Minas de Riotinto (south Spain) forest fire: Burned area assessment and fire severity mapping using Landsat 5‐TM, Envisat‐MERIS, and Terra‐MODIS postfire images

Roldán-Zamarrón, A.; Miguel, S. Merino de; González-Alonso, F.; García-Gigorro, S.; Cuevas, J. M.

doi:10.1029/2005jg000136

Cited by 25 publications

(18 citation statements)

References 18 publications

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“…Diferentes autores han propuesto y usado estos y otros índices y diferentes técnicas en teledetección (Fernández-Manso et al, 2016;Chuvieco, 2002;Roldán-Zamarrón et al, 2006) que han sido aplicadas con distintos sensores y satélites, como MODIS y Landsat principalmente. El lanzamiento de la nueva misión Sentinel-2 de la ESA abre nuevas perspectivas en el estudio de incendios debido a su calidad espectral, radiométrica, su alta resolución espacial y su tiempo de revisita.…”

Section: Introductionunclassified

Estimación del grado de severidad de incendios en el sur de la provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8

et al. 2018

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Assessment of rural fire severity is fundamental to evaluate fire damages and to analyze recovery processes in a low-cost and efficient way. Burnt areas covering shrubs and grasslands were estimated in more than 30,000 km2 in Argentina from December 2016 to January 2017. The study area presented in this work is located in the South of the Buenos Aires province, and it covers a semiarid area with the presence of xerophilous shrubs and grasslands. This is one of the most abundant ecosystem in Central and Southern Argentina. Field campaigns were carried out over the area affected by the fire in order to georreference the burnt plots and characterized the fire severity in 5 levels. The objective of this work is to analyze the feasibility of new satellites Sentinel-2 for fire studies, as well as provide a comparison to Landsat-8 derived results, because this mission has been one of the most used in it. Pre-fire and postfire Sentinel-2 and Landsat-8 imagery were used to analyze different band combinations to compute a Normalized Difference Spectral Index (NDSI), as well as the difference of this index before and after the fire (dNDSI). Results show a significant correlation (R2 =0.72 and estimation error of 0.77) between dNDSI derived from Sentinel-2 and the severity levels obtained in the field campaign using bands 8a and 12 (NIR and SWIR), the same bands as used in the Normalized Burn Ratio (NBR). Moreover, results derived from Sentinel-2 are better than results derived from Landsat-8 (R2 =0.63 and estimation error of 0.92). Furthermore, it is observed that the correlation is improved when Sentinel-2 bands 6 and 5 (located in the Red-Edge region) are considered (R2 =0.74 and estimation error of 0.76). An inverse correlation has been observed between the recovery of vegetation four months after the fire and the fire severity level.

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Section: Introductionunclassified

Estimación del grado de severidad de incendios en el sur de la provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8

et al. 2018

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“…The NBR (Key and Benson, 1999) is a spectral index that integrates NIR and SWIR bands, both of which register the strongest responses, albeit in opposite ways, to burning (Lentile et al, 2006, Roldán-Zamarrón et al, 2006. The NBR was estimated using the following equation:…”

Section: Methodsmentioning

confidence: 99%

“…In general terms, burned area can be estimated using remote sensing techniques in a rapid, economic, and effective way (Lentile et al, 2006), and the results may be directly integrated into a GIS (Geographic Information System). However, in relation to fire, remote sensing techniques and data may be also used in many other applications: fire occurrence and fire patterns (Pereira et al, 2005;Riaño et al, 2007;Ichoku et al, 2008), fire emissions (Palacios-Orueta et al, 2005;Van der Werf et al, 2010), active fire detection (Roy, 1999;Li et al, 2004;Calle et al, 2008), fire and burn severity (De Santis and Chuvieco, 2007;Roldán-Zamarrón et al, 2006), effects of f ire on vegetation and soil (Wittenberg et al, 2007), and post-f ire vegetation recovery (Riaño et al, 2002, Van Leeuwen et al, 2010.…”

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confidence: 99%

An intercomparison of Satellite Burned Area Maps derived from MODIS, MERIS, SPOT-VEGETATION, and ATSR images. An application to the August 2006 Galicia (Spain) forest fires

