Ecosystem mapping in the Lower Foothills Subregion of Alberta: Application of fuzzy logic

Abstract

Predictive ecosite mapping involves developing computer models that consistently identify and map ecosystems. This method of predicting ecosystem occurrence on the landscape uses basic inventory information and expert knowledge, and is an effective integrated planning tool for providing a record of the location and spatial distribution of ecosystems within a management area. Fuzzy logic technology can be used to computerize essential elements of ecosystem identification, and the outputs can be linked to a Geographic Information System for map production. A pilot study was undertaken on the application of this technology to the Alberta Ecological Land Classification database and the resulting ecosite map for a township located in central Alberta (Tp42R9W5). The range of attributes used in the program was constrained by the attributes recorded on mapped polygons. Three maps with suitable attributes were available for the township studied: a Digitized Elevation Model map, an Alberta Vegetation Inventory map, and a reconnaissance soil survey map. Attributes of all polygons from all three maps were compiled and seven attributes (humus form, Ah thickness, surface texture, aspect, organic thickness, slope angle, and Alberta Vegetation Inventory moisture regime) were chosen to produce a computerized program for ecosite identification. Four sets of data were used to calibrate the program, as well as a small-plot data set collected from the township studied. The computer program was used to analyze the polygon data corresponding to two sets of data collected in the field and resulted in 72% and 70% similarity between the choices of experts and of the computer program. The quality of the original polygon attributes contributed to errors in identification. In addition, the reconnaissance soil survey map gave only an estimate of four attributes (Ah horizon thickness, organic thickness, humus form, and surface texture). Key words: ecosystem classification, site classification, fuzzy logic, fuzzy sets, predictive ecosystem mapping, predictive site mapping