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117 results for “Burn severity”
Supporting code and data for: Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds
<p>This is the R code and shapefiles of the coniferous HUC-12 watersheds and their western US boundary for the publication, "Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds" </p>
Effects of nurse shrubs and biochar on planted conifer seedling survival and growth in a high-severity burn patch in New Mexico, USA
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Supporting code and data for: Proportion of forest area burned at high-severity increases with increasing forest cover and connectivity in western US watersheds
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Anaktuvuk River Burn Eddy Flux Measurements, 2008 Severe Burn Site, North Slope Alaska
We deployed three eddy covariance towers along a burn severity gradient (i.e. severely-, moderately-, and un-burned tundra) to monitor post fire Net Ecosystem Exchange of CO2 (NEE) within the large 2007 Anaktuvuk River fire scar during the summer of 2008. This data represents the first post fire growing season's energy and mass exchange at the severe burn site.
Anaktuvuk River Burn Eddy Flux Measurements, 2009 Severe Burn Site, North Slope Alaska
We deployed three eddy covariance towers along a burn severity gradient (i.e. severely-, moderately-, and un-burned tundra) to monitor post fire Net Ecosystem Exchange of CO2 (NEE) within the large 2007 Anaktuvuk River fire scar during the summer of 2008. This data represents the 2009 post fire energy and mass exchange at the severe burn site.
Anaktuvuk River Burn Eddy Flux Measurements, 2010 Severe Burn Site, North Slope Alaska
We deployed three eddy covariance towers along a burn severity gradient (i.e. severely-, moderately-, and un-burned tundra) to monitor post fire Net Ecosystem Exchange of CO2 (NEE) within the large 2007 Anaktuvuk River fire scar during the summer of 2008. This data represents the 2010 post fire energy and mass exchange at the severe burn site.
Anaktuvuk River Burn Eddy Flux Measurements, 2011 Severe Burn Site, North Slope Alaska
We deployed three eddy covariance towers along a burn severity gradient (i.e. severely-, moderately-, and un-burned tundra) to monitor post fire Net Ecosystem Exchange of CO2 (NEE) within the large 2007 Anaktuvuk River fire scar during the summer of 2008. This data represents the 2011 post fire energy and mass exchange at the severe burn site.
Anaktuvuk River Burn Eddy Flux Measurements, 2012 Severe Burn Site, North Slope Alaska
We deployed three eddy covariance towers along a burn severity gradient (i.e. severely-, moderately-, and un-burned tundra) to monitor post fire Net Ecosystem Exchange of CO2 (NEE) within the large 2007 Anaktuvuk River fire scar during the summer of 2008. This data represents the 2012 post fire energy and mass exchange at the severe burn site.
Fire Severity Estimates: 2001 Survey Line Burn
Field-based estimates of fire severity for 85 plots located near the Tanana River in the Survey Line Burn. Composite Burn Index estimates were taken during June-August 2002. The locations of each plot are stored as differentially corrected GPS files and as an ArcGIS point shapefile. Digital aerial photography of the post-fire area from August and September 2002 are in the data base. Database also includes remotely sensed fire severity indices of the entire Survey Line Burn including the Normalized Burn Ratio from a 21-July-2002 Landsat ETM+ image and the differenced Normalized Burn Ratio from a 6-June-2001 pre-fire Landsat ETM+ image and the post-fire 21-July-2002 Landsat ETM+ image.
