MONITORING POST-FIRE REVEGETATION: MILFORD FLAT FIRE, UTAH
Lisa Langs Stoner, GIS Analyst
Remote Sensing & GIS Laboratory
Utah State University
Logan, UT 84322
[email protected]
ABSTRACT
In July 2007, Utah experienced the largest fire in the state’s history, burning approximately 1,469 square kilometers
in Millard and Beaver counties, involving several different plant communities, primarily salt desert scrub,
sagebrush, and juniper. The Milford Flat Fire was lightning-caused but fueled by a number of converging conditions
including drought, high winds, and an abundance of annual weeds. The Remote Sensing & GIS Laboratory at Utah
State University worked with the Utah Bureau of Land Management (BLM) to develop a remote sensing-based
monitoring protocol to assess the effects of the fire on the landscape and to evaluate seeding program success. A 10year sequence of phenological profiles were developed from MODIS 16-day composite Normalized Differenced
Vegetation Index (NDVI) data to compare vegetation responses before and after the fire at both a fire-wide and
treatment scale. The phenological response of annual species such as cheatgrass (Bromus tectorum) was assessed
relative to the re-establishment of perennial vegetation. With a favorable climate during the three years following the
fire, the BLM’s seeding program is on a positive trajectory.
KEYWORDS: wildfire, revegetation, monitoring, MODIS, Utah
INTRODUCTION
The Milford Flat Fire (MFF), Utah’s largest wildland fire, burned from 6 July 2007 to 15 July 2007. This
lightning-caused, fast moving fire started three miles north of the town of Milford, Utah, consuming more than
1,469 square kilometers in the Black Rock Desert region of Beaver and Milford Counties (Figure 1). A number of
environmental conditions contributed to the severity of the fire including periods of sustained drought prior to the
fire, high winds, high temperatures, and an abundance of invasive annual weeds. Average annual precipitation in
this area is between 20 and 52 centimeters that come mostly as snow and monsoonal thunderstorms in late summer.
The vegetation fueling the fire included salt desert scrub, sagebrush, and juniper intermixed with an understory of
annual weeds. Most of the area within the fire boundary burned contiguously, leaving only a few small unburned
islands. Approximately 75% of the land is Bureau of Land Management (BLM)-owned, followed by private (17%),
state (8%) and Bureau of Indian Affairs (0.2%).
Substantial investments from federal, state, and private sources were directed toward fire rehabilitation,
comprised of revegetation treatments and post-fire monitoring. Soil stabilization was the immediate goal following
the fire. The success of seed treatments and high seedling survival rates were of primary importance. Ultimately, the
desired outcome was to increase the perennial plant cover and minimize invasive annuals and noxious weeds. Postfire vegetation and wildlife surveys were conducted by the BLM and the Utah Division of Wildlife Resources
(UDWR). The Remote Sensing & GIS Laboratory (RS/GIS Lab) at Utah State University (USU) was contracted to
develop a remote sensing-based monitoring protocol to assess the effects of the fire on a landscape scale and to
evaluate seeding program success.
PROJECT OBJECTIVES
The primary objective of this project was to develop a remote sensing-based monitoring system to: 1) determine
the extent of the Milford Flat Fire, 2) identify land cover types affected by the fire, 3) map pre- and post-fire
distribution and density of cheatgrass (Bromus tectorum), 4) identify pre- and post-fire phenological patterns,
including greenness onset, peak, and end dates, and 5) assess the effectiveness of post-fire seeding treatments.
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Figure 1. Location of the Milford Flat Fire in Utah.
