Analysis of MASTER Thermal Data in the 

Greeley Area of the Front Range Urban 

Corridor, Colorado—Delineation of Sites 

Infrastructure Resource Characterization 

U.S. Department of the Interior 

U.S. Geological Survey 

U.S. Geological Survey Bulletin 2196 
Analysis of MASTER Thermal Data in the 

Greeley Area of the Front Range Urban Corridor, 

Colorado—Delineation of Sites for 

Infrastructure Resource Characterization 

By K. Eric Livo and Ken Watson 

U.S. Geological Survey Bulletin 2196 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

Gale A. Norton, Secretary 

U.S. Geological Survey 

Charles G. Groat, Director 

U.S. Geological Survey, Reston, Virginia: 2004 

This report is only available on-line at: http://pubs.usgs.gov/bul/b2196


For more information about the USGS and its products:


Telephone: 1-888-ASK-USGS


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Any use of trade, product, or firm names in this publication


is for descriptive purposes only and does not imply endorsement


by the U.S. Government


Although this report is in the public domain, it contains copyrighted 


materials that are noted in the text. Permission to reproduce those 


items must be secured from the individual copyright owners.

Contents 

Abstract.................................................................................................................................................. 1


Introduction ........................................................................................................................................... 1


Geologic Setting ................................................................................................................................... 2


Data Sets................................................................................................................................................ 2


Image Setting ........................................................................................................................................ 3


Methodology ......................................................................................................................................... 3


Classification of TIR Data.................................................................................................................... 4


Field Examination.................................................................................................................................. 6


Examination of Spectra ....................................................................................................................... 8


Distribution and Analysis of Classes Within the Flightline 5 Image............................................. 10


Summary ................................................................................................................................................ 11


References Cited .................................................................................................................................. 12 


Figures 

1. 	 Diagram showing MASTER convolved inverse wave emissivity spectra for groups of 


common rock-forming minerals ................................................................................................. 2


2. 	 MASTER color-infrared-composite image, Front Range Urban Corridor, 


South Platte and St. Vrain Rivers............................................................................................... 4


3. 	 Diagram showing temperature on the Platte River at the Englewood Gage station on 


the day of the overflight ............................................................................................................. 5


4. 	 Diagram showing model atmosphere parameter values derived from water radiance 


measurements .............................................................................................................................. 5


5. 	 Diagram showing observed radiance versus computed radiance for 


water and ground ......................................................................................................................... 5


6. 	 MASTER inverse wave emissivity images showing variation of intensities with 


channel number............................................................................................................................ 6


7. 	 MASTER temperature image, Front Range Urban Corridor, South Platte and 


St. Vrain Rivers.............................................................................................................................. 7


8. 	 MASTER classification image (soils), Front Range Urban Corridor, South Platte and 


St. Vrain Rivers.............................................................................................................................. 7


9. 	 Diagram showing inverse wave emissivity spectra of unsupervised soil and 


vegetation classes ....................................................................................................................... 8 


10. AVIRIS imaging spectroscopy clay and mica map, South Platte and St. Vrain Rivers .... 9


11. 	 Diagram showing MASTER convolved emissivity spectra for quartz, potassium 


feldspar, and clay ......................................................................................................................... 10


12. 	 Diagram showing comparison between observed MASTER spectrum from an area in 


a sand and gravel quarry with a linear mixture of laboratory spectra................................ 10


Tables 

1. MASTER system ........................................................................................................................... 3


2. AVIRIS system............................................................................................................................... 3


3. Color coding for isoclus image................................................................................................... 8


II

Analysis of MASTER Thermal Data in the 

Greeley Area of the Front Range Urban Corridor, 

Colorado—Delineation of Sites for Infrastructure 

Resource Characterization 

By K. Eric Livo and Ken Watson 

Abstract 

Sand and soils southwest of Greeley, Colorado, were characterized for mineral composition and industrial quality. Radiance data from the thermal channels of the MASTER simulator 

were calibrated using estimated atmospheric parameters. Channel emissivities were approximated using an estimated ground 

temperature. Subsequently, a decorrelation algorithm was used 

to calculate inverse wave emissivity images. Six soil classes, one 

vegetation class, water, and several small classes were defined 

using an unsupervised classification algorithm. Ground covered 

by each of the derived emissivity spectral classes was studied 

using color-infrared air photos, color-infrared composite MASTER data, geologic maps, NASA/JPL Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data, and field examination. 

Spectral classes were characterized by their responses and 

related to their mineral content through field examination. 

Classes with a minimum at channel 44, and having a similar 

spectral shape to quartz, field checked as containing abundant 

quartz. Classes with a minimum at channel 45, and having a 

spectral shape similar to the sheet minerals, were found in the 

field to contain abundant mica and clay. Sandy soil was found to 

have a positive slope at the longer wavelengths; the more clay 

rich soils had a negative slope. Spectra with a strong downturn 

at channel 50 generally indicated low vegetation cover, whereas 

an upturn indicated more vegetation cover. 

Mapping revealed a range of classified soils with varying 

amounts of quartz, silt, clay, and plant humus. Sand and gravel 

operations along the St. Vrain River, gravel lots, and some fields 

spectrally classified as quartz-rich sands were confirmed through 

field examination. Other fields mapped as sandy soils, ranging 

from quartz-rich sandy soil to quartz-rich silt-sand soil with clay. 

Flood plains mapped as sandy-silty-organic-rich clay. 

The city of Greeley contained all classes of materials, with 

the sand classes mapping as various types of asphalt. Abundant 

quartz gravel was apparent within the asphalt during field checking. The clay classes mapped silt-clay soils in areas of irrigated 

grass landscaping, some fields, and roofing materials. 

