GIS-based modeling of forest soil moisture regime classes: Using Rinker Lake in northwestern Ontario, Canada as a case study

There is a need to develop geospatial technological approaches to predict and map relative soil moisture (soil moisture regime) classes on forested landscapes. This is because soil moisture regime (SMR) maps are continuously being sought by forest managers for application in planning and operations. The aim of the study was to develop a GIS-based modeling approach to predict and map relative soil moisture classes on forested landscape. A rule-based geographic information system (GIS) modeling technique was developed to predict SMR classes (dry, fresh, moist and wet) using: 1) soil textural classes derived from Quaternary geology maps, and 2) water receiving areas derived from topographic attributes generated from 15 m digital elevation model. The rule-based GIS modeling approach produced a SMR map with an overall accuracy of about 65% relative to 54% generated from soil wetness index reclassification approach when compared to observed ground plot data. This novel geospatial technique could be easily applied by forest managers for enhanced forest resource inventory.