usda forest service, remote sensing applications center, fsweb: www: monitoring vegetation...
TRANSCRIPT
USDA Forest Service, Remote Sensing Applications Center, FSWeb: http://fsweb.rsac.fs.fed.us
WWW: http://www.fs.fed.us/eng/rsac/
Monitoring Vegetation Regeneration after Wildfire
Jess Clark
USFS Remote Sensing Applications CenterIn cooperation with:
Marc Stamer (San Bernardino NF), Kevin Cooper (Los Padres NF), Carolyn Napper (San Dimas T&D), Terri Hogue (UCLA)
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Need for Post-fire Monitoring
• Wildfire Effects• BAER Assessments and Treatments• Monitoring Requirements
– Who, how often, for how long, who pays?• Values at Risk
Photo credit: Robert Leeper
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Role of Remote Sensing
• Severity mapping (NBR / dNBR)• Monitoring (NDVI / EVI)• Predictive Modeling (Regression)• Decision Support Tools
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Role of Remote Sensing
• Vegetation Indices– NDVI, EVI, NBR
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Severity Mapping
• Snapshot in time (NBR / dNBR)– e.g., BAER, RAVG, MTBS– Classes / protocols well defined
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Monitoring
• NDVI / EVI for monitoring over time– Trends Analysis
• Current compared to pre-fire condition
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Monitoring
• NDVI / EVI for monitoring over time– Hybrid Static Cover Layer
• Pixel values represent actual cover values
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Project Objectives
• Assess effectiveness of remote sensing to monitor vegetation regeneration– Methods:
• Field data collection• Remote sensing based observations• Correlation analysis / predictive modeling• Application
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Locations
• Six fires: Old (2003), Am. River Complex (2008), La Brea (2009), Station (2009), Bull (2010), Canyon (2010)
Am. River Complex
Bull / Canyon
Old
StationLa Brea
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Methods – Field Data Collection
• Pole-mast photography– “Plot” = area of
homogeneous ground condition
– Between 4 and 10 photos per plot
– Photos interpreted later
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Methods – Photo Interpretation
• Pole-mast photography– Each photo interpreted cover vs. no-cover– Stats summarized by plot (4 to 10 photos)
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Methods – Satellite Imagery
• Imagery collected pre- and post-fire• NDVI / EVI creation• Pixel values summarized by plot areas
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Results
• NDVI and EVI both showed relatively high correlation to ground cover
• Leads to application of thresholds for thematic output
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80
0
20
40
60
80
100
120
f(x) = − 466.037310938107 x² + 519.495828059246 x − 40.0240177691607R² = 0.723908499042396
Ground Cover and EVI
0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80
0
20
40
60
80
100
120
f(x) = − 238.492333272508 x² + 390.067742280946 x − 51.0262670408163R² = 0.64865204261544
Ground Cover and NDVI
EVI Value NDVI Value
% G
roun
d Co
ver
% G
roun
d Co
ver
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Discussion and Limitations
• Less than perfect field data collection– Clumping of fires, not values
• Some historic (recovered) fires and some current (still black) fires
– Muddy results in critical data range• Poor linear function in the 0.2 – 0.35 NDVI
data range
0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80
0
20
40
60
80
100
120
Ground Cover and NDVI
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Application for New Fires
• Time series imagery for new fires– Horseshoe 2,
Monument, Schultz• NDVI and EVI
cover map• Available for
evaluation
Schultz
Horseshoe 2
Monument
Phoenix
Tucson
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Schultz (2010) – Applied Results
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Horseshoe 2 (2011) – Applied Results
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Monument (2011) – Applied Results
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Decision Support Tool
• Tool for resource managers / line officers
• When has the risk sufficiently lessened?
USDA Forest Service, Remote Sensing Applications Center, http://fsweb.rsac.fs.fed.us
Decision Support Tool
• Post-fire Watershed Planning Decision Support Process
1. Define critical values2. Define AOI3. Acquire imagery and VI4. Summarize VI by AOI5. Probability of damage6. Identify risk7. More ESR work needed?
USDA Forest Service, Remote Sensing Applications Center, FSWeb: http://fsweb.rsac.fs.fed.us
WWW: http://www.fs.fed.us/eng/rsac/
Comments / Questions?
Jess Clark
801-975-3769