1

United States Department of Agriculture / Forest Service RMRS-RN-43 April 2011

Introduction

Areas at potentially elevated risk of soil erosion and runoff after large, 

severe wildfires are often treated with agricultural straw mulch to protect 

sensitive resources from large-scale erosion events (Bautista and others 

2009; Cerda and Robichaud 2009; Neary and others 2005; Robichaud 2005; 

Robichaud and others 2000). The mulch treatment is ideally applied before 

the first rainfall event, and the rate of coverage should be ~2 Mg ha–1

(1 ton ac–1) (~70% ground cover) (USDA 1995). Mulch treatments can 

be efficiently applied from a helicopter (heli-mulching) as long as wind 

and other weather factors are suitable (Napper 2006). Agricultural straw 

mulch is effective at reducing erosion, especially in the first year after the 

fire (Bautista and others 1996; Groen and Woods 2008; Wagenbrenner and 

others 2006), and at providing short-term soil stabilization before vegetation has a chance to reestablish after the fire (Taskey and others 1989). 

Straw mulch is also cost effective, and it decomposes relatively quick 

without appearing to significantly inhibit native vegetation (Dodson and 

Peterson 2010; Kruse and others 2004) as long as it is thinly spread (less 

than 5 cm, or 2 in deep). A possible negative impact of straw mulch is 

the potential for spreading weeds if the mulch is contaminated (Bautista 

and others 2009; Beyers 2004; Graham 2003). Straw mulch is also easily 

redistributed by wind or water, making it ineffective on steep, exposed 

slopes during high winds (Bautista and others 2009).

After heli-mulching, forest managers commonly question how to measure the treatment coverage and then potentially monitor the longevity 

and effectiveness of the treatment (Napper 2006). Treatment specifications 

are generally checked early in the application process to fine-tune the 

application rate according to weather, wind speed, and mulch moisture 

content. A subsequent check is often made via a helicopter flight but does 

not normally encompass the entire treated area. Post-fire treatments can 

be very expensive ($2000 per ha–1 ($800 ac–1) and up), especially when 

Using QuickBird Imagery to Detect 

Cover and Spread of Post-Fire Straw 

Mulch After the 2006 Tripod Fire, 

Washington, USA

Sarah A. Lewis1 and Peter R. Robichaud2

Rocky Mountain Research Station

Research Note

Abstract—Agricultural straw mulch is a commonly applied treatment for protecting resources at risk from runoff and erosion events 

after wildfires. High-resolution QuickBird 

satellite imagery was acquired after straw 

mulch was applied on the 2006 Tripod 

Fire in Washington. We tested whether the 

imagery was suitable for remotely assessing 

the areal coverage of the straw mulch treatment. Straw mulch was easily identified in 

the imagery because of the distinct spectral 

signature of the mulch against the burned 

background. The measured straw cover on 

the ground was correlated to the modeled 

cover in the imagery with a correlation 

coefficient of r = 0.47, and a rank analysis 

indicated the ability to predict relative 

straw cover amounts on the plots with a 

rank correlation of ρ = 0.40. Better correlations may be possible if the time between 

the image acquisition and field validation 

was shorter. Our results encourage further 

exploration of the use of high-resolution 

imagery for research applications and postfire management.

Keywords: QuickBird, remote sensing, straw 

mulch, Tripod Fire, Burned Area Emergency 

Response (BAER), erosion mitigation

Lewis, Sarah A.; Robichaud, Peter R. 2011. 

Using QuickBird imagery to detect cover 

and spread of post-fire straw mulch after 

the 2006 Tripod Fire, Washington, USA. 

Res. Note RMRS-RN-43. Fort Collins, CO: 

U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 9 p.

1 Civil Engineer, U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station, Moscow, Idaho.

2 Research Engineer, U.S. Department of 

Agriculture, Forest Service, Rocky Mountain 

Research Station, Moscow, Idaho.
2 USDA Forest Service Research Note RMRS-RN-43. 2011

applied over thousands of hectares, and forest managers 

need to ensure that money has been spent appropriately 

and effectively to stabilize the areas at greatest risk (Groen 

and Woods 2008; Robichaud and others 2000).

