Next Article in Journal
LiDAR—A Technology to Assist with Smart Cities and Climate Change Resilience: A Case Study in an Urban Metropolis
Next Article in Special Issue
Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge
Previous Article in Journal
DASSCAN: A Density and Adjacency Expansion-Based Spatial Structural Community Detection Algorithm for Networks
Previous Article in Special Issue
Saddle Position-Based Method for Extraction of Depressions in Fengcong Areas by Using Digital Elevation Models
Open AccessArticle

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

DICAM-ARCES, University of Bologna, 40136 Bologna, Italy
Department of Engineering, University of Roma TRE, 00146 Rome, Italy
Department of Civil Engineering, University of Salerno, 84084 Fisciano (SA), Italy
DICAM, University of Bologna, 40136 Bologna, Italy
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(4), 160;
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)
PDF [6355 KB, uploaded 3 May 2018]


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top