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Cross-Correlation between World Fire Atlas and Environmental Classifications

Cross-Correlation between World Fire Atlas and Environmental Classifications. Diane Defrenne, SERCO Olivier Arino, ESA. CLASSIFICATIONS USED TO CROSS CORRELATE WITH THE WFA. CROSS-CORRELATION WITH METEOROLOGICAL DATA. CROSS-CORRELATION WITH ATHMOSPHERIC DATA. FURTHER DEVELOPMENTS.

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Cross-Correlation between World Fire Atlas and Environmental Classifications

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  1. Cross-Correlation between World Fire Atlas and Environmental Classifications Diane Defrenne, SERCO Olivier Arino, ESA

  2. CLASSIFICATIONS USED TO CROSS CORRELATE WITH THE WFA. CROSS-CORRELATION WITH METEOROLOGICAL DATA. CROSS-CORRELATION WITH ATHMOSPHERIC DATA. FURTHER DEVELOPMENTS. • Objective of the Cross Correlation with Environmental Classifications: • Develop a set of Fire Behaviour Rules. • Develop a set of Emission prediction factor. • Increasing the WFA confidence.

  3. ENVIRONMENTAL CLASSIFICATIONS • Classification Criteria: • Temporal coverage about 5 years between November 1995 and May 2005. • Global Geographical coverage almost complete. • Classification available: • Meteorological data  ECMWF 40 Years Re-Analysis, monthly fields (resolution 2.5°). • Vegetation Classification  GLC2000 (30 sec°). • Atmospheric chemistry observation  TEMIS, monthly fields (0.25°). • System used to generate the correlations: • Use of the World Fire Atlas Tool that permits the discrimination of the WFA data in time and space. • Microsoft Visual Basic 6.0 • Free GIS object library (www.inovagis.org). • Preliminary processing on the input files: • Generate from each classification file in various format (netcdf, grd, …) a raster file on Plate Carré. • Manage the various resolution of each classification.

  4. WFA and METEOROLOGICAL CLASSIFICATION OBJECTIVE  Fire Behaviour Rules. Input: WFA ECMWF Temperature from ECMWF ERA 40 ECMWF Precipitationfrom ECMWF ERA 40 Vegetation classification from GLC2000 • Output: Hot spots detected from WFA. • Mean of monthly temperature over the region. • Mean of monthly cumulated precipitation over the region. • Classification of Vegetation index from GLC2000 (% of surface use by each vegetation). Region: South America, Africa, Siberia.

  5. Methodology • Difference between local climate (Temperature and precipitation). • Vegetation classification for each tile. • Correlation between number of fire by month and meteorological parameters (the precipitation and the temperature). • Extract some fire behaviour rules.

  6. AMAZON F(time): Time rule, F(T): Temperature rule , F(P): Precipitation rule

  7. AFRICA Fires depends of Vegetation and Precipitation.

  8. Fires depends of temperature.

  9. Cross-correlation between NO2 concentration and hot spots detected First Results:

  10. Using satellite data to better understand Ozone budget. N.Savage Predicting the No2 Concentration from the hot spots detected by the satellite: • METHODOLOGY: • Divide the region of interest in small tile (5°x5°). • Group the tiles having a similar vegetation following the GLC2000 index. • Select inside each zone a central region of study with enough fires. • Perform a cross-correlation between the number of fires by month and the No2 mean concentration in a month from march 1996 to may 2005 (without Jan 1998 and November 2003 because data from TEMIS are not available or complete) for each region selected. • Perform a linear regression over each region. • Try to generate an emission prediction factor for each region and to find the natural NO2 emission cycle. • ASSUMPTION: • GLC2000 is a fixed vegetation classification  the vegetation has globally changed between 1996 and 2005. • TEMIS NO2 tropospheric concentration data available from April 1996 to date and WFA data available from July 1996 to date. • The emission predictor factor is considering linear and doesn’t take in consideration the natural NO2 emission cycle.

  11. Discriminate region by GLC2000 Classification REPARTITION OVER AFRICA: GLC2000 Index Vegetation: 12  Shrub Cover, closed-open, deciduous (with or without sparse tree layer) 13  Herbaceous Cover, closed-open 16  Cultivated and managed areas 1  Tree Cover, broadleaved, evergreen 2  Tree Cover, broadleaved, deciduous, closed 3  Tree Cover, broadleaved, deciduous, open

  12. NOT ENOUGH HOT SPOTS !! Emission prediction factor 1 C=152,09+31.49(HS) 2 C=88.07+26.46(HS) 3 C=? 4 C=163.36+24.90(HS) 5 C=159.84+65.046(HS)

  13. FURTHER DEVELOPMENTS • Behaviour Model: • Fully define F(time), F(precipitation) and F(temperature) from Cross-correlation over some significant regions • Use ERA 40 4 time daily data set to refine in time the Behaviour model. • Emission prediction factor: • Results are very encouraging. • Complete the study to define the NO2 emission prediction factor and seasonal cycle. • Vegetation Classification: • Cross-correlate the WFA data with the classification from GLOBCOVER for 2005 (available by the end of 2007). Thank You!

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