Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni‐temporal perspective

Quintano, Carmen; Fernández–Manso, Alfonso; Fernández‐Manso, O.; Shimabukuro, Yosio E.

doi:10.1080/01431160500212195

Cited by 54 publications

(34 citation statements)

References 35 publications

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“…The segmentation tool is a procedure commonly utilized to separate the targets of the images, facilitating automatic or non-automatic classifications. Several studies have used this procedure, such as [37], which presented a semi-automatic procedure utilizing segmentation for burned area extraction in VEGETATION/SPOT and AVHRR/NOAA imagery; [38], which applied LSMM, segmentation and unsupervised classification to AVHRR/NOAA and TM/Landsat data to map burned forest areas in the Mediterranean, and [39], which used LSMM and segmentation in Landsat images to map deforested areas in the Amazon rainforest.…”

Section: Burned Area Mapping In Tm and Etm+ Images And Field Datamentioning

confidence: 99%

Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest

et al. 2014

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Abstract:The objective of this study was to analyze the spatial and temporal distribution of burned areas in Rondônia State, Brazil during the years 2000 to 2011 and evaluate the burned area maps. A Linear Spectral Mixture Model (LSMM) was applied to MODIS surface reflectance images to originate the burned areas maps, which were validated with TM/Landsat 5 and ETM+/Landsat 7 images and field data acquired in August 2013. The validation presented a correlation ranging from 67% to 96% with an average value of 86%. The lower correlation values are related to the distinct spatial resolutions of the MODIS and TM/ETM+ sensors because small burn scars are not detected in MODIS images and higher spatial correlations are related to the presence of large fires, which are better identified in MODIS, increasing the accuracy of the mapping methodology. In addition, the 12-year burned area maps of Rondônia indicate that fires, as a general pattern, occur in areas that have already been converted to some land use, such as vegetal extraction, large animal livestock areas or diversified permanent crops. Furthermore, during the analyzed period, land use conversion associated with climatic events significantly influenced the occurrence of fire in Rondônia and amplified its impacts.

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Section: Burned Area Mapping In Tm and Etm+ Images And Field Datamentioning

confidence: 99%

Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest

et al. 2014

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“…The use of image classification has proved a very practical technique for burnt area mapping in exploiting the spectral changes of the objects after a fire (Sunar and Ozkan, 2001;Epting et al, 2005;Kokaly et al, 2007;Escuin et al, 2008). For more details see Quintano et al (2006) and Smith et al (2007).…”

Section: Introductionmentioning

confidence: 99%

Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007

Petropoulos

Knorr

Scholze

et al. 2010

Nat. Hazards Earth Syst. Sci.

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Abstract. Remote sensing is increasingly being used as a cost-effective and practical solution for the rapid evaluation of impacts from wildland fires. The present study investigates the use of the support vector machine (SVM) classification method with multispectral data from the Advanced Spectral Emission and Reflection Radiometer (ASTER) for obtaining a rapid and cost effective post-fire assessment in a Mediterranean setting. A further objective is to perform a detailed intercomparison of available burnt area datasets for one of the most catastrophic forest fire events that occurred near the Greek capital during the summer of 2007. For this purpose, two ASTER scenes were acquired, one before and one closely after the fire episode. Cartography of the burnt area was obtained by classifying each multiband ASTER image into a number of discrete classes using the SVM classifier supported by land use/cover information from the CORINE 2000 land nomenclature. Overall verification of the derived thematic maps based on the classification statistics yielded results with a mean overall accuracy of 94.6% and a mean Kappa coefficient of 0.93. In addition, the burnt area estimate derived from the post-fire ASTER image was found to have an average difference of 9.63% from those reported by other operationally-offered burnt area datasets available for the test region.

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“…In Mediterranean Europe, where burned areas can be small and very fragmented, regional scale studies [5] require medium to high spatial resolution satellite images. Landsat TM/Enhanced TM plus (ETM+) data have therefore largely been used [6][7][8] thanks to their suitable geometric and spectral characteristics for mapping areas affected by fires. Nonetheless, analyses based on the exclusive use of optical images can be limited by weather conditions (e.g., cloud cover) and errors due to spectral overlaps [9].…”

Section: Introductionmentioning

confidence: 99%

“…As far as the optical and infrared imaging sensors are concerned, several techniques have been exploited to map burned areas [2][3][4][5][6][7][8]. In particular, large-scale studies have relied on the availability of coarse resolution satellite data, such as NOAA-AVHRR (National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer) [2][3][4], SPOT-VGT (Satellite Pour l'Observation de la Terre/VEGETATION) [9,10], NASA-MODIS (National Aeronautics and Space Administration-Moderate Resolution Imaging Spectrometer) [11] and the geostationary Meteosat Visible and InfraRed Imager (MVIRI)/Spinning Enhanced Visible and InfraRed Imager (SEVIRI) satellites [12], as well as the Geostationary Operational Environmental Satellites (GOES) [13], offering the advantage of multi-temporal and frequent acquisitions.…”

Section: Introductionmentioning

confidence: 99%

Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions

et al. 2015

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Abstract:The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer's accuracy (omission error < 10%) and user's accuracy (commission error < 5%), OPEN ACCESSRemote Sens. 2015, 7 1321 an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets.

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Mapping burned areas in Mediterranean countries using spectral mixture analysis from a uni‐temporal perspective

Cited by 54 publications

References 35 publications

Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest

Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest

Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007

Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions

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