Probabilistic prediction of forest decline in Lorestan province using a combined modeling approach

Identifying forest areas susceptible to decline in order to take preventive measures can play a significant role in inhibiting this phenomenon. Previous studies suggest the high performance of modeling to identify such areas. Hence, we used 15 models to identify forest areas prone to decline in Lorestan province. For modeling, forest areas with over 50% tree mortality were used as dependent variable, and environmental factors including annual mean rainfall, annual mean temperature, relative humidity, aridity index, evapotranspiration, dust storm index, drought index, distance to surface waters and agricultural lands, slope, aspect and NDVI as independent variables were introduced into the models. AUC of each model was multiplied by its output and the mean of these models was considered as the combined model. The forest decline risk map resulted from the combined model indicated a decline trend from central parts of the Lorestan’s forests to the south and south-western parts. The Random forest and Support vector machine were recognized as the best models with AUC value of 1 and the Bioclim as the weakest model with AUC of 0.75. According to the combined model, approximately 23.7%, 7.5%, and 19.5% of the studied forests had low, medium, and high risk of decline respectively. The climatic factors including aridity index, rainfall, temperature, and evapotranspiration were the most influencing environmental factors, respectively. The present research, in addition to emphasizing the modeling efficiency in identification of forest areas susceptible to decline, indicated that the combination of models yields better result rather than their separate use.