Introduction 

An important focus of current studies in the U.S. Geological Survey National Infrastructure Project is the characterization 

of the mineral composition and industrial quality of construction 

materials, particularly sand and gravel deposits. One of the 

areas chosen for this investigation is the Front Range Urban 

Corridor of Colorado. This study focuses on data acquired along 

the South Platte and St. Vrain River drainage from Denver to 

Greeley, Colo. Remotely sensed data, primarily multispectral 

thermal infrared, supplemented with broadband reflectance data 

for vegetation information and reflectance imaging spectroscopy 

data for mica and clay identification, were used in the study. 

The thermal-infrared (TIR) region of the electromagnetic 

spectrum (3–35 μm) contains diagnostic spectral features of the 

most common rock-forming minerals (fig. 1) and is an ideal wavelength region to collect remote sensing data for surficial studies. 

A new airborne system called the MODIS/ASTER (MASTER) 

airborne simulator, a joint development involving NASA and the 

U.S. Geological Survey (USGS EROS Data Center), provides the 

means to acquire 10 channels of TIR data (Hook, Myers, Thome, 

and others, 2001). MASTER data have been acquired along seven 

flightlines in the Front Range Urban Corridor of north-central 

Colorado. This study reports on the initial interpretation of data 

acquired along a northeast line of about 40 km from Interstate 25 

to Greeley following the St. Vrain and South Platte River drainages. This urban corridor is an area of multiple use that includes 

agricultural land and open space, surface and ground-water 

bodies, sand and gravel mining, coal mining, gas and oil production, and land development. The data presented here provide surficial characterization of these areas that can be used to provide 

informed land-use policies for multiple use. 

The diagnostic spectral features of common rock-forming 

minerals in the TIR occur due to the presence of the fundamental 

vibrational frequencies of silicate and carbonate molecules and 

can be detected in the spectral emissivity (the TIR equivalent of 

spectral reflectivity) of a material. Laboratory studies have provided fairly extensive databases (Christensen and others, 2000, 

among others) that can be used to analyze TIR remote sensing 

data. The utility of these data depends on several issues: (1) spatial resolution (pixel size), (2) spectral resolution (number of 

channels), and (3) data analysis methods. The MASTER instrument used in this study is an airborne system that limits the data 

availability to a limited number of evaluation sites (http:// 

masterweb.jpl.nasa.gov/). The MASTER data in this study were 

acquired at a ground resolution of 8 m. Global satellite data are 

available from the Moderate Resolution Imaging SpectroRadiometer (MODIS) (http://modis.gsfc.nasa.gov/) and from the 

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (http://asterweb.jpl.nasa.gov/), but with a 

coarser spatial (MODIS: 1,000 m; ASTER: 90 m) and spectral 

resolution than MASTER data have. Although the spatial resolution of thermal satellite data is currently too low for most 

infrastructure projects, improvements are occurring rapidly. The 

10-channel spectral data from MASTER represent an 
0 

-1 

-2 

pyroxenes 

alkali feldspars 

calcic feldspars 

intermediate feldspars 

sheet 

amphiboles 

olivines 

quartz 

41 42 43 44 45 46 47 48 49 50 

MASTER CHANNEL 

Figure 1. MASTER convolved inverse wave 

emissivity spectra for groups of common rockforming minerals. The algorithm to compute the 

inverse wave emissivity from emissivity is described in the text. Generally the spectra are very 

similar in appearance to emissivity spectra because the algorithm only applies a slight tilt to the 

shape. This illustration is also online at http:// 

speclab.cr.usgs.gov/PAPERS/Bulletin-2196_figs/ 

index.html. 

improvement over the 5 channels of ASTER and the 6 channels 

of the previous airborne Thermal-Infrared Multispectral Scanner 

(TIMS) system (Palluconi and Meeks, 1985), but the MASTER 

resolution remains considerably less than in some experimental 

airborne systems (such as Spatially Enhanced Broadband Array 

Spectrograph System (SEABASS) with >200 channels (http:// 

www.aero.org/home.html)). Analysis techniques are also continually being developed and refined to take advantage of 

improvements in instrument development. In this study we 

employ a new algorithm that enhances spectral contrast and 

reduces errors introduced by temperature changes, and we display the results using an unsupervised classification algorithm. 

This classification algorithm has the advantage that it does not 

require collection of ground spectra to guide the selection of 

classes. 

INVERSE WAVE EMISSIVITY (OFFSET) 

Geologic Setting 

The study area is located north of Denver, Colo., on the 

high plains just east of the mountain front. The gently eastward 

sloping sedimentary terrain is cut by numerous streams and rivers that join the South Platte River throughout the area, bringing 

erosional detritus from the Precambrian and Tertiary rocks 

exposed within the interior of the Front Range mountains. The 

South Platte River carries this sand and gravel towards the Missouri River within its broad flood plain. 

The South Platte River system in this area is composed of 

braided stream river channels set within a broad flood plain cut 

into the surrounding sedimentary bedrock, with banks containing several ages of Quaternary terraces and a pediment upland 

surface. Paleochannels have been cut to a depth of more than 30 

m below the present channel within and near the present river 

course. Channel depths can reach 18 m; terrace deposits contain 

as much as 12 m of silt, sand, and gravel, and the pediment surfaces contain a thin layer of alluvium. South of the study area, 

the South Platte system is being mined for aggregate, but within 

the area, the gravel is too sparse for present economic recovery. 

The St. Vrain River joins the South Platte near the center of 

the study area, transporting a coarser matrix of sand and gravel 

that is presently being mined in shallow aggregate open-pits 

within the flood plain. The sand and gravel are derived a short 

distance away, from granitic, volcanic, and metamorphic terranes within the mountains and from sedimentary rocks eastward of the mountain front. 

Local bedrock is composed of Cretaceous age sedimentary shales and sandstones that dip gently eastward: the Fox 

Hills Sandstone on the west is progressively overlain by the 

Laramie and Arapahoe Formations. The Fox Hills Sandstone 

was deposited as beach sands within a deltaic environment as 

regional uplift shifted the inland sea shoreline eastward. The 

clay matrix of the Fox Hills Sandstone is principally kaolinite. Muds and sands of the Laramie followed on the emerging 

coastal plain where lush vegetation growth later became coal. 

