An applied statistical method to identify desertification indicators in northeastern Iran

Abstract

Background: Desertification could be considered ultimate consequence of land degradation in an ecosystem. Iran with more than 75% arid and semi-arid areas involves fragile and susceptible ecosystems to desertification. We applied a statistical algorithm including regression trees and random forest techniques for determining main factors affecting desertification based on ESAs in Taybad-Bakharz region at northeastern Iran. Results: The results indicated a significant correlation between the desertification hazard value with variables of wind erosion, precipitation, aridity index, technology development, slope index, vegetation state and land use changes. Conclusions: Regression trees and random forest techniques in desertification hazard provide an absolute estimation of the relationship between dependent and independent variables. We can use a robust base for further investigations and refined with findings from in-depth studies carried out at the local scale.