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Assessment of burn severity and vegetation recovery following the 2025 Palisades fire using Landsat-based spectral indices

2025 Palisades yangını sonrasında yangın şiddetinin ve bitki örtüsünün iyileşmesinin Landsat tabanlı spektral indeksler kullanılarak değerlendirilmesi

Nezih Furkan Erbaş(TAPU VE KADASTRO 22. (YOZGAT) BÖLGE MÜDÜRLÜĞÜ)0000-0002-5888-4916
Nizamettin Erbaş(YOZGAT BOZOK UNIVERSITY)0000-0002-6379-3023
Abdullah Varlık(NECMETTIN ERBAKAN UNIVERSITY)0000-0003-2072-3313
Pages
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Publication Date
2026-10-27
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

The burned–unburned classification achieved an Overall Accuracy of 83.58% and a Cohen’s Kappa coefficient of approximately 0.64.

📌 3 Key Takeaways

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Objective & Hypothesis:Background and Aims Wildfires can cause substantial changes in vegetation condition and ecosystem structure, making rapid and reliable post-fire assessment essential for environmental management.
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Key Findings & Data:This study evaluated vegetation change, burn severity, and short-term post-fire response following the 2025 Palisades Fire in California, USA.
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Conclusion & Impact:The burned–unburned classification achieved an Overall Accuracy of 83.58% and a Cohen’s Kappa coefficient of approximately 0.64.
🔬Design:Araştırma Makalesi
📍Scope:2025 Verileri
⚙️Variables:Orman yangını · uzaktan algılama · Landsat

Abstract

Background and Aims Wildfires can cause substantial changes in vegetation condition and ecosystem structure, making rapid and reliable post-fire assessment essential for environmental management. This study evaluated vegetation change, burn severity, and short-term post-fire response following the 2025 Palisades Fire in California, USA. Methods Multi-temporal Landsat 8 and Landsat 9 Collection 2 Level-2 Surface Reflectance imagery acquired on 21 December 2024 (pre-fire), 14 January 2025 (active-fire) and 15 February 2025 (early post-fire) was analyzed. Vegetation dynamics were assessed using the Normalized Difference Vegetation Index (NDVI) and differenced NDVI (dNDVI), while burn severity was evaluated using the Normalized Burn Ratio (NBR) and conventional pre-fire–post-fire differenced NBR (dNBR). Burn severity was classified into five classes. An independent accuracy assessment was performed using Sentinel-2 imagery acquired on 21 February 2025. Results Vegetation condition declined markedly during the active-fire period, followed by localized early post-fire spectral improvement. Within the 3,045.87 ha common valid pre-fire–post-fire analysis area, Moderate-High Severity represented the largest class at 1,173.78 ha (38.54%), followed by Moderate-Low Severity at 684.18 ha (22.46%) and High Severity at 592.65 ha (19.46%). Together, these three classes accounted for 80.46% of the valid analysis area. The burned–unburned classification achieved an Overall Accuracy of 83.58% and a Cohen’s Kappa coefficient of approximately 0.64. Conclusion The integration of Landsat-derived vegetation and burn-sensitive spectral indices with independent Sentinel-2 validation provides an effective framework for rapid wildfire assessment. However, early post-fire spectral improvements should not be interpreted as complete ecological recovery, and longer-term monitoring is required to evaluate ecosystem resilience.

Özet

Giriş ve Hedefler Orman yangınları bitki örtüsü durumu ve ekosistem yapısında önemli değişimlere neden olabilmekte olup, yangın sonrası etkilerin hızlı ve güvenilir biçimde değerlendirilmesi çevresel yönetim açısından önem taşımaktadır. Bu çalışmada, ABD’nin Kaliforniya eyaletinde meydana gelen 2025 Palisades Yangını sonrasında bitki örtüsü değişimi, yanma şiddeti ve kısa dönem yangın sonrası tepki değerlendirilmiştir. Yöntemler Yangın öncesi 21 Aralık 2024 tarihli Landsat 8, aktif yangın dönemine ait 14 Ocak 2025 tarihli Landsat 9 ve erken yangın sonrası 15 Şubat 2025 tarihli Landsat 9 Collection 2 Level-2 Surface Reflectance görüntüleri analiz edilmiştir. Bitki örtüsü dinamikleri Normalized Difference Vegetation Index (NDVI) ve differenced NDVI (dNDVI), yanma şiddeti ise Normalized Burn Ratio (NBR) ve geleneksel yangın öncesi–yangın sonrası differenced NBR (dNBR) kullanılarak değerlendirilmiştir. Yanma şiddeti beş sınıfta incelenmiş ve 21 Şubat 2025 tarihli Sentinel-2 görüntüsü kullanılarak bağımsız doğruluk değerlendirmesi gerçekleştirilmiştir. Bulgular Aktif yangın döneminde bitki örtüsü durumunda belirgin bir azalma gözlenmiş, erken yangın sonrası dönemde ise bazı alanlarda yerel spektral iyileşmeler belirlenmiştir. Yangın öncesi ve erken yangın sonrası görüntülerin ortak geçerli 3.045,87 ha analiz alanında en büyük sınıf 1.173,78 ha (%38,54) ile Orta-Yüksek Şiddet olmuştur. Bunu 684,18 ha (%22,46) ile Orta-Düşük Şiddet ve 592,65 ha (%19,46) ile Yüksek Şiddet izlemiştir. Bu üç sınıf birlikte geçerli analiz alanının %80,46'sını oluşturmuştur. Yanmış–yanmamış sınıflandırmasının Genel Doğruluğu %83,58, Cohen’s Kappa katsayısı ise yaklaşık 0,64 olarak hesaplanmıştır. Sonuçlar Landsat tabanlı bitki örtüsü ve yangına duyarlı spektral indekslerin bağımsız Sentinel-2 doğrulamasıyla birlikte kullanılması, hızlı yangın sonrası değerlendirme için etkili bir yaklaşım sunmaktadır. Bununla birlikte, erken dönemde belirlenen spektral iyileşmeler tam ekolojik iyileşme olarak değerlendirilmemeli ve ekosistem dayanıklılığının belirlenmesi için uzun dönemli izleme yapılmalıdır.

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Cite This Article

Erbaş, N. F., Erbaş, N., Varlık, A. (2026). Assessment of burn severity and vegetation recovery following the 2025 Palisades fire using Landsat-based spectral indices. Anadolu Orman Araştırmaları Dergisi, Sayı: 13. https://doi.org/10.53516/ajfr.2019764

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