A fuzzy logic based method for modeling the spatial distribution of indicators of decomposition in a high mountain environment
|CLASSIFICATION; DECISION TREE APPROACH; DIGITAL ELEVATION MODELS; Environmental Sciences; Environmental Sciences & Ecology; EXPERT KNOWLEDGE; FOREST; Geography, Physical; GIS; HUMUS FORMS; PEDOTRANSFER FUNCTIONS; Physical Geography; REGRESSION TREES; SOIL INFERENCE
|INST ARCTIC ALPINE RES
|ARCTIC ANTARCTIC AND ALPINE RESEARCH
Upscaling of sample data on indicators of decomposition to the landscape scale is often necessary for extensive ecological assessments. The amount of such data is mostly scarce even with high sampling efforts. Moreover, environmental conditions are very heterogeneous in high mountain regions. Therefore, the aim was to find a suitable technique for spatial modeling under these circumstances. A method combining decision tree analysis and the construction of fuzzy membership functions is introduced for a GIS-based mapping of decomposition indicating parameters. It is compared with an approach solely based on decision trees. Within a case study in the Italian Alps the spatial distribution of humus forms, classified by the occurrence of an OH (humified residues) horizon, is examined. There appears to be a strong relationship with elevation and a minor correlation with slope exposition. The fuzzy logic-based approach proves to be suitable for modeling the spatial distribution of indicators of decomposition. Mapping fuzzy values allows for the representation of small-scale variability and uncertainty of data due to a relatively low sample size in a very heterogeneous environment.
Show full item record
checked on Feb 26, 2024