Distribution Modeling of Vegetation Types in Venabygdsfjellet, Oppland.

Inglés. This study explores the effect of increasing sample units density withpresence-only data (PO data) on the ability to predict the distribution of threecommon (2e dwarf shrub heath, 4b bilberry birch forest and 9c fen) andthree rare (3b tall forb meadow, 8d rich swamp forest and 9d mud- bottomfens 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 NorwegianInstitute for Forest and Landscape. The vegetation map was used as material forthe PO data in the prediction modeling. In beforehand, this map was qualityassessed. To evaluate the quality of the map, necessary fieldwork and statisticalanalysis was conducted. As a result of this evaluation, 84 % of all observationscorrespond to the mapped distribution on the vegetation map. The PO data fordistribution modeling were collected in a point grid with different densities (100m 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 fivedensities: 3×3 km, 4,5×4,5 km, 6×6 km, 7,5×7,5 km and 9×9 km. In addition toPO data, 12 environmental variables were used as explanatory predictors (thedigital elevation model, basin, curvatures, flow accumulation, flow direction,groundwater, slope, satellite image, the Normalized Difference VegetationIndex (NDVI), the Topographic Wetness index (TWI), sediment and soil maps).Using the PO data and these environmental variables, each vegetation type wasmodeled in all five densities of the PSU grid using a maximum entropymodeling method using a custom-made software called MaxEnt.In total, 26 out of 30 planned prediction models were run. The fourmissing 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-measureprovided by MaxEnt (> 0.80 AUC). The statistical comparison of the predictedand true distribution of the modeled vegetation types showed that only 7prediction models can be considered as good (2e in densities 3×3 km and4.5×4.5 km, 4b in densities 3×3 km and 4.5×4.5 km, 9c in densities 3×3 km and7.5×7.5 km and 3b in density 3×3 km). The vegetation types 8d and 9d were notmodeled 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-Knifetest, showed that the DEM (the digital elevation model), NDVI index (theNormalized Difference Vegetation Index), slope and satellite images in blueband were the most important environmental variables among all vegetationtype models.

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