Remote sensing is a potential solution to this uncertainty 

because of the ability to rapidly acquire a relatively cheap 

and accurate “snapshot” of post-fire conditions. High 

spatial resolution satellite sensors such as QuickBird 

(DigitalGlobe Inc., Longmont, Colorado) and IKONOS 

(GeoEye, Dulles, Virgina) can provide a clear picture of 

ground conditions after the fire that is similar in quality and scale to a traditional aerial photograph (Goetz 

and others 2003) but with the advantage of greater 

spectral information. QuickBird and IKONOS satellites 

collect four spectral bands of data (visible through the 

near infrared region of the electromagnetic spectrum), 

whereas Landsat, which is most often used for post-fire 

mapping, collects seven spectral bands (visible through 

shortwave infrared). The shortwave infrared band is 

highly sensitive to the changes in soil after fire, which 

makes it so useful for mapping burn severity. However, 

the pixel size of a QuickBird image is much finer (2.4-m 

(8-ft) versus 30-m (100-ft) Landsat pixels), allowing for 

greater spatial discrimination of ground components. 

The spatial resolution of QuickBird imagery is more 

closely matched to the spatial variability of the post-fire 

environment, which makes it well-suited for mapping 

fine-scale characteristics such as straw mulch coverage.

QuickBird imagery has been used to help guide post-fire 

treatments, determine immediate post-fire tree mortality, 

and refine soil burn severity maps (Chirici and Corona 

2005; Corona and others 2008). These high-resolution images give users the ability to identify vegetation patches, 

roads, bodies of water, and other resources at risk and 

allow users to assess the color and char condition (green, 

brown, or black) of any remaining vegetation. IKONOS 

was used to measure crop residue on agricultural fields 

with modest success (Bannari and others 2006) using 

a linear spectral unmixing model of soil, crop (green 

vegetation), and residue (senesced crop). This model 

is comparable to a post-fire scene in that there are few 

ground cover components and the major spectral signatures are live and dead vegetation and exposed soil. If a 

treatment is applied, such as straw mulch, it has a similar 

spectral signature to crop residue. To improve results, 

Bannari and others (2006) suggest that the ideal time to 

spectrally discriminate crop residue is early in the growing 

season before there is abundant green vegetation so that 

soil and crop residue are the primary contributors to the 

spectral signature. Thus, the ideal time to detect mulch 

in a post-fire scene is immediately after it is applied and 

before green vegetation has had a chance to re-establish.

Due to the limited scale of this project, QuickBird 

imagery was primarily collected over areas that were 

known to have been burned at high severity, were considered at risk for increased runoff and erosion, and were 

treated with a helicopter-applied mulch treatment. The 

objectives of this study were: 1) to determine if satellite 

imagery could be used to detect straw mulch cover on 

the ground; and 2) to learn whether straw mulch cover 

could be measured with satellite imagery.

Methods

Study Area

Lightning storms ignited the Spur and Tripod Fires 

on 3 July and 24 July 2006, which eventually burned 

together and became the Tripod Complex (called 

the Tripod Fire hereafter) (USDA 2007). The fire was 

declared contained on 31 October 2006 after 71,000 ha 

(175,000 ac) burned. Mean annual precipitation is 

150-300 mm (6-12 in), depending on elevation. Tree 

species composition of the burned areas at the low- to 

mid-elevation was generally mixed coniferous stands 

of ponderosa pine (Pinus ponderosa), Douglas-fir (Pseudotsuga menziesii), and western larch (Larix occidentalis), 

with pockets of Engelmann spruce (Picea engelmannii) 

and lodgepole pine (Pinus contorta). The high-elevation 

montane forests were dominated by Engelmann spruce, 

lodgepole pine, and subalpine fir (Abies lasiocarpa).

