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Cross-Correlation between World Fire Atlas and Environmental Classifications

This study focuses on the cross-correlation between the World Fire Atlas (WFA) and various environmental classifications such as meteorological data, atmospheric data, and vegetation classification. The objective is to develop fire behavior rules, emission prediction factors, and increase the confidence in the WFA data.

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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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