Within the study area, clays of the Laramie Formation are principally montmorillonite. Conglomerates of the Arapahoe Formation contain Precambrian rocks derived from the uplifting 

Front Range. 

Unconsolidated sediments overlie most of the bedrock in 

the study area. These Quaternary sediments occur as alluvium 

within stream channels, colluvium along the steeper flanks of the 

stream valleys, and eolian deposits of sand and silt within the 

upland areas. Channel alluvium generally is a variable composition of gravel, sand, silt, clay, and organic matter. Colluvium 

consists of sandy silt and clay, and eolian material forms sand 

dunes and loess deposits downwind from flood plains. 

Data Sets 

The primary data source for this study was the MASTER 

system (table 1). 

See http://masterweb.jpl.nasa.gov for the central wavelength and bandwidth of the 50 channels. Thermal emissivity 

and temperature were obtained using channels 41–50 of the 

2 MASTER Thermal Data and Infrastructure Resource Characterization 
Table 1. MASTER system.


[flight: 9/21/99]

Spectral channels: 50 (16-bit resolution). 10 TIR channels

TIR Channel : Effective Wavelength

41 : 7.75 μm 

42 : 8.17 μm 

43 : 8.61 μm 

44 : 9.05 μm 

45 : 9.68 μm 

46 : 10.09 μm 

47 : 10.62 μm 

48 : 11.32 μm 

49 : 12.11 μm 

50 : 12.88 μm 

Instantaneous field of view: 2.5 mrad 

Ground resolution: nominally 12–50 meters (variable with altitude). 

8 meters was obtained for this study. 

Total field of view: 85.92 degrees 

MASTER data as described in the following section. In addition 

channels 2, 5, 8 of the MASTER data were used to make a 

Color-Infrared-Composite (CIC) image for examination of vegetation cover. 

Color infrared air photos (10 in. × 10 in.) were acquired 

simultaneously with the MASTER data. These were rolled 

transparencies with overlapping frames yielding full stereo coverage of the flightline. 

The mica and clay identification data source for this study 

was the NASA–JPL Airborne Visible and Infrared Imaging 

Spectrometer (AVIRIS) system (table 2). 

See http://aviris.jpl.nasa.gov for further specifications. A 

map of clay and mica minerals developed from AVIRIS data 

analysis was used to assist in characterizing soils. (See Clark, 

Swayze, and Gallagher, 1993.) 

Image Setting 

Line 5 of the MASTER data is centered on the St. Vrain 

and South Platte River drainage, extending from I-25 and the St. 

Vrain River on the southwest (bottom of image) to the city of 

Greeley on the northeast, as shown on the color-infrared-composite image (fig. 2). Features within the image include sand 

and gravel operations (A) that are located along the St. Vrain 

River at the bottom (south) of the image area, the Fort St. Vrain 

power plant (B) located at the confluence of the St. Vrain and 

South Platte Rivers—a third of the way up from the bottom of 

the image, and upland fields that are south of the South Platte 

River (C) halfway up the image on the right. A large feedlot (D) 

is located on the south side of the confluence of the Big Thompson and South Platte Rivers—two-thirds of the way up the 

image, and the city of Greeley (E) is at the top. 

Table 2. AVIRIS system. 

[flight: 7/1/99] 

Spectral channels: 224 (16-bit resolution). 

Ground resolution: nominally 20 meters (variable with altitude). 

Flight altitude: 20 kilometers

Pixels/scanline: 640

Wavelengths: 0.4–2.5 micrometers

Methodology 

The thermal radiation detected at the aircraft by the MASTER instrument can be expressed by the following: 

Ri = τi

{εiBi(T) + (1 - εi)Hskyi} + Hpathi i =41,...50 

where 

i - MASTER channel number 

Ri - radiance detected at aircraft 

τi - transmission in the ith channel from the ground to the 

aircraft 

T - ground temperature 

Bi

(T) - Planck function where Bi(T)=c1ec2/λT/λ5(1- ec2/λT) 

λ is the wavelength of the ith channel, and c1,c2 are the 

radiation constants 

Hskyi - hemispherical downward radiance from the sky on 

the ground 

Hpathi

 - radiance from the atmosphere in the path from the 

ground to the aircraft 

εi

 is the spectral emissivity of the ground. 

If the atmospheric properties are known (τ, Hsky, Hpath) 

either from an atmospheric modeling program such as MODerate resolution TRANSsmittance (MODTRAN) (http:// 

www2.bc.edu/~sullivab/soft/modtran4.html) or National Centers 

for Environmental Prediction (NCEP) (http:// 

www.ncep.noaa.gov/) or from in situ measurements, then we 

have 10 equations with 11 unknowns: the 10 εi's and T. A 

number of different schemes have been proposed to resolve this 

dilemma, none of which is completely satisfactory. In this study 

we use the following method. We first estimate the ground temperature, then compute the emissivity, and lastly apply a decorrelation algorithm to minimize the error introduced by estimating 

the temperature (Watson, 1997). The steps are outlined as 

follows: 

Introduce Fcori={Ri -Hpathi}/τi =εiBi(T)+(1-εi)Hskyi. 

We then compute the brightness temperature Θi

 such that 

Bi

(Θi)=Fcori and estimate T using Test=max(Θi). Note that if εi 

=1 for any i then the solution is exact. 

We then use Test to estimate ε : 

εesti

={Fcori -Hskyi}/{Bi(Test)-Hskyi}. 

Lastly we decorrelate a λ-1 dependence from εest as follows: 

Let ε2i=εesti -α/λ-β

where α=(10*S4 -S1 *S3)/S2 

β=(S3 -α*S1)/10. The term ε2i will be called the inverse 

wave emissivity to distinguish it from the εesti

 and εi

. 

The sums S1,S2,S3,and S4 are determined: 

Methodology 3 
Figure 2. MASTER color-infrared-composite image, Front Range Urban Corridor, South Platte and St. Vrain Rivers. A 

higher resolution image is also online at 

http://speclab.cr.usgs.gov/PAPERS/ 

Bulletin-2196_figs/index.html. 

