1. Monitoring Vegetation Regeneration after Wildfire
Jess Clark
USFS Remote Sensing Applications Center
In 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,
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2. Need for Post-fire Monitoring
Wildfire Effects
BAER Assessments and Treatments
Monitoring Requirements
Who, how often, for how long, who pays?
Values at Risk
This slide provides the conceptual framework for this project. After wildfires occur, the severity of the fire is mapped and delivered to BAER teams. They make professional judgments of treatment needs based on the severity map and potential values at risk.
After the BAER team leaves, the local forest (district ranger) is responsible for vegetation monitoring; however, the BAER team is still allowed to monitor the treatments they implemented and can use BAER funds to re-apply treatments for up to three years if the need persists.
At some point, district rangers get pressure from the public to remove invasive treatments (like the k-rail shown in the picture above) but rangers are unsure whether the risk has fallen sufficiently where they can confidently allow treatment removal.
USDA Forest Service, Remote Sensing Applications Center,
Photo credit: Robert Leeper

3. Severity mapping (NBR / dNBR) Monitoring (NDVI / EVI)
Role of Remote Sensing
Severity mapping (NBR / dNBR)
Monitoring (NDVI / EVI)
Predictive Modeling (Regression)
Decision Support Tools
USDA Forest Service, Remote Sensing Applications Center,

4. Role of Remote Sensing Vegetation Indices NDVI, EVI, NBR
All are image derivatives that take advantage of the spectral richness in Landsat to discern differences in plant vigor and soil moisture content.
USDA Forest Service, Remote Sensing Applications Center,

5. Snapshot in time (NBR / dNBR)
Severity Mapping
Snapshot in time (NBR / dNBR)
e.g., BAER, RAVG, MTBS
Classes / protocols well defined
Using remote sensing technology is well defined and understood for mapping post-fire effects (e.g., severity). BAER, RAVG, and MTBS are three examples of programs national in scope that do this. People understand the value and limitations in each of these programs and (hopefully) use them appropriately.
USDA Forest Service, Remote Sensing Applications Center,

6. NDVI / EVI for monitoring over time
Trends Analysis
Current compared to pre-fire condition
This first example shows a possible method, which is to track the NDVI trend of zones / polygons over time. This approach will allow someone to see whether certain zones have recovered to pre-fire greenness condition. In this example, you can see an immediate green-up in some areas by early May the following year and then another drastic green-up during May-June Some zones, however, exhibit a much slower recovery.
USDA Forest Service, Remote Sensing Applications Center,

7. NDVI / EVI for monitoring over time
Hybrid Static Cover Layer
Pixel values represent actual cover values
This approach is a bit of a hybrid approach. This approach is a composite of all images available during a growing season with the maximum NDVI value chosen and displayed. This allows us to account for vegetation that greens in May but browns in August.
While this is the approach we have carried through, it isn’t the panacea. Maybe an area was relatively barren before the fire occurred; this layer does not account for change.
USDA Forest Service, Remote Sensing Applications Center,

8. 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,

9. Locations
Six fires: Old (2003), Am. River Complex (2008), La Brea (2009), Station (2009), Bull (2010), Canyon (2010)
Am. River Complex
All fires were in California due to cooperating partners: San Bernardino NF, Los Padres NF, San Dimas T&D, and UCLA.
Bull / Canyon
La Brea
Station
Old
USDA Forest Service, Remote Sensing Applications Center,

10. Methods – Field Data Collection
Pole-mast photography
“Plot” = area of homogeneous ground condition
Between 4 and 10 photos per plot
Photos interpreted later
Landsat pixels represent 900 sq. meters of ground on the earth, so we tried to focus on areas that would encompass at least 2 Landsat pixels. Anything less homogeneous would be adversely affected by spatial error in the Landsat imagery as well as GPS locations.
USDA Forest Service, Remote Sensing Applications Center,

11. Methods – Photo Interpretation
Pole-mast photography
Each photo interpreted cover vs. no-cover
Stats summarized by plot (4 to 10 photos)
We interpreted 600 points per plot and we calculated that we only “needed” 400 to properly meet statistics assumptions. To achieve a 95% confidence interval, we need to interpret at least 400 points per plot, regardless of the number of photos acquired in the plot.
95% Confidence Interval = 2 * sqrt (p(1-p)/n)
95% CI = +/- 0.05; p = 0.5 (worst case scenario; equal chance of the ground exhibiting cover or bare ground)
Solved for n; n = 400
We decided to collect 600 points so we could ignore or throw out points that intersected anomalous things in the photos (humans, monopod, etc.)
Cover is defined as living plant material, those that either have sprouted since the fire or those that did not die in the initial fire. One major caveat here is in plots that have existing canopies – high (living) canopy cover but little understory. The monopod setup does not get above the canopy, so we’re poorly suited to deal with that.
USDA Forest Service, Remote Sensing Applications Center,

