The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 543–547, 2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 543–547, 2018

  06 Mar 2018

06 Mar 2018


C.-B. Vîlceanu1, A. C. Badea2, and S. Herban1 C.-B. Vîlceanu et al.
  • 1Department Overland Communication Ways, Foundations and Cadastral Survey, Civil Engineering Faculty, Politehnica University Timisoara, Traian Lalescu 2, postal code 300223, Timisoara, Romania
  • 2Surveying and Cadastre Department, Faculty of Geodesy, Technical University of Civil Engineering Bucharest, Bd. Lacul Tei no. 122 – 124, postal code 020396, sector 2, Bucharest, Romania

Keywords: landslide monitoring, data processing, geostatistics, geomatics, GIS

Abstract. Management of spatial data by means of Geographic Information System (GIS) plays an essential role based on the latest achievements in Geomatics domain. Geomatics offers the possibility to share, compare, and exchange data between researcher and users in unambiguous and accessible ways for map production and user-friendly technologies for results communication.
Although sometimes the geodesist’s contribution to certain projects for landslide monitoring meant to develop early-warning-systems or risk maps is not adequately appreciated and he is only seen as supplier of measured geometric data, the geodesist has a significant contribution through his abilities regarding the modelling of dynamic systems, like strategic constructions (dams, tall buildings etc.) or landslides and data processing and interpretation.
This study focuses on using geomorphological characteristics to detect the changes and the effects of landsliding using the ArcGIS 10.1 extension, Geostatistical Analyst.
One of the main uses of geostatistics is to predict values of a sampled variable over the whole area of interest, which is referred to as spatial prediction or spatial interpolation.
The extension allows creating a surface from data measurements occurring over an area where collecting information for every possible location would be impossible and gives us the possibility to fully understand the qualitative and quantitative aspects of the data.
By providing us with the opportunity to predict and model spatial phenomena based on statistics and incorporating powerful exploration tools, ArcGIS Geostatistical Analyst effectively bridges the gap between geostatistics and geographic information system (GIS) analysis.