Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Fire Monitoring Ivan Csiszar 1(GOVERNMENT PRINCIPAL INVESTIGATOR), Brad Pierce 2, Shobha Kondragunta 1, Wilfrid Schroeder 3, Chris Schmidt 4 1 NOAA/NESDIS/STAR/SMCD, 2 NOAA/NESDIS/STAR/CoRP, 3 UMd/CICS, 4 CIMSS Requirements: Research area: Improve weather forecast and warning accuracy and amount of lead-time – Primary research activity: Improve NOAA’s incident support services for fires and other high impact events by expanding the number if trained personnel and shortening response time to 12 hours Research area: Provide information to air quality decision makers and improve NOAA’s national air quality forecast capability – Primary research activity: Improved measurement tools for monitor long-term trends Science: How can the accuracy of satellite-derived fire detections be quantified and improved? Benefit: Improved fire data for NOAA and other US Government Agencies and their customers: – Disaster management – Resource management – General public Well characterized, high quality data for global initiatives and regional fire management – GOFC-GOLD: Global Observation of Forest and Landcover Dynamics program of the Global Terrestrial Observing System Science Challenges: Fire characterization and validation of area/temperature and Fire Radiative Power products Enhanced products – Multi-sensor monitoring and data fusion – Fire Radiative Energy (time-integrated FRP) Pre- and post-fire assessments – Fire danger, fuel characterization and burn severity Long-term fire data records (in the framework of GEO, GTOS and GCOS) Next Steps: VIIRS and ABI algorithms finalized and delivered; validation systems developed Develop capability for fuel characterization and biomass estimation using NASA Decadal Survey missions (DESDynI, HyspIRI) Introduce fire as land ecosystem disturbance in national and GEO/CEOS carbon monitoring programs Transition Path: Enhanced VIIRS algorithm funded by IPO and will be evaluated together with expected sensor changes ABI algorithm is part of the standard GOES-R development effort User communities are involved through interagency and international coordination (i.e. GOFC-GOLD) 1. Basics of Satellite-based Fire Detection Fire signals in medium-to-coarse resolution imagery: Radiometric signal: distinct emitted signal at select wavelengths –Approach: increased radiance at 4 µm contrast between 4 µm and 11 µm –Issues: potential false alarm from reflected solar radiation, hot surfaces, cloud edges etc. insufficient sensor specifications, spurious behavior at saturation Spatial signal: separate fire signal from non-burning background –Approach: spatial contrast between potential fire and surrounding pixels –Issues: insufficient number of background pixels clouds hot surface large fires STAR plays and active role in ensuring continuity of active fire observing capabilities through CGMS/GSICS, CEOS and GOFC- GOLD 300K ~ surface; 600K ~ smoldering; 1000K ~ flaming 4. NESDIS’s Operational Fire Monitoring Products Geostationary fire products –WF-ABBA to process GOES data –Global product for GOES Imager, MSG/SEVIRI and MTSAT JAMI –Developed through partnership with CIMSS –Disseminated through OSDPD –NESDIS’s response to GOFC-GOLD request to support global monitoring and emissions estimates –Used by the international fire management and emissions community NOAA Hazard Mapping System –Coverage over North America –Includes fire detections from GOES, MODIS and AVHRR –Adapted or used by international partners (e.g. National Meteorological Service of Mexico) –Disseminated through OSDPD in graphical (web-GIS) and digital formats –STAR is providing science support and updates WF_ABBA v65 Fires: MET-9 GOES-12 MTSAT-1R 2. Fire Product Development Polar: NPP/NPOESS VIIRS –Development of an enhanced algorithm for unaggregated data –Methodology for VIIRS fire proxy data generation using ASTER and MODIS –In collaboration with University of Maryland and NASA Geostationary: GOES-R ABI –Builds on heritage WF-ABBA algorithm for current GOES, Met-8/-9, and MTSAT-1R –Uses simulated ABI data created from models by CIRA and MODIS data remapped to ABI from CIMSS as proxies –Product includes detection and characterization Instantaneous fire area and temperature Fire Radiative Power –Fire detection threshold roughly 75 MW in terms of Fire Radiative Power (aggregated)(unaggregated) Detection envelopes for AVHRR, MODIS and aggregated and unaggregated VIIRS. Shaded areas correspond to probability of detection > 90%A Validation methodology: reference data from simultaneous higher resolution imagery –Moderate resolution (i.e. Landsat-class) –Airborne (e.g. NASA, USFS) –Detection probabilities as a function of fire pixels within the pixel footprint from reference imagery –Collaboration with CEOS Working Group on Calibration and Validation; Land Product Validation Subgroup Products validated –NASA EOS Terra MODIS Global (Stage 3) validation using ASTER Part of NASA EOS program Collaboration with NASA and University of Maryland –GOES Imager (WF-ABBA) Part of GOES reprocessing and validation within LBA (Large-Scale Biosphere-Atmosphere Program in the Amazon) Collaboration with CIMSS –NPP/NPOESS VIIRS Leadership on cal/val development team –GOES-R ABI Validation protocol as part of AWG activities Fire detected GOES fire pixels Fires undetected Active fires (in yellow) on a Landsat-7 image on 17 May UTC at 55.8W 11.3S (Brazil) Landsat/ETM+ β 0 = Stde=0.151 β 1 =0.010 Stde=0.002 N=1215 GOES N HR (p=50%)=320 ASTER β 0 = Stde=0.122 β 1 =0.042 Stde=0.003 N=1264 MODIS N HR (p=50%)=57 Detection probabilities of MODIS and GOES as a function of Landsat/ETM+ or ASTER fire pixels 5. NESDIS’ Fire Product Utilization WRF-CHEM regional aerosol assimilation experiments –SEVIRI fire detections WF ABBA algorithm including estimates of Fire Radiative Power –NESDIS emission factors from Wiedinmyer et al., Atmospheric Environment 40 (2006) 3419–3432“ –Nested RAQMS global/WRF-CHEM regional analysis system Assimilation of MODIS aerosol optical depth (AOD) August 16, 2006 NESDIS Organic Carbon emissions are larger than standard WRF-CHEM emissions leading to improved prediction of aerosol loading WRF-CHEM AOD 12Z August 16, 2006 Algorithm principles for medium and coarse resolution sensors Fire: excess radiance Thermal anomaly Reflectance anomaly Nighttime visible light T(4μm) is high - absolute signal T(4μm) - T(11μm) is large -spectral contrast T(4μm) and/or T(4μm) - T(11μm) differ significantly from surrounding background – spatial contrast tests to minimize false detection (VIS/NIR reflectance; internal cloud mask; water mask; sun-glint test etc.) 3. Fire Product Validation Detection capabilities of GOES Imager as a function of fire size and temperature Comparison of aerosol optical depth from WRF-CHEM (top) and MODIS (bottom) Comparison of organic carbon emissions from WRF-CHEM and NESDIS