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212 IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT Among the indexes used in this test for Gran Canaria Island the SVI is the one that yields better results in the application to ASTER data categorization of pixels affected by a fire, this index presents minor errors of commission for classified burnt pixels, and the NDVI is the one that presents minor errors of omission. INDEX ACCURACY KAPPA COEFFICIENT SAMPLING ERROR CONFIDENCE INTERVAL (F) RGB 92,05% 0,8385 26,10% 92,0 ± 0,5117 NDVI 86,39% 0,7180 33,10% 86,4 ± 0,6486 ASTER SAVI 85,59% 0,7014 33,88% 85,6 ± 0,6641 SVI 86,50% 0,7213 32,98% 86,5 ± 0,6463 TVI 86,41% 0,7188 33,99% 85,5 ± 0,6663 RGB 95,72% 0,8813 43,19% 95,7 ± 0,8466 MODIS NBR 77,48% 0,4861 164,59% 77,5 ± 3,2259 BAIM 77,59% 0,5257 159,53% 77,6 ± 3,1268 NDVI SAVI SVI TVI ASTER Comis. Omis. Comis. Omis Comis. Omis. Comis. Omis. Burnt 16,14 4,76 16,08 4,65 17,01 5,41 15,98 4,82 Volcanic 4,6 9,7 5,12 16,94 4,66 9,76 4,65 9,68 Unburned 8,65 27,16 10,05 27,51 9,64 25,75 8,84 26,87 NBR BAIM MODIS Comis. Omis. Comis. Omis Burnt 0 27,41 0 28,92 Unburned 55,77 0 49,84 0 Table 1 - Results of the SVM algorithm application to Gran Canaria ASTER and MODIS images. Also, results of sampling error, comission and omission errors and confidence interval. In relation to the MODIS sensor data study the NBR presents worse percentages of accuracy (Table 1) compared to the BAIM values. Nevertheless commission and omission errors for these indexes are too high comparing to the ASTER ones, probably as an effect of the different spatial resolution for these two types of satellite data. The sampling errors and confidence intervals are worse for MODIS indexes than those obtained for ASTER, probably because of the vegetation composition inside the fire perimeter and the orographic influence in an insular environment in which the spatial resolution of 250 and 500 m. of MODIS channels may be not enough.
4 - Conclusions Burnt area index using MODIA and ASTER data 213 This paper shows the estimation of burnt area in a large wildfire that took place during summer 2007 in Gran Canaria island (Spain), limiting the number of classes to the minimum, in order to check if a rapid assessment of the affected area with only one ASTER or MODIS post-fire image is possible. The orographic complex effects in an island like this, make the ASTER derived indexes more suitable due to their better spatial resolution. Nevertheless, high errors of commission and omission are detected in some cases due to the special vegetation characteristics of this test area. A future work under development will also analyze the error due to the pixel resolution in terms of a classification method based on object-oriented and neural networks. Acknowledgments This work has been supported by the “Ministerio de Educacion y Ciencia” (Spain), under Project CGL2007-66888-C02-01/CLI. References Chuvieco, E., Ventura, G., Martín, M.P., Gómez, I., 2005. Assessment of multitemporal techniques of MODIS and AVHRR images for burned land mapping. Remote Sensing of Environment 94: 450-462. ERSDAC, 2007. ASTER User’s Guide. Earth Remote Sensing Data Analysis Center. http://www.science.aster.ersdac.or.jp/en/documents/users_ guide/ González-Alonso, F., Merino de Miguel, S., Cuevas Gonzalo, J.M., 2007. Un nuevo algoritmo para la cartografía de áreas quemadas a partir de información NIR, SWIR y TIR. Revista de Teledetección. ISSN: 1133-0953. 2007. 28: 97-105. Key, C., Benson, M., 1999. The Normalized Burned Ratio, a Landsat TM radiometric index of burn severity incorporating multi-temporal differencing. U.S. Department of the Interior Northern Rocky Mountain Science Center. Martín, M.P., Gómez, I., Chuvieco, E., 2005. Performance of a burned-area index (BAIM) for mapping Mediterranean burned scars from MODIS data. Proceedings of the 5th International <strong>Workshop</strong> on Remote Sensing and GIS applications to Forest Fire Management: Fire Effects Assessment. (Zaragoza), GOFC-GOLD, EARSeL: 193-198. Nikolakopoulus, K.G., 2003. Use of Vegetation Indexes with ASTER VNIR data for burned areas detection in Western Peloponnese, Greece. Geoscience and Remote Sensing Symposium, IGARSS apos; 03. Proceedings. 2003 IEEE International Volume 5, Issue, 2003 Page(s): 3287-3289.
