SavedAdd your journal

Journals / Türkiye Uzaktan Algılama Dergisi / 2026 / Volume: 8

Long-term analysis of the meteorological factors on vegetation recovery after forest fire

Yusuf Üzümlü(Necmettin Erbakan University)0000-0002-6075-773X
İlkay Buğdaycı(Necmettin Erbakan University)0000-0001-8361-1306
Mustafa Üstüner(Samsun University)0000-0003-0553-2682
Pages
—
Publication Date
2026-07-03
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results show that relative and specific humidity, soil moisture, solar radiation, and precipitation were positively associated with vegetation growth, while surface temperature, evapotranspiration, and air temperature showed a negative correlation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Global climate change exerts significant pressure on natural ecosystems through rising temperatures, irregular precipitation patterns, and increasing drought frequency.
📊
Key Findings & Data:This study examines vegetation recovery following the 2008 forest fire in the Menderes district of İzmir Province, a region highly susceptible to fires due to its Mediterranean climate and dense forest cover.
💡
Conclusion & Impact:Results show that relative and specific humidity, soil moisture, solar radiation, and precipitation were positively associated with vegetation growth, while surface temperature, evapotranspiration, and air temperature showed a negative correlation.
🔬Design:Modeling & Simulation
📍Scope:2008 Verileri
⚙️Variables:Climate change · Forest fire · Ridge regression

Abstract

Global climate change exerts significant pressure on natural ecosystems through rising temperatures, irregular precipitation patterns, and increasing drought frequency. These conditions create more favorable environments for forest fires by reducing fuel moisture and extending the fire season. This study examines vegetation recovery following the 2008 forest fire in the Menderes district of İzmir Province, a region highly susceptible to fires due to its Mediterranean climate and dense forest cover. The relationship between the Enhanced Vegetation Index (EVI), derived from Landsat satellite imagery, and meteorological variables was modeled using the Ridge Regression method. Results show that relative and specific humidity, soil moisture, solar radiation, and precipitation were positively associated with vegetation growth, while surface temperature, evapotranspiration, and air temperature showed a negative correlation. The experimental results proved that meteorological factors explain approximately 70% of post-fire vegetation recovery. The remaining 30% is attributed to soil properties, topography, post-fire restoration efforts, and ecological processes.

🔖

Cite This Article

Üzümlü, Y., Buğdaycı, İ., Üstüner, M. (2026). Long-term analysis of the meteorological factors on vegetation recovery after forest fire. Türkiye Uzaktan Algılama Dergisi, Volume: 8. https://doi.org/10.51489/tuzal.1873909

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (58)