A Forest Service Burned Area Emergency Response 

(BAER) team mapped soil burn severity and determined 

emergency stabilization needs within weeks of the fire’s 

containment. Twenty-three percent of the burned area 

was classified as high soil burn severity, 39% as moderate, and 38% as low or unburned. BAER treatments 

prescribed after the Tripod Fire included seeding, fertilizing, and mulching to protect watersheds at risk for 

erosion and runoff (USDA 2007). Wheat straw mulch 

was applied to 5500 ha (13,500 ac) via helicopter at the 

rate of 2 Mg ha–1 (1 ton ac–1) beginning in October 2006. 

Treatment application resumed in June 2007 and was 

completed the first week of July 2007. The majority of 

this straw came from certified weed-free, wheat-grain 

fields in Washington that had inspection reports or from 

Montana, Idaho, and California, which have state weedfree certification programs (USDA 2007).
USDA Forest Service Research Note RMRS-RN-43. 2011 3

Field Sampling

On 8 August and 19-20 October of 2007, 10 field sites 

were sampled for validation of the straw heli-mulching 

application rate. The areas that were known to have high 

straw coverage were the focus areas for the field validation. 

On selected heli-mulched hillslopes, a 60- by 60-m (200- 

by 200-ft) sampling area (called a site) was established 

with nine plots arranged with the slope aspect and across 

the hillslope (fig. 1). The location of the center of each 

site (plot A) was recorded with a GPS (Garmin GPSMap76S, Garmin International Inc., Olathe, Kansas). Within 

~ 5 m (16 ft) of each plot center, five 1- by 1-m (~ 3- by 

3-ft) square subplots were sampled for ground cover. A 

string-gridded plot frame with 100 string intersections 

was laid on the ground, and the ground cover type (mineral soil, vegetation, litter, or straw mulch) was recorded 

at each intersection. The mean ground cover of the five 

subplots was assigned to each of the nine plot locations 

to create a spatial representation of mean ground cover 

types across the hillslope. This sampling scheme was 

repeated on 10 hillslopes, for a total of 90 ground cover 

plots on the Tripod Fire study area.

QuickBird Imagery Analysis

QuickBird imagery was acquired on 26 July 2007 

over an 8000-ha (20,000-ac) portion of the burned area. 

The imagery was delivered as multiple geotiff files that 

were combined into a single, orthorectified image mosaic. 

Preliminary examination of the color (red-green-blue 

color composite) image allowed for the identification 

of the major ground cover components: blackened 

(charred) vegetation, brown (scorched) vegetation, green 

(unburned) vegetation, soil, and straw mulch. The straw 

mulch cover was easily identified because the straw was 

highly visible against the otherwise black background.

Spectral mixture analysis (SMA) was used to model the 

percent cover of the mulch in the treated areas. Spectral 

endmembers for image processing were selected from 

the most homogenous patches of green, scorched, and 

charred vegetation, soil, and straw mulch located in the 

imagery (fig. 2). By examining the spectral signature 

of the purest pixels and given our knowledge of the 

fire area, we were able to select endmember spectra for 

spectral mixture modeling. Once endmember spectra 

were identified, spectral unmixing of individual pixels 

was used to estimate the fractional component spectra 

(spectral fractions) of the pixels (ENVI 2007) and, in turn, 

the physical fractional component of the materials on 

the ground (Adams and others 1985; Roberts and others 

1993; Theseira and others 2003). The outputs of SMA are 

grayscale fractional cover images that are scaled 0 to 1 

(or 0 to 100%). 

Figure 1—The location of the field sites and the field plot layout on the Tripod Fire located on the Wenatchee-Okanogan 

National Forest in north-central Washington State.
4 USDA Forest Service Research Note RMRS-RN-43. 2011

In order to compensate for some of the geolocational 

uncertainty in the imagery associated with locating field 

plots on the imagery (6 to 15 m (20 to 50 ft) accuracy), pixel 

values within a 5-m (16-ft) radius around each plot location were averaged (~24 pixels). These spectral fractions 

were compared to the ground data at the same spatial 

scale to evaluate how well the image model compared 

to the conditions on the ground.

