| Popis: |
An objective method for inductively modelling the distribution of mountain land units using GIS managed topographic variables is presented. The landscape of a small high mountain catchment in the Spanish Pyrenees, covered with grassland, was classified in ten land units by hierarchical agglomerative clustering, using a sample of 194 random plots, in which classes of vegetation, soils and landforms were defined. Additionally, seven layers of topographic variables (altitude, slope angle, aspect, solar radiation, topographic wetness index, specific catchment area, and regolith thickness) were created from a Digital Elevation Model. The affinity of each land unit to the topographic variables was calculated using Binary Discriminant Analysis (BDA), after dichotomising the latter around their mean values. Then, the distribution of each land unit was predicted by boolean operations combining step by step distributions for the seven topographic variables ordered, for each unit, after the absolute values of the Haberman’s residuals in BDA. The predicted distributions were tested (χ2) against that of the observed sampling plots. From the original ten land units, the distributions of eight of them were successfully predicted (four are related to the slope sequence, two reflect the water accumulation in the soil, and two respond to geomorphic processes) while the remaining two had to be rejected. Part of the catchment (39%) was not assigned to any land unit, probably because more distributed variables accounting for snow distribution are necessary. |