  1. Agne, M. C., Fontaine, J. B., Enright, N. J., & Harvey, B. J. (2022). Fire interval and post-fire climate effects on serotinous forest resilience. Fire Ecology, 18(1). https://doi.org/10.1186/s42408-022-00145-4
  2. Almeida, M., & Coelho, P. (2023). A first assessment of ERA5 and ERA5-Land reanalysis air temperature in Portugal. International Journal of Climatology, 43(14), 6643–6663. https://doi.org/10.1002/joc.8225
  3. Beghin, R., Lingua, E., Garbarino, M., Lonati, M., Bovio, G., Motta, R., & Marzano, R. (2010). Pinus sylvestris forest regeneration under different post-fire restoration practices in the northwestern Italian Alps. Ecological Engineering, 36(10), 1365–1372. https://doi.org/10.1016/j.ecoleng.2010.06.014
  4. Blanco-Rodríguez, M. Á., Ameztegui, A., Gelabert, P., Rodrigues, M., & Coll, L. (2023). Short-term recovery of post-fire vegetation is primarily limited by drought in Mediterranean forest ecosystems. Fire Ecology, 19(1). https://doi.org/10.1186/s42408-023-00228-w
  5. Bonshoms, M., Ubeda, J., Liguori, G., Körner, P., Navarro, Á., & Cruz, R. (2022). Validation of ERA5-Land temperature and relative humidity on four Peruvian glaciers using on-glacier observations. Journal of Mountain Science, 19(7), 1849–1873. https://doi.org/10.1007/s11629-022-7388-4
  6. Calderisi, G., Salaris, E., Cogoni, D., Rossetti, I., Murtas, F., & Fenu, G. (2025). Relationship between post-fire vegetation recovery and soil temperature in the Mediterranean Forest. Fire, 8(3). https://doi.org/10.3390/fire8030091
  7. Chen, Y., Morton, D. C., & Randerson, J. T. (2024). Remote sensing for wildfire monitoring: Insights into burned area, emissions, and fire dynamics. One Earth, 7(6), 1022–1028. https://doi.org/10.1016/j.oneear.2024.05.014
  8. Choudhary, M. K., Amal Raj, V., Gowri Sankar, S., & Ravindran, P. (2025). Composition and structure based GGA Bandgap prediction using machine learning approach. Advanced Theory and Simulations, 8(12), e00771. https://doi.org/10.1002/adts.202500771
  9. Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M., & Oliva, P. (2020). Satellite remote sensing contributions to wildland fire science and management. Current Forestry Reports, 6(2), 81–96. https://doi.org/10.1007/s40725-020-00116-5
  10. De Luca, G., Silva, J. M. N., & Modica, G. (2022). Short-term temporal and spatial analysis for post-fire vegetation regrowth characterization and mapping in a Mediterranean ecosystem using optical and SAR image time-series. Geocarto International, 37(27), 15428–15462. https://doi.org/10.1080/10106049.2022.2097482
  11. De Rigo, D., Libertà, G., Durrant, T., Artes, T., & San-Miguel-Ayanz, J. (2017). Forest fire danger extremes in Europe under climate change: variability and uncertainty. Publications Office of the European Union. https://doi.org/10.2760/13180
  12. Dilekçi, S., Marangoz, A. M., & Ayhan, A. (2021). Detection of forest fire risk areas of Zonguldak and Ereğli regional forest directorate. Journal of Geomatics, 6(1), 44-53. https://doi.org/10.29128/geomatik.660623
  13. Ermitão, T., Gouveia, C. M., Bastos, A., & Russo, A. C. (2024). Recovery following recurrent fires across Mediterranean Ecosystems. Global Change Biology, 30(12). https://doi.org/10.1111/gcb.70013
  14. Ertuğrul, M., & Kaya, L. G. (2021). Türkiye’nin Akdeniz iklim kuşağında çıkan orman yangınlarının incelenmesi ve alınması gereken önlemler. Türkiye Bilimler Akademisi. https://doi.org/10.53478/TUBA.2021.047
  15. Hao, B., Xu, X., Wu, F., & Tan, L. (2022). Long-term effects of fire severity and climatic factors on post-forest-fire vegetation recovery. Forests, 13(6). https://doi.org/10.3390/f13060883
  16. Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67. https://doi.org/10.1080/00401706.1970.10488634
  17. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Romete Sensing of Enviroment, 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2
  18. Ibarra-Bonilla, J. S., Pinedo-Alvarez, A., Prieto-Amparán, J. A., Siller-Clavel, P., Santellano-Estrada, E., Álvarez-Holguín, A., & Villarreal-Guerrero, F. (2024). Post-fire vegetation dynamics of a temperate mixed forest: An assessment based on the variability of Landsat spectral indices. Trees, Forests and People, 17. https://doi.org/10.1016/j.tfp.2024.100648
  19. Jarchow, C. J., Didan, K., Barreto-Muñoz, A., Nagler, P. L., & Glenn, E. P. (2018). Application and comparison of the MODIS-derived enhanced vegetation index to VIIRS, landsat 5 TM and landsat 8 OLI platforms: A case study in the arid colorado river delta, Mexico. Sensors, 18(5). https://doi.org/10.3390/s18051546