REMOTE SENSING DATA
Data from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat Thematic Mapper (TM), and
the National Agriculture Imagery Program’s (NAIP) 1-meter resolution digital ortho photos, all provided critical
information for this project. The Normalized Differenced Vegetation Index (NDVI) derived from MODIS 16-Day
(cloud-corrected) composite images at 250-meter spatial resolution, was a primary data set in this project for
measuring changes in vegetation growth and productivity from years 2000 to 2010. NDVI is a ratio based on two
bands, the visible red (.620–.670 micrometers) and the NIR (.841–.876 micrometers) regions of the electromagnetic
spectrum. The visible red region of incoming sunlight is strongly absorbed by chlorophyll in plants, whereas the
NIR region is reflected. NDVI is calculated as follows:
NDVI = (NIR – red)/ (NIR + red)
The NDVI produces a range of values between -1 and 1, where, increasing positive values are associated with
increasing greenness. By exploiting the differences in plant reflectance, the spatial distribution of vegetation growth
patterns can be determined.
The finer spatial resolution of Landsat TM (30-meter2) was used to detect and delineate the area that burned. As
with MODIS, we created a Landsat-derived fire intensity index by exploiting the near-infrared and middle-infrared
bands of a Landsat image acquired on July 27, 2007.
Lastly, the NAIP county-wide digital ortho-photos were invaluable sources of data used in base maps and
selection of field sites.
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Figure 2. Progression from the original Landsat TM image captured July 27, 2007, to the Landsat-derived fire
intensity index (panchromatic), to the Landsat-derived classification of the fire area (red) with BLM fire boundary
(black).
METHODS
Identifying Fire Extent
Fire-related decreases in vegetation are evident spectrally by decreases in reflectivity in the visible and NIR
bands, and conversely, increases in the MIR band reflectance values. Accordingly, vegetation loss by fire can be
detected remotely if the amount of vegetation removed changes significantly the amount of solar radiation reflected
or absorbed (Patterns and Yool, 1998, and references therein). Figure 2 shows the raw Landsat TM image acquired
on July 27, 2007 (visualized by bands: 7 (MIR), 4 (NIR), and 3 (red)). The intensity of the red color coincides with
the amount of black ash resulting from the fire. Darker red areas occur where juniper was prevalent creating more
ash. Lighter red areas seem to coincide with areas that had lower ground cover.
To identify burned areas immediately after the MFF, we created a Normalized Difference Fire Index (NDFI)
using Landsat’s band 4 (NIR, 0.76-0.90 micrometers) and band 7 (MIR, 2.08-2.35 micrometers) in the following
calculation:
NDFI = (NIR – MIR) / (NIR + MIR).
In the Landsat-derived fire intensity map (Figure 2, panchromatic image) brighter areas indicate higher relative fire
intensity due to higher amounts of combustible material. The third image in Figure 2 shows the classification of the
areas that burned with the BLM-generated fire boundary for comparison. The method of mapping fire extent using
the NDFI provided a more detailed depiction of what lands actually burned within the fire boundary. However, for
this project, we used the extent of the BLM-generated fire boundary to retain the unburned areas for our analyses.
Pre-fire Land Cover
The vast majority (910 km2) of the pre-fire land cover present within the MFF boundary, were shrublands
dominated by Wyoming big sagebrush (Artemisia tridentata spp. wyomingensis), black sagebrush (A. nova),
shadscale saltbush (Atriplex confertifolia), fourwing saltbush (A. canescens), and greasewood (Sarcobatus
vermiculatus). Juniper woodlands made up the second highest land cover type prior to the fire (285 km2), followed
by a mixture of grasslands and forblands (115 km2).
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Distribution and Density of Cheatgrass
Assuming that peak greenness for cheatgrass occurs around April 15 th (+/- 2 to 3 weeks) in this portion of Utah
(R. Beckstrand (BLM-FFO), pers. comm.), an elevated early spring green-up is a possible indication of cheatgrass
cover. Depending on local climate conditions, late June to early July is when “greenness” values would be at a
minimum; after the cool season grasses have senesced and before the green-up of the warm season grasses. An index
for cheatgrass density swas derived by selecting two dates of MODIS NDVI images for each year between 2000 and
2010, one to identify peak greenness in spring, and the second for typical summer greenness. Using the formula:
(Spring NDVI – Summer NDVI) / (Spring NDVI + Summer NDVI)
a normalized difference index was created, producing a range of greenness values between -1 and +1. Values above
zero indicate areas that were greener in spring than they were in summer. Higher positive values should indicate
higher cheatgrass cover and density.
