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128 II - VALIDATION OF RS PRODUCTS FOR FIRE MANAGEMENT pre-fire season may therefore represent an added value for fire management in Portugal (Gouveia et al., 2009). In fact, the Iberian Peninsula is recurrently affected by drought episodes and a combined effect of lack of precipitation over a certain period with other climatic anomalies, such as high temperature, high wind and low relative humidity over a particular area may result in reduced green vegetation cover. In fact, major fire events in Iberia are frequently preceded by drought periods, reflected by the decrease of vegetation activity before the fire episode. This effect is further aggravated by a quick response of dead fuels to changing meteorological conditions, such that even short periods of drought may lead to an exponential increase in fire risk. 2 - Results Burned area (BA) data covering the period 1982-2006 were obtained from the official data supplied by the National Forest Authority (AFN). Figure 1 presents the inter-annual variability of burned area over Portugal during the considered period. The close resemblance between the annual and the summer curves implies that the annual fire regime in Portugal is mostly dominated by events occurring in July and August. Vegetation stress prior to the begining of the fire season was assessed using averaged values in May of the Normalized Difference Vegetation Index (NDVI) as obtained from the Global Inventory Modelling and Mapping Studies (GIMMS) database (Zhou et al., 2001), covering the considered 25-year period. Values of NDVI in May were then spatially averaged over the forested regions of Portugal as identified by downgrading the Global Land Cover (GLC) for the year 2000 (Bartholome et al., 2002) to the GIMMS scale. Meteorological fire danger in summer 1982-2006 was estimated using July + August means of the Daily Sevirity Rating (DSR), an index that may be derived from the so-called Canadian Fire Weather Index (FWI) system (van Wagner, 1987). DSR is a numeric rating of the difficulty of controlling fires and reflects the expected efforts required for fire suppression (CFS, 2007). Regression tree analysis was used to assess how yearly amounts of BA in the fire season could be related with vegetation stress prior to the fire season and meteorological conditions during the fire season. Regression tree (Breiman et al., 1984) is a nonparametric technique that is very effective in selecting from given variables the interactions among them that are most important in determining the outcome variable to be explained. Figure 2 presents a scatter plot of NDVI in May versus DSR in summer 1982- 2006 (left panel) together with the recorded amounts of BA during the same period(right panel). Figure 3 provides a schematic overview of the obtained regression tree. It may be noted that years of weak activity are associated to both low values of NDVI in May (i.e. low biomass) and DSR in summer (i.e. meteorological conditions that do not favour the onset and spreading of fire). The largest event (in 2003) is, on the contrary, associ-
Operational use of remote sensing in forest fire management in Portugal 129 ated to large values of both variables. The role of NDVI is also particularly important in controlling the impact of high values of DSR on the BA amounts. Finally the apparent tendency of not having high values of NDVI associated to high values of DSR may reflect the role of soil moisture in controlling the impact of extreme hot and dry spells on the onset and spreading of wildfires. References Bartholomé, E., Belward, A., Achard, F., Bartalev, S., Carmona-Moreno, C., Eva, H., Fritz, S., Gregoire, J., Mayaux. P., Stibig, H., 2002. GLC 2000: Global Land Cover mapping for the year 2000. EUR 20524 EN, European Commission, Luxembourg. Breiman, L., Friedman, J. H., Olshen, R. A., Stone, C. J., 1984. Classification and regression trees. Monterey, Calif., U.S.A., Wadsworth, Inc. CFS, 2007: Canadian wildland fire information system. http://fire.nofc.cfs.nrcan.gc.ca/en/background/bi_FWI_summary_e.ph. Gouveia C., Trigo, R.M., DaCamara, C.C., 2009. Drought and Vegetation Stress Monitoring in Portugal using Satellite Data. Nat. Hazards Earth Syst. Sci., 9: 185-195. Pereira, M.G., Trigo, R.M., DaCamara, C.C., Pereira, J.M.C., Leite, S.M., 2005. Synoptic patterns associated with large summer forest fires in Portugal. Agr. Forest Met., 129: 11-25. Trigo R.M., Pereira, J.M.C., Pereira, M.G., Mota B., Calado, M.T., DaCamara C.C., Santo, F.E., 2006. Atmospheric conditions associated with the exceptional fire season of 2003 in Portugal. Int. J. of Climatology 26 (13): 1741-1757. van Wagner, C.E., 1987. Development and structure of the Canadian Forest Fire Index System. Canadian Forestry Service, Ottawa, Ontario, Forestry Technical Report 35. Zhou, L., Tucker, C.J., Kaufmann, R.K., Slayback, D.N., Shabanov, V., Myneni, R.B., 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geoph. Res., 106: 20069-20083.
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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
Page 79 and 80: Fuel model mapping using ikonos ima
Page 81 and 82: FIRST STEPS TOWARDS A LONG TERM FOR
Page 83 and 84: First steps towards a long term for
Page 85: 3 - Conclusion and further research
Page 88 and 89: 86 I - PRE-FIRE PLANNING AND MANAGE
Page 90 and 91: 88 I - PRE-FIRE PLANNING AND MANAGE
Page 93 and 94: FUEL MOISTURE CONTENT ESTIMATION BA
Page 95 and 96: 3 - Results Fuel moisture content e
Page 97 and 98: CYBERPARK PROJECT: MULTITEMPORAL SA
Page 99 and 100: Cyberpark project: Multitemporal sa
Page 101 and 102: REMOTE SENSING-BASED MAPPING OF FUE
Page 103 and 104: Remote Sensing-based Mapping of fue
Page 105 and 106: ASSESSING CRITICAL FUEL PARAMETERS
Page 107 and 108: Assessing critical fuel parameters
Page 109: II VALIDATION OF RS PRODUCTS FOR FI
Page 112 and 113: 110 II - VALIDATION OF RS PRODUCTS
Page 117 and 118: RELATIONSHIPS BETWEEN COMBUSTION PR
Page 119 and 120: Relationships between combustion pr
Page 121: Relationships between combustion pr
Page 129: OPERATIONAL USE OF REMOTE SENSING I
Page 133: Operational use of remote sensing i
Page 141 and 142: GLOBAL MONITORING OF THE ENVIRONMEN
Page 143 and 144: Global monitoring of the environmen
Page 145: Global monitoring of the environmen
Page 153 and 154: DAILY MONITORING OF PRE-FIRE VEGETA
Page 155 and 156: Daily monitoring of pre-fire vegeta
Page 157 and 158: THE GLOBAL MODIS BURNED AREA PRODUC
Page 159 and 160: The global MODIS burned area produc
Page 161: III FIRE DETECTION AND FIRE MONITOR
Page 164 and 165: 162 III - FIRE DETECTION AND FIRE M
Page 166 and 167: 164 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
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References System for early forest
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182 III - FIRE DETECTION AND FIRE M
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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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IV BURNED LAND MAPPING, FIRE SEVERI
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204 IV - BURNED LAND MAPPING, FIRE
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Abstract: The aim of this paper is
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Burnt area index using MODIA and AS
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4 - Conclusions Burnt area index us
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SATELLITE DERIVED MULTI-YEAR BURNED
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Satellite derived multi-year burned
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Satellite derived multi-year burned
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MODIS REFLECTIVE AND ACTIVE FIRE DA
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3 - Methodology and results MODIS r
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MODIS reflective and active fire da
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SOME NOTES ON SPECTRAL PROPERTIES O
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Some notes on spectral properties o
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MONITORING POST-FIRE VEGETATION REG
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Abstract: SAR data from a test site
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Backscatter properties of x- and c-
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6 - Conclusions Backscatter propert
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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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