Landsat-7 ETM+ as an observatory for land cover Initial radiometric and geometric comparisons

Landsat-7 ETM+ as an observatory for land cover

Initial radiometric and geometric comparisons with

Landsat-5 Thematic Mapper

Jeffrey G. Masek*, Miroslav Honzak, Samuel N. Goward, Ping Liu, Edwin Pak

Department of Geography, University of Maryland, College Park, MD 20742, USA

Received 23 May 2000; received in revised form 14 February 2001; accepted 30 April 2001

Abstract

The Landsat-7 ETM+ sensor offers several enhancements over the Landsat-4,5 Thematic Mapper (TM) sensor, including increased

spectral information content, improved geodetic accuracy, reduced noise, reliable calibration, the addition of a panchromatic band, and

improved spatial resolution of the thermal band. In this paper, we present some initial comparisons between Landsat-5 TM and

Landsat-7 ETM+ imagery in order to quantify these improvements. We find that the ETM+ continues the record of TM observations,

and, in many respects, substantially improves upon the earlier sensor. Specific improvements include lower spatial noise levels,

improved information content, and geodetic accuracy of systematically corrected products to 50–100 m. These improvements are

likely to have significant benefits for land-cover mapping and change detection applications. D 2001 Published by Elsevier Science

Inc. All rights reserved.

1. Introduction

The Landsat-7 mission, successfully placed into orbit

on April 15, 1999, continues the 29-year record of land

remote sensing measurement that started in 1972 with

the Earth Resources Technology Satellite (ERTS, later

Landsat-1). From the beginning, Landsat sensors have

provided calibrated multispectral measurements of land

conditions, which have proven valuable in tracking the

Earth’s terrestrial forests, grasslands, agricultural activity,

urban growth, and surface hydrology (Goward & Wil-

liams, 1997). Landsat’s success is revealed not only in

the diverse applications of the acquired information but

also the many complimentary spaceborne land remote

sensing systems that are now actively monitoring the

Earth. The recent launch of Terra, with the MODIS,

ASTER, and MISR sensors, combined with Landsat-7

Enhanced Thematic Mapper Plus observations, comprise

a major US commitment to study potential changes in

the Earth system at the beginning of the 21st century.

The Landsat-7 mission represents a significant change in

operations in comparison to Landsat-4 and -5. Following

design studies in the mid-1970s, Landsat-4 introduced a new

sensor system, the Thematic Mapper (TM), to replace the

original four-spectral band Multispectral Scanner (MSS)

flown on Landsat-1, -2, and -3. The TM instrument signific-

antly refined the original MSS sensor, with more spectral

bands (seven vs. four), finer spatial resolution (30 m), and

improved radiometric precision (8 bits vs. 6 bits) (Markham

& Barker, 1985).

Coincident with the deployment of TM on Landsat-4, the

entire Landsat system was transferred to the private sector,

under the management of the National Oceanic and Atmo-

spheric Administration (Sheffner, 1994). A component of

this commercialization effort was the development of the

sensor system for Landsat-6. The EOSAT undertook a

redesign effort that introduced several changes in the

original TM sensor design in an effort to enhance

the competitiveness of the observations in comparison to

the French SPOT system. The new sensor design was called

the Enhanced Thematic Mapper (ETM).

The ETM instrument incorporated a new, 15-m pan-

chromatic band, as well as a 9-bit analog to digital

converter. The panchromatic band was added to provide

0034-4257/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved.

PII: S0034 -4257 (01 )00254 -1

* Corresponding author.

E-mail address: [email protected] (J.G. Masek).

www.elsevier.com/locate/rse

Remote Sensing of Environment 78 (2001) 118–130

higher spatial resolution observations, as a compliment to

the 30-m multispectral measurements. However, in order

to provide sufficient signal, the spectral bandpass of the

panchromatic band covered both the visible and near-

infrared portions of the spectrum (0.5–0.9 mm). In order

to keep the telemetered data stream to a reasonable

volume the 9-bit A-to-D system was designed so that

either the upper or lower 8 bits of the radiometry could

be provided from the system. In effect, this introduced the

potential for two gain states (high and low), which,

particularly for dark landscapes, could increase the radio-

metric signal (while running the risk of increased satura-

tion of the signal for bright targets). Unfortunately, neither

the applications nor science community had a chance to

evaluate the ETM instrument. Landsat-6 was lost during

launch in 1993.

