Improved Fire Severity Prediction Using Pre-Fire Remote Sensing and Meteorological Time Series: Application to the French Mediterranean Area DOI
Victor Pénot, Thomas Opitz, François Pimont

и другие.

Опубликована: Янв. 1, 2024

Fire severity, or how an environment is affected by fire, can be estimated over large areas using remotely sensed fire severity indices, such as the Relative Burnt Ratio (RBR). RBR predictions are generally based on data collected a single date immediately before aggregated time to scalar value. However, accurate temporal and spatial prediction of remains challenging. To improve predictability RBR, we build new predictive models series spanning several months fuel proxies, derived from optical remote sensing meteorological data. The approach applied fires French Mediterranean area during summers 2016-2021 period. Lagged Generalized Additive Model (LGAM) Functional Linear (FLM) used estimate influence explanatory variables up prior while (GAM), which relies immediate pre-fire predictors at date, benchmark. Training carried out fire–land-cover scale with training dataset composed independent those in test datasets. FLM achieves best accuracy (R=0.68, RMSE=0.057) compared LGAM (R=0.60, RMSE=0.063) benchmark (R=0.52, RMSE=0.069) also less sensible overfitting. selected correctly predicts even highest values when Normalized Difference Vegetation Index decreases faster than average fire-weather Duff Moisture Code increases 65 days fire. 17% decrease RMSE GAM shows that knowledge dynamics two provides valuable information for ranking according severity.

Язык: Английский

Canadian forests are more conducive to high-severity fires in recent decades DOI
Weiwei Wang, Xianli Wang, Mike Flannigan

и другие.

Science, Год журнала: 2025, Номер 387(6729), С. 91 - 97

Опубликована: Янв. 2, 2025

Canada has experienced more-intense and longer fire seasons with more-frequent uncontrollable wildfires over the past decades. However, effect of these changes remains unknown. This study identifies driving forces burn severity estimates its spatiotemporal variations in Canadian forests. Our results show that fuel aridity was most influential driver severity, summer months were more prone to severe burning, northern areas influenced by changing climate. About 6% (0.54 14.64%) modeled significant increases number days conducive high-severity burning during 1981 2020, which found 2001 2020 spring autumn. The extraordinary 2023 season demonstrated similar spatial patterns but more-widespread escalations severity.

Язык: Английский

Процитировано

10

Immediate assessment of forest fire using a novel vegetation index and machine learning based on multi-platform, high temporal resolution remote sensing images DOI Creative Commons
Hanqiu Xu, Jiahui Chen,

Guojin He

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 134, С. 104210 - 104210

Опубликована: Окт. 11, 2024

Язык: Английский

Процитировано

6

Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions DOI Creative Commons
Christopher R. Hakkenberg, Matthew L. Clark, Theodore N. Bailey

и другие.

Communications Earth & Environment, Год журнала: 2024, Номер 5(1)

Опубликована: Ноя. 20, 2024

Drivers of forest wildfire severity include fuels, topography and weather. However, because only fuels can be actively managed, quantifying their effects on has become an urgent research priority. Here we employed GEDI spaceborne lidar to consistently assess how pre-fire fuel structure affected across 42 California wildfires between 2019–2021. Using a spatial-hierarchical modeling framework, found positive concave-down relationship GEDI-derived severity, marked by increasing with greater loads until decline in the tallest most voluminous canopies. Critically, indicators canopy volumes (like biomass height) became decoupled from patterns extreme topographic weather conditions (slopes >20°; winds > 9.3 m/s). On other hand, vertical continuity metrics like layering ladder more predicted – especially where sparse understories were uniformly associated lower levels. These results confirm that estimates overcome limitations optical imagery airborne for interactive drivers severity. Furthermore, these findings have direct implications designing treatment interventions target versus entire canopies delineating risk conditions. Wildfire is such as rather than total range conditions, according analysis data fires

Язык: Английский

Процитировано

3

Oxygen isotope values of charred tree bark as an indicator of forest fire severity DOI Creative Commons

Eric V. McDonald,

Elizabeth Webb, Jeffery P. Dech

и другие.