SeverusPT - A multi-source burn severity dataset for mainland Portugal
<h3>Abstract (EN)</h3> <p>The SeverusPT project aims to periodically and timely provide relevant and standardized information on burn severity supported by satellite and field observations. Key objectives include developing a spatially explicit framework for assessing, mapping, and predicting burn severity and delivering a co-designed product/service to enhance institutional and operational capacity for fire hazard management and post-fire ecosystem restoration.</p> <p>The project currently provides standardized satellite-based datasets on mainland Portugal’s observed/historical burn severity, leveraging multiple satellite missions (Sentinel-2, Landsat, MODIS), spectral indices (e.g., Normalized Burn Ratio – NBR, Tasseled Cap Transformation – TCT), and burn severity indicators. The datasets are derived from pre-calculated severity products through algorithms that integrate satellite image time series (SITS) in two main approaches: (i) a delta-based pipeline, employing “classical” severity measurements (e.g., delta NBR) and focused primarily on high spatial resolution satellites; and (ii) a trajectory-based pipeline supported by SITS and the analysis of post-fire trajectories for multiple dimensions of ecosystem functioning and primarily focusing on high-temporal/moderate spatial resolution satellites.</p> <p>Field assessments, critical for validating satellite products and obtaining nuanced results regarding post-fire effects, were used to provide information on burn severity across different structural components of vegetation. The project used a purposive stratified approach for field surveys, focusing on the 2022 fire season across mainland Portugal. Selection criteria based on fire size, location, main vegetation type, and other ancillary layers enabled comprehensive coverage and diversity in post-fire conditions. Approximately 111 sites in 28 burned areas were surveyed in north and centre Portugal (the wildfire foci in the country) using the Geometrically Structured Composite Burn Index (GeoCBI) protocol. Two methods were used to validate the delta-based products by comparing in situ GeoCBI and satellite burn severity estimates: (i) non-parametric linear correlation (Spearman method) and nonlinear correlation; and (ii) a nonlinear exponential model adapted from pre-existing studies.</p> <p>Delta-based SeverusPT products agreed well with GeoCBI field measures of burn severity. The best linear correlation results were bounded between 0.64 and 0.71. Sentinel-2 and the NBR spectral index with RBR generally ranked higher when compared to Landsat-8. For nonlinear correlation, results were between 0.65 and 0.76, with the best results for Landsat-8 TCTG, closely followed by Sentinel-2 NBR spectral index with RDT or RBR indicators. The results for the nonlinear model validation were similar, with the best marks attained by the RdNBR, RBR, and dNBR indicators (R2= 0.64, 0.62, and 0.60, respectively).</p> <p>The project’s data portal is a centralized gateway for accessing and downloading project data and metadata. It offers two primary levels of data access: Level 1 includes the products, and Level 2 comprises image data files along with metadata. The trajectory-based pipeline and products are still under active development and will be added to the SeverusPT Data Portal.</p> <p>SeverusPT builds on a comprehensive approach combining satellite data with rigorous field validation, yielding significant insights into wildfire severity. The project’s innovative methodologies and the data portal’s accessibility contribute to the field of wildfire severity assessment, offering valuable data and tools for fire management and prioritizing post-fire mitigation and recovery strategies.</p> <p><strong>SeverusPT Data Products Manual</strong>: <a href="https://doi.org/10.5281/zenodo.10640961" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10640961</a></p> <p>___</p> <h3>Resumo (PT)</h3> <p>O projecto SeverusPT tem como objetivo disponibilizar, de forma regular e atempada, informação relevante e pradonizada da severidade da área ardida, baseada em dados de satélite e observações no terreno. Os principais objetivos incluem o desenvolvimento de uma moldura de análise espacialmente explícita para avaliar, mapear e prever a severidade da área ardida, fornecendo um produto/serviço resultante de co-desenho para melhorar a capacidade institucional e operacional para a gestão do risco de incêndio e o restauro dos ecossistemas pós-incêndio.