Geospatial analysis of lake and landscape interactions within the Toolik Lake region, North Slope of Alaska.

摘要

The Arctic region of Alaska is experiencing severe impacts of climate change. The Arctic lakes ecosystems are bound to undergo alterations in its trophic structure and other chemical properties. However, landscape factors controlling the lake influxes were not studied till date. This research has examined the currently existing lake landscape interactions using Remote Sensing and GIS technology. The statistical modeling was carried out using Regression and CART methods.Remote sensing data was applied to derive the required landscape indices. Remote sensing in the Arctic Alaska faces many challenges including persistent cloud cover, low sun angle and limited snow free period. Tundra vegetation types are interspersed and intricate to classify unlike managed forest stands. Therefore, historical studies have remained underachieved with respect thematic accuracies. However, looking at vegetation communities at watershed level and the implementation of expert classification system achieved the accuracies up to 90%.The research has highlighted the probable role of interactions between vegetation root zones, nutrient availability within active zone, as well as importance of permafrost thawing. Multiple regression analyses and Classification Trees were developed to understand relationships between landscape factors with various chemical parameters as well as chlorophyll readings. Spatial properties of Shrubs and Riparian complexes such as complexity of individual patches at watershed level and within proximity of water channels were influential on Chlorophyll production of lakes. Till-age had significant impact on Total Nitrogen contents. Moreover, relatively young tills exhibited significantly positive correlation with concentration of various ions and conductivity of lakes. Similarly, density of patches of Heath complexes was found to be important with respect to Total Phosphorus contents in lakes.All the regression models developed in this study were significant at 95% confidence level. However, the classification trees could not achieve high predictabilities due to limited number of lakes sampled.Keywords: Landscape factors, Lake primary productivity, Arctic, Climate change, Regression, CART