Provision of eucalyptus wood farming potential map in Iran: An application of land cover, ecological, climatic, hydrologic, and edaphic analysis in a GIS-based fuzzy AHP framework

Abstract

Iran is a low-forest cover country (<10% of country area is forested) and has problems with deforestation and increasing demands for wood products. This wood scarcity has prompted the country to curtail industrial logging to meet other demands for wood. The costs of importing wood are rising, so farming wood industrially with fast-growing species may help with this problem. Eucalyptus is a fast-growing species that can be used for industrial production and has been proven adaptable to the environments of southern Iran. This study identifies the most suitable locations for eucalyptus wood-farming in Khuzestan Province. Twenty-two factors and fuzzy analytic hierarchy process (FAHP) were used to identify the best locations for eucalyptus wood-farming in this Province in southern Iran. The independent variables used reflect four components of the environment: soil, water, climate, and land cover. Maps of twenty-two factors were prepared from satellite data, ground sampling, and other geospatial data sources. Eucalyptus plantations in Khuzestan Province were mapped by combining GPS-located ground surveys with data from the Khuzestan Natural Resources Administration. The ecological needs of E. camaldulensis were determined from a review of eucalyptus adaptation studies conducted in southern Iran. The factor maps were classified based on their suitability for eucalyptus farming and these classes served as within-layer weights. The weights of effective factors were calculated using FAHP and Chang's fuzzy-extent analysis (outside-of-layer weights). The accuracies of the fuzzy weights of effective factors were assessed by consistency ratio. A eucalyptus wood-farming potential map was created by overlaying the weighted sub-factor maps. This map was validated with real data (the map of current eucalyptus plantations), overall accuracy assessment, and the kappa index. The results showed that among the primary factors, water (weight: 0.34) and land cover (weight: 0.32) were the most influential determinants of the best locations for eucalyptus farming. The accuracy assessment revealed that the FAHP method produced an overall accuracy of 82% and kappa index of 0.71. This is acceptably strong for the identification of the best eucalyptus farm locations in Khuzestan Province. The wood-farming potential map developed from this research, can be used as a foundation to plan eucalyptus farming in southern Iran to meet the demand for wood in Iran.