Statistics

Correlations were first assessed between the ground 

cover and the spectral fractions (image model) using 

the Pearson correlation statistic (SAS proc CORR) (SAS 

Institute Inc. 2008) at the site scale (n = 90) and then at 

the plot scale (the mean of five subplots, n = 9). Correlations were considered significant if p<0.05. In order 

to analyze trends in the data and account for the likely 

under-prediction of straw mulch in the imagery, the 

Spearman rank correlation was also calculated. Scatterplots with ground data as the independent variables 

and image data as the dependent variables were used 

to further examine the ground and image relationships. 

Linear regressions (SAS proc REG) (SAS Institute Inc. 2008) 

Figure 2—QuickBird spectral signatures of the five endmembers used in the spectral 

mixture analysis.

were calculated from these data, and linear regression 

estimator lines and coefficients of determination were 

reported on the scatterplots.

Results and Discussion

Detection of Straw Mulch After the 

Tripod Fire

The straw mulch appeared on the image in two patterns: 

wide swaths from the helicopter flight paths, and smaller 

patches from individual straw bales (fig. 3). In the first 

post-fire growing season, minimal vegetation returned to 

the most severely burned areas. The resultant vegetation 

cover averaged only 6% and was not a key contributor to 

the overall color (reflectance) of the scene. The dominant 

spectra in the mulched areas were charred (black) and 

scorched (brown) trees, soil, and straw mulch. These were 

ideal conditions for detection of the mulch because the 

mulch was much lighter and brighter (higher albedo) 

than the charred background, which was primarily dark 

(fig. 2). Fewer spectra contributing to the overall scene 

lends to a higher chance of target spectrum (straw mulch) 

detection (Bannari and others 2006).
USDA Forest Service Research Note RMRS-RN-43. 2011 5

Figure 3—Fractional cover predictions of the straw mulch cover in an area approximately 1 km2 (250 ac) surrounding Site 1. In c), white pixels indicate ~100% straw mulch cover, gray pixels indicate ~50% straw mulch 

cover, and black pixels indicate 0% straw mulch cover. Inset figures: a) a close-up of the pixels with the Site 1 

plots overlaid; and b) a photograph of a typical plot. The plot frame is 1-m2

 (3.3-ft), and the four flags surrounding the rebar are the other subplot locations. 

Predicting Straw Mulch Cover

When all data were analyzed together (n = 90), the 

correlation between straw mulch on the ground and 

modeled in the image was significant (r = 0.47; p<0.001). 

The regression line on the scatterplot (fig. 4a) indicates 

considerably more mulch was measured on the ground 

(range 4% to 94%) than was predicted in the image (2% to 

35%). This result is fairly typical in remotely sensed field 

studies; the image often shows less of the target material 

than is measured on the ground (Lewis and others 2007; 

Mundt and others 2006; Robichaud and others 2007). 

Predicted cover fractions are more likely to be relative 

amounts (for example, the ratio of exposed soil to litter 

or straw mulch cover) rather than absolute values. The 

ranked data (fig. 4b) have a greater scatter than the raw 

data (fig. 4a), but the correlation is similar (ρ = 0.40), 

which indicates there is a significant trend between straw 

mulch cover on the ground and modeled in the image. 

The ranked data provide a relative straw mulch cover 

prediction compared to the other plots (that is, higher 

than or less than), rather than a percent cover.
6 USDA Forest Service Research Note RMRS-RN-43. 2011

Figure 4—Measured straw mulch cover on the ground 

versus straw mulch cover in the image from the SMA for all 

data at the plot scale (n = 90). a) is the raw data (percent 

straw mulch cover); and b) is the ranked data.