2013

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Aim of study: The following paper presents an inter-comparison of three global products: MCD45A1 (MODIS - MODerate resolution Imaging Spectrometer - Burned Area Product), L3JRC (Terrestrial Ecosystem Monitoring Global Burnt Area Product), and GLOBCARBON Burnt Area Estimate (BAE) Product; and three local products, two of them based on MODIS data and the other one based on MERIS (MEdium Resolution Imaging Spectrometer) data.Area of study: The study was applied to the Galician forest fires occurred in 2006.Materials and Methods: Materials used involved the three already mentioned global products together with two MODIS and one MERIS reflectance images, and MODIS thermal anomalies. The algorithm we used, which is based on the determination of thresholds values on infrared bands, allowed the identification of burned pixels. The determination of such threshold values was based on the maximum spatial correlation between MODIS thermal anomalies, and infrared reflectance values. This methodology was applied to MODIS and MERIS reflectance bands, and to the NBR (Normalized Burn Ratio). Burned area validation was evaluated using burned area polygons as derived from an AWiFS (Advanced Wide Field Sensor) image of 60m pixel size.Main results: Best results were reached when using the MERIS infrared bands, followed by the MODIS infrared bands. Worst results were reached when using the MCD45A1 product, which clearly overestimated; and when using the L3JRC product, which clearly underestimated.Research highlights: Since the efficiency of the performance of the available burned area products is highly variable, much work is needed in terms of comparison among the available sensors, the burned area mapping algorithms and the resulting products.Keywords: forest fires; MODIS; MERIS; MCD45A1; L3JRC; GLOBCARBON-BAE; SPOT-VEGETATION; ATSR.Abbreviations used: ATSR: Along Scanning Radiometer; AVHRR: Advanced Very High Resolution Radiometer; AWiFS: Advanced Wide Field Sensor; EOS: Earth Observation System; ESA: European Space Agency; GBA2000: Global Burnt Area 2000; GLOBCARBON-BAE: GLOBCARBON Burnt Area Estimate Product; L3JRC: Terrestrial Ecosystem Monitoring Global Burnt Area Product; MCD45A1: MODIS Burned Area Product; MERIS: MEdium Resolution Imaging Spectrometer; MOD09GA: Terra MODIS Surface Reflectance Daily L2G Global 500 m; MOD09GQ: Terra MODIS Surface Reflectance Daily L2G Global 250 m; MODIS: MODerate resolution Imaging Spectrometer; NBR: Normalized Burn Ratio; NDVI: Normalized Difference Vegetation Index; NIR: near-infrared; SPOT: Satellite Pour l’Observation de la Terre; SWIR: short-wave infrared; UTM: Universal Transverse Mercator.

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“…MERIS was mainly designed for ocean colour applications, as it provides high spectral resolution in the range of the blue to the near infrared regions (Gower and Borstad 2004). The application of MERIS data to fire applications is scarce: identification of smoke plumes (Huang and Siegert 2004), discrimination of burn severity (De Santis and Chuvieco 2007;Roldan-Zamarron, Merino-De-Miguel et al 2006). Mapping BA with MERIS has only been performed at regional level (Oliva, Martin et al 2011) using different vegetation indices while (González-Alonso 2009) combined fire hotspots from MODIS and NIR reflectance values from MODIS and MERIS imagery.…”

Section: Introductionmentioning

confidence: 99%

Global burned area maps from MERIS

Alonso-Canas¹,

Chuvieco²

2014

Advances in Forest Fire Research

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A navegação consulta e descarregamento dos títulos inseridos nas Bibliotecas Digitais UC Digitalis, UC Pombalina e UC Impactum, pressupõem a aceitação plena e sem reservas dos Termos e Condições de Uso destas Bibliotecas Digitais, disponíveis em https://digitalis.uc.pt/pt-pt/termos.Conforme exposto nos referidos Termos e Condições de Uso, o descarregamento de títulos de acesso restrito requer uma licença válida de autorização devendo o utilizador aceder ao(s) documento(s) a partir de um endereço de IP da instituição detentora da supramencionada licença.Ao utilizador é apenas permitido o descarregamento para uso pessoal, pelo que o emprego do(s) título(s) descarregado(s) para outro fim, designadamente comercial, carece de autorização do respetivo autor ou editor da obra. Na medida em que todas as obras da UC Digitalis se encontram protegidas pelo Código do Direito de Autor e Direitos Conexos e demais legislação aplicável, toda a cópia, parcial ou total, deste documento, nos casos em que é legalmente admitida, deverá conter ou fazer-se acompanhar por este aviso. Global burned area maps from MERIS Autor(es):Alonso-Canas, Itziar; Chuvieco, Emilio AbstractBiomass burning is a global scale phenomenon that affects annually around 3.5 million Km2. This phenomenon has a very critical relevance for vegetation dynamics, atmospheric emissions and carbon budgets, increasingly affecting human lives and property, particularly in catastrophic conditions (heat waves, drought, strong winds). Properly characterizing burned areas on a global scale has become a relevant factor when studying these processes. The Fire CCI project, in the framework of the ESA's Climate Change Initiative, aims at providing consistent time series of burned areas (BA) globally, over the period 1995 to 2009. Global BA products are obtained by merging information from three sensors: the ATSR series, VEGETATION and MERIS. In this case, the algorithm developed to obtain burned areas from the MERIS sensor will be presented. The MERIS BA algorithm is based on MERIS reflectance bands, spectral indices and both post-fire and multi-temporal analysis controlled by HS locations. BA detection is performed in a two phase process: the first one aims to detect seed pixels while the second one will develop contextual criteria around these seed pixels. Global BA maps have been obtained with the MERIS algorithm for years 2006 to 2008.

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Minas de Riotinto (south Spain) forest fire: Burned area assessment and fire severity mapping using Landsat 5‐TM, Envisat‐MERIS, and Terra‐MODIS postfire images

Cited by 25 publications

References 18 publications

Estimación del grado de severidad de incendios en el sur de la provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8

Estimación del grado de severidad de incendios en el sur de la provincia de Buenos Aires, Argentina, usando Sentinel-2 y su comparación con Landsat-8

An intercomparison of Satellite Burned Area Maps derived from MODIS, MERIS, SPOT-VEGETATION, and ATSR images. An application to the August 2006 Galicia (Spain) forest fires

Global burned area maps from MERIS

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