mohammad javad behi

Vegetation distribution modeling, as a tool that uses statistical methods and GIS, plays an important role in examining the complex relationships between plant community distribution and influential environmental variables. These models are based on data on the presence and absence of species and their correlation with environmental variables and on the hypothesis that environmental factors control the distribution of plant species. One of the objectives of this study was to present a model for predicting the habitat distribution of plant species and to prepare a map for predicting their relative distribution by the logistic regression method. In this study, random sampling method was used to sample vegetation and soil in Molla Fahleh watershed area of Firoozabad, Fars province,. At each sampling point, 4 soil samples were taken from two depths of 0-15 and 15-30 cm. Soil physical and chemical parameters along with physiographic factors (slope, slope direction and elevation) were recorded for each sample. Habitat distribution modeling was performed by logistic regression method using SPSS software for major plant species in the region. According to the results, soil texture, organic matter, clay content, lime content, acidity, slope and height have the underlying role in the distribution of the studied species. The results show that the logistic regression method is able to provide an accurate prediction model most of the plant species in the area.

The purpose of this study is to evaluate the temporal and spatial changes of vegetation in Mullah Fahleh of Firoozabad region of Fars province. The standard vegetation index data of Landsat 5, 7 and 8 satellites, the precipitation data and Palmer index, which are available as a remote sensing product, were extracted for 1992 to 2020 from the Google Earth Engine. Markov chain method was used to predict future vegetation changes. Using data from 1992 and 2002, the model was first run for 2020 and based on its acceptable performance (the kappa index of 75%), prediction was extended to 2030. The results showed an improving trend in vegetation status, so that the dense cover will increase from 4% in the initial period to 19% in 2030. The results of this study can help rangeland managers in the region to better exploit the region's natural resources and prevent the destruction of this ecosystem.