S1 =Σλi 

S1a =∑λ-1 

i


S2 =S1a*10-S1 

2

S3=∑εi 

S4 =∑εi/λi. 

Finally it remains to show why decorrelating a λ-1 dependence from εest reduces the temperature error. Let us start with 

the standard equation that defines emissivity: 

εi=Ri /Bi (T) 

and introduce εoi=Ri/Bi(To) where T=To+δ. To is the estimated temperature and δ is a small error term. 

Then εi=Ri/Bi (To+δ). 

Now using a Taylor's expansion 

Bi (To+δ)=Bi (To){1+δ*B-1∂B/∂T+ ....} 

and since ∂B/∂T ~ -c2 *Bi (To)λ*To 

2 

thus εi ~εoi{1-c2δ/λTo 

2

}~ εoi-γ/λ

where γ =c2δ/To 

2 

. 

Hence if we decorrelate a 1/λ dependence out of εi we 

remove the temperature error (at least to first order). 

In this study we estimated the atmospheric parameters 

using the NCEP atmospheric model and then further refined 

these values by comparing the computed inverse wave emissivity for the Platte River with the inverse wave emissivity of tap 

water (Salisbury and D’Aria, 1992), using the water temperature 

measured at the Englewood Gage station (fig. 3). The two atmospheric models are compared in figure 4, and a plot of mean 

observed radiances for water and terrain are compared to fluxes 

computed at the aircraft for water and terrain using the refined 

atmospheric model, assumed temperatures, and a range of emissivities (fig. 5). The fit for water observations appears good. 

The fit for the ground observations is somewhat suspect at channel 41 (which appears to be low) but otherwise satisfactory. 

These atmospheric parameters were then used to compute a 10-

channel inverse wave emissivity image (fig. 6) and a single 

channel estimated temperature image (fig. 7). 

Classification of TIR Data 

An unsupervised classification algorithm called isoclus 

(PCI Inc., Richmond Hill, Ontario, Canada) was then applied to 

the 10-channel inverse wave emissivity image in order to extract 

a distinct set of 15 spectral classes. Six soil classes (2, 3, 4, 6, 9, 

11), one vegetation class (8), water (class 10), and several 

classes (1, 5, 7, 12, 13, 14, 15) associated with man-made structures were identified. An image was prepared to display the 

classification of soils (fig. 8) using the color coding scheme 

shown in table 3. The spectra of the soil, vegetation, and water 

classes are plotted in figure 9. The image was then examined 

using both field reconnaissance and information from existing 

maps and images, and the spectra were compared to mineral 

spectra (fig. 1). 

Ground covered by each of the 15 derived spectral classes 

was studied using color-infrared air photos, color-infrared composite MASTER data (channels 8, 5, 2: RGB), field examination, the AVIRIS clay/mica image, and geologic maps to make a 

preliminary identification of each class’s composition and spatial distribution. We also examined the use of singular value 

4 MASTER Thermal Data and Infrastructure Resource Characterization 
20 10,000 

19 

18 

9,000 

17 

16 

15 8,000 

14 

0 10 20 

TIME (MDT) 

Figure 3. Temperature on the Platte River at the Englewood Gage 


station on the day of the overflight. The gaging station is on MASTER 7,000


line 4 (not shown in report). This illustration is also online at http://


speclab.cr.usgs.gov/PAPERS/Bulletin-2196_figs/index.html. 


1.0 4,000


0.9


0.8 

0.7 41 42 43 44 45 46 47 48 49 50 

0.6 2,000 MASTER CHANNEL 

0.5 

0.4 1,000 Figure 5. Observed radiance (solid) versus computed radiance 

0.3 (dashed) for water and ground. Computed radiance used atmospheric 

3,000 6,000


ε=0.91 

ε=0.85 

ε=0.98 

ground terrain 

water 

Solid—Observed 

Dashed—Computed 

EXPLANATION 

7 8 9 10 11 12 13 7 8 9 10 11 12 13 model (fig. 4) and emissivities indicated on the figure. This illustration is Wavelength μm Wavelength μm also online at http://speclab.cr.usgs.gov/PAPERS/Bulletin-2196_figs/ 

4,000 

-0.1 

7 8 10 11 12 13 

Wavelength μm 

9 

index.html. 

3,000 EXPLANATION decomposition (Press and others, 1992), using a linear mixing 

2,000 Solid—Fitted model model for the class 11 spectrum to estimate relative abundances 

1,000 

Dash—NCEP model as an aid to advance our understanding of material identification. 

Color-infrared photography and composite MASTER 

0 images, both acquired during the MASTER flight, yielded timedependent information on the distribution and general intensity 

-0.2 of vegetation throughout the area, and whether the vegetation 40 was crop, riparian, or urban landscaping. Many fields had 

exposed soil following fall harvest, greatly enhancing soil characterization in the autumn 1999 images. Air photos yielded spa20 tial detail for locating test sites, and geologic maps supplied 

preliminary composition and geomorphic features for integration with the color-infrared images in order to make preliminary 0.0

78 9 10 11 12 13 

0

78 9 10 11 12 13 materials identification of the classes. 

Wavelength μm Wavelength μm AVIRIS analysis used a field site to calibrate the radiance 

data to absolute reflectance relative to ground (Clark, Swayze, Figure 4. Model atmosphere parameter values (solid lines) derived 

from water radiance measurements. Dashed lines show the National Heidebrecht, and others, 1993). Spectral analysis using the U.S. 