  20. Justice, C. O., Vermote, E., G Townshend, J. R., Defries, R., Roy, D. P., G, J. R., Leeuwen, van, Authors Christopher Justice, A. O., Hall, D. K., Salomonson, V. V, Privette, J. L., Riggs, G., Strahler, A., Lucht, W., Myneni, R. P., Knyazikhin, Y., Running, S. W., Nemani, R. R., Wan, Z., … van Leeuwen are, W. (1998). The Moderate Resolution Imaging Spectroradiometer (MODIS): Land Remote Sensing for Global Change Research. IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1228-1249. https://doi.org/10.1109/36.701075
  21. Kavzoğlu, T., & Sefercik, U. G. (2021). Orman yangınlarında uzaktan algılama teknolojileri ile erken uyarı, tespit, izleme ve müdahale stratejileri. Türkiye Bilimler Akademisi. https://doi.org/10.53478/TUBA.2021.044
  22. Kurbanov, E., Vorobev, O., Lezhnin, S., Sha, J., Wang, J., Li, X., Cole, J., Dergunov, D., & Wang, Y. (2022). Remote sensing of forest burnt area, burn severity, and post-fire recovery: A review. Remote Sensing, 14(19). https://doi.org/10.3390/rs14194714
  23. Liu, S., & Dobriban, E. (2020). Ridge Regression: Structure, cross-validation and sketching.
  24. Liu, W., Guan, H., Hesp, P. A., & Batelaan, O. (2023). Remote sensing delineation of wildfire spatial extents and post-fire recovery along a semi-arid climate gradient. Ecological Informatics, 78. https://doi.org/10.1016/j.ecoinf.2023.102304
  25. Liu, Z. (2016). Effects of climate and fire on short-term vegetation recovery in the boreal larch forests of Northeastern China. Scientific Reports, 6. https://doi.org/10.1038/srep37572
  26. López-Vicente, M., Cerdà, A., Kramer, H., & Keesstra, S. (2021). Post-fire practices benefits on vegetation recovery and soil conservation in a Mediterranean area. Land Use Policy, 111. https://doi.org/10.1016/j.landusepol.2021.105776
  27. Mandre, A., Hebbar, D. R., Rao, J. S., Keshav, A., Kamal, S., & Rao, T. (2023). Early forest-fire detection by linear regression, Ridge regression and Lasso regression. Proceedings - 2023 International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023, Bengaluru, India, 273–277. https://doi.org/10.1109/CIISCA59740.2023.00060
  28. Mısır, N., & Mısır, M. (2021). Orman Yangınlarının İklim Değişikliği Açısından Değerlendirilmesi. Türkiye Bilimler Akademisi. https://doi.org/10.53478/TUBA.2021.042
  29. Mohammed, J. A. (2024). Trend analysis of extreme rainfall indices from CHIRPS precipitation estimates over the Lake Tana sub-basin, Abbay Basin of Ethiopia. Environmental Monitoring and Assessment, 196(6). https://doi.org/10.1007/s10661-024-12722-y
  30. Moreira, F., Viedma, O., Arianoutsou, M., Curt, T., Koutsias, N., Rigolot, E., Barbati, A., Corona, P., Vaz, P., Xanthopoulos, G., Mouillot, F., & Bilgili, E. (2011). Landscape - wildfire interactions in southern Europe: Implications for landscape management. Journal of Environmental Management, 92(10), 2389–2402). https://doi.org/10.1016/j.jenvman.2011.06.028
  31. Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., & Thépaut, J. N. (2021). ERA5-Land: A state-of-the-art global reanalysis dataset for land applications. Earth System Science Data, 13(9), 4349–4383. https://doi.org/10.5194/essd-13-4349-2021
  32. Nagler, P. L., Jarchow, C. J., & Glenn, E. P. (2018). Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico. Proceedings of the International Association of Hydrological Sciences, 380, Cordoba, Spain, 45–54. https://doi.org/10.5194/piahs-380-45-2018
  33. Nioti, F., Xystrakis, F., Koutsias, N., & Dimopoulos, P. (2015). A remote sensing and GIS approach to study the long-term vegetation recovery of a fire-affected pine forest in southern Greece. Remote Sensing, 7(6), 7712–7731. https://doi.org/10.3390/rs70607712
  34. O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality and Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-006-9018-6
  35. OGM. (2025). Retrieved October 5, 2025, from https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler
  36. Özel, B., Ateşoğlu, A., & Kırdar, E. H. (2021). Orman Yangınları Sonrası Yanan Alanların Ağaçlandırılması, İzleme ve Değerlendirme. Türkiye Bilimler Akademisi. https://doi.org/10.53478/TUBA.2021.050
  37. Vatcheva, K. P., & Lee, M. (2016). Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology, 6(2). https://doi.org/10.4172/2161-1165.1000227
  38. Peña-Molina, E., Moya, D., Marino, E., Tomé, J. L., Fajardo-Cantos, Á., González-Romero, J., Lucas-Borja, M. E., & de las Heras, J. (2024). Fire vulnerability, resilience, and recovery rates of Mediterranean Pine forests Using a 33-year time series of satellite imagery. Remote Sensing, 16(10). https://doi.org/10.3390/rs16101718
  39. Pérez-Cabello, F., Montorio, R., & Alves, D. B. (2021). Remote sensing techniques to assess post-fire vegetation recovery. Current Opinion in Environmental Science and Health, 21. https://doi.org/10.1016/j.coesh.2021.100251
