Title of article :
Estimating plant species occurrence in MTB/64 quadrants as a function of DEM-based variables—a case study for Medvednica Nature Park, Croatia
Author/Authors :
Jelaska، نويسنده , , Sven D and Antoni?، نويسنده , , Oleg and Nikoli?، نويسنده , , Toni and Hr?ak، نويسنده , , Vladimir and Plazibat، نويسنده , , Josip، نويسنده ,
Pages :
11
From page :
333
To page :
343
Abstract :
Croatia is among those European countries without an Atlas of Flora produced till today as a result of constant lack of greater number of active botanists and inconsistency in gathering data in the field. Recently, a standard for collection of data, based on the Central European MTB (abbreviation of German term “Meßtischblätter” that stands for a sheet of topographic map) grid was proposed and tested in the field on the “Medvednica Nature Park” on Medvednica mountain near the city of Zagreb. Using the data collected in 97 MTB/64 quadrants (presence/absence of plant species), we tested the potential of estimating species occurrence at the proposed grid by models in a function of the Digital Elevation Model (DEM)-based variables, namely altitude, terrain slope, terrain aspect, and flow accumulation potential. Because of significant spatial variability of environmental factors within MTB/64 quadrants, each one was represented by descriptive statistics (median, 5-, 25-, 75- and 95-percentiles) of DEM-based variables. Thirty-seven plant species were selected arbitrarily, on the basis of their frequency in the studied area (40–60% of all quadrants). Three methods for development of predictive model were used and compared: discriminant analyses, logistic regression, and classification trees. Yielded results suggest that spatial modelling could be probably applied in flora mapping, which would optimise fieldwork. However, decreasing of mapping unit area is recommended, especially for rare species. For larger areas, inclusion of other environmental predictors (macroclimatic, lithological, landuse) in models is probably needed.
Keywords :
CT , logistic regression , GIS , predictive models , Flora mapping , Discriminant analyses
Journal title :
Astroparticle Physics
Record number :
2037875
Link To Document :
بازگشت