7th Workshop on Forest Fire Management - EARSeL, European ...

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172 III - FIRE DETECTION AND FIRE MONITORING to be false detects and adding fires that the algorithms have not detected. False detects can be due to a number of conditions including urban heat islands, solar specular reflection off water surfaces, highly reflective clouds, relatively brighter vegetated land embedded within darker areas, instrument noise, etc. Fires not detected by the algorithms can be attributed to partial obscuration by clouds, overhead vegetation canopy, detected temperature that is not sufficiently hot or hotter than the surrounding area, etc. Validation results (Schroeder et al., 2008) using 30m ASTER data found that fires added by analysts reduced the omission error rate compared to WFABBA and MODIS detections. However, the commission error rate was not reduced by automated detections that were deleted, with actual fires being erroneously removed. Validation using ground based reports from Florida, Montana, Idaho and Manitoba supported the finding of reduced omission errors in the HMS compared to the automated only product (WFABBA, MODIS and FIMMA) with approximately twice as many detections, although the overall detection rate was only 25-30%. The low detection rate is ascribed to the small size (less than 2 ha) of many of the fires as well as prohibitive cloud cover. HMS products may be accessed at www.osdpd.noaa.gov/ml/land/hms.html. 2 - Smoke Detection and Model Transport Smoke detection is exclusively performed with visible imagery, primarily utilizing animated GOES data, although polar imagery is occasionally used. Detection is optimized over the contiguous US with GOES-West in the morning and GOES-East in the evening due to favorable solar zenith and satellite viewing angles. Analysts graphically depict the smoke extent by manually drawing polygons. An estimate of the vertically integrated smoke concentration is assigned to each polygon. Three ranges of values (in µm/m 3 ) are available for the analyst to assign to the polygon. The automated GOES Aerosol and Smoke Product (GASP) (Knapp et al., 2005) which generates Aerosol Optical Depth (AOD) is a tool to aid analysts in this determination. The smoke depicted may be attached to actively burning fires or may be several days old and have drifted hundreds or thousands of km from the source. Many fires do not produce emissions that are detectable. Clouds obscure some smoke emissions while other fires may have minimal emissions due to the short duration of the fire, limited biomass or the engineering of the fire in the case of agricultural/prescibe burns. For fires producing smoke that is detected the analyst provides an estimate of the initiation and duration of emissions and a coarse estimate of the fire size. The size is ideally estimated using higher (1km) resolution polar data with the fire close to the suborbital track. This is not always possible, especially in dynamic wildfire situations in the mid and lower latitudes. In these cases the analyst pro-
Noaa’s operational fire and smoke detection program 173 vides the input information based on visual observation of the amount of smoke being produced and experience. This information is used as input for NOAA’s operational air quality forecast capability (Rolph et al., 2009) viewed at www.nws.noaa.gov/aq/ Emission information from the HMS is used by the HYSPLIT model (Draxler and Hess, 1998) in conjunction with the North American Mesoscale (NAM) meteorological model to generate smoke dispersion and trajectories for the ensuing 48 hours for the contiguous US. Smoke emission rates are obtained from the US Forest Service BlueSky (www.airfire.org/bluesky) emission algorithm, which includes a fuel type database and consumption and emissions models. An automated satellite-based smoke detection and tracking technique has recently been developed for verifying the smoke predictions. A source apportionment technique matches identified fires with maps of AOD from GASP and tracks identified smoke at a 30 minute interval. Initial validation results comparing the smoke optical depth from this product to the Ozone Mapping Instrument (OMI) total optical depth for absorbing aerosol indicate agreement within 0.2 AOD. References Draxler, R.R. and Hess, G.D., 1998. An Overview of the HYSPLIT_4 Modelling System for Trajectories, Dispersion, and Deposition. Australian Meteorological Magazine, 47, 295-308. Giglio, L., Descloitres, J., Justice, C.O., Kaufman Y.J., 2003. An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sens. Environ, 87, 273-282. Justice, C.O., Giglio, L., Korontzi, S., Owens, J., Morisette, J., Roy, D., Descloitres, D.J., Alleaume, S., Petitcolin, F., Kaufman, Y., 2002. The MODIS Fire Products. Remote Sens. Environ, 83, 244-262. Knapp, K.E., Frouin, R., Kondragunta, S., Prados, A., I., 2005. Towards Aerosol Optical Depth Retrievals Over Land from GOES Visible Radiances: Determining Surface Reflectance, Int. J. Rem. Sens., 26, 4097-4116. Li, Z., Nadon, S., Cihlar., J., 2000. Satellite Detection of Canadian Boreal Forest Fires: Development and Application of an Algorithm, Int. J. Rem. Sens., 21, 3057-3069. Prins, E.M., Menzel, W.P., 1992. Geostationary Satellite Detection of Biomass Burning in South America, Int. J. Remote Sens., 13, 2783-2799. Rolph, G.D., Draxler, R.R., Stein, A.F., Taylor, A, Ruminski, M.G., Kondragunta, S., Zeng, J., Huang, H., Manikin, G., McQueen, J.T., Davidson, P.M., 2009. Description and Verification of the NOAA Smoke Forecasting System: The 2007 Fire Season, Weather and Forecasting, 24, 361-378.
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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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112 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
Page 124 and 125: 122 II - VALIDATION OF RS PRODUCTS
Page 129 and 130: OPERATIONAL USE OF REMOTE SENSING I
Page 131 and 132: Operational use of remote sensing i
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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 169 and 170: REAL-TIME MONITORING OF THE TRANSMI
Page 171 and 172: Real-time monitoring of the transmi
Page 173: 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
Page 206 and 207: 204 IV - BURNED LAND MAPPING, FIRE
Page 208 and 209: 206 IV - BURNED LAND MAPPING, FIRE
Page 211 and 212: Abstract: The aim of this paper is
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
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222 IV - BURNED LAND MAPPING, FIRE
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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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