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Examining Relationships Between Socioeconomic Factors and Landscape Metrics in the Southern Basin of the Caspian Sea
Authors:Bahareh Ghafouri  Bahman Jabbarian Amiri  Afshin Alizadeh Shabani  Melissa Songer
Institution:1.Department of Environmental Science, Faculty of Natural Resources,University of Tehran,Karaj,Iran;2.Smithsonian Conservation Biology Institute,National Zoological Park,Front Royal,USA
Abstract:Socioeconomic forces are not only among the main drivers of landscape dynamics; they are also influenced by landscape patterns. Landscape structure and functions are closely related to natural and social factors. The objective of this study was to investigate the relationships among some human-related factors and landscape ecological metrics as landscape pattern indicators and to identify suitable metrics for modeling these relationships. To this goal, landscape ecological metrics were calculated for each of the 32 counties of Mazandaran and Guilan provinces located in the southern basin of the Caspian Sea using land use/cover maps in class level. Stream network metrics were calculated using a digital elevation model, road density metrics were calculated using map of main roads separately, and significant metrics were selected according to results of correlation tests and factor analysis. The correlations between these metrics and socioeconomic factors were tested, and their relationships were modeled with multiple linear regressions. Significant relationships were found among socioeconomic factors and landscape ecological metrics, and land use/cover data are applicable for modeling socioeconomic factors, especially demographic and employment structure factors. Among the landscape metrics applied in this study, road density, mean patch size, mean nearest neighbor distance, and percentage of a land use/cover class in landscape were important metrics for predicting socioeconomic factors. Our findings indicated that road density metric and percentages of urban class are useful for predicting urban socioeconomic factors and percentage of agriculture and forest classes in the landscape are suitable metrics for predicting rural socioeconomic factors.
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