Community Wildfire Protection Plans Pikes Peak Wildfire Prevention Partners Workshop
Use of NASA Assets for Predicting Wildfire Potential for Forest Environments in Guatemala
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The University of Mississippi Geoinformatics Center NASA MRC RPC review meeting, 14-15 April Use of NASA Assets for Predicting Wildfire Potential for Forest Environments in Guatemala Rapid Prototyping Capability Project
description
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 - PowerPoint PPT Presentation
Transcript of Use of NASA Assets for Predicting Wildfire Potential for Forest Environments in Guatemala
The University of Mississippi Geoinformatics CenterNASA MRC RPC review meeting, 14-15 April 2008
Use of NASA Assets for Predicting Wildfire Potential for Forest Environments in
Guatemala
Rapid Prototyping Capability Project
The University of Mississippi Geoinformatics CenterNASA MRC RPC review meeting, 14-15 April 2008
Project team
Greg Easson & Laura JohnsonThe University of Mississippi
Bill Cooke & Rekha PillaiMississippi State University
David LewisInstitute for Technology Development Inc. at Stennis Space Center
Victor Hugo RamosNational Council for Protected Areas (CONAP), Guatemala
Collaborators
The University of Mississippi Geoinformatics CenterNASA MRC RPC review meeting, 14-15 April 2008
Research Question
Can VIIRS data facilitate the development of a near real time forest fire potential decision support system in the tropics?
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008 1
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008 1
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
MODIS Active Fire Product (SERVIR)
Fires in Mesoamerica
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
Shifting from Fire Detection to Determining Fire Potential
• Detecting Active Fire• Bright spot
• Detecting Fire Potential• Vegetation moisture content
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
Project Purpose
• To evaluate the potential of VIIRS data for monitoring vegetation moisture condition of tropical forests
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The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
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
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The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
Background
Fire potential is 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
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The University of Mississippi Geoinformatics CenterNASA MRC RPC review meeting, 14-15 April 2008 1
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.
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008 1
MODIS, April 18, 2003
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
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
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
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
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The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
Study Area Mayan Biosphere Reserve, Guatemala
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008 1
MODIS, April 18, 2003
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
FIRE Potential
Weather
combustion model based on vegetation type
Slope
Vegetation Greenness
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
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
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
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
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The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
Project Timeline
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008 2
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Synthetic VIIRS NDVI – Surface Reflectance Based Products
Time SeriesProduct Tool
Application Research Toolbox
Filter Options
MedianSavitzky-Golay
Running Mean
TemporalProcessing
Daily Modis
Spatial OptionsN x N Mean
Median
Spatial
Quality Criteria• Scan Angle• Clouds• etc.
Spatial Synthesis to VIIRS GSD(~400 m)
Reprojection to Application Coordinates/Grid
Daily VIIRS NDVI from Surface Reflectance
Filtered VIIRS NDVI Time Series (from Surface Reflectance)
Model Noise Characteristics
Add Scene Geometry Effects
MOD09: (R,NIR)250m GSD
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Oct-99
Jun-0
0
Feb-01
Oct-01
Jun-0
2
Jan-0
3
Sep-03
May-04
Jan-0
5
Sep-05
May-06
Dec-06
Aug-07
0
500
1000
1500
2000
2500
Date
KB
DI
KBDI 1999-2007
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
5 x 5
NVDI Values
Date
ND
VI
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
Project Outcomes & Expected Impacts
The University of Mississippi Geoinformatics CenterNASA MRC RPC Review Meeting: 14-15 April 2008
Greg Easson, Director UMGC
662 915 5995 [email protected]
Contact Information:
Laura Johnson research associate
662 915 5818 [email protected]
