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Assessing vegetation restoration potential under different land uses and climatic classes in northeast Iran

Ahmad Emamian, Alireza Rashki, Dimitris G. Kaskaoutis and 3 more

Ecological Indicators · 2021

doi:10.1016/j.ecolind.2020.107325 (opens in a new tab)

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Abstract

This study examines the trends in vegetation cover using the Growing Season NDVI (GSN) time series in moderate spatial resolution (250 m) over Khorasan Razavi province, in northeast Iran, during the period 2004–2015. The province is largely desert, with extra-arid, arid, and semi-arid de Martonne climate zones dominating, while rangelands, shrublands and deserts cover most areas, making it an ideal territory for monitoring vegetation trends and implement future restoration projects. Most parts of the province and land-cover classes show no trends in vegetation cover, but large decreasing trends occur in areas covered by sand dunes, previously reforested lands and clay pit areas. Trends in various land-cover types are also examined as functions of the climatic class and the Terrain Niche index (TNI), which is characteristic of the topography, revealing large decreasing trends in the extra-arid climatic zone. In addition, most of the areas exhibit Hurst exponent values around 0.5, implying stochastic time series without any consistency and a likelihood of random vegetation and land cover changes in the future. This study also aims to determine likely future vegetation status and the most favourable areas for restoration projects through analysis of two indexes (Future Restoration Dispersal Index, FRDI and Future Uncertainty Dispersal Index, FUDI). The results show that reforestation, sand dunes and clay pits areas are the most favourable for implementing restoration projects, while the spatial distribution of the potential restoration classes reveals that the southern and northeastern parts of Khorasan Razavi province are the most favourable areas for establishing environmental restoration activities in order to avoid further degradation of ecosystems.

Abstract licence: CC BY-NC

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