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250 IV - BURNED LAND MAPPING, FIRE SEVERITY DETERMINATION, AND VEGETATION RECOVERY ASSESSMENT 3 - Preliminary results The time-series of EVI values for Guarda (2797 ha), Santarém (19466 ha) and Faro (25471 ha) fires and respective control areas, are shown in Fig.1. All the three fires occurred during the first two weeks of August 2003, as revealed by the sharp drop in EVI values for the burned areas (fig. 2 - black line). Prior to the fires, the EVI time series for fire and control areas are very similar, especially for the Santarém and Guarda fires. At the Faro site, pre-fire EVI values of the control area appear consistently lower and more strongly seasonal than those within the fire perimeter. The fire induced EVI decrease is larger at the Santarém and Faro fires than at Guarda fire. There are also clear differences in post-fire EVI response dynamics. At Santarém, EVI values at the burned area take over two years to reach values similar to those of the control area, while at Faro the recovery is faster (about 16months). The post-fire vegetation response displays a distinct pattern of the Guarda site. Recovery is very fast, such that five months after the fire, EVI values are higher within the fire perimeter then in the unburned control area. By October 2004, i.e., 14 months after the fire, the fire affected area and control EVI time series converge to very similar values. Fig. 2 – EVI time series between February 2000 and December 2008 for Guarda (a), Santarém (b) and Faro (c) fires.
Monitoring post-fire vegetation regeneration of the 2003 burned areas in Portugal using a time-series of MODIS enhanced vegetation index 251 4 - To do next After this previous analysis of the data we intent to de-seasonalise the data using empirical mode decomposition (), an approach which does not rely on stationary assumptions. Then, using Spatial data on land cover, topography, previous-fire history, soil maps and climate data, we want to adjust a model to the trend tendency of vegetation recovery function of these variables. References Arnan, X., Rodrigo, A., et al., 2007. “Post-fire regeneration of Mediterranean plant communities at a regional scale is dependent on vegetation type and dryness.” Journal of Vegetation Science 18(1): 111-122. Bisson, M., Fornaciai, A., Coli, A., Mazzarini F. and Pareschi M.T., 2008. The Vegetation Resilience After Fire (VRAF) index: Development, implementation and an illustration from central Italy. International Journal of Applied Earth Observation and Geoinformation,10: 312-329. Bond, W.J. and Keeley, J.E., 2005. “Fire as a global ‘herbivore’: the ecology and evolution of flammable ecosystems.” Trends in Ecology and Evolution 20(7): 387-394. Díaz-Delgado, R., Lloret, F., Pons, X. and Terradas, J., 2002. Satellite evidence of decreasing resilience in Mediterranean plant communities after recurrent wildfires. Ecology, 83: 2293-2303. Colditz, R.R., Conrad, C., et al., 2008. TiSeG: A Flexible Software Tool for Time-Series Generation of MODIS Data Utilizing the Quality Assessment Science Data Set, IEEE Transactions on Geoscience and Remote Sensing. 46: 3296-3308. Efe, R., Cravins, G., et al., Eds., 2008. Natural Environment and Culture in the Mediterranean Region. Newcastle, Cambridge Scholars Publishing. García-Haro, F.J., Gilabert, M.A., Meliá, J., 2001. Monitoring fire-affected areas using Thematic Mapper data. International Journal of Remote Sensing, 22: 533-549. Naveh, Z., 1975. “The evolutionary significance of fire in the mediterranean region“ Vegetatio 21: 199-208. Solans Vila, J.P. and Barbosa P., 2009. Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data. Ecological Modelling, in press. Viedma, O., Meliá, J., Segarra, D., Garcia-Haro J., 1997. Modeling rates of ecosystem recovery after fires by using Landsat TM data. Remote Sensing of Environment, 61: 383-398.
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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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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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Noaa’s operational fire and smoke
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SYSTEM FOR EARLY FOREST FIRE DETECT
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3 - Result in Germany System for ea
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References System for early forest
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ADVANCING THE USE OF MULTI-RESOLUTI
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Advancing the use of multi-resoluti
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Advancing the use of multi-resoluti
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Page 203: IV BURNED LAND MAPPING, FIRE SEVERI
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Page 213 and 214: Burnt area index using MODIA and AS
Page 215 and 216: 4 - Conclusions Burnt area index us
Page 217 and 218: SATELLITE DERIVED MULTI-YEAR BURNED
Page 219 and 220: Satellite derived multi-year burned
Page 221: Satellite derived multi-year burned
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: Monitoring post-fire vegetation reg
Page 261 and 262: Abstract: SAR data from a test site
Page 263 and 264: Backscatter properties of x- and c-
Page 265: 6 - Conclusions Backscatter propert
Page 273 and 274: CORRECTION OF TOPOGRAPHIC EFFECTS I
Page 275 and 276: Correction of topographic effects i
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Page 279 and 280: BURNED AREAS MAPPING BY MULTISPECTR
Page 281 and 282: Burned areas mapping by multispectr
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Page 285 and 286: 4 - Conclusions Burned areas mappin
Page 287 and 288: Abstract: In this paper NDVI VEGETA
Page 289 and 290: Post Fire vegetation recovery estim
Page 291: Post Fire vegetation recovery estim
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Page 296: Finito di stampare nel mese di agos
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