Arktis-alpiinisen fytomassan mallintaminen paikkatieto- ja kaukokartoitusaineistojen avulla

• Main Author: Riihimäki, Henri
• 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 2013
• Subjects: Geography; Maantiede; Geografi; Arctic; Norway; Arktis; Arktis*
• Online Access: http://hdl.handle.net/10138/41686

The ongoing global change challenges us to examine the key factors of rapidly changing northern ecosystems. One of the most important factors in these environments is living vegetation biomass, also known as phytomass. This thesis examines above ground phytomass in an artic-alpine environment, located in northwesternmost Finland and Ráisduottarháldi –area, Norway. The most important aim of the study was to produce a best possible estimate of the phytomass in the study area. Typically, phytomass modelling in artic-alpine areas has been done by using linear regression models having spectral vegetation index (SVI), usually NDVI, as an explanatory variable. Goodness of the model is typically assessed by coefficient of determination (R2). This thesis expands this approach and tests different SVI's alongside NDVI. Bias, root mean square error (RMSE), and correlation of observed and predicted phytomasses are used in addition. The effect of sample size is also briefly tested. Factors affecting phytomass, such as topography, were also examined. Topographic variables, such as the topographic wetness index (TWI), slope, potential yearly radiation and curvature were derived from digital elevation model and used as a predictors. Rock and soil variables were also used, but the quality of the data was found poor. In addition to linear regression models (LM), generalized linear models (GLM) and variation partition were used to find out wether the simple SVI-models can be improved by adding topographic factors into the models. Boosted regression trees (BRT) were utilized to find out the importance of individual effects of topographic factors to phytomass. NDVI was found to be the best SVI to predict phytomass (R2 61,6 %, RMSE 593,5 g/m2). However, the model was slightly biased (–4,3 %), although not statistically significantly. Forest areas cause significant deviaton to the data, which might explain why the explanatory power of the NDVI model is lower compared to other similar studies carried in pure arctic environments. Based ...