Quantifying vegetation recovery under different reforestation practices using time series analysis: a case study in Cyprus
Journal
Ecological Indicators
ISSN
1470-160X
Date Issued
September 2026
Abstract
In recent decades, wildfires have increased in frequency, intensity, and spatial extent, causing extensive ecological damage and emphasizing the necessity for effective post-fire restoration measures. Earth Observation (EO) and Remote Sensing (RS) technologies offer an efficient and reliable way to monitor landscape recovery over long periods, supporting both research and forest management. This study explores the contribution of EO methods in evaluating reforestation actions implemented after the 2016 Solea fire in Cyprus, a representative wildfire event in the Troodos region, dominated by Pinus brutia forest stands. Using a 12-year Landsat-based Normalized Burn Ratio (NBR) time series (2013–2024), complemented by climatic and topographic variables harmonized to a common analysis grid, this study assesses vegetation recovery under five restoration approaches: Planting, Terraces, Sowing, Spot Sowing, and Natural Regeneration. Recovery dynamics were assessed through established indicators, including the Relative Recovery Index (RRI) and Year-to-Year (YrYr) slopes. Our findings indicate that areas restored through planting and terraces were associated with faster and more consistent vegetation recovery than areas undergoing natural regeneration. The statistical analysis also demonstrated that fire severity and pre-fire vegetation condition were the most influential determinants of post-fire regrowth, and that linear regression models provided the strongest predictive performance. By combining EO time series with ecological insights, this work highlights the strengths and weaknesses of different restoration practices and provides evidence-based guidance for future forest recovery planning in fire-prone Mediterranean landscapes.
Funding
This study was carried out in the framework of AI-OBSERVER Twinning project titled “Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence” that is funded by the European Union with Grant Agreement No. 101079468. The authors would also like to acknowledge the ‘EXCELSIOR’: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (www.excelsior2020.eu). The ‘EXCELSIOR’ project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.
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