Remotely-Sensed Fire Danger Rating System to Support Forest/Land Fire Management in Indonesia Orbita Roswintiarti Indonesian National Institute of Aeronautics and Space (LAPAN) SE Asia Regional Research Information Network (SEARRIN) Presented at International Workshop on Advances in Operational Weather Systems for Fire Danger Rating Edmonton, Canada, 14-16 16 July 2008.
Background The number of weather stations in Indonesia are limited. Spatial distribution of these weather stations is not dense. Local scale of FDR information is needed for the local government. The use of satellite remote sensing data is the best alternative. 2
WMO Weather Station Distribution Indonesia -- Malaysia -- Philippines -- Thailand -- Laos -- 4 WMO Weather Stations: --127 Brunei -- 2 -- 27 Singapore 4 -- 59 Myanmar 23 -- 64 Viet Nam -- 21 3
FWI System Inputs Required inputs to the FWI system are the: Air temperature Relative humidity 24-hour precipitation 10-m wind speed Previous day s FWI calculation All inputs measured at 12:00 LST (ideally) 4
Remote Sensing-based Inputs TXLAPS Qmorph, TRMM DEM NOAA/ AVHRR Wind speed Rainfall Elevation Latitude Land Surface Temperature Air Temperature Land Use: Soil, Vegetation, Water Relative Humidity FWI System Fine Fuel Moisture Content (FFMC) Drought Code (DC) Initial Spread Index (ISI) Fire Weather Index (FWI) TXLAPS: Tropical extended Area Prediction System (BoM, Australia) 00:00 UTC Qmorph: (NOAA) 06:00 UTC to 05:00 UTC TRMM: Tropical Rainfall Measuring Mission (NASA and JAXA) 06:00 UTC to 05:00 UTC DEM: Digital Elevation Model NOAA/AVHRR: National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA-16 between 08:00 UTC and 11:00 UTC) e RH = e a s x100% 194. 64 [ 4. 1082 * T a ] 5
Remote Sensing-based Inputs TXLAPS Qmorph, TRMM Terra/Aqua MODIS Wind speed Rainfall Reflectance Ch1 to Ch7 Tb 31, Tb 32 EVI Water Vapor NDVI Albedo Land Surface Temperature Air Temperature DEM Relative Humidity FWI System Fine Fuel Moisture Content (FFMC) Drought Code (DC) Initial Spread Index (ISI) Fire Weather Index (FWI) MODIS: T b : EVI: NDVI: Moderate Resolution Imaging Spectroradiometer Temperature brightness Enhanced Vegetation Index Normalized Difference Vegetation Index 6
Qmorph - NOAA 24-hour Rainfall Spatial coverage: 60.0 N-60.0 60.0 S S (by 8 km and 0.25 ) Temporal coverage: Dec 2002 recent (every 30 minutes) Rainfall estimates are produced from the microwave observation of DMSP-13 (SSMI), DMSP-14 (SSMI), DMSP-15 (SSMI), NOAA-15 (AMSU-B), NOAA-16 (AMSU-B), NOAA-17 (AMSU-B), TRMM (TMI), AQUA (AMSR-E) Tropical Rainfall Measuring Mission (TRMM) NASA and JAXA Spatial coverage: 50.0 N-50.0 50.0 S (by 0.25 ) Temporal coverage: Jan 1998 recent (every 3 hour and monthly) The 3B42RT estimates are produced in four stages; (1) the microwave estimates precipitation are calibrated and combined, (2) infrared precipitation estimates are created using the calibrated microwave precipitation, (3) the microwave and IR estimates are combined, and (4) rescaling to monthly data is applied. 7
Qmorph Data 8
TRMM 3B42RT Data 9
10-m m Wind Speed http://www.bom.gov.au/cgi-bin/nmoc/latest_y.pl?idcode bin/nmoc/latest_y.pl?idcode=idytx003.10m.000 10
Data Grid (2.5 km x 2.5 km) 11
FWI Inputs (19 Feb 2008) 12
FWI Inputs (19 Feb 2008) 13
FWI Inputs (19 Feb 2008) 14
FWI Inputs (19 Feb 2008) 15
Fine Fuel Moisture Code (19 Feb 2008) 16
Fine Fuel Moisture Code (19 Feb 2008) 17
Drought Code (19 Feb 2008) 18
Drought Code (19 Feb 2008) 19
Haze Plume (19 Feb 2008) 20
Initial Spread Index (19 Feb 2008) 21
Fire Weather Index (19 Feb 2008) 22
FWI Interpretations (Indonesia and Malaysia) Low Low Class Moderate High Class Moderate High Extreme Extreme Ignition Potential Fine Fuel Moisture Code Interpretation Low probability of fire starts Moderate probability of fire starts in areas of local dryness Grass fuels becoming easily ignitable; high probability of fire starts Grass fuels highly flammable; very high probability of fire starts Difficulty of Control Initial Spread Index Interpretation Low fire intensity in grasslands. Fire will spread slowly or be self-extinguishing. Grassland fires can be successfully controlled using handtools. Moderate fire intensity in grasslands. Handtools will be effective along the fire s flanks, but water under pressure (pumps, hose) may be required to suppress the head fire in grasslands. High fire intensity in grasslands. Direct attack at the fire s head will require water under pressure, and mechanized equipment may be required to build control lines (e.g. bulldozer) Very high fire intensity in grasslands. Fire control will require construction of control lines by mechanized equipment and water under pressure. Indirect attack by back-burning between control lines and the fire may be required. Class Smoke Potential Drought Code Interpretation Low Typical wet-season conditions. More than 30 dry days until DC reaches threshold. Severe haze periods unlikely. Moderate High Extreme Low Class Moderate High Extreme Normal mid dry-season conditions. Between 15 and 30 dry days until DC reaches threshold. Burning should be regulated and monitored as usual. Normal dry-season peak conditions. Between 5 and 15 dry days until DC reaches threshold. All burning in peatlands should be restricted. Weather forecasts and seasonal rainfall assessments should be monitored closely for signs of an extended dry season. Approaching disaster-level drought conditions. Less than 5 dry days until DC reaches threshold, at which point severe haze is highly likely. Complete burning restriction should be enforced. General Fire Danger Fire Weather Index Interpretation Fire will be self-extinguishing. Fires can be easily suppressed with handtools Most fires can be successfully controlled using power pumps and hose Some fires will be difficult to control 23
Monthly Average of FFMC (Jun 2008) 24
LAPAN website: http://www.rs.lapan.go.id www.rs.lapan.go.id/simba 25
ASEAN Secretariat Haze-on on-line: http://www.haze-online.or.id online.or.id/ 26
GOFC-GOLD GOLD website: http://gofc-fire.umd.edu/projects/index.asp fire.umd.edu/projects/index.asp 27
Concluding Remarks Advantages of using satellite remote sensing data: Provide comprehensive and multi-temporal temporal coverage of large areas in real-time and at frequent intervals. Provide mapping at a regular spatial resolution. Cost-effective. Limitations: Do not directly estimate the meteorological parameters. Data processing is more complex. Clouds block land observation. 28
Thank you for your attention 29