Trees Forests and People, Год журнала: 2025, Номер unknown, С. 100786 - 100786

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Optimising fire severity mapping using pixel-based image compositing DOI Creative Commons
Néstor Quintero, Olga Viedma, Sander Veraverbeke

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 321, С. 114687 - 114687

Опубликована: Март 6, 2025

Язык: Английский

Процитировано

0

Changes in GEDI-based measures of forest structure after large California wildfires relative to pre-fire conditions DOI Creative Commons
Matthew L. Clark, Christopher R. Hakkenberg, Theodore N. Bailey

и другие.

Remote Sensing of Environment, Год журнала: 2025, Номер 323, С. 114718 - 114718

Опубликована: Апрель 4, 2025

Язык: Английский

Процитировано

0

Exploration of Suitable Spectral Bands and Indices for Forest Fire Severity Evaluation Using ZY-1 Hyperspectral Data DOI Open Access
Xinyu Hu, Feng Jiang, Xianlin Qin

и другие.

Forests, Год журнала: 2025, Номер 16(4), С. 640 - 640

Опубликована: Апрель 7, 2025

Satellite remote sensing has been widely recognized as an effective tool for estimating fire severity. Existing indies predominantly rely on broad-spectrum multispectral data, limiting the ability to elucidate intricate relationship between severity and spectral response. To address this challenge, optimal bands indices assessment were explored using ZY-1 hyperspectral which captured pre- post-fire conditions of a forest site in Yuxi City, Yunnan Province, China. Separability contrast threshold segmentation methods applied perform sensitivity analysis original constructed derived from surface reflectance image combination, respectively. The findings indicate following: (1) exhibited superior separability classification capabilities compared difference image, with highest accuracy 78.99% achieved at 800 nm central wavelength. (2) normalized index category combination outperformed vegetation other 83.39% 2048 1106 (3) Unburned areas strong separability, facilitating segmentation, but burned showed poor severities, particularly low moderate–high severity, remains primary limitation assessment. In conclusion, study advances understanding response by leveraging narrow-band advantages. It aims enhance satellite-based estimation, offering valuable technical guidance theoretical insights assessing impacts recovery.

Язык: Английский

Процитировано

0

Spatial Representation of Soil Erosion and Vegetation Affected by a Forest Fire in the Sierra de Francia (Spain) Using RUSLE and NDVI DOI Creative Commons

Gloria Fernández,

Leticia Merchán, José A. Sánchez

и другие.

Land, Год журнала: 2025, Номер 14(4), С. 793 - 793

Опубликована: Апрель 7, 2025

Extreme weather events are increasing the frequency and intensity of forest fires, generating serious environmental socio-economic impacts. These fires cause soil loss through erosion, organic matter depletion, increased surface runoff release greenhouse gases, intensifying climate change. They also affect biodiversity, terrestrial aquatic ecosystems, quality. The assessment by remote sensing, such as use Normalised Difference Vegetation Index (NDVI), allows rapid analysis damaged areas, monitoring vegetation changes design restoration strategies. On other hand, models RUSLE key tools for calculating erosion planning conservation measures. A study impacts on soils in south Salamanca, where one worst province took place 2022, has been carried out using NDVI models, respectively. confirms that significantly properties, increase hinder recovery, highlighting need effective It was observed intensifies after (the maximum rate before is 1551.85 t/ha/year, while it 4899.42 t/ha/year) especially areas with steeper slopes, which increases vulnerability, according to model. showed a decrease recovery most affected (with value 0.3085 event 0.4677 before), indicating slow regeneration process. generation detailed cartographies essential identify critical prioritise actions. Furthermore, highlights importance implementing measures, designing sustainable agricultural strategies developing policies focused mitigation land degradation fire-affected ecosystems.

Язык: Английский

Процитировано

0

An integrated framework for wildfire emergency response and post-fire debris flow prediction: a case study from the wildfire event on 20 April 2021 in Mianning, Sichuan, China DOI
Yao Tang,

Yuting Luo,

Wang Li-juan

и другие.

Natural Hazards, Год журнала: 2025, Номер unknown

Опубликована: Май 8, 2025

Язык: Английский

Процитировано

0

Improved fire severity prediction using pre-fire remote sensing and meteorological time series: Application to the French Mediterranean area DOI Creative Commons
Victor Pénot, Thomas Opitz, François Pimont

и другие.

Agricultural and Forest Meteorology, Год журнала: 2025, Номер 371, С. 110588 - 110588

Опубликована: Май 9, 2025

Язык: Английский

Процитировано

0