When the Landsat program was returned to US gov-

ernment operations in 1992, specifications were developed

for a new sensor system on Landsat-7. Of primary

concern was to ensure continuity of the Landsat obser-

vation record from previous missions (e.g., Landsat-4 and

-5). This pressure directed attention toward building a

duplicate of the Landsat-6 ETM instrument, which was

viewed as the quickest way to produce a backup to the

aging Landsat-4 and -5. Thus, the Landsat-7 ETM+

instrument is essentially the EOSAT Landsat-6 heritage

instrument. It replicates the Landsat-6 ETM design, with

the significant exception of the thermal infrared band,

where the ground resolution has been improved from 120

to 60 m, and the addition of a Partial Aperture Calibrator

(PAC) and Full Apature Calibrator (FAC) to allow solar

calibration of the instrument.

Beyond the technical changes represented in the ETM+

instrument, the technical and scientific teams associated

with the mission, directed from the NASA Goddard Space

Flight Center, brought better than 20 years experience to

the design and development of the Landsat platform.

Lessons learned over this time period offered considerable

promise that the quality of the Landsat-7 mission could

easily exceed the Landsat-4 and -5 missions, simply

because so much had been learned from these previous

missions with respect to radiometric calibration, geometric

accuracy, and image quality.

2. Objectives, approach, and data sources

With this history and technical potential well in mind,

we undertook an initial examination of selected Landsat-7

ETM+ observations, in comparison to both historical and

contemporary Landsat-5 observations. The specific goals

of this study were to evaluate the degree to which

Landsat-7 observations will permit continued large area

monitoring of land-cover dynamics. In addition, we set

out to understand how Landsat-7 observations may assist

in improved automation of regional and global scale

studies in support of the US Global Change Research

Program and NASA Earth Observation System goals

(Goward et al., 1999; Goward & Williams, 1997). This

study represents a first, ‘‘quick look’’ at the data charac-

teristics in comparison to Landsat-5 TM. We anticipate

that other studies will refine these results as more data

become available.

Several data attributes must be well understood to fully

exploit Landsat observations for global change science.

For this study, we selected three key attributes to compare

between Landsat-5 TM and Landsat-7 ETM+:

� Geodetic accuracy refers to the absolute accuracy of

projected pixel locations within the image, using the

corner coordinates provided by the ground process-

ing system.� Radiometric information content specifies the amount

of information (bits) used to represent the land surface

in any given image or image subset.� Periodic sensor noise refers to the coherent noise

(striping, banding, stippling) commonly found in

Landsat-5 TM images.

In essence, these attributes constitute a chain of informa-

tion. Degrading any aspect of this chain could limit the

ability to extract useful information from the imagery

(Vogelmann, Helder, Morfitt, Choate, & Merchant, 1998).

Radiometric calibration, while also a key part of this

information chain, is being examined by other members of

the Landsat Science Team and has not been addressed in this

study (Teillet et al., 2001; Vogelmann, Choate, Helder,

Merchant, & Bulley, 1999).

To intercompare Landsat-5 and Landsat-7 data, we

examined a suite of scenes for two regions, Washington,

DC and Manaus, Brazil, acquired during the last three

years (Table 1). The Washington, DC region includes

urban, suburban, deciduous forest, water, and wetlands

land-cover types. The Manaus scene includes primary

tropical forest, two types of river water, regenerating

forest, pasture, and the urban center of Manaus itself.

These two locations, in the mid- and tropical latitudes of

the Earth provide a representative sample of the condi-

tions that may be encountered any of the more humid

portions of the Earth’s land areas. ETM+ images were

processed to Level 1G, HDF format using LPGS soft-

ware. TM images were processed to Level 1G using the

EDC NLAPS system.

Of particular note are the two scenes of the Washing-

ton, DC area from June 2, 1999, acquired as Level 0

format. These scenes were acquired as part of the Land-

sat-5/Landsat-7 underfly experiment, in which the two

sensors simultaneously (within 4 min) imaged overlapping

ground swaths as Landsat-7 ascended to its final orbit.

This data set allowed direct comparison of radiometry

between the TM and ETM+ sensors. In addition, we used

multidate imagery for identical ground targets for some

J.G. Masek et al. / Remote Sensing of Environment 78 (2001) 118–130 119

analyses. Although multidate images incorporate variable

atmospheric effects and land-cover conditions, as well as

sensor changes, intercomparing many scenes from mul-

tiple dates can reveal consistent changes attributable to

sensor performance.

