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Characterizing Vegetation Fire Regimes in Brazil Through Adjusted Satellite Fire Detection Data Wilfrid Schroeder, Jeffrey T.Morisette, Louis Giglio, Ivan.

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Presentation on theme: "Characterizing Vegetation Fire Regimes in Brazil Through Adjusted Satellite Fire Detection Data Wilfrid Schroeder, Jeffrey T.Morisette, Louis Giglio, Ivan."— Presentation transcript:

1 Characterizing Vegetation Fire Regimes in Brazil Through Adjusted Satellite Fire Detection Data Wilfrid Schroeder, Jeffrey T.Morisette, Louis Giglio, Ivan Csiszar, Douglas Morton, Christopher Justice, João A. R. Pereira LC-23 Group Special Session : S19 Accuracy Assessment and their Implications for Fire and Deforestation Monitoring III LBA Scientific Conference Brasília, July 27-29 2004

2 Goal To understand how different satellite derived fire products describe surface conditions To address major driving elements affecting numbers To propose a method to adjust (correct) resulting numbers and possibly start data integration (multi-satellite approach)

3 Major Brazilian Biomes In absolute numbers: Floresta Amazônica, Floresta Estacional and the Cerrado account for approx 80% of Brazil and respond to 84% of the total number of hot spots detected Target-relative numbers: Approx 28% (1Million km 2 ) of Floresta Amazônica show near zero hot spot numbers (no human presence) Complexo do Pantanal and Caatinga also show large use of fire

4 Hot Spot Distribution per Biome Type Absolute NumbersAbsolute Numbers HotSpotDensityHotSpotDensity

5 Amazon States The Brazilian Amazon states respond to approx 70% of the total number of hot spots detected Different fire seasons (timing) observed within the region Intense land transformation taking place Strong correlation between fire x deforestation

6 Hot Spot Distribution over the Brazilian Amazon Absolute Numbers Hot Spot Density

7 Hot Spot Distribution over the Brazilian Amazon

8 BR163 & BR230 Fish bone deforestation pattern observed at different stages of development Maintenance x Conversion fires representing different fractions of total fires in each road section Social elements playing major role to fire numbers

9 Development Scenarios

10 Hot Spot Distribution over Road Sections MODIS/Terra AVHRR/NOAA-12 Similar fire spatial distribution Different fire numbers involved

11 Major issues affecting fire product (from Fire & Deforestation Accuracy Workshop) Satellite coverage Viewing geometry (pixel size & view angle) Fire size/temperature/duration Clouds Biome Type Diurnal cycle

12 Data Adjustment Hot Spots in Mato Grosso State Adjusted Numbers: 39.5% increase Hot Spots in Mato Grosso State Adjusted Numbers: 46.4% increase Hot Spots in Mato Grosso State Adjusted Numbers: 51.3% increase Hot Spots in Mato Grosso State Adjusted Numbers: 56.0% increase Hot Spots in Mato Grosso State Adjusted Numbers: 61.3% increase

13 Major driving elements affecting satellite derived fire numbers are due to physical, social and sensor-inherent factors Timing of peak fire season is the less affected Ranking of distinct target areas may be subjected to strong variation among satellites Major implication to public policies Non-trivial approach to combine data sets is required Potential use of adjusted numbers/multi- satellite approach reinforces the need to attack the problem Conclusion **Article submitted to LBA Special issue at Earth Interactions Journal


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