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

Model Prediction of the Soil Moisture Regime and Soil Nutrient Regime Based on DEM-Derived Topo-Hydrologic Variables for Mapping Ecosites

“…The accuracy of the produced SMR map, i.e., 61% correctness (with 90% within ±1 class), was higher than the reported 48% correctness (with 83% within ±1 class), where SMRs, using the same database as in this study, were estimated from modeling soil drainage classes [11], as well as higher than the reported 55% correctness (with 94% within ±1 class), where SMRs with six classes were estimated from plant indicators [7], but a little lower than the reported 65% correctness, where SMRs with four classes were generated from a rule-based GIS model [10]. Compared with Yang's matrix [11], although many very dry plots in the estimated SMR map were still classified as dry or fresh plots and several wet plots were classified as moist or moist/wet plots in this study, there was an increase in OA value from 0% to 37% for very dry plots and from 23% to 50% for wet plots, respectively.…”

“…In addition to field assessments using easily observable site features, indicator vegetation, and easily identified soil properties [7], many studies focused on the model predictions of SNRs and SMRs. The models are often based on interpolation schemes [8] and statistics-based schemes [9,10] using varying model predictors from field-based plant indicators [7], model-based clay content [9], model-based soil drainage [11], remote sensing data [12], and map-based soil texture [10], with varying class number, map resolution, and model accuracies. However, there is a lack of model studies that estimated SNRs and SMRs with high resolution (i.e., ≤10 m) and high accuracy using easily accessible model predictors.…”