Distribution Modeling of Vegetation Types in Venabygdsfjellet, Oppland.
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- Master's theses (INA) 
This study explores the effect of increasing sample units density with presence-only data (PO data) on the ability to predict the distribution of three common (2e dwarf shrub heath, 4b bilberry birch forest and 9c fen) and three rare (3b tall forb meadow, 8d rich swamp forest and 9d mud- bottom fens and bogs) vegetation types. The chosen study area was Venabygdsfjellet in Ringebu municipality, Oppland. In 2001 the vegetation in the study area was mapped by Norwegian Institute for Forest and Landscape. The vegetation map was used as material for the PO data in the prediction modeling. In beforehand, this map was quality assessed. To evaluate the quality of the map, necessary fieldwork and statistical analysis was conducted. As a result of this evaluation, 84 % of all observations correspond to the mapped distribution on the vegetation map. The PO data for distribution modeling were collected in a point grid with different densities (100 m for common and 25 m for rare vegetation types) within the sample units (1500×600m size). The sample unit was equivalent to a Primary Statistical Unit (PSU) of the AR18×18 survey system and given in a grid net with five densities: 3×3 km, 4,5×4,5 km, 6×6 km, 7,5×7,5 km and 9×9 km. In addition to PO data, 12 environmental variables were used as explanatory predictors (the digital elevation model, basin, curvatures, flow accumulation, flow direction, groundwater, slope, satellite image, the Normalized Difference Vegetation Index (NDVI), the Topographic Wetness index (TWI), sediment and soil maps). Using the PO data and these environmental variables, each vegetation type was modeled in all five densities of the PSU grid using a maximum entropy modeling method using a custom-made software called MaxEnt. In total, 26 out of 30 planned prediction models were run. The four missing models did not have any PO-points in some of the PSU grid density. Out of 26, 23 prediction models performed well according to the AUC-measure provided by MaxEnt (> 0.80 AUC). The statistical comparison of the predicted and true distribution of the modeled vegetation types showed that only 7 prediction models can be considered as good (2e in densities 3×3 km and 4.5×4.5 km, 4b in densities 3×3 km and 4.5×4.5 km, 9c in densities 3×3 km and 7.5×7.5 km and 3b in density 3×3 km). The vegetation types 8d and 9d were not modeled successfully any PSU grid densities, although they had high AUCvalues. The best modeled vegetation type was 4b in a 3x3 km PSU grid density. The variable importance analysis conducted by MaxEnt trough the Jack-Knife test, showed that the DEM (the digital elevation model), NDVI index (the Normalized Difference Vegetation Index), slope and satellite images in blue band were the most important environmental variables among all vegetation type models.