Spatial modeling of the main factors on the degradation of Zagros forests (Case study: Khorramabad sub-basin)

Water and soil protection, providing living conditions for human communities and production of by-products are the most important performances and characteristics of Zagros forests. About one third of the total population of the country lives in the Zagros region and more than 70% of the total nomads of the country are in this area; in terms of livestock, 50% of the country's livestock are located in this area. Unfortunately, today the Zagros forests degradation is happening for various human and ecological reasons. Due to the importance of degradation in the Zagros forests and the unfavorable conditions of this ecosystem, the present study was conducted with the aim of geographical modeling of forest degradation factors in the Khorramabad basin.

Based on studies, five main causes of degradation were identified, including overgrazing, nomadic migration routes, distribution of residences, distribution of access roads and farming under the forest canopy. For each factor, a digital map was prepared and normalized by fuzzy method in ArcGIS. Fuzzy layers were weighted by AHP and degradation potential layer was obtained by WLC method. In order to validate this layer, five classes were considered and in each class 12 sample plots of 35 × 35 m were implemented and two factors of farming under the forest canopy and tree decline were examined and measured. Then, based on the numerical values of the forest degradation potential layer (as a response variable) and the field surveys of five degradation factors (as independent variables) at 30 random points, the modeling was performed by geographically weighted regression method.

The results of weighting the factors affecting forest degradation showed that the two factors of farming under the forest canopy and overgrazing are more important than other factors. Based on geographical modeling, the results of forest degradation prediction in the sample points showed that the residuals standard deviation without any specific spatial pattern are distributed throughout the forest. The continuous forest degradation prediction map also showed that there is potential for degradation in all parts of the forest, but with different intensities. There are three critical points on this map that should be given special attention in the management of these forests.

Various researchers have often used classical regression methods to model and investigate the factors affecting forest degradation, but this study has used geographical regression and many georeferencing errors of predictive data are automatically eliminated. Therefore, this method can be used with more confidence and used to prepare prediction maps.