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ISPRS Int. J. Geo-Inf. 2018, 7(4), 160; https://doi.org/10.3390/ijgi7040160

Use of DEMs Derived from TLS and HRSI Data for Landslide Feature Recognition

1
DICAM-ARCES, University of Bologna, 40136 Bologna, Italy
2
Department of Engineering, University of Roma TRE, 00146 Rome, Italy
3
Department of Civil Engineering, University of Salerno, 84084 Fisciano (SA), Italy
4
DICAM, University of Bologna, 40136 Bologna, Italy
*
Author to whom correspondence should be addressed.
Received: 5 February 2018 / Revised: 10 April 2018 / Accepted: 19 April 2018 / Published: 23 April 2018
(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)
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Abstract

This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking into account the uncertainties associated with DEM production. In order to obtain a georeferenced and edited point cloud, the two data sets require quite different processes, which are more complex for satellite images than for TLS data. The differences between the two processes are highlighted. The point clouds are interpolated on a DEM with a 1 m grid size using kriging. Starting from these DEMs, a number of contour, slope, and aspect maps are extracted, together with their associated uncertainty maps. Comparative analysis of selected landslide features drawn from the two data sources allows recognition and classification of hierarchical and multiscale landslide components. Taking into account the uncertainty related to the map enables areas to be located for which one data source was able to give more reliable results than another. Our case study is located in Southern Italy, in an area known for active landslides. View Full-Text
Keywords: HRSI; Geo-Eye-1; TLS; DEM; kriging; uncertainty; morphometric feature HRSI; Geo-Eye-1; TLS; DEM; kriging; uncertainty; morphometric feature
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Barbarella, M.; Di Benedetto, A.; Fiani, M.; Guida, D.; Lugli, A. Use of DEMs Derived from TLS and HRSI Data for Landslide Feature Recognition. ISPRS Int. J. Geo-Inf. 2018, 7, 160.

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