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Proceedings of the VII Internationa
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SCIENTIFIC COMMITTEE Olivier Arino
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Analysis of human-caused wildfire o
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Monitoring post-fire vegetation reg
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10 Agency, Italian National Forestr
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I PRE-FIRE PLANNING AND MANAGEMENT
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16 I - PRE-FIRE PLANNING AND MANAGE
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USING SPATIAL ANALYSIS TO CHARACTER
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Using spatial analysis to character
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Abstract: Fire is the major stand-r
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Fire and climate change in Boreal F
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PROJECTING FUTURE BURNT AREA IN THE
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Projecting future burnt area in the
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Projecting future burnt area in the
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MULTISCALE CHARACTERIZATION OF SPAT
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Multiscale characterization of spat
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Multiscale characterization of spat
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CLASSIFICATION OF SITE AND STAND CH
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Classification of site and stand ch
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Classification of site and stand ch
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FUEL MOISTURE CONTENT ESTIMATION: A
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Fuel moisture content estimation: a
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Fuel moisture content estimation: a
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FUEL MODEL MAPPING USING IKONOS IMA
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Fuel model mapping using ikonos ima
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FIRST STEPS TOWARDS A LONG TERM FOR
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First steps towards a long term for
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3 - Conclusion and further research
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FUEL MOISTURE CONTENT ESTIMATION BA
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3 - Results Fuel moisture content e
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CYBERPARK PROJECT: MULTITEMPORAL SA
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Cyberpark project: Multitemporal sa
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REMOTE SENSING-BASED MAPPING OF FUE
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Remote Sensing-based Mapping of fue
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ASSESSING CRITICAL FUEL PARAMETERS
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Assessing critical fuel parameters
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II VALIDATION OF RS PRODUCTS FOR FI
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110 II - VALIDATION OF RS PRODUCTS
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RELATIONSHIPS BETWEEN COMBUSTION PR
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Relationships between combustion pr
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Relationships between combustion pr
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OPERATIONAL USE OF REMOTE SENSING I
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Operational use of remote sensing i
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Operational use of remote sensing i
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GLOBAL MONITORING OF THE ENVIRONMEN
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Global monitoring of the environmen
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Global monitoring of the environmen
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DAILY MONITORING OF PRE-FIRE VEGETA
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Daily monitoring of pre-fire vegeta
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THE GLOBAL MODIS BURNED AREA PRODUC
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The global MODIS burned area produc
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III FIRE DETECTION AND FIRE MONITOR
Page 164 and 165: 162 III - FIRE DETECTION AND FIRE M
Page 169 and 170: REAL-TIME MONITORING OF THE TRANSMI
Page 171 and 172: Real-time monitoring of the transmi
Page 173 and 174: NOAA’S OPERATIONAL FIRE AND SMOKE
Page 175 and 176: Noaa’s operational fire and smoke
Page 177 and 178: SYSTEM FOR EARLY FOREST FIRE DETECT
Page 179 and 180: 3 - Result in Germany System for ea
Page 181: References System for early forest
Page 189 and 190: ADVANCING THE USE OF MULTI-RESOLUTI
Page 191 and 192: Advancing the use of multi-resoluti
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Page 203: IV BURNED LAND MAPPING, FIRE SEVERI
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Page 211 and 212: Abstract: The aim of this paper is
Page 213: Burnt area index using MODIA and AS
Page 217 and 218: SATELLITE DERIVED MULTI-YEAR BURNED
Page 219 and 220: Satellite derived multi-year burned
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Page 229 and 230: MODIS REFLECTIVE AND ACTIVE FIRE DA
Page 231 and 232: 3 - Methodology and results MODIS r
Page 233: MODIS reflective and active fire da
Page 245 and 246: SOME NOTES ON SPECTRAL PROPERTIES O
Page 247 and 248: Some notes on spectral properties o
Page 249 and 250: MONITORING POST-FIRE VEGETATION REG
Page 251 and 252: Monitoring post-fire vegetation reg
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Page 261 and 262: Abstract: SAR data from a test site
Page 263 and 264: Backscatter properties of x- and c-
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6 - Conclusions Backscatter propert
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266 IV - BURNED LAND MAPPING, FIRE
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268 IV - BURNED LAND MAPPING, FIRE
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CORRECTION OF TOPOGRAPHIC EFFECTS I
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Correction of topographic effects i
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Correction of topographic effects i
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BURNED AREAS MAPPING BY MULTISPECTR
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Burned areas mapping by multispectr
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Burned areas mapping by multispectr
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4 - Conclusions Burned areas mappin
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Abstract: In this paper NDVI VEGETA
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Post Fire vegetation recovery estim
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Post Fire vegetation recovery estim
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292 FORTUNATO G. pag. 191 FRENCH N.
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Finito di stampare nel mese di agos
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