Journal
REMOTE SENSING
Volume 13, Issue 16, Pages -Publisher
MDPI
DOI: 10.3390/rs13163152
Keywords
cliff monitoring; UAV; photogrammetry; point cloud registration; iterative closest point; coastal erosion
Categories
Funding
- seed grant through the National Science Foundation project Guam EPSCoR [1457769]
- Office Of The Director
- Office of Integrative Activities [1457769] Funding Source: National Science Foundation
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This study introduces a novel cliff monitoring methodology that does not rely on georeferencing efforts, producing reliable monitoring results by processing 3D point clouds and aligning them using a rigid registration protocol. Experimental findings demonstrate the efficiency of this approach in small-scale experiments and full-scale field validation, highlighting its significance for underserved coastal communities.
Cliff monitoring is essential to stakeholders for their decision-making in maintaining a healthy coastal environment. Recently, photogrammetry-based technology has shown great successes in cliff monitoring. However, many methods to date require georeferencing efforts by either measuring geographic coordinates of the ground control points (GCPs) or using global navigation satellite system (GNSS)-enabled unmanned aerial vehicles (UAVs), significantly increasing the implementation costs. In this study, we proposed an alternative cliff monitoring methodology that does not rely on any georeferencing efforts but can still yield reliable monitoring results. To this end, we treated 3D point clouds of the cliff from different periods as geometric datasets and further aligned them into the same coordinate system using a rigid registration protocol. We examined the performance of our approach through a few small-scale experiments on a rock sample as well as a full-scale field validation on a coastal cliff. The findings of this study would be particularly valuable for underserved coastal communities, where high-end GPS devices and GIS specialists may not be easily accessible resources.
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