فهرست مطالب پائولو دی اودوریکو

Although land degradation is a worldwide challenge and a destructive phenomenon, little studies have been done on the application of new numerical methods (data mining and statistically), for spatial simulation of this phenomenon and identification of areas sensitive to land degradation. The aim of this study is to spatially simulate land degradation in the Qazvin plain using the frequency ratio model to identify areas prone to land degradation. For this purpose, using the trend of changes in net primary production during the years 2001 to 2020, the points of occurrence of land degradation in the Qazvin plain were determined. Approximately 70% and 30% of the points were used to prepare the land degradation vulnerability map and validate the model's efficiency, respectively. For this research, 15 parameters affecting land degradation (directly and indirectly) including temperature, rainfall, slope, aspect, elevation, EC and SAR of ground water, ground water level, annual ground water decline, land use, normalized difference vegetation index, normalize difference salinity index, vegetation soil salinity index, normalized difference moisture index, and visible and shortwave infrared drought index, were introduced into the model as predictors factors or independent parameters. Finally, using the area under the ROC curve, the effectiveness of the frequency ratio model for spatial simulation of land degradation was assessed. The map of land degradation susceptibility shows that the areas prone to degradation are located in the northeast, north, northwest, west, southwest, and south of the Qazvin plain, which mainly includes good, moderate and poor rangelands. For the land use parameter, the highest frequency ratio was associated with the sum of good, moderate, and poor rangeland (5.66). The value of AUC = 0.7 indicates the good performance of the frequency ratio model in spatial simulation of land degradation.