</p> <p>Atualmente este projeto disponibiliza conjuntos de dados derivados de imagens de satélite para Portugal Continental acerca da severidade histórica/observada da área ardida. Estes conjuntos de dados fazem uso de informação proveniente de múltiplas missões espaciais (Sentinel-2, Landsat, MODIS), índices espetrais (p.ex. “Normalized Burn Ratio” – NBR, “Tasseled Cap Transformation” – TCT), e indicadores da severidade da área ardida. Os conjuntos de dados derivam de produtos pré-calculados de severidade através de algoritmos que integram séries temporais de imagens de satélite (SITS) em duas abordagens principais: (i) a cadeia de processamento (“pipeline”) baseado em deltas, que implementa medidas “clássicas” de severidade (p.ex. delta-NBR) e que se foca principalmente em satélites de alta resolução espacial; e (ii) a cadeia de processamento baseado em trajetórias, que se baseia na análise de trajetórias pós-incêndio para múltiplas dimensões do funcionamento dos ecossistemas e que se foca em dados de satélite de alta resolução temporal e resolução espacial moderada.</p> <p>No sentido de fornecer informação acerca da severidade da área ardida em vários componentes estruturais da vegetação, foram utilizados dados recolhidos no terreno, os quais são cruciais para validar produtos derivados de imagens de satélite e obter resultados pormenorizados relativamente a efeitos pós-incêndio. No âmbito do projeto foi usada uma abordagem estratificada para efetuar os levantamentos no terreno, focada na época de incêndios de 2022 em Portugal Continental. Critérios de seleção baseados no tamanho da área ardida, localização, tipo de vegetação principal e outras camadas auxiliares de informação permitiram cobrir uma maior diversidade de condições pós-incêndio. Foram visitados aproximadamente 111 locais pertencentes a um total de 28 áreas ardidas no Norte e Centro de Portugal Continental (as zonas do país mais afetadas por incêndios), usando o protocolo “Geometrically Structured Composite Burn Index” (GeoCBI). Foram utilizados dois métodos para validar os produtos baseados em deltas, através da comparação do GeoCBI in situ com estimativas obtidas por satélite: (i) correlações lineares não paramétricas (método de Spearman) e correlações não-lineares; e (ii) um modelo exponencial não-linear adaptado de estudo pré-existentes.</p> <p>Os produtos SeverusPT baseados em deltas apresentaram elevada concordância com as medidas de severidade da área ardida recolhidas no terreno através do GeoCBI. Os valores resultantes mais elevados para a correlação linear situaram-se entre 0,64 e 0,71. Foram obtidos valores geralmente mais elevados para Sentinel-2 e para o índice espetral NBR e o indicador de severidade RBR, em comparação com os resultados obtidos para Landsat-8. Relativamente às correlações não-lineares, foram obtidos valores entre 0,65 e 0,76, tendo os valores mais elevados sido obtidos para o índice espetral TCTG derivado de Landsat-8, seguido do índice espetral NBR derivado de Sentinel-2 com os indicadores de severidade RDT ou RBR. Foram obtidos resultados semelhantes para a validação através de modelo não-linear, com os valores mais elevados correspondendo aos indicadores de severidade RdNBR, RBR e dNBR (R2 = 0,64, 0,62 e 0,60, respetivamente).</p> <p>O portal de dados do projeto constitui uma via de acesso centralizada para a visualização e descarregamento de dados e metadados do projeto, oferecendo dois níveis primários de acesso: o Nível 1 inclui os produtos e o Nível 2 é constituído por ficheiros de imagens e metadados. Os produtos provenientes da cadeia de processamento baseada em trajetórias estão ainda em desenvolvimento activo e serão adicionados posteriormente no Portal de Dados do SeverusPT.</p> <p>O SeverusPT assenta numa abordagem abrangente que combina dados provenientes de satélites com uma rigorosa validação baseada em dados recolhidos no terreno, oferecendo uma melhor compreensão da severidade dos incêndios. As suas metodologias inovadoras e a acessibilidade do seu portal de dados contribuem para o campo da avaliação da severidade dos incêndios, providenciando dados e ferramentas valiosos para a gestão do fogo e para a priorização de estratégias de mitigação e recuperação pós-incêndio.</p> <p> </p>
Burn severity data from: The heterogeneity of burn severity affects bird density in an abandoned mountain landscape of the Atlantic-Mediterranean transition
<p><strong>[Abstract]</strong></p> <p>Fire regimes in mountain landscapes of southern Europe have been shifting from their baselines due to the accumulation of fuel fostered by long-standing rural abandonment and fire exclusion policies. Understanding the role of fire on biodiversity is paramount to implement adequate management to mitigate the impacts of altered fire regimes and land abandonment on biodiversity. Here, we explored to what extent the spatiotemporal variation in burn severity has affected bird abundance of a mountain abandoned landscape located in the Atlantic-Mediterranean transition (NW Iberia). We took advantage of: (1) satellite images of Sentinel 2 and Landsat missions to compute burn severity indicators from 2010 to 2020, and (2) standardized bird