When analyzed at the plot level, correlations between 

straw mulch measured in the field and predicted in the 

image had a wide range (r = 0.1 to 0.7). Rank correlations 

(Spearman) also spanned a similar range (ρ = 0 to 0.65) 

and were as strong or stronger on most of the plots. The 

best result from both correlation analyses was on Site 3 

(r = 0.73; ρ = 0.65). On Site 3, plots A and G had the least 

amount of straw measured on the ground and in the 

image, and overall, the ranks of the data show a strong 

agreement (table 1). Plots C and H were the biggest 

outliers (in terms of rank) on this site. Plot C had the 

third smallest amount of measured straw mulch but had 

the second largest (eighth smallest) amount predicted 

in the imagery; however, these values only differed by 

10%. Plot H had the most measured straw (83%), but only 

18% predicted straw (sixth smallest). This site had more 

remaining canopy than most of the sites, and the large 

Table 1—Straw mulch cover on Site 3 at individual plots, sorted 

by lowest to highest measured coverage.

Ground Ground Image Image

Plot (%) (rank) (%) (rank)

A 10 1 6 2

G 12 2 5 1

C 30 3 20 8

F 35 4 9 4

E 38 5 7 3

I 39 6 12 5

B 41 7 18 7

D 75 8 27 9

H 83 9 18 6

under-prediction of straw values was probably a result 

of occlusion of the ground by the canopy.

The percent straw mulch cover and the ranks of the 

straw mulch cover from Site 3 were plotted to visually 

compare the raw and ranked data (fig. 5). The coefficients 

of determination were similar (R2 = 0.53 and 0.42), as 

expected from the correlation analysis. The biggest differences in the scatterplots were the slopes of the linear 

regression estimator lines. By ranking the data, the range 

of the ground and image data are the same (1 to 9); therefore, the slope of the linear regression estimator line is 

similar to an imagined 1:1 line. Whereas on the percentage 

cover graph, the slope was steep (slope = 2.4), highlighting the discrepancy in the range of straw mulch cover 

values found on the ground and in the imagery. Thus, 

the relationship between the percent straw mulch cover 

on the ground and in the image is significant, but the 

ability to predict a percent straw mulch cover accurately 

is weak. The ranked data indicate a relative increasing 

relationship between the straw mulch cover on the ground 

and compared to the amount modeled in the imagery. 

Therefore, the straw cover modeled in the image can be 

validated by field plots that have adequate straw mulch 

cover for erosion mitigation, and the image can then be 

used to determine which areas outside of the field plots 

also have sufficient straw mulch cover.
USDA Forest Service Research Note RMRS-RN-43. 2011 7

and erosion. It has been suggested that 50 to 70% straw 

mulch coverage is sufficient to provide erosion control 

and soil stabilization (Robichaud 2000). Therefore, we 

hypothesize if 50 to 70% of the pixels in the treated area 

of interest have straw mulch cover, the treatments would 

be considered satisfactory.

To test, using our field sites, we extracted pixels in a 

120- by 120-m (400- by 400-ft, 50 by 50 pixels) area centered on each site to evaluate if sufficient mulch had been 

applied. All 10 sites had mulch present in at least 70% 

of the pixels. Nine out of 10 sites had at least 50% mulch 

cover predicted (greater than 15% in image; fig. 4a) in at 

least 70% of the pixels, and 7 out of 10 sites had at least 

70% cover predicted (greater than 20% in image; fig. 4a) 

in at least 70% of the pixels. Thus, our analysis would 

have concluded that the treatment specifications had 

been met in these areas.

It is important to remember the field data were collected 

3 to 12 months after the mulch was initially applied, and 

the image was acquired 3 months before the field data 

were collected. Redistribution and decomposition of 

the straw mulch is likely within this time frame, and 

this study was not implemented to evaluate the mulch 

application, rather to test methods that may be used for 

assessment and monitoring. Ideally, the image would 

be acquired immediately after the mulch was applied, 

and the field data would be collected within days of 

the image acquisition. However, such is rarely the case 

with field studies, and the logistics of a ground-truthing 

campaign often span weeks or months. Potential explanations for inconsistencies between the ground and image 

data include:

	 •	 Slope steepness, which may skew the view angle

from the satellite. 

	 •	 The heterogeneity of the straw mulch distribution—

both from the helicopter’s initial application or from 

the redistribution overtime—can lead to discrepancies between what was measured on the ground and 

predicted in the image. 