We compared the output of cheatgrass density indices
Table 1. Dates for estimating annual cheatgrass
using the higher spatial resolution of Landsat-derived NDVI
density using MODIS NDVI data
images versus the coarser MODIS-derived NDVI images. We
used MODIS NDVI data to calculate the probability of
Year
Spring
Summer
cheatgrass across the extent of the MFF for the following
2000
Apr-22
Jul-11
reasons: 1) more dates to select from, enabling more
2001
Apr-23
Jul-12
phenologically accurate representations of the difference
2002
Apr-23
Jun-26
between peaks and troughs in annual greenness curves, 2)
2003
Apr-23
Jul-12
MODIS 16-day composites are cloud free, and 3) spatially, the
2004
Apr-22
Jul-11
results were similar between Landsat and MODIS when the
2005
May-09
Jul-12
images were acquired on the same dates by the two sensors.
A list of dates used to create annual cheatgrass density
2006
May-09
Jul-12
estimates from MODIS NDVI data is provided in Table 1. The
2007
Apr-07
Jun-26
peak of spring greenness during the years 2000 to 2010
2008
Apr-06
Jul-11
occurred between April 6 and May 9. In 2007 and 2008, the
2009
Apr-23
Jul-12
greenness peaked a little early during the first week of April.
2010
May-09
Jul-12
Also, in 2007, an early summer date (June 26th) was selected
for minimum greenness, since the next available date, July 12 th, was while the fire was burning and would have
biased the cheatgrass estimate.
Phenological Profiles
The MODIS NDVI images were also used to create annual “greenness curves” or phenological profiles for each
year between 2000 and 2010. By plotting mean NDVI values over time, changes in vegetation can be seen at both
fire-wide and treatment scales. A total of 253 images of MODIS NDVI 16-day composite data were downloaded (23
dates/year * 11 years) from the USGS Land Processes Distributed Active Archive Center (https://lpdaac.usgs.gov/).
Each date required three scenes to encompass the state of Utah, which were mosaicked, reprojected, and subset for
subsequent analysis at the scale of the Milford Flat Fire. We used phenological profiles to develop within- and
among-year comparisons, including identification of greenness onset, peak, and end dates, and as an overall index of
the response of vegetation to the treatments.
Seed Treatments and Remote Sensing Monitoring Field Sites
The BLM’s seeding program began in the fall of 2007 and continued through the spring of 2009. Four types of
mechanical treatments were employed depending on the terrain and remnant vegetation. Areas with steeper slopes
received aerial seeding only, while areas with more even terrain were seeded by chaining or drilling. A small
proportion of the treatments involved imprinting in areas with remnant greasewood. Approximately 647 km2 were
treated initially. However, some areas required subsequent re-seeding, which took place in 2008 and 2009.
Approximately 1100 km2 were seeded in total.
Emergency stabilization seed mixes for the MFF were composed primarily of cool season perennial
bunchgrasses and forbs and some warm season perennial bunchgrasses (both native and non-native species). A
shrub component was included in some areas during re-seeding. Shrubs included fourwing saltbush, shadscale
saltbush, and Gardner’s saltbush (A. gardneri) in lower precipitation zones, and Wyoming big sagebrush, Mountain
big sagebrush (A. tridentata spp. vaseyana), and antelope bitterbrush (Purshia tridentata) in higher precipitation
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zones. Forage kochia (Bassia prostrata) was commonly used in re-seeded areas. Seeding application occurred
during the fall, winter, and spring. Consequently, many large areas that were once dominated by shrubland and
juniper were converted to grasses and forbs following the initial soil stabilization efforts.
Twenty-five field sites representing a range of pre-fire vegetation types and post-fire treatments were selected
within the perimeter of the MFF to evaluate the ability of remotely-sensed imagery to monitor treatment success.
