Extraction and analysis of ground fissures from airborne LiDAR data

doi:10.6046/gtzyyg.2014.04.18

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Abstract Airborne laser scanning (ALS) data have been used to construct the digital terrain model under dense vegetation, but its reliability for recognition ground fissures in the tropics remains unknown. In this paper, Langshitan located in Lengshuijiang City was selected as the study area, and the method for extracting ground fissures and analyzing micro-topography features based on airborne LiDAR point cloud data in the dense vegetation were studied. First, the point clouds were separated into ground points and non-ground points through adaptive TIN filter method, elevation filter, echo intensity difference filter, to ensure that the bare-earth reserved micro-topography features in the dense vegetation. Second, on the basis of ground data, triangulated irregular network was built to generate digital elevation models by inverse distance weighted interpolation; afterwards, ground fissures identification parameters could be extracted, and then linear detection could be performed by the method of minimum curvature. Finally, ground fissures stability was analyzed by the profile information of LiDAR and identification parameters. The results achieved by the authors have shown that qualitative and quantitative identification parameters extracted by the LiDAR data can determine the location, slope and aspect as well as length and depth information of ground fissures and, on such a basis, ground fissures stability can be determined by the profile information of identification parameters and micro-topography features. It is thus proved that DEM constructed by ground points and low vegetation points could reserve micro-topography features.

Keywords hyperspectral image band grouping and reordering Prim algorithm fuzzy similarity maximum and minimum closeness(MMC)

TP79

Issue Date: 17 September 2014

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	ZHANG Zhuan
	MA Yu
	CAI Wei

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ZHANG Zhuan,MA Yu,CAI Wei. Extraction and analysis of ground fissures from airborne LiDAR data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 111-118.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.04.18 OR https://www.gtzyyg.com/EN/Y2014/V26/I4/111

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