	 •	 Geolocation of plots and sites on the ground and

then in the image can cause spatial errors unless 

there is perfect agreement between the locations. Site 

centers were the only locations that were recorded 

with GPS; all other plot locations were calculated. 

Recording all plot and subplot locations would likely 

improve the spatial agreement between the ground 

and image data.

Figure 5—Measured straw mulch cover on the ground versus 

straw mulch cover in the image from the SMA for the data at 

Site 3 (n = 9). a) is the raw data (straw mulch cover); and b) 

is the ranked data.

The ability to predict a relative straw mulch cover is 

potentially useful when considering the efficiency and 

quality of the straw mulch application. It may not be 

necessary to know the exact percentage of straw mulch 

at any given spot on the ground, but rather that straw 

mulch was applied to enough of the area to reduce runoff 
8 USDA Forest Service Research Note RMRS-RN-43. 2011

Conclusion

Straw mulch was easily identified in the QuickBird 

imagery because of the distinct spectral signature of the 

mulch against the burned background. Straw mulch 

coverage rates were predicted by analysis of the imagery 

with a correlation coefficient of r = 0.47, and a rank correlation of p = 0.40. Stronger correlations would probably occur if the image acquisition and field validation 

occurred closer to one another and if the locations of the 

individual plots were more accurately identified on the 

image. The correlation analysis indicated the ability to 

predict relative straw mulch cover beyond the sites where 

explicit straw cover measurements were collected. It is 

encouraging that we were able to identify (presence or 

absence) the straw mulch coverage and estimate high or 

low coverage from the high-resolution imagery.

Management Implications

Our results encourage further exploration of the use 

of high-resolution imagery for research applications and 

post-fire management. It is generally assumed (and minimally field-verified) that the heli-mulch treatment was 

applied as prescribed because it is difficult to measure 

treatment rates due to the spatial extent of the fire and 

remote nature of wildfires. This is unfortunate because 

of the high cost of post-fire erosion control treatments 

and the importance of protecting resources at risk by 

ensuring adequate straw mulch cover is applied to the 

disturbed soil. A remotely sensed image provides a means 

to estimate the treatment coverage and to potentially 

verify that the heli-mulch contract specifications were 

met, especially in large treatment areas. These methods 

probably will not be used in all post-fire treatment situations, but they are potentially useful where remote 

contract validation and/or monitoring are needed. 

The longevity of the treatment application and native 

vegetation response could also be monitored through the 

acquisition of subsequent annual or semi-annual images. 

This study focused on straw mulch, but the analysis techniques would probably apply to other mulch treatments 

such as wood shreds or wood strands or hydromulch.

Acknowledgments

This research project was supported by the USDA Forest Service, Rocky Mountain Research Station and the 

Wenatchee-Okanogan National Forest, Methow Valley, 

and the Tonasket Ranger Districts. We would like to 

thank three internal reviewers who provided thoughtful 

comments that improved the quality of this manuscript.

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Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526
Rocky

 Mountain

 Research Station

The Rocky Mountain Research Station develops scientific information 

and technology to improve management, protection, and use of the 

forests and rangelands. Research is designed to meet the needs of 

the National Forest managers, Federal and State agencies, public and 

private organizations, academic institutions, industry, and individuals. 

Studies accelerate solutions to problems involving ecosystems, range, 

forests, water, recreation, fire, resource inventory, land reclamation, 

community sustainability, forest engineering technology, multiple use 

economics, wildlife and fish habitat, and forest insects and diseases. 

Studies are conducted cooperatively, and applications may be found 

worldwide.

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Rocky Mountain Research Station

240 W Prospect Road

Fort Collins, CO 80526

(970) 498-1100

Research Locations

Reno, Nevada

Albuquerque, New Mexico

Rapid City, South Dakota

Logan, Utah

Ogden, Utah

Provo, Utah

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Flagstaff, Arizona

Fort Collins, Colorado

Boise, Idaho

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Missoula, Montana