Field site selection was based on the following principles: 1) site familiarity developed during field reconnaissance
lead by the BLM prior to remote sensing analyses, 2) stratification across the three most prevalent treatment types,
and identification of untreated and unburned areas, 3) easily accessed by roads in preparation for the second field
reconnaissance, and 4) areas that could be correlated to BLM vegetation monitoring sites. The orientation of the
field sites started in the southern portion of the fire within the Cedar City Field Office (CCFO) district which is
generally within higher precipitation zones, and run northward into the Fillmore Field Office (FFO) district which is
generally within a lower precipitation zone (Figure 3). Of the 25 field sites, 3 were aerially seeded, 12 were chained,
6 were drilled, 3 burned but with no treatment, and one was unburned.
We intersected each MODIS NDVI 16-day composite image with the field site boundaries to calculate mean
greenness values for all dates at each site. Similarly to the fire-wide analysis (see Phenological Profiles section), we
plotted mean NDVI values over time to show differences in phenology (i.e. greenness) within and among years by
treatment type.
Figure 3. Distribution of BLM treatments and field sites selected for remote sensing monitoring.
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RESULTS
Distribution and Density of Cheatgrass
To identify potential hotspots for cheatgrass before and after the MFF, we calculated a normalized difference
index by optimizing the differences between spring and summer NDVI greenness values. We created a series of
maps to show the estimated distribution and abundance of cheatgrass from year 2000 to 2010 (Figure 4). The same
histogram breaks were used each year to show relative cheatgrass density. Density levels were classified as the
following: none (clear/transparent), low (green), medium (orange), and high (red).
Figure 4. Milford Flat Fire relative cheatgrass density indices for 2000 through 2010. (Density classes: low = green,
medium = orange, high = red, no cheatgrass predicted = transparent).
There is a noticeable increase in both cheatgrass distribution and density in the years leading up to the fire, with
a spike in cheatgrass density in 2001, 2005, and 2007. This part of Utah received an unusual amount of precipitation
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in the fall of 2000, 2004, and 2006. The map for 2007 is an estimate of cheatgrass immediately preceding the MFF,
which clearly shows higher cheatgrass density across the MFF landscape.
In the three years post-fire, 2008 to 2010, the distribution and relative amount of cheatgrass were significantly
reduced compared to pre-fire densities. The first year following the fire, the landscape was mostly void of
vegetation. In 2008, the density of cheatgrass was generally low with only a few areas of high density (red). There
was an increase in density in 2009, but the actual value for greenness (i.e. NDVI means) in spring was lower than all
other years (except 2008) and was slightly delayed (see Phenological Patterns section, Figure 5). The cheatgrass
response in 2009 was higher than expected, being that the fall of 2008 was not particularly wet. That said, the
density calculated for 2009 may be a response to other species in addition to cheatgrass growing at the same time.
Plants such as forage kochia and alfalfa (Medicago sativa), both have early spring greenness similar to cheatgrass,
but have active growing seasons throughout the summer and fall. Therefore, areas with a high component of forage
kochia and/or alfalfa would lower the spring-to-summer difference. The climate in 2010 appeared abnormal, with
lower than average temperatures in April, and an increase in spring precipitation (D. Whitaker (BLM-FFO), pers.
comm.). Cheatgrass density decreased in 2010 compared to 2009 estimates, but had more high density areas than
were present in 2008.
Figure 4 shows the variability in the area estimated to have cheatgrass and changes in density levels from 2000
to 2010. The area with cheatgrass including all density classes (i.e. low, medium, and high levels) before the fire
(2000 – 2007) averaged approximately 1175 km2 (SD ± 231), or 87% of the MFF burn area. The area with
cheatgrass after the fire (2008 – 2010) dropped to an average of 811 km2 (SD ± 150), or 60% of the MFF burn area.
The average area with just “high” levels of cheatgrass dropped from 35% to 11% of the MFF burn area, with 473
km2 (SD ± 310) before the fire and 145 km2 (SD ± 188) after the fire.
Percent cover of cheatgrass within the different cheatgrass density levels was not available, as this information
requires additional ground-truth data to calibrate. These maps represent relative values of cheatgrass identifying “hot
spots” for each year throughout the MFF burned area.