12. Methods – Satellite Imagery
Imagery collected pre- and post-fire
NDVI / EVI creation
Pixel values summarized by plot areas
I used normal Landsat delivery methods (USGS-GloVis) as well as a new approach (Google Earth Engine). Google has the entire Landsat data record spinning on their servers and allow for “clip-zip-ship” functionality as well as cloud-based computing and generation of a number of indices. I downloaded image chips around the fires and their accompanying vegetation indices.
I then created zonal stats based on plot locations (all pixels that intersected the plot) and summarized then in tabular and graphical form.
USDA Forest Service, Remote Sensing Applications Center,

13. NDVI and EVI both showed relatively high correlation to ground cover
Results
NDVI and EVI both showed relatively high correlation to ground cover
Leads to application of thresholds for thematic output
Both EVI and NDVI performed fairly well in terms of the big picture. The NBR performed very poorly and was not included in further analysis. Armed with these equations, I was able to apply the algorithm to new fires that had no field data and make a map of predicted ground cover.
% Ground Cover
% Ground Cover
EVI Value
NDVI Value
USDA Forest Service, Remote Sensing Applications Center,

14. 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
Even with 6 different fires, we could minor clumping of fires rather than values; for example, Bull/Canyon tended to be the lower end of the scale (newer fire) while the Old Fire represented much of the higher end of the scale.
Also, the correlation in what I would consider the critical data range (40% cover) is poor. Overall, the correlation is strong, but locally, there are plenty of weaknesses.
USDA Forest Service, Remote Sensing Applications Center,

15. Application for New Fires
Time series imagery for new fires
Horseshoe 2, Monument, Schultz
NDVI and EVI cover map
Available for evaluation
Schultz
Phoenix
Again, I used Google Earth Engine to gather new Landsat imagery (all cloud-free imagery acquired after the fire) and stacked the maximum NDVI / EVI value into a single composite.
Tucson
Horseshoe 2
Monument
USDA Forest Service, Remote Sensing Applications Center,

16. Schultz (2010) – Applied Results
The Schultz Fire burned outside Flagstaff during These are still preliminary results with no proper field validation.
USDA Forest Service, Remote Sensing Applications Center,

17. Horseshoe 2 (2011) – Applied Results
Horseshoe 2 burned south of Phoenix last year. This fire had considerable green-up in portions.
USDA Forest Service, Remote Sensing Applications Center,

18. Monument (2011) – Applied Results
Monument burned last year and is the fire we’re going to visit tomorrow. I will have a few print outs to do a quick qualitative assessment while we’re in the field.
USDA Forest Service, Remote Sensing Applications Center,

19. Tool for resource managers / line officers
Decision Support Tool
Tool for resource managers / line officers
When has the risk sufficiently lessened?
This project includes the creation of a decision support tool that will help resource managers better answer the question, “Can we open the Graham Harbor Campground yet?” The graphic shows the mountains along Lake Chelan in Washington. The Pot Peak Fire (2004) burned 47,000+ acres but the Graham Harbor Campground was saved. However, the entire hillslope above the campground was burned fairly severely.
USDA Forest Service, Remote Sensing Applications Center,

20. Post-fire Watershed Planning Decision Support Process
Decision Support Tool
Post-fire Watershed Planning Decision Support Process
Define critical values
Define AOI
Acquire imagery and VI
Summarize VI by AOI
Probability of damage
Identify risk
More ESR work needed?
After the predictive modeling is performed and validated, it is used as an input to a decision support tool. The district ranger or other land manager will use the flowchart shown above to evaluate whether watersheds need additional attention or whether their recovery has sufficiently reduced the risk of future damage (to life and property).
USDA Forest Service, Remote Sensing Applications Center,

21. Jess Clark jesstclark@fs.fed.us 801-975-3769
Comments / Questions?
Jess Clark
USDA Forest Service, Remote Sensing Applications Center,
FSWeb:
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