Study the role of geomorphologic parameters in distribution of vegetation cover using spatial regression analysis (case study, Arsbaran catchments: naposhtehcay, ilghinehcay and mardanqumcay)

Introduction: Special and sensitive place of plants as the basis of ecosystems and role of them in moderating hazards such as floods, erosion and pollution of water resources make us to understand the environmental variables affecting the growth and development of vegetation cover. In light of this understanding and maintaining the interweaving relations between environmental variables and vegetation cover, we will be able to maintain and support the live coverage of vegetation. In this context, geomorphic variables as special appearance of other environmental elements and factors have closely related to vegetation cover in mountainous catchments. Awareness of the role of geomorphic variables in distribution of vegetation cover requires analysis the spatial relationships and scientifically accurate spatial modeling. In this regard, the emergence and development of remote sensing (RS) and Geographic Information System (GIS) and access to digital maps of geomorphic variables have provided the development and implementation of predictive models in investiagting the spatial variations of vegetation cover. This study aimed to assess and determine the spatial geomorphic-vegetation relationships using the pixel-based spatial approach in Arasbaran catchments (3 catchments: Naposhtehchhay, Ilginehchay and Mardanqumchay). Arasbaran mountainous catchments, NW Iran, include worthwhile forest and range ecosystems maintaining the great storage of biodiversity and particular uncommon species. Materials and Methods: Our approach is based on spatial multiple regression analysis between geomorphological parameters and abundance of vegetation cover. In this regard, 27 geomorphomety parameters as independent variables and NDVI as the dependent variable were computed from Landsat imagery (ETM sensor) and SRTM digital elevation model (DEM). First, preprocessing operations including atmospheric correction (noise reduction) and geometric correction was performed on the sattellite image. DEM is preprocessed by removal of sinks in GIS environment. After radiometric and geometric corrections, raster layers of geomorphic parameters computed and prepared using GIS and SAGA softwares and NDVI layer computed using IDRISI software. It is necessary to normalize the scale of data (-1 - +1) because of various scales of the variables using the following formula: Xnormalize= x- x(min)/x(max) - x(min) In the formula, x: raw value of the variable; min (x): minimum of the variable; max (x): maximum of the variable. We use the SAGA for performing the multiple regression (stepwise method) with 0/01 signisicance level. Results and Discussion: Preliminary results of the regression analysis showed that many of geomorphological parameters had significant relations with vegetation cover in spite of low correlation coefficients. Independent variables that positively correlated to the dependent variable were as follows: slope, transformed aspect, slope position, earth surface convexity, plan curvature, profile curvature, convergence index, flow path length, flow accumulation, Melton ruggedness number. Independent variables were negatively correlated to the dependent variable were as follows: valley depth, elevation, topography position index, slope length, flow width. The results of rgression steps indicated that 8 parameters including valley depth, topography position index, elevation, slope, slope position, transformed aspect, earth surface convexity and general curvature were the most important inependent variables explained most of variance of the dependent variable. Final results of regression analysis showed that the best linear regression model abtained in Mardanqumchay catchment with 0/32 R2 value. In contrast, the weakest regression model is abtained in Naposhtehcay with 0/11 R2 value. It appears that Ilghinehcay catchment have moderate phytogeomorphic conditions having rgression model with 0/21 R2 value. It is found that there is a correspondence between ruggedness of catchments and prediction power and efficiency of the regression models. Conclusion: This study attempts to analyze the relationships between geomorphology and vegetation cover using a geographic information system (GIS) and remote sensing (RS) approach in Arasbaran catchments, NW Iran. Identification of the most important independent geomorphic variables and comparison of the regression models in order to select the best regression model provided from the spatial regression analysis. Geomorphic parameters including valley depth, topography position index, elevation, slope, slope position, transformed aspect, earth surface convexity and general curvature valley are the most effective independent variables for explaining the spatial variations of vegetation cover abundance. The selected geomorphic variables, in the Whole, are enough reflection of geomorphology of a site, having not only the relation between form and process in them, being the special representative of other environmental factors. Comparison of the ruggedness of catchments with prediction power and efficiency of the regression models is interesting result of the research stressed the close and interweaved relationships between geomorphology and vegetation cover in the study area. Overall, although significant portion of the spatial variations of the vegetation cover abundance could not be explained by final regression models, but the predictive models can discover and determine important variables that affect the spatial patterns of vegetation cover and processes underlined in the patterns, leading to inhance understanding the geomorphic-vegetation relationships, considering the comprehensive spatial approach in regression analysis in one hand and complex non-linear relationships between vegetation cover and geomorphology in other hand.