Centers for Environmental Prediction model parameters for comparison. Geological Survey Tetracorder algorithm identified the clay and 

Wavelengths (x-axis) in micrometers. Significant differences between mica mineralogy (Clark, Swayze, and Gallagher, 1993). The 

atmospheric models can be seen in the first channel in all plots. This il- results for a portion of the study are shown in the mica-clay 

lustration is also online at http://speclab.cr.usgs.gov/PAPERS/Bulletin- image (fig. 10). Muscovite occurs in the rolling terrain on the 

2196_figs/index.html. northwest, muscovite with kaolinite in the sand and gravel 

Classification of TIR Data 5 

MASTER 

9/21/99 Englewood Gage, S. Platte R. 

TEMPERATURE, IN DEGREES CELSIUS 

Transmission 

Angular path radiance factor 

RADIANCE (mW/m2.ster.μm) 

Sky radiance 

mW/m2.ster.μm 

Path radiance Angular transmission mW/m2.ster.μm factor 
CHAN. 41 CHAN. 42 CHAN. 43 CHAN. 44 CHAN. 45 CHAN. 46 CHAN. 47 CHAN. 48 CHAN. 49 CHAN. 50 

Figure 6. MASTER inverse wave emissivity images showing variation of intensities with channel number. A higher resolution image is also 

online at http://speclab.cr.usgs.gov/PAPERS/Bulletin-2196_figs/index.html. 

quarries, montmorillonite in the upland fields, and kaolinite and 

muscovite southeast of the upland fields (Livo and others, 1999). 

Field Examination 

Mineral composition of the classes was identified through 

field examination. Sites were selected within homogeneous 

class areas and visually characterized for major quartz, feldspar, silt (particle size), clay (mineral), mica, and mafic minerals, as well as organic debris. Surface vegetation was also 

noted. The study area was traversed from the southwest to the 

northeast over a period of several days in the autumns of 2000 

and 2001, when exposed ground conditions would be seasonally comparable to those during the overflight. Differences 

were observed that were due to changes in ground cover as a 

result of differences in crop plantings between the dates of data 

acquisition (Sept. 21, 1999) and field examination. These were 

identified in discussions with individual farmers. Sites were 

examined in relationship to bare ground as exposed during the 

overflight on September 21, 1999. 

Generally, the soil ranged from a quartz-potassic feldsparrich sand and gravel (class 11) to silty sand (class 9), through a 

silt-sand with clay (class 6), a clayey silt-sand with humus (class 

4), sand-silt organic-rich clay (classes 3 and 2), and a vegetated 

ground (class 8). The unsupervised classification separates these 

soils into discrete units with arbitrary boundaries, whereas field 

and data examinations show a continuous variation between soil 

types. We discuss the classes in the order just described. 

Class 11 was visually identified as being a very fine 

grained quartz-rich sandy soil with potassic feldspar and some 

muscovite and silt. The soil occurs in upland fields southeast of 

the South Platte River. The eolian silts and sands are derived 

from the St. Vrain and South Platte Rivers. The soil is very friable, does not clump well, and contains little plant detritus. 

This class contains coarser sand and gravel that form sandbars, 

sandy ground, and gravel deposits along the river drainage. It is 

also a constituent of asphalt road surfaces, many gravel roads, 

most gravel lots, and the majority of asphalt parking lots in the 

town of Greeley. Class 11 grades into class 9. 

Class 9 was found to be a very fine grained quartz-rich siltsand soil with some clay, potassic feldspar, and muscovite. The 

soil occurs in upland fields south of both the St. Vrain and South 

Platte Rivers along with eolian silts and sands derived from 

these rivers. The soil is friable, poorly clumps, and contains little plant detritus. This class represents the majority of asphalt 

roadways and some gravel roads. Road classification will either 

occur in class 9 or in class 11. Class 9 grades into class 6. 

Class 6 is a very fine grained quartz-rich silt-sand soil with 

clay and plant humus. The soil occurs throughout the area, 

especially in the upland fields and in regions near riverbanks. 

Examined field sites have very similar soils as class 9, with the 

6 MASTER Thermal Data and Infrastructure Resource Characterization 
Figure 7. MASTER temperature image, 

Front Range Urban Corridor, South Platte Figure 8. MASTER classification image 

and St. Vrain Rivers. A higher resolution (soils), Front Range Urban Corridor, 

image is also online at http:// South Platte and St. Vrain Rivers. A highspeclab.cr.usgs.gov/PAPERS/Bulletin- er resolution image is also online at http:/ 

2196_figs/index.html. /speclab.cr.usgs.gov/PAPERS/Bulletin2196_figs/index.html. 

Field Examination 7 
 Table 3. Color coding for isoclus image. 

Class 2 Red 

Class 3 Green 

Class 4 Blue 

Class 6 Magenta 

Class 8 Black 

Class 9 Yellow 

Class 10 Black 

Class 11 Cyan 

Class 1,5,7,12–15 Black 

addition of plant humus and clay. The soil clumps into hard dry 

balls. In upland fields, Class 6 occurs in fields of class 8 vegetation as bare soil between plants. In Greeley, class 6 maps streets 

where the spectral response appears to be integrated between 

asphalt, roofs, and trees. Class 6 grades into class 4. 

Class 4 was not examined independent of class 3 in the 

field but was observed to grade into class 3 on the classification 

image. 

Class 3 is a sandy-silty-organic-rich clay soil that occurs as 

over-bank fill in flood plains, riverbanks, and stockyards, and as 

soil in turf farms and irrigated grass landscaping. This class is 

the dominant soil in the riparian areas of the St. Vrain and Platte 

River drainages and has open canopy vegetation that may 

explain the upturn in channel 50 similar to class 8 vegetation 

spectra (fig. 9). Class 3 grades into class 2. 

Class 2 is a sandy-silty-organic-rich clay soil very similar 

to class 3. It occurs in areas that are marshy during part of the 

year, such as flood plains, drainage ditches, and irrigated fields, 

where trees, rushes, and other high-chlorophyll plants are abundant. In Greeley, roofing materials and some fields were identified in class 2. 

Class 8 contains abundant vegetation, generally closed canopy cover, occurring in the upland fields as crops and trees and 

in the city of Greeley as trees and other landscaping. 

