IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v14y2025i3p448-d1596376.html
   My bibliography  Save this article

Driving Factors of Post-Fire Vegetation Regrowth in Mediterranean Forest

Author

Listed:
  • Catarina de Almeida Pinheiro

    (CECS (Communication and Society Research Centre), Department of Geography, University of Minho, 4710-057 Braga, Portugal
    CEGOT (Centre of Studies on Geography and Spatial Planning), Department of Geography and Tourism, University of Coimbra, 3004-530 Coimbra, Portugal)

  • Bruno Martins

    (CEGOT (Centre of Studies on Geography and Spatial Planning), Department of Geography and Tourism, University of Coimbra, 3004-530 Coimbra, Portugal
    RISCOS (Portuguese Association of Risks, Prevention and Security), University of Coimbra, 3004-530 Coimbra, Portugal)

  • Adélia Nunes

    (CEGOT (Centre of Studies on Geography and Spatial Planning), Department of Geography and Tourism, University of Coimbra, 3004-530 Coimbra, Portugal
    RISCOS (Portuguese Association of Risks, Prevention and Security), University of Coimbra, 3004-530 Coimbra, Portugal)

  • António Bento-Gonçalves

    (CECS (Communication and Society Research Centre), Department of Geography, University of Minho, 4710-057 Braga, Portugal)

  • Manuela Laranjeira

    (CECS (Communication and Society Research Centre), Department of Geography, University of Minho, 4710-057 Braga, Portugal)

Abstract

Large wildfires have increased in the Mediterranean region due to socio-economic and land-use changes. The most immediate and concerning consequence of the wildfires is the loss of vegetation. However, there are few studies on the relationship between wildfire and vegetation recovery, especially on the complex relationship between species composition, burn severity and geo-environmental context. This study focuses on the analysis of post-fire vegetation regrowth (RV) in Mediterranean forests. Therefore, two objectives were set: (i) to analyse the influence of pre-fire conditions, burn severity and topographic variables on growth rates for each stage of recovery and (ii) to identify the drivers of post-fire vegetation recovery. The results show that NDVI increases rapidly in the first two years after the wildfire and more slowly in the following years. Except for the first year, RV shows a positive relationship with burn severity. In the first year, the importance of topographical features, especially curvature and flow accumulation, stands out. In the fourth year, when NDVI values are highest, RV is mainly explained by the presence of pre-fire vegetation, followed by burn severity and altitude. These results can be an important step towards more effective local management strategies leading to a resilient and sustainable territory.

Suggested Citation

  • Catarina de Almeida Pinheiro & Bruno Martins & Adélia Nunes & António Bento-Gonçalves & Manuela Laranjeira, 2025. "Driving Factors of Post-Fire Vegetation Regrowth in Mediterranean Forest," Land, MDPI, vol. 14(3), pages 1-19, February.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:448-:d:1596376
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/14/3/448/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/14/3/448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thorsten Thadewald & Herbert Buning, 2007. "Jarque-Bera Test and its Competitors for Testing Normality - A Power Comparison," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(1), pages 87-105.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Timothy Kiessling & Lars Isaksson & Burze Yasar, 2016. "Market Orientation and CSR: Performance Implications," Journal of Business Ethics, Springer, vol. 137(2), pages 269-284, August.
    2. Elhachemi Abdelkader Hacine Gherbi & Gamal Sayed Ahmed Khalifa & Aleksy Kwilinski, 2024. "Forecasting Entrepreneurial and Employability Opportunities in the UAE: A Government Finance Analysis," Virtual Economics, The London Academy of Science and Business, vol. 7(4), pages 92-121, December.
    3. A. H. M. Mehbub Anwar & Mohammad Altelmesani & Abdulrahman Alwosheel, 2024. "Unveiling the causal nexus between Saudi’s seaborne trade and economy: evidence from an ARDL model," Journal of Shipping and Trade, Springer, vol. 9(1), pages 1-21, December.
    4. Muhammad Aslam & Rehan Ahmad Khan Sherwani & Muhammad Saleem, 2021. "Vague data analysis using neutrosophic Jarque–Bera test," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-9, December.
    5. Bharat Ram Dhungana & Jitendra Kumar Singh & Samrat Dhungana, 2024. "Life expectancy and health care spending in South Asia: An econometric analysis," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-14, December.
    6. Wanfang Chen & Marc G. Genton, 2023. "Are You All Normal? It Depends!," International Statistical Review, International Statistical Institute, vol. 91(1), pages 114-139, April.
    7. Lan Khanh Chu, 2024. "The role of technological innovation and population aging in environmental degradation in the Organization for Economic Co-operation and Development countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(1), pages 735-773, January.
    8. Tanweer Ul Islam, 2018. "Preliminary Tests of Homogeneity - Type I Error Rates under Non-Normality," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(5), pages 144-150, May.
    9. Mynbayev, Kairat & Aipenova, Aziza, 2013. "Testing a differential condition and local normality of densities," MPRA Paper 87045, University Library of Munich, Germany, revised 2014.
    10. Sébastien Charles & Jonathan Marie, 2020. "A Note on the Competing Causes of High Inflation in Bulgaria during the 1990s: Money Supply or Exchange Rate?," Review of Political Economy, Taylor & Francis Journals, vol. 32(3), pages 433-443, July.
    11. Yong Bao, 2013. "On Sample Skewness and Kurtosis," Econometric Reviews, Taylor & Francis Journals, vol. 32(4), pages 415-448, December.
    12. Liu, Ying & Lin, Boqiang & Xu, Bin, 2021. "Modeling the impact of energy abundance on economic growth and CO2 emissions by quantile regression: Evidence from China," Energy, Elsevier, vol. 227(C).
    13. Czesław Domański & Piotr Szczepocki, 2020. "Comparison of selected tests for univariate normality based on measures of moments," Statistics in Transition New Series, Polish Statistical Association, vol. 21(5), pages 151-178, December.
    14. Wicaksono, Ahmad Tibrizi Soni, 2022. "Islamic Bank Deposits during COVID-19 Pandemic: A Spatial Finance Approach," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 56(1), pages 135-149.
    15. Shujaat Naeem Azmi & Shakeb Akhtar, 2023. "Interactions of services export, financial development and growth: evidence from India," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 4709-4724, October.
    16. Islam, Tanweer ul, 2008. "Normality Testing- A New Direction," MPRA Paper 16452, University Library of Munich, Germany.
    17. Bufalo, Michele & Orlando, Giuseppe, 2023. "A three-factor stochastic model for forecasting production of energy materials," Finance Research Letters, Elsevier, vol. 51(C).
    18. Choudary Ihtasham Ali & Sami Ullah & Umar Ijaz Ahmed & Abd Ur Rehman & Hafiz Zahid Mehmood & Mudassar Yasin & Mohsin Raza, 2023. "Short and Long Run Effects of Monetary Policy on Food Inflation: A Study of Pakistan," Journal of Economic Impact, Science Impact Publishers, vol. 5(2), pages 146-154.
    19. Damian Jelito & Marcin Pitera, 2021. "New fat-tail normality test based on conditional second moments with applications to finance," Statistical Papers, Springer, vol. 62(5), pages 2083-2108, October.
    20. Sasan Barak & Navid Parvini, 2023. "Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1695-1726, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:448-:d:1596376. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.