Phenological Patterns
Greenness curves constructed from MODIS NDVI mean values for water years 2000 to 2010 were used to
assess changes in phenology before and after the MFF (Figure 5). We used “water years,” defined as the 12-month
period from October 1 to September 30. Values for each date from the first six water years, 2000 to 2006, were
averaged together to provide a “benchmark” for comparing post-fire revegetation efforts. All subsequent years, 2007
through 2011, are shown as single lines for viewing purposes, but represent the same type of data as the 2000 to
2006 mean. Each of these response curves represent average “greenness” across the extent of the fire. The year of
the fire, 2007, is shown as a black line. The timing and height of peak greenness during the spring of 2007, is a
typical response for cheatgrass. The black vertical line represents the start date of the fire, after which, greenness
drops immediately. The pattern in the vegetation phenologically, was variable during the first two years following
the fire. However, at three years post-fire, the response in greenness is promising. In 2010 (Figure 5, red line), the
onset of greenness is nearly one month after the 2000-2006 average onset. The gradual increase in greenness, and
trend of increased greenness continuing later into the summer beyond the typical green-up and senescence of
cheatgrass, indicates other species have emerged. The continued elevated greenness pattern is likely a response by
warm season grasses, perennial forbs, or shrubs that were seeded during post-fire treatments, re-sprouted, or
immigrated into the area following the MFF. Regardless of the species present, the prolonged greenness into
summer months is a positive result.
MODIS NDVI greenness curves were used to identify the approximate dates for greenness onset, peak, and end
between 2000 and 2010 (Figure 6). In pre-fire years, greenness onset began in early to middle March (March 5 th –
22nd). The greenness peak occurred in late April and early May (April 22 nd - May 9th), with a decline in greenness
values in middle to early July (June 11th – July 12th). The year of the MFF was an unusual year, in several respects.
In the fall of 2006, there was a slight increase in greenness values relative to prior years, indicating a possible fall
emergence of cheatgrass. In 2007, greenness peaked earlier than normal, approximately April 7 th, following an
initial steep green-up, surpassing all years’ peak greenness values (Figure 5). The green-up period lasted through
late June (June 26th), approximately 2 weeks prior to the fire’s starting date of July 6 th. In 2009 and 2010, the start
date for greenness onset was approximately April 23 rd, which was related to later than normal snow cover.
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Figure 5. Greenness curves at Milford Flat for water years 2000 to 2011.
Figure 6. Dates for greenness onset, peak, and end at Milford Flat, 2000 - 2010.
If cheatgrass receives sufficient fall precipitation, and temperatures are agreeable, germination will begin that
same season, leading to potentially greater cheatgrass density the following spring (R. Beckstrand (BLM-FFO) and
E. Schupp, pers. comm.). The cheatgrass density maps for 2001, 2005 and 2007 (Figures 4) may be an indication of
this tendency. By adding climate data to the NDVI greenness curves, this phenological pattern was observed: spring
greenness values were higher following a wet fall, particularly if the precipitation came during the month of October
(Figure 7). A similar pattern was apparent in 2001 and 2005 of relatively high spring greenness following an
unusually wet October. October precipitation in 2001, 2005, and 2007, was at least 6.7 cm (≥1 SD) above the 2000 –
2011 mean. The relationship between mean October precipitation was positively correlated with peak spring
greenness (R2 = 0.58, p = 0.006) (Figure 8, inset). We applied this relationship using the recorded mean October
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2010 precipitation of 6.4 cm to predict peak spring greenness for water year 2011. The predicted spring greenness
for water year 2011 was 0.34, also above the 2000 to 2010 average (Figure 8).
Figure 7. Monthly greenness (NDVI means) plotted against mean precipitation for the 2007 water year.
Figure 8. October precipitation and peak spring greenness by water year at Milford Flat.
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Seed Treatment Assessment
The final objective for this project was to evaluate the response of post-fire seeding treatments with remotelysensed imagery. Twenty-five sample field sites (Figure 3) were examined including: 3 aerial seedings, 12 chained
and seeded sites, 6 drilled and seeded sites, 3 burned sites that did not receive a treatment, and 1 unburned site.