3. Sensor performance

3.1. Geodetic accuracy

Geodetic accuracy (the geographic navigation accuracy

of a particular pixel) is vital for not only locating image

features, but also for determining what has changed between

images from differing time periods. Geodetic accuracy is a

function of knowing the spacecraft position in time (eph-

emeris), spacecraft attitude, cameral model (focal plane),

and having an accurate software model to convert these data

into a geographically projected image. Applying the satellite

ephemeris data to the raw imagery, and resampling the

observations to a projected grid is part of the ‘‘systematic’’

correction done during L0R to L1G processing (Bernard,

1997). For Landsat-7, the geodetic error after systematic

correction was required to be less than 250 m (1s devi-

Fig. 1. Geodetic accuracy of Landsat-7 ETM+ image of Washington, DC acquired on July 28, 1999. (a) GPS locations (road intersections) identified in the

image are depicted, together with error vectors showing the magnitude and direction of offset. Vectors are shown at 1000 times true distance. Image outline and

counties in theWashington metropolitan region are also shown. (b) Crosshair plot of all geodetic errors from (a).

Table 1

List of TM and ETM+ scenes used in this study

Location (path, row) Acquisition date Sensor (TM/ETM+)

Washington, DC (15, 33) July 11, 1996 TM

October 15, 1996 TM

July 14, 1997 TM

June 2, 1999 TM

June 2, 1999 ETM+

July 28, 1999 ETM+

Manaus, Brazil (231, 62) July 4, 1996 TM

July 7, 1997 TM

August 7, 1999 ETM+

J.G. Masek et al. / Remote Sensing of Environment 78 (2001) 118–130120

ation). This is considerably stricter than Landsat-5, where

corner coordinates may often be in error by up to 1–2 km.

Systematic correction should not be confused with ‘‘pre-

cision’’ georeferencing, in which the image is warped to

known control points, or orthorectification, in which topo-

graphy is included in a three-dimensional viewing model.

Neither of these procedures is part of the standard ETM+

processing chain carried out by EROS Data Center.

To evaluate the geodetic accuracy of Landsat-7, we

collected accurate ground control points for the Washington,

DC area, located these points on the July 28, 1999 image,

and calculated the differences in the actual and reported

locations (error). Most of the control points consisted of

road intersections, clearly visible in the imagery, located in

the field using differential GPS. Given uncertainties in: (a)

the GPS values ( ± < 1 m); (b) our knowledge of where the

points were gathered ( ± 15 m); and (c) the exact geographic

pixel location due to nearest-neighbor resampling in L1G

production ( ± 15 m), this error analysis is probably accurate

to � 20–25 m for this scene.

The results indicate that, for these examples, the geodetic

accuracy of Landsat-7 imagery is well within specification

(Fig. 1). The navigation error for the Washington, DC image

indicates a predominately along-track (N–S) translation of

� 100 m, with little evidence of crosstrack error. In fact,

using postpass ephemeris processing, experiments at USGS

and NASA suggest that the navigation error for ETM+

imagery can be routinely reduced to less than 50 m (J.

Storey, personal communication). Having the capability to

automatically determine the location of any pixel to within

50 m, without going through time-consuming manual

georegistration, will certainly prove invaluable for land-

cover applications, particularly change detection.

3.2. Spatial noise content

On earlier Landsat missions, both the MSS and TM

instruments exhibited spatial noise. Most notable were sta-

tionary, periodic noises such as banding and striping. Both the

TM and ETM+ sensors use 16 separate detectors that sweep

in the crosstrack direction (so called ‘‘whisk-broom’’ geo-

metry). These 16 separate sensors must be crosscalibrated in

order to produce a seamless, noise-free image. Striping

(detector-by-detector variability) arises from miscalibration

of individual detectors during systematic radiometric pro-

cessing (Poros & Peterson, 1985). Banding, the variable

brightness of forward and reverse scans, reflects the so-called

‘‘memory effect,’’ an onboard electronic ‘‘smearing’’ of

bright or dark targets in the scan direction (Crippen, 1989;

Helder, Quirk, & Hood, 1992). These noises, usually at the

1–2 digital number (DN) level, are particularly noticeable on

uniform dark targets, such as water or forest.