Class 10 is water and water-bearing soil, including the 

South Platte and St. Vrain Rivers, ponds, and marshes with 

exposed water. Fields that most likely were heavily irrigated 

were also mapped, but not field checked. 

Examination of Spectra 

In the thermal infrared wavelength region, minerals emit 

electromagnetic radiation with diagnostic spectral shapes caused 

by molecular vibrations. The laboratory spectra for quartz, olivines, amphibolites, sheet minerals (mica, clay, and chlorite), 

feldspars, and pyroxenes are shown (convolved to MASTER 

wavelengths and converted to inverse wave emissivities) in 

figure 1. Quartz has a strong asymmetric minimum centered at 

channel 44, with steep slopes, a broad maximum at the longer 

wavelengths past the minimum, and a downturn at channel 50. 

The sheet minerals (mica, clay, and chlorite) generally have a 

100 

0 

-100 

-200 

class 2 

3 

4 

6 

8 

9 

10 

11 

41 42 43 44 45 46 47 48 49 50 

MASTER CHANNEL 

Figure 9. Inverse wave emissivity spectra 

of unsupervised soil and vegetation classes. Class spectra were defined by the unsupervised classification algorithm. This 

illustration is also online at http:// 

speclab.cr.usgs.gov/PAPERS/Bulletin2196_figs/index.html. 

maximum at channel 42, a minimum at channel 45, and a spectrum that flattens at the longer wavelengths. Feldspars have 

flattened spectra with multiple minimums: at channels 43 and 45 

for alkali feldspars, at channels 43 and 45-46 for intermediate 

feldspars, and at channels 43 and 47 for calcium feldspars. 

Pyroxenes have minimums at channels 44 and 47-48 whereas 

amphiboles have a minimum at channel 46. Olivines have an 

asymmetric minimum centered at channel 47 and shoulders at 

channels 45 and 48. 

Eight spectral classes proved useful in characterizing the 

most abundant surface materials within the MASTER data. Six 

soil spectral classes (classes 2, 3, 4, 6, 9, and 11: red, green, blue, 

magenta, yellow, and cyan), the vegetation class (class 8), and 

the water class (class 10) have distinct spectral emissivity characteristics that can be related to their material content. 

Comparisons of these distinct features with laboratory mineral 

spectra help explain the features and their meaning in terms of 

material composition. 

We begin with an empirical examination of the class spectra 

(fig. 9) to discover the most significant features. The top six spectra in figure 9 (classes 2, 3, 4, 6, 8, and 9) have a similar shape with 

a maximum at channel 43, a minimum at channel 45, and some 

variation in the shape at wavelength channels greater than 47. 

Class 11 (fig. 9), distinctly different from the other classes, 

exhibits a sharp decrease to the minimum at channel 44, a rise 

to a maximum centered at channels 48 and 49, and a strong INVERSE WAVE EMISSIVITY (OFFSET) 

8 MASTER Thermal Data and Infrastructure Resource Characterization 
Figure 10. AVIRIS imaging spectroscopy clay and mica map, South Platte and St. Vrain Rivers. A higher resolution image is also online 

at http://speclab.cr.usgs.gov/PAPERS/Bulletin-2196_figs/index.html. 

downturn at channel 50. This spectrum is similar to the quartz 

spectrum seen in figure 1. Class 9 has a shape intermediate 

between class 11 and class 6 with a broad minimum at channels 44 and 45. This suggests a quartz-rich soil with mica and 

(or) clay. The channel 45 minimum displayed by the other soil 

classes (2, 3, 4, and 6) corresponds to the minimum that can be 

observed in the sheet mineral spectrum (fig. 1) and indicates 

the presence of mica and (or) clay minerals. Class 4 (which 

was lumped with class 3 in the field investigation) appears to 

differ only at channel 50, suggesting that the difference 

between the two classes is due to a greater vegetation component in class 3 (fig. 9). 

The spectral character at the higher channels (fig. 9) 

appears quite diagnostic for material classification. Classes with 

a downturn at channel 50—2, 4, 6, 9, and 11—generally have 

low vegetation cover while classes with an upturn—3, 8, and 

10—are soil with grasses and other open canopy vegetation 

(class 3), closed canopy vegetation (class 8), and water (class 

10), based on field observation. Sandier soils (classes 6, 9, and 

11) have a positive slope at the longer wavelength channels (45– 

49) whereas the more clay rich soils (classes 2, 3, and 4) have a 

negative slope (fig. 9). Class 10, which we associate with water, 

has a flattened spectrum. The water spectrum has a significant 

upturn at channel 50 that is not inconsistent with vegetation 

(mixed water-vegetation pixels). 

MASTER soil class spectral features are formed from a 

combination of mineral spectra. An example is the sand and 

gravel class (class 11: cyan). Field examination of material in 

this class revealed a quartz-rich sandy soil with potassic feldspar 

and some muscovite and silt. Thus we expect to find a combination of these mineral spectra in the spectra of the class. A model 

with a linear combination of the spectra for the minerals quartz, 

potassium-feldspar, and clay (fig. 11) was least squares fitted 

using single value decomposition to a subset of class 11 representing the sand and gravel quarry near the bottom of the image. 

Figure 12 shows a comparison between the fit and the observed 

spectra. The abundances of the fitted spectrum: 55 percent 

quartz, 20 percent potassium-feldspar, 23 percent clay (and (or) 

mica and (or) chlorite), with a 41 percent spectral scaling (flattening) factor correlated well with the field-examined quartz 

arkose with muscovite, a trace of mafic minerals, and little clay. 

Results of linear spectral mixing of minerals for the other classes 

Examination of Spectra 9 
1.0 1.0 

0.9 

quartz 

potassium feldspar 

clay 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

41 42 43 44 45 46 47 48 49 50 

MASTER CHANNEL 

Figure 11. MASTER convolved emissivity spectra for quartz, potassium 

feldspar, and clay. The feldspar and clay spectra are averages computed from several mineral spectra. This illustration is also online at http:// 

speclab.cr.usgs.gov/PAPERS/Bulletin-2196_figs/index.html. 

were not conclusive. For the other classes, no models with a linear combination of mineral spectra agreed with observed soil 

mineralogy. 