Changes in mean NDVI values were used to assess the response of the treatments in the following analyses:
1. Phenological curves for water years 2000 to 2011 were created for each field site (similar to the Figure 5,
not included in this document due to page length restrictions), using mean greenness values from all dates
using the mean greenness value from water years 2000 to 2006 as a “benchmark” to compare the vegetation
response in pre- versus post-fire years.
2. Mean greenness values in 2010 were used to compare post-fire differences in spring (May 25th) versus
summer (July 28th) (Figure 9a) to assess cheatgrass potential three years post-fire.
3. Differences in mean greenness between pre-fire benchmarks (i.e. mean between 2000 and 2006) and postfire (2010) in spring (May 25th) and summer (July 28th) were calculated to see before and after greenness by
season by field site (Figure 9b).
Patterns in phenology at the field site scale are similar to the fire-wide results, but there are some notable differences
based on pre-fire vegetation, treatment type, and location. When comparing year 2010 (Figure 5, red line) relative to
the benchmark (Figure 5, gold bars), the average greenness at juniper sites dropped considerably after the fire
compared to pre-fire values, particularly in the summer. As would be expected, live juniper had higher greenness
values pre-fire compared to the vegetation signal three years post-fire. The opposite response was seen at many of
the big sagebrush sites where greenness values were higher in the summer post-fire than the pre-fire summer
benchmark, evidence of the establishment of perennial seedlings. At some sites, the cheatgrass population may be
increasing based on a precipitous drop in spring versus summer greenness values.
Figures 9a and b summarize seasonal changes in phenology at each field site, color-coded by treatment type. In
general, chained sites had slightly higher greenness than aerial and drilled sites in both spring and summer, but the
response among treatment types was similar (i.e. no significant differences among treatment types) (Figure 9a). In
areas where juniper dominated pre-fire, the 2010 summer greenness was greater at chained sites versus those that
were aerially seeded. There is greater growth in chained areas compared to aerially seeded sites (D. Fletcher BLMCCFO, pers. comm.). The 2010 summer greenness was higher in drilled sites relative to pre-fire benchmarks, except
for two sites, #24 (salt desert scrub pre-MFF) and #25 (salt desert scrub/ greasewood flat pre-MFF). These two sites
are probably the driest sites, the latter with highly saline soils that experienced extreme wind erosion after the fire.
Of the three untreated sites, there were minimal differences in summer greenness in pre-fire benchmarks and 2010
values, with the exception of a small peak in greenness on August 13th, possibly a response of warm season grasses
to the precipitation received in late July/early August. Both big sagebrush and James’ galleta (Pleuraphis jamesii)
seedlings were re-establishing post-MFF at field site #7. Prior seed treatments put in place following the 2006
wildfires may be responsible for the increased greenness at field site #16. The amount of greenness in 2010 at the
unburned site, site #3, a juniper site with sagebrush understory, was analogous to pre-fire benchmarks.
Figure 9b shows the seasonal difference in greenness between pre-fire benchmarks (first bar) and post-fire
values (second bar). Positive NDVI values (i.e. NDVI > 0) indicate increased greenness after the fire, while negative
NDVI values indicate lower greenness after the fire. Field sites #1 to #9, #12, and #13 were all dominated by juniper
prior to the MFF. Field sites #1 to #6 show negative greenness values in summer when comparing 2010 to the prefire average. For these sites, in summer, live juniper (i.e. the pre-fire condition) had higher greenness than the
vegetation in 2010. Field sites #24 and #25 prior to the fire were classified as Inter-Mountain Basins Mixed Salt
Desert Scrub and Inter-Mountain Basins Greasewood Flat, respectively. These sites have experienced substantial
erosion post-fire where revegetation has been difficult, hence the negative greenness values in summer when
comparing pre-fire to 2010 means. As described above, the MFF covered a broad area fueled by intense winds. The
fire raged through canyons, crossing over ridges, and into multiple precipitation zones. The northern most portion of
the fire passed through areas and vegetation types where fire is relatively infrequent and less resilient to disturbance.
