Delayed Vegetation Mortality After Wildfire: Insights from a Mediterranean Ecosystem

Plants, Journal Year: 2025, Volume and Issue: 14(5), P. 730 - 730

Published: Feb. 27, 2025

Wildfires, one of the most important ecological disturbances, influence composition and dynamics ecosystems all around world. Changes in fire regimes brought on by climate change are making their effects worse increasing frequency size fires. This study examined issue delayed mortality at species community levels, concentrating Mediterranean forests dominated Quercus ilex suber. research areas lacking spectral recovery following a megafire, which, although relatively small compared to total burned area, represented significant disturbances. The results highlighted distinct post-fire both woodland levels. Q. experienced higher mortality, particularly lower severity (NR), likely due increased intra-specific competition. Because its thick bark, which offers stronger resistance encourages regeneration even high-severity zones (HR), suber showed greater resilience. Responses from shrub layer varied, some species, such as Pteridium aquilinum Cytisus villosus, proliferation. To improve our knowledge ecosystem resilience guide forest management fire-prone areas, these findings highlight intricacy processes need integrate species-specific features with more general community-level patterns.

Language: Английский

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Sentinel-2 ve Landsat-8 ile Bulut Tabanlı Orman Yangın Analizi

Geomatik, Journal Year: 2025, Volume and Issue: 10(3), P. 316 - 330

Published: March 8, 2025

Orman yangınları, doğal ve insan kaynaklı faktörlerden kaynaklanan önemli bir afettir. Bu yangınlar, kuraklık iklim değişikliği gibi ekolojik sorunlara neden olmanın yanı sıra, müdahale sürecinde yangın sonrası hasar tespiti ile analiz çalışmalarında hem maddi de manevi kayıplara yol açmaktadır. Günümüzde, orman yangınlarının hasarların belirlenmesinde Uzaktan Algılama (UA) teknikleri Coğrafi Bilgi Sistemleri (CBS) yaygın şekilde kullanılmaktadır.Bu çalışmada, 29 Temmuz 2021 tarihinde Muğla ili Köyceğiz ilçesinde başlayan 14 gün süren yangını ele alınmıştır. Yangının analizi, Google Earth Engine (GEE) platformunda uzaktan algılama kullanılarak gerçekleştirilmiştir. Yangın öncesine ait sonrasına 27 Ağustos tarihli Sentinel-2A Landsat-8 uydu görüntüleri değerlendirilmiştir. Çalışma kapsamında, bölgeye eğim, bakı NDVI parametreleri risk modeli haritası oluşturulmuş yanan alanların bu riskli bölgelerle örtüştüğü tespit edilmiştir. etkilerini belirlemek amacıyla Normalize Edilmiş Vejetasyon İndeksi (NDVI), Yanma Şiddeti (NBR), indekslerin farkları olan dNDVI dNBR, ayrıca Yanık İzi (BSI) Yanmış Alan (BAI) hesaplanarak tahrip alanlar Son aşamada, dNBR görüntülerine USGS FIREMON (Yangın Etkilerini İzleme Envanter Protokolü) tarafından belirlenmiş eşik değerler uygulanarak çalışma alanına yanma şiddeti oluşturulmuştur.

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Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(3), P. 768 - 768

Published: Jan. 29, 2023

The wall-to-wall prediction of fuel structural characteristics conducive to high fire severity is essential provide integrated insights for implementing pre-fire management strategies designed mitigate the most harmful ecological effects in fire-prone plant communities. Here, we evaluate potential point cloud density LiDAR data from Portuguese áGiLTerFoRus project characterize surface and canopy structure predict wildfire severity. study area corresponds a pilot flight around 21,000 ha central Portugal intersected by mixed-severity that occurred one month after survey. Fire was assessed through differenced Normalized Burn Ratio (dNBR) index computed pre- post-fire Sentinel-2A Level 2A scenes. In addition continuous data, also categorized (low or high) using appropriate dNBR thresholds communities area. We several metrics related distribution fuels strata with mean 10.9 m−2. Random Forest (RF) algorithm used capacity set accuracy RF regression classification model respectively, remarkably (pseudo-R2 = 0.57 overall 81%) considering only focused on variables loading. highest contribution models were proxies horizontal continuity (fractional cover metric) loads openness up 10 m height (density metrics), indicating increased higher load vertical continuity. Results evidence technical specifications acquisitions framed within enable accurate predictions density.

Language: Английский

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Post-Fire Vegetation (Non-)Recovery across the Edges of a Wildfire: An Unexplored Theme

Fire, Journal Year: 2024, Volume and Issue: 7(7), P. 250 - 250

Published: July 13, 2024

Wildfires have a significant influence on ecosystems globally, shaping vegetation, biodiversity, landscapes, soil properties, and other ecosystem processes. Despite extensive research different aspects of wildfires, the edges burned areas remain understudied, even though they involve complex dynamics. In this study, we analyzed post-fire vegetation recovery across large wildfire in Mediterranean area. The investigations were focused patches woodlands that, previous showed normalized burn ratio (NBR) decline one year after fire. Field surveys carried out characterized by NBR rates outside area as controls. Five hypotheses tested, identifying delayed tree mortality key factor linked to decline, particularly low-severity fire zones proximity edges. Delayed mortality, observed predominantly near edges, may also affect unburned or less severely within main perimeter, highlighting need for ongoing monitoring. As these play crucial role succession dynamics, understanding second-order effects is imperative effective management. This study underscores importance long-term assessment impacts, emphasizing necessity field alongside remote sensing. Continued observation essential elucidate enduring impacts wildfires facilitate informed restoration strategies.

Language: Английский

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Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(2), P. 361 - 361

Published: Jan. 16, 2024

Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent often devastating. Accurate assessments of the initial severity are required for management mitigation efforts negative impacts fire. Evapotranspiration (ET) crucial hydrological process that links vegetation health water availability, making it valuable indicator understanding dynamics ecosystem recovery after wildfires. This study uses Mapping at High Resolution with Internalized Calibration (eeMETRIC) Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery estimate five large forest fires occurred Spain Portugal 2022 from two perspectives: uni- bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution particularly relevant heterogeneous landscapes different types availability. was significantly affected by according eeMETRIC (F > 431.35; p-value < 0.001) SSEBop 373.83; metrics, reductions 61.46% 63.92%, respectively, wildfire event. A Random Forest machine learning algorithm used predict severity. We achieved higher accuracy (0.60 Kappa 0.67) when employing (eeMETRIC SSEBop) as predictors compared utilizing conventional differenced Normalized Burn Ratio (dNBR) index, which resulted value 0.46. conclude fine valid be indicators countries. research highlights importance Landsat-based accurate tools improve analysis

Language: Английский

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