses from Airborne LiDAR point clouds, where the vertical and horizontal characteristics of crevasses are employed
Summary
As a visible surface feature of glacier ice, crevasses form when a yield tensile stress or critical strain rate is reached, and they are frequently distributed on glaciers, ice sheets and ice shelves [1,2]. In the last two decades, multi-source remote sensing data (e.g., optical images, synthetic aperture radar (SAR), and LiDAR) obtained by aircraft and satellites have been applied to detect crevasses [9,10,11,12,13] For most of these methods, only two-dimensional crevasses are obtained. These stereophotogrammetry methods are efficient for the three-dimensional mapping of crevasses, they are still limited by three factors: (1) The difficulty of surveying evenly distributed ground control points; (2) the low contrast of the texture over a glacier surface; and (3) the lack of light at the bottom and sidewalls of a crevasse To overcome these issues, some researchers have attempted to map crevasses using laser scanning systems [20,21,22], regardless of the low contrast of the glacier surface texture and the shadow within the region of a crevasse
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