A landscape pattern caused by northeasterly winds is clearly evident in both NAIP and Landsat imagery. In some
areas, seeds eroded along with the soil and/or were buried due to high winds (R. Beckstrand (BLM-FFO), pers.
comm.). The environmental conditions of the lower, drier sites and those with heavy erosion problems have likely
contributed to a lower success rate of seedling establishment and productivity and will ultimately be slower to
recover.
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Figure 9. Post-fire NDVI greenness in spring (May 25th) and summer (July 28th) by treatment type (a).
Differences in NDVI greenness between pre-fire benchmarks (means from 2000-2006) and post-fire (2010) in
spring (May 25th) and summer (July 28th) by treatment type (b). (Treatment type: aerially seeded = blue, chained =
lavender, drilled = yellow, no treatment = tan, unburned = green.)
CONCLUSIONS
Relative to the state of Utah, MFF covered ~0.7% of the total land area. The intensity of this fire removed most
of the pre-existing vegetation and being a highly wind-prone area, the unprotected soils were therefore susceptible to
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severe wind erosion. The immediate concern of the BLM was emergency stabilization of the soil. The BLM’s
seeding treatments started during the fall of 2007 and were completed by spring 2008. In 2009, the RS/GIS
Laboratory began to work with the BLM to develop a remote sensing monitoring program to evaluate the success of
post-fire landscape treatments.
From a remote sensing perspective, the outcome of the BLM’s seeding program was positive. The MODISderived phenological profiles showed that although the vegetation responded slowly at first, the relative greenness
detected in summer months increased compared to pre-fire benchmarks. This response was likely a result of
perennial species (i.e. grasses, forbs, and some shrub seedlings) that were seeded, and/or certain immigrant species
such as Russian thistle (Salsola iberica) that are occupying some areas. From annual cheatgrass density maps, the
amount and distribution of cheatgrass was lower when compared to pre-fire estimates. At the field site scale, we
found that areas that were chained (i.e. mostly juniper sites) had higher greenness in the summer compared to sites
that were aerially seeded. Many of the sites that were drill seeded also had a strong greenness response in summer,
often greater than the greenness exhibited by pre-existing vegetation (i.e. salt desert scrub and low elevation
sagebrush sites). No matter how much greenness increased in each treatment, the greenness peak in all plots
occurred nearly one month later in 2010. The delayed greenness response in 2010 was largely due to climate
variation due to an extended winter and snow cover through April, but the gradual increase in spring values and
continued elevated response through summer was likely a result of the treatments. While the trajectory for recovery
is positive, as our data shows, the response was variable in certain areas, and our results are based mostly on the
vegetation responses in a single year, 2010. Additional monitoring will be required to properly assess the success of
seeding treatments across the MFF landscape.
The 2011 water year will be interesting. The points of concern are mostly related to the favorable conditions for
cheatgrass growth during the 2011 water year. The higher than average precipitation received in the fall of 2010,
may promote early germination of cheatgrass. Greenness recorded in the fall of 2010 was also elevated compared to
pre-fire benchmarks. Temperatures rose statewide in early spring of 2011, followed by additional precipitation.
Fortunately, the response of the treatments appears to be good, evidenced by elevated summer greenness patterns in
2010 at the majority of the MFF field sites.
REFERENCES
Aronoff, S., 2005. Remote sensing basics, In: Remote sensing for GIS managers. S. Aronoff, editor. ESRI Press,
Redlands, CA-USA., pp. 53–67.
Patterns, M.W., and S.R. Yool, 1998. Mapping Fire-Induced Vegetation Mortality Using Landsat Thematic Mapper
Data: A Comparison of Linear Transformation Techniques, Remote Sensing of the Environment, 65(2):132142.
Piwowar, J.M., 2005. Digital image analysis, In: Remote sensing for GIS managers. S. Aronoff, editor. ESRI Press,
Redlands, CA-USA., pp. 288-335.
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