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Use of NASA Assets for Predicting Wildfire Potential for Forest Environments in Guatemala

Use of NASA Assets for Predicting Wildfire Potential for Forest Environments in Guatemala. Rapid Prototyping Capability Project. Greg Easson & Laura Johnson The University of Mississippi Bill Cooke & Rekha Pillai Mississippi State University. Project team. Collaborators. David Lewis

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Use of NASA Assets for Predicting Wildfire Potential for Forest Environments in Guatemala

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  1. Use of NASA Assets for Predicting Wildfire Potential for Forest Environments in Guatemala Rapid Prototyping Capability Project

  2. Greg Easson & Laura Johnson The University of Mississippi Bill Cooke & Rekha Pillai Mississippi State University Project team Collaborators David Lewis Institute for Technology Development Inc. at Stennis Space Center Victor Hugo Ramos National Council for Protected Areas (CONAP), Guatemala

  3. Research Question Can VIIRS data facilitate the development of a near real time forest fire potential decision support system in the tropics?

  4. 1

  5. 1

  6. MODIS Active Fire Product (SERVIR) Fires in Mesoamerica

  7. Shifting from Fire Detection to Determining Fire Potential • Detecting Active Fire • Bright spot • Detecting Fire Potential • Vegetation moisture content

  8. Project Purpose • To evaluate the potential of VIIRS data for monitoring vegetationmoisture condition of tropical forests 1

  9. Project objectives • To determine how select MODIS and simulated VIIRS vegetation indices compare with Keetch Byram Drought Code (KBDI) values in significant and non-significant fire years 1

  10. Background Fire potentialis the susceptibility of a location to the ignition and spread of fire. Factors include topography, vegetation type, fuel load, live and dead fuel moisture content, temperature, and humidity. KBDI is most commonly used input for measuring fire potential 1

  11. Keetch Byram Drought Code (KBDI) • KBDI is a drought index • Current practice of computing KBDI from point source weather data and its manual interpolation across large areas is subject to uncertainties. 1

  12. MODIS, April 18, 2003 1

  13. Rationale • Verbesselt et al (2007) found that SPOT based NDII is better correlated with KBDI compared to NDVI. • Anderson et al (2007) found MODIS-based EVI is better correlated to KBDI than NDVI or NDWI • Fensholt et al (2006) has shown that MODI- based SIWSI performs better than NDWI and NDII in predicting canopy water stress • More work is needed to identify the MODIS based vegetation index is most closely correlated with KBDI; and no work as yet has been done using VIIRS data

  14. Vegetation indices Normalized Difference Vegetation Index NDVI = NIR (band2) - Red (band1) / NIR + Red Enhanced Vegetation Index EVI = 2.5 x (NIR + Red) / [(NIR + 6) x (Red – 7.5) x (Blue (band3) + 1)] Shortwave Infrared Water Stress Index SIWSI = NIR – SWIR2 (band 6) / NIR + SWIR2 1

  15. Study AreaMayan Biosphere Reserve, Guatemala

  16. MODIS, April 18, 2003 1

  17. FIRE Potential Weather Vegetation Greenness combustion model based on vegetation type Slope

  18. Data requirements • Keetch Byram Drought Index (KBDI) • Daily maximum temperatures • Daily 24 hour rainfall • Time series vegetation indices from 2001 to 2005 • MODIS NDVI 8-day 500 meter • MODIS EVI 8-day 500 meter • MODIS SIWSI 8-day 500 meter • Simulated VIIRS NDVI 8-day 400 meter

  19. Methods • Building a continuous KBDI for 1999 to 2005 • Obtaining 8-day time series MODIS and simulated VIIRS data between 2001 to 2005 • Graph NDVI, EVI, SIWSI and KBDI values for 2001 to 2005 • Correlate the coefficients for the NDVI, EVI, SIWIS and KBDI 3

  20. Project Timeline

  21. Synthetic VIIRS NDVI – Surface Reflectance Based Products Daily Modis Filtered VIIRS NDVI Time Series (from Surface Reflectance) MOD09: (R,NIR) 250m GSD Daily VIIRS NDVI from Surface Reflectance Application Research Toolbox Time Series Product Tool Spatial Synthesis to VIIRS GSD (~400 m) Temporal Processing Filter Options • Quality Criteria • Scan Angle • Clouds • etc. Median Savitzky-Golay Reprojection to Application Coordinates/Grid Running Mean Model Noise Characteristics Spatial Spatial Options Add Scene Geometry Effects N x N Mean Median

  22. KBDI 1999-2007 1

  23. NVDI Values 5 x 5

  24. Project Outcomes & Expected Impacts

  25. Contact Information: • Greg Easson, Director UMGC • 662 915 5995 • geasson@olemiss.edu • Laura Johnson • research associate • 662 915 5818 • lj@olemiss.edu

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