PREDICTION SPATIAL PATTERNS OF WINDTHROW PHENOMENON IN DECIDUOUS TEMPERATE FORESTS USING LOGISTIC REGRESSION AND RANDOM FOREST
ABSTRACT Forest management needs to evaluate various hazards where may cause economic or other losses to forest owners. The aim of this study is to prepare windthrow hazard maps based on logistic regression and random forest models in Nowshahr Forests, Mazandaran Province, Iran. First of all, 200 windthrow locations were identified from extensive field surveys and some reports. Out these, 140 (70%) locations were randomly selected as training data and the remaining 60 (30%) cases were used for the validation goals. In the next step, 10 predictive variables such as slope degree, slope aspect, altitude, Topographic Position Index (TPI), Topographic Wetness Index (TWI), distance to roads and skid trails, wind effect, soil texture, forest type and stand density were extracted from the spatial database. Subsequently, windthrow hazard maps were produced using logistic regression and RF models, and the results were plotted in ArcGIS. Finally, the area under the curves (AUC) and kappa coefficient were made for performance purposes. The validation of results presented that the area under the curve and kappa have a more accuracy for the random forest (97.5%, and 95%, respectively) than logistic regression (96.667%, and 93.333%, respectively) model. Therefore, this technique has more potentiality to be applied in the evaluation of windthrow phenomenon in forest ecosystems. Additionally, both models indicate that the spatial distribution of windthrow incidence likelihood is highly variable in this region. In general, the mentioned findings can be applied for management of future windthrow in favor of economic benefits and environmental preservation.