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224 IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT high correlation (R 2 =0.845) between observed and simulated GeoCBI and an almost 1:1 linear fitting (slope ~1 and constant ~0) as well as a very low dispersion of the values. The total RMSE was also very low (RMSE= 0.14). In addition, the accuracy was quite homogeneous throughout the range of GeoCBI values observed in the field (1.68 to 3.00). For the validation of BE estimation, the results of the linear fitting between dNDVI and both BEref and BEadj (table 3) showed that the correlation coefficients were considerably higher in the case of BEadj, ranging from 0.45 (shrubs) to 0.72 (hardwoods). In the case of BE, the correlation was practically inexistent (table 3) for all vegetation types. 4 - Discussion and conclusions The simulation model selected for this study had already been successfully applied in other Mediterranean fires in Spain and Portugal (De Santis et al., 2009). Validation results showed that the accuracy of the model inversion was considerably homogeneous in all ranges of GeoCBI values, and the RMSE = 0.14 was, in fact, slightly lower than the ones obtained in Spain and Portugal (RMSE = 0.18-0.21). In addition, the model was tested over a wider range of GeoCBI values. The results confirmed the consistency of the model, which was independent of specific site characteristics, at least within Mediterranean/temperate environments. The BE validation confirmed that the use of GeoCBI as an adjustment factor improved BE estimation considerably for all vegetation types analyzed. From a broader perspective, this study must be regarded as a first attempt to improve BE estimation. The fundamental improvements shown in BE estimation still lies on the high accuracy of BS estimation. Therefore, there is room for improvement, especially concerning alternative ways of optimizing the use of BS to adjust BE estimation. References Barbosa, P.M., Cardoso Pereira, J.M., and Gregoire, J.-M., 1998. Compositing criteria for burned area assessment using multitemporal low resolution satellite data. Remote Sens. Environ. 65, 38-49. Crutzen, P. and Andreae, M., 1990. Biomass burning in the tropics: Impact on atmospheric chemistry and biogeochemical cycles, Science, 250, 1669-1678. De Santis, A., Chuvieco, E. & Vaughan, P.J., 2009. Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models. Remote Sensing of Environment, 113 (1), 126-136. De Santis, A. & Chuvieco, E., 2009. GeoCBI: a modified version of the Composite Burn Index for the initial assessment of the short-term burn severity from remotely sensed data. Remote Sensing of Environment, 113
Burn severity and burning efficiency estimation using simulation models and geoCBI 225 (3), 554-562. IPCC2006 Guidelines for National Greenhouse Gas Inventories. Volume 4: Agriculture, Forestry and Other Land Use. Harald Aalde (Norway), Patrick Gonzalez (USA), Michael Gytarsky (Russian Federation), Thelma Krug (Brazil), Werner A. Kurz (Canada), Rodel D. Lasco (Philippines), Daniel L. Martino (Uruguay), Brian G. McConkey (Canada), Stephen Ogle (USA), Keith Paustian (USA), John Raison (Australia), N.H. Ravindranath (India), Dieter Schoene (FAO).
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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
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162 III - FIRE DETECTION AND FIRE M
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164 III - FIRE DETECTION AND FIRE M
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REAL-TIME MONITORING OF THE TRANSMI
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Real-time monitoring of the transmi
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NOAA’S OPERATIONAL FIRE AND SMOKE
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Page 181: References System for early forest
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Page 189 and 190: ADVANCING THE USE OF MULTI-RESOLUTI
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Page 213 and 214: Burnt area index using MODIA and AS
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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 273 and 274: 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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