  40. Rokem, A., & Kay, K. (2021). Fractional ridge regression: A fast, interpretable reparameterization of ridge regression. GigaScience, 9(12). https://doi.org/10.1093/gigascience/giaa133
  41. Sabuncu, R., Kavgacı, A., Alan, M., & Çalışkan, A. (2023). Orman Yangınları Sonrası Restorasyon. Türkiye Ormancılar Derneği Yayını, 425-442.
  42. San-Miguel-Ayanz, J., Moreno, J. M., & Camia, A. (2013). Analysis of large fires in European Mediterranean landscapes: Lessons learned and perspectives. Forest Ecology and Management, 294, 11–22. https://doi.org/10.1016/j.foreco.2012.10.050
  43. Scikit-learn. (2025). Retrieved October 5, 2025, from https://scikit-learn.org/stable/modules/linear_model.html#ridge-regression-and-classification
  44. Smith-Ramírez, C., Castillo-Mandujano, J., Becerra, P., Sandoval, N., Fuentes, R., Allende, R., & Paz Acuña, M. (2022). Combining remote sensing and field data to assess recovery of the Chilean Mediterranean vegetation after fire: Effect of time elapsed and burn severity. Forest Ecology and Management, 503. https://doi.org/10.1016/j.foreco.2021.119800
  45. Spatola, M. F., Borghetti, M., & Nolè, A. (2023). Elucidating factors driving post-fire vegetation recovery in the Mediterranean forests using Landsat spectral metrics. Agricultural and Forest Meteorology, 342. https://doi.org/10.1016/j.agrformet.2023.109731
  46. Stankova, N. (2023). Post-fire recovery monitoring using remote sensing: A Review. Aerospace Research in Bulgaria, 35, 192–200. https://doi.org/10.3897/arb.v35.e19
  47. Suprapto, S., Wahyuningtyas, A., Madurani, K. A., & Ni’mah, Y. L. (2025). Predicting ash content and water content in coal using full infrared spectra and machine learning models. South African Journal of Chemical Engineering, 51, 170–179. https://doi.org/10.1016/j.sajce.2024.11.006
  48. Tüfekcioğlu, İ., Ergan, G., Kaynaş, B., Aktepe, N., & Tavşanoğlu, Ç. (2022). Developing recommendations by rapid ecological assessment for post-fire restoration in low altitude forests and shrublands in Mediterranean climate region: A case study for Datça-Bozburun Special Protection Area. Turkish Journal of Forestry, 163–177. https://doi.org/10.18182/tjf.1118883
  49. Tulger Kara, G., & Elbir, T. (2024). Evaluation of ERA5 and MERRA-2 Reanalysis Datasets over the Aegean Region, Türkiye. DEU Muhendislik Fakultesi Fen ve Muhendislik, 26(76), 9–21. https://doi.org/10.21205/deufmd.2024267602
  50. Viana-Soto, A., Aguado, I., & Martínez, S. (2017). Assessment of post-fire vegetation recovery using fire severity and geographical data in the mediterranean region (Spain). Environments, 4(4), 90. https://doi.org/10.3390/environments4040090
  51. Wu, R. (2016). Forecast Analysis of Securities Index Based on Ridge Regression—In Case of Shanghai Composite Index. Business and Globalization, 04(02), 47–55. https://doi.org/10.12677/bglo.2016.42007
  52. Yilmaz, M. (2023). Accuracy assessment of temperature trends from ERA5 and ERA5-Land. Science of the Total Environment, 856. https://doi.org/10.1016/j.scitotenv.2022.159182
  53. Yılmaz, M. (2022). Verification of ERA5 Hourly Air Temperature Data over Turkey. Journal of Natural Hazards and Environment, 8(2), 207–220. https://doi.org/10.21324/dacd.1001820
  54. Yoantika, A. F., & Susiswo. (2021). Comparing the Principal Regression Analysis Method with Ridge Regression Analysis in Overcoming Multicollinearity on Human Development Index (HDI) Data in Regency/City of East Java in 2018. Journal of Physics: Conference Series, 1872(1). https://doi.org/10.1088/1742-6596/1872/1/012024
  55. Yoo, W., Mayberry, R., Bae, S., Singh, K., Qinghua, P., & Lillard Jr, J. W. (2014). A study of effects of multicollinearity in the multivariable analysis. International Journal of Applied Science and Technology, 4(5), 9-19.
  56. Zahura, F. T., Bisht, G., Li, Z., McKnight, S., & Chen, X. (2024). Impact of topography and climate on post-fire vegetation recovery across different burn severity and land cover types through random forest. Ecological Informatics, 82. https://doi.org/10.1016/j.ecoinf.2024.102757
  57. Zhang, C., Xiao, X., Wang, X., Yi, S., Meng, C., Qin, Y., Yao, Y., Yin, L., Celis, J., Pan, L., Pan, B., Yang, X., & Dong, J. (2025). Climate-induced losses of surface water and total water storage in Northeast Asia. Communications Earth and Environment, 6(1). https://doi.org/10.1038/s43247-025-02449-0
  58. Zhao, F. R., Meng, R., Huang, C., Zhao, M., Zhao, F. A., Gong, P., Yu, L., & Zhu, Z. (2016). Long-term post-disturbance forest recovery in the greater yellowstone ecosystem analyzed using Landsat time series stack. Remote Sensing, 8(11). https://doi.org/10.3390/rs8110898