surveys carried out over 206 point-counts along the breeding season of 2021. Bird abundance models were built from burn severity metrics together with well-known fire regime attributes (% of burnt area and time since fire). Our results showed that the spatiotemporal variation of burn severity significantly correlated with the abundance of the 39% of the modeled species, supporting the role of pyro-diversity in driving bird populations in our region. The burnt area also explained abundance patterns for 28% of species. Time since fire only correlated with the abundance of 3 species. Our findings confirm the importance of incorporating burn severity indicators into the toolkit of decision makers to anticipate the response of birds to fire management.</p> <p><strong>[Dataset Description]</strong></p> <p>For each year between 2010 and 2020, we used a pair of satellite images, one before (April - July) and one after (September - November) the fire season. In order to use the best available information, we selected different satellites along the study period: for years 2010 and 2011, we used Landsat 5 imagery, whereas for 2012 we used Landsat 7, since Landsat 5 imagery was not useful due to high cloudiness. Since its launch in 2013, we shifted to Landsat 8 data, and finally to Sentinel 2 data from 2015 onwards. For each of these images we calculated the Normalized Burn Ratio (NBR), which is the normalized ratio between near infrared (NIR) and short wave infrared (SWIR) radiation (Eq. 1). NIR and SWIR bands of satellite sensors respond in opposite ways to burned vegetation, allowing to identify burned areas.</p> <p>NBR =(NIR - SWIR) / (NIR +SWIR) (1)</p> <p>To obtain a quantitative measure of change for each year, we calculated the dNBR by subtracting the NBR of the post-fire season image from the NBR of the pre-fire season image (Eq. 2). Finally, dNBR values were used as an estimate for fire severity.</p> <p>dNBR = NBRprefire- NBRpostfire (2)</p>
A method for creating a burn severity atlas: an example from Alberta, Canada
<p>Relativized Burn Ratio (RBR) grids derived from Landsat imagery for large fires in Alberta and adjacent national parks (1985 - 2018).</p>
Day of burning maps and burn severity landscape metrics in the southwestern United States 2002-2020
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Long-term benefits of burns for large mammal habitat undermined by large, severe fires in the American West
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Data from: Learning from wildfires: a scalable framework to evaluate treatment effects on burn severity
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Food webs for three burn severities after wildfire in the Eldorado National Forest, California
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Data from: The effects of prescribed fire severity and time post-burn on beetle assemblages in a temperate deciduous forest
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Wildfire burn severity and emissions inventory: an example implementation over California
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Data set: Burn severity and soil chemistry are weak drivers of early vegetation succession following a boreal mega-fire in a production forest landscape
<p>Data used in the analyses of the study. Data set on soil chemistry (pH, organic content, nutrient content), plots distance to the fire perimeter, and plant communities (vascular plants and bryophytes) recorded in permanent plots on clearcuts in a burn-severity gradient two and five years following a wildfire. Data collected from a 13000 hectare wildfire that occurred in 2014, in boreal southern Sweden. Calculated trait means from data bases, and plant community data from unburned plots from another study, are are also included here.</p>
MOSEV: A global burn severity database from MODIS (2000-2020)
<p>To advance in the fire discipline as well as in the study of CO<sub>2</sub> emissions it is of great interest to develop a global database with estimators of the degree of biomass consumed by fire, which is defined as burn severity. We present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area (BA) products scenes since November 2000 to near real time. To build the database we combined Terra MOD09A1 and Aqua MYD09A1 surface reflectance products to obtain dense time series of the Normalized Burn Ratio (NBR) spectral index, and we used the MCD64A1 product to identify BA and the date of burning. Then, we calculated for each burned pixel the difference of the NBR (dNBR), and its relativized version (RdNBR), as well as the post-burn NBR which are the most commonly used burn severity spectral indices. The database also includes the pre-burn NBR used for calculations, the date of the pre- and post-burn NBR and the date of burning.</p>
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