Abstract
This paper deals with an automated feature identification process, specifically identification of landforms and their attributes. The feature identification process is GIS-integrated and is carried out on the commercial platform ArcGIS. Spatio-explorative analysis offers a wide range of methods and techniques for the feature identification. An automated process is helpful for experts to identify many feature types over large areas. Our study case are thermokarst lakes (as prime climate indicators) in two different areas: North Canada and North Siberia. For the analysis of variance and correlation we have established a required significance level up to five percent. We have found correlations between the existing feature parameters and use regression analysis to optimize the identification process as well as to be able to better distinguish individual landforms from each other. Our goal is to provide GIS-integrated object-identification tools to identify and characterize landforms indicative of climate change, to allow extracting parameters required to assess climatic boundary conditions.
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Tyrallová, L., Gonschorek, J. (2012). Spatio-Explorative Analysis and Its Benefits for a GIS-integrated Automated Feature Identification. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2012. ICCSA 2012. Lecture Notes in Computer Science, vol 7334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31075-1_17
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DOI: https://doi.org/10.1007/978-3-642-31075-1_17
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