Distribution and Analysis of Classes Within the 

Flightline 5 Image 

The sand and gravel operations (A: fig. 2) along the St. 

Vrain River were classified with the MASTER data as class 11 

(cyan). Class 11—quartz-rich sand quarries (cyan) are fringed 

by class 9—quartz-rich silt-sand soil (yellow) and contain class 

9 material bearing roads. Field verification (which used these 

quarries as one of several sites for defining class 11 mineralogy) 

showed that the extracted sand and gravel was a quartz-rich 

potassic-feldspar arkose with muscovite and a trace of mafic 

minerals, and little clay. Quartz dominates the mineral grains, 

and the site has a very strong quartz absorption feature in the 

sand and gravel quarry 

linear mixture 

f (quartz)=.55 

f(potassium feldspar)=.20 

f(clay)=.23 

f(flat)=.41 

EXPLANATION 

0.9 

0.8 

0.7 

41 42 43 44 45 46 47 48 49 

MASTER CHANNEL 

Figure 12. Comparison between observed MASTER spectrum from an 

area in a sand and gravel quarry with a linear mixture of laboratory 

spectra. (See fig. 11.) The fractions were derived using a single value 

decomposition algorithm. This illustration is also online at http:// 

speclab.cr.usgs.gov/PAPERS/Bulletin-2196_figs/index.html. 

MASTER data. The roads and fringing areas map as having 

some clay with the quartz sands (fig. 8). 

The Fort St. Vrain power plant area (B: fig. 2) mapped as 

quartz-rich sands (cyan: class 11) that were field verified as 

quartz-rich gravel. The main buildings are surrounded by gravel 

lots where fill was hauled to the site. Likewise, dirt roads in the 

area are quartz-rich gravel that map with varying amounts of clay 

(cyan and yellow: classes 11 and 9). Other introduced quartzrich gravel in the region, seen as squares with a few pixels on 

each side (cyan: class 11), are gravel lots used for power substations. The AVIRIS image (fig. 10) shows muscovite in the gravel. 

Upland fields (C: fig. 2) mapped as a group of sandy soils, 

ranging from quartz-rich sandy soil (cyan: class 11), to quartzrich silt-sand (particle size) soil with some clay mineral (yellow: 

class 9), and to quartz-rich silt-sand soil with clay and plant 

humus (magenta: class 6). Class 4, silt-sand-clay (blue), is interwoven throughout class 6, especially near boundaries with class 

3 (green). The soils appear to have a natural variability in silt 

EMISSIVITY (OFFSET) 

EMISSIVITY 

10 MASTER Thermal Data and Infrastructure Resource Characterization 

50 
and clay content. Eolian sands and silts were derived from the 

South Platte River. Field investigations show that the soils have 

a continuous range in composition, but the unsupervised classification algorithm statistically places the soils into discrete 

classes. Feathering of the edges of adjacent classes shows this 

effect. Variation of composition between fields can be attributed 

to farmers’ amending their fields by plowing under crop remains 

and fertilizing fields with manure. The change in clay and 

humus is seen in the class spectra, with longward shift in the 

spectral minimums, from quartz-feldspar absorption features, to 

clay absorption features. Progressive flattening of the spectra 

longward of the main absorption features occurs with increasing 

clay and humus content. The AVIRIS image shows montmorillonite clay as the dominant sheet mineral. 

Soils from the large feedlot and a smaller feedlot (D: fig. 2) 

3 km to the east mapped as sandy-silty-organic-rich clay (green 

and red: classes 3 and 2) in the MASTER soil classification 

image (fig. 8). Field checking verified the organic composition 

of the soil. No growing vegetation existed within the stockyards. 

Moisture in the soil may suppress spectral maximums but will 

not change the overall characteristics of the class spectra. 

The city of Greeley (E: fig. 2) contains all classes of materials. The sand classes mapped mainly as various types of asphalt 

whereas the three clay classes mapped clay-rich ground and roofing material. The quartz-rich sands class (cyan: class 11) occurs 

as asphalt road surfaces and as the majority of asphalt parking 

lots. Abundant quartz gravel in this pavement was apparent during field checking. The quartz-rich silt-sand soil with some clay 

class (yellow: class 9) comprises the majority of asphalt roadways. This pavement also has quartz gravel, though the difference between the class 11 and this class variety of asphalt was not 

studied. A third class of asphalt occurs with class 6 (magenta), a 

quartz-rich silt-sand soil with clay and plant humus class. Class 

6 occurs on streets where the spectral response appears to be integrated among asphalt, roofs, and trees. 

The three clay classes within Greeley (classes 4, 3, and 2: 

blue, green, and red) mapped a more diverse group of materials. 

Generally, classes 4 and 3 mapped silt-clay soils in areas of irrigated grass landscaping, whereas class 2 mapped some fields, 

and roofing materials on residential houses and businesses, particularly on the west side of the image area. Exactly what materials in the roofs correlate with class 2 is unclear. 

Flood plains of the South Platte and St. Vrain Rivers 

mapped as sandy-silty-organic-rich clay throughout the drainage 

(blue, green, and red: classes 4, 3, and 2); the associated river 

channels and eroded ground mapped as quartz-rich sands (cyan: 

class 11). Throughout the flood plains, the soils appear to be a 

consistent mixture of the three classes. Likewise, the channel 

sands remain homogeneous throughout their lengths within the 

MASTER classified soil image. Sand and gravel occur under the 

well-developed organic-rich soil and are visible in eroded areas 

and river bottoms where burrowing animals have brought sand to 

the surface. Whereas MASTER mapping classified sheet silicate 

minerals within the area, AVIRIS mapping identifies the sheet silicate minerals as muscovite within the sand and gravel and muscovite and kaolinite within mined land. Vegetation prevented the 

over-bank soil from being identified using AVIRIS data. 

