Are variations of forest vegetation consistent with trends of meteorological parameters in the northern Zagros region of Iran?

Document Type : Research Paper

Authors

1 PhD Candidate of Silviculture and Forest Ecology, Faculty of Natural Resources, University of Tehran, I. R. Iran

2 Prof., Dept. of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, I. R. Iran

Abstract

Deterioration of Zagros forests may partly be related to changes in climatic parameters. In the present study, the trends of precipitation and temperature changes in relation to the trend of forest cover changes in Sardasht area, Northern Zagros, was studied using 30 years data (1988-2017) recorded at Sardasht Synoptic Weather Station. Mann-Kendall statistical tests, the Sen’s slope estimator and Pettitt test were used for trend detecting and finding mutation points of climatic parameters. Trends of forest cover changes in Sardasht were also investigated using NDVI and SAVI vegetation indices. The relationship between the climatic parameters and the vegetation indices was carried out by the simple linear regression analysis and stepwise multivariate regression. Mann-Kendall test showed that the annual, seasonal, and monthly temperatures of January, February, March, May, June, July, August, and September had a significant increasing trend. The trend of changes in NDVI and SAVI indices were not significant, however, these indices pointed out significant relationships with climatic parameter so that temperature of April and SAVI demonstrated the highest simple linear correlation coefficient (0.499). Stepwise multivariate regression analysis displayed that the SAVI had the highest multivariate correlation (0.810) with temperature of August, temperature of spring, precipitation of July and temperature of winter. Regarding the impact of climate change on decline of Zagros forests, the regression models presented in this study can be used to make an appropriate decision to protect these forests. Managers should think of the unexpected changes in meteorological parameters owing to global warming.

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