期刊
REMOTE SENSING
卷 13, 期 13, 页码 -出版社
MDPI
DOI: 10.3390/rs13132504
关键词
laser scanning; point cloud; segmentation; ground point filtering; mobile mapping system; SLAM; vegetation; high-resolution DTM
类别
资金
- Interreg V-A Italy-Switzerland Cooperation Program-A.M.AL.PI.2018 Alpi in Movimento, Movimento nelle Alpi. Piuro 1618-2018 [594274]
The paper discusses the creation process of a multi-source high-resolution Digital Terrain Model (DTM) in dense vegetated areas for landslide movement understanding. Challenges from the area's characteristics and processed data were faced, but it provided valuable testing opportunities for the technologies used through the complexity of the case study.
The paper presents the first part of a research project concerning the creation of 3D terrain models useful to understand landslide movements. Thus, it illustrates the creation process of a multi-source high-resolution Digital Terrain Model (DTM) in very dense vegetated areas obtained by integrating 3D data coming from three sources, starting from long and medium-range Terrestrial Laser Scanner up to a Backpack Indoor Mobile Mapping System. The point clouds are georeferenced by means of RKT GNSS points and automatically filtered using a Cloth Simulation Filter algorithm to separate points belonging to the ground. Those points are interpolated to produce the DTMs which are then mosaicked to obtain a unique multi-resolution DTM that plays a crucial role in the detection and identification of specific geological features otherwise visible. Standard deviation of residuals of the DTM varies from 0.105 m to 0.176 m for Z coordinate, from 0.065 m to 0.300 m for X and from 0.034 m to 0.175 m for Y. The area under investigation belongs to the Municipality of Piuro (SO) and includes both the town and surrounding valley. It was affected by a dramatic landslide in 1618 that destroyed the entire village. Numerous challenges have been faced, caused both by the characteristics of the area and the processed data. The complexity of the case study turns out to be an excellent test bench for the employed technologies, providing the opportunity to precisely identify the needed direction to obtain future promising results.
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