Periglasiaalisten ilmiöiden alueellinen mallintaminen subarktisessa ympäristössä

• Main Author: Aalto, Juha
• Other Authors: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta, Geotieteiden ja maantieteen laitos, University of Helsinki, Faculty of Science, Department of Geosciences and Geography, Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten, Institutionen för geovetenskaper och geografi
• Format: Master Thesis
• Language: Finnish
• Published: Helsingfors universitet 2011
• Subjects: Geography; Maantiede; Geografi; Gam; Kilpisjärvi; Subarctic; Lapland
• Online Access: http://hdl.handle.net/10138/29478

Periglacial processes act on cold, non-glacial regions where the landscape deveploment is mainly controlled by frost activity. Circa 25 percent of Earth's surface can be considered as periglacial. Geographical Information System combined with advanced statistical modeling methods, provides an efficient tool and new theoretical perspective for study of cold environments. The aim of this study was to: 1) model and predict the abundance of periglacial phenomena in subarctic environment with statistical modeling, 2) investigate the most import factors affecting the occurence of these phenomena with hierarchical partitioning, 3) compare two widely used statistical modeling methods: Generalized Linear Models and Generalized Additive Models, 4) study modeling resolution's effect on prediction and 5) study how spatially continous prediction can be obtained from point data. The observational data of this study consist of 369 points that were collected during the summers of 2009 and 2010 at the study area in Kilpisjärvi northern Lapland. The periglacial phenomena of interest were cryoturbations, slope processes, weathering, deflation, nivation and fluvial processes. The features were modeled using Generalized Linear Models (GLM) and Generalized Additive Models (GAM) based on Poisson-errors. The abundance of periglacial features were predicted based on these models to a spatial grid with a resolution of one hectare. The most important environmental factors were examined with hierarchical partitioning. The effect of modeling resolution was investigated with in a small independent study area with a spatial resolution of 0,01 hectare. The models explained 45-70 % of the occurence of periglacial phenomena. When spatial variables were added to the models the amount of explained deviance was considerably higher, which signalled a geographical trend structure. The ability of the models to predict periglacial phenomena were assessed with independent evaluation data. Spearman's correlation varied 0,258 - 0,754 between the observed ...