Riverbanks and rolling terrain mapped primarily as quartzrich silt-sand soil with clay and plant humus (magenta: class 6) 

with class 4 (blue) and class 3 (green) scattered throughout. 

Generally, these areas field checked as silty soil with natural 

dryland vegetation. The AVIRIS image shows muscovite as the 

dominant sheet mineral. 

Summary 

MASTER data were processed using an algorithm to compute the inverse wave emissivity. The processing employed an 

atmospheric model derived from an existing regional model by 

using the observed MASTER radiance observations of the South 

Platte River and ground radiances computed using water temperature measurements. An unsupervised classification algorithm 

was applied to the results to identify a spatially unique set of 

spectral classes. These data were then analyzed by field examination and comparison with other maps and images and mineral 

spectral databases. 

Quartz-rich sands were directly identified using their spectral signature, as were sand and gravel mines, sandbars, and sandy 

soil (as well as asphalt and gravel lots). Analysis of a MASTER 

spectrum of the sand and gravel operation (labeled A, fig. 2) led to 

discovering a spectral mineral mixture of quartz, feldspar, and 

sheet silicate minerals that agreed with the field observations. 

Data spatial resolution allowed fine structures such as sandbars within the river channels, dirt roads, and small gravel lots to 

be seen. Eolian quartz-rich sands derived from the river channels have been carried by the prevailing wind to form soils in 

fields south of the South Platte and St. Vrain Rivers. The identification of eolian sands might be used to locate river segments 

with abundant sands that may indicate sand and gravel deposits 

of economic interest. 

A range of soils with varying amounts of quartz, silt, 

clay, and plant humus are mapped in the upland farm fields 

south of the South Platte River. The quartz and clay abundance could be qualitatively estimated in these soils by examination of their spectral features. Emissivity minimums at 

channels 44, 45, and 50, maximums at channels 43 and 48–49, 

and spectral slopes were used to coarsely characterize quartz 

and clay abundances. Quality of the quartz sands is indicated 

by these spectral responses and might be used to help evaluate the sand’s desirability for construction, farming, or stability from erosion. 

Two river bottom soils capping river fill were mapped. 

These sand-silt-organic-rich clay soils are characteristic of the 

flood plains of both rivers. One soil comprises over-bank deposits; the other soil is generally found in areas that are marshy during part of the year. The two soils have very similar spectra with 

the exception of the last channel. These clay-rich soils are distinctly different from the upland sandy soils, in both geomorphology and spectral character, with a very flattened spectral 

response longward of channel 45. This type of soil could possibly be used in other areas to indicate conditions permissible for 

sand and gravel operations. 

Summary 11 
The results of this study indicate that MASTER thermal 

data provide a useful tool for characterizing the composition 

and quality of materials that commonly occur in areas useful for 

infrastructure resources. Satellite data of similar spectral and 

spatial resolution could be used to characterize and map soil 

mineralogy and physical parameters over large geographic 

areas. 

References Cited 

Clark, R.N., Swayze, G.A., and Gallagher, A.J., 1993, Mapping minerals 

with imaging spectroscopy, in Scott, R.W., Jr., Detra, P.S., and Berger, B.R., eds., Advances related to United States and international 

mineral resources—Developing frameworks and exploration technologies: U.S. Geological Survey Bulletin 2039, p. 141–150. 

Clark, R.N., Swayze, G.A., Heidebrecht, K.B., Goetz, A.F.H., and Green, 

R.O., 1993, Comparison of methods for calibrating AVIRIS data to 

ground reflectance, in Green, R.O., ed., Summaries of the fourth 

annual JPL airborne geosciences workshop, Volume 1: AVIRIS 

workshop, Jet Propulsion Laboratory Publication 93-26, p. 31–34. 

Christensen, P.R., Bandfield, J.L., Hamilton, V.E., Howard, D.A., Lane, 

M.D., Piatek, J.L., Ruff, S.W., and Stefanov, W.L., 2000, A thermal 

emission spectral library of rock-forming minerals: Journal of 

Geophysical Research, v. 105, p. 9735–9739 (available online at 

URL http://speclib.jpl.nasa.gov/). 

Hook, S.J., Myers, J.J., Thome, K.J., Fitzgerald, M., and Kahle, A.B., 2001, 

The MODIS/ASTER airborne simulator (MASTER)—A new instrument for earth science studies: Remote Sensing of the Environment, 

v. 76, Issue 1, p. 93–102. 

Livo, K.E., Hummer-Miller, Susanne, and Knepper, D.H., Jr., 1999, Surface 

mineral composition of selected flood plains within the Colorado 

Front Range Urban Corridor mapped using remotely sensed data, in 

Bon, R.L., Riordan, R.F., Tripp, B.T., and Krukowski, S.T., eds., Proceedings of the 35th Forum on the Geology of Industrial Minerals— 

The Intermountain West Forum 1999: Utah Geological Survey Miscellaneous Publication 01-2, p. 240–241. 

Palluconi, F.D., and Meeks, G.R., 1985, Thermal infrared multispectral 

scanner (TIMS)—An investigator’s guide to TIMS data: Jet Propulsion Laboratory Publication 85-32. 

Press, W.H., Teukolsky, S.A., Vellerling, W.T., and Flannery, B.P., 1992, 

Numerical recipes in Fortran 77, Second Edition: Cambridge University Press, section 2.6, p. 51–63. 

Salisbury, J.W., and D’Aria, D.M., 1992, Emissivity of terrestrial materials 

in the 8-14 micrometer atmospheric window: Remote Sensing of the 

Environment, v. 42, p. 83–106. 

Watson, Ken, 1997, Three algorithms to extract spectral emissivity information from multispectral thermal data and their geologic applications: First JPL Workshop on remote sensing of Land Surface 

Emissivity, Pasadena, Calif., May 6–8, 1997. 

12 MASTER Thermal Data and Infrastructure Resource Characterization