Landsat-7 ETM+ has been designed with improved

detector-by-detector calibration and improved electronics,

so it should exhibit reduced levels of banding and striping

compared to Landsat-5 TM. To verify this, we compared

image subsets of the Chesapeake Bay from both a Landsat-7

image (P15,R33; July 28, 1999) and from three Landsat-5

Fig. 1. (continued )


images (July 14, 1997, October 15, 1996, and July 11,

1996). The target area was a 15� 15 km region of the Bay

south of Washington, DC. Each image was first converted to

units of at-sensor radiance (L) using the standard calibration

equation (Eq. (1)):

Lk ¼ gkDNþ bk ð1Þ

where gk and bk refer to the gain and bias for band k

(Landsat 7 Science Data Users Handbook: http://

ltpwww.gsfc.nasa.gov/IAS/handbook/). To ensure that all

images exhibited comparable total radiance levels, regard-

less of atmospheric conditions, the minimum value in each

subset was subtracted (e.g. simple dark-object subtraction)

(Chavez, 1996). Images for each band demonstrate visually

that ETM+ exhibits dramatically reduced levels of periodic

noise (Fig. 2). Bottom features previously masked by

striping in Landsat-5 scenes become crisp in the Landsat-7

scene. It should be noted that different bands on Landsat-5

TM exhibit different levels and patterns of noise. Bands 1–3

show narrow striping associated with detector miscalibra-

tion, while band 4 shows broader banding associated with

memory effect. Bands 5 and 7 show an odd ‘‘stippling’’

pattern unlike the patterns of the VNIR bands.

To quantify the noise reduction, we have used Fourier

spectral analysis to isolate the noise component in each

Fig. 2. Histogram-equalized images of the Chesapeake Bay for bands 4 and 5 from (a, c) Landsat-5 TM (acquired 7/11/97) and (b, d) Landsat-7 ETM+

(acquired 7/28/99). Note the pronouced reduction in noise (banding, striping, stipling) in the Landsat-7 images.


band. To ‘‘build’’ the Fourier noise signal, each 512� 512

pixel subset of the Chesapeake Bay was segmented into

nine overlapping 256� 256 pixel windows. The Fourier

amplitude spectrum was calculated for each window

separately, and then summed to increase the signal

coming from periodic noise. To reduce ‘‘ringing’’ asso-

ciated with edge discontinuities, each window was first

multiplied by the Hanning window (Eq. (2)):

wðrÞ ¼ 1

21� cos

2prN � 1

� �� ð2Þ

where r is the distance from the edge of the image window

and N is the window size (Press, Flannery, Teukolsky, &

Vettering, 1988). The windowed data h(x,y) were then

transformed into the Fourier domain according to the

standard transform (Eq. (3)):

Hðkx; kyÞ ¼XN0

XN0

hðx; yÞeikxxeikyyDxDy ð3Þ

where kx and ky represent the x and y wavenumber

components, respectively, and H(kx,ky) is a complex array.

The complex array H(kx,ky) is converted to a real

amplitude array by taking the magnitude of each complex

value. The amplitude array from each of the 256� 256

windows is then added to ‘‘build’’ the periodic noise

signal and reduce components associated with particular

image content (e.g. varying atmospheric haze, sediment

patterns, etc.). To convert the two-dimensional summed

amplitude spectrum to a one-dimensional radial power

spectrum (RPS), the maximum value for each circum-

ferential band of discrete wavenumber kr (=(kx2 + ky

2)1/2)

was retained.

Radial amplitude spectra from all bands show a similar

pattern (Fig. 3). For low wavenumbers (k < 20), image

content dominates the spectra. For shorter wavelengths,

regular noise peaks occur at specific wavenumbers for each

band and each sensor. The position and (to a lesser extent)

amplitude of noise peaks in Landsat-5 TM subsets are

consistent regardless of acquisition date. In contrast, Land-

sat-7 ETM+ data typically show fewer discrete noise peaks,

and lower overall noise amplitude. It should be noted that

while the overall brightness of each image is about the same,

Landsat-7 tends to concentrate more of the image amplitude

in the longer-wavelength part of the spectrum associated

with actual image content, rather than shorter wavelengths

associated with noise.

Integrating each radial amplitude spectrum from wave-

number k = 20 to k = 128 gives a rough measure of the total

noise content in each band (Fig. 4). This integration reveals

that Landsat-7 ETM+ exhibits about 20–40% less noise in

each band compared to Landsat-5. The greatest relative

improvement appears in bands 5 and 7, while the least

improvement appears in band 3. It should be noted that

these figures are only valid for dark targets. Since striping

may arise from errors in detector gains, some part of the

spatial noise is likely to be correlated with target brightness.

A future task will be to fully quantify ETM+ spatial noise

levels as a function of target brightness.

3.3. Radiometric information content

The two-gain state characteristic of the Landsat-7 ETM+

instrument presents the interesting potential of providing

more radiometric precision, and, thus, better land-cover

discrimination than previous TM observations. The gain

attributes of the ETM+ were set such that for spectral

Fig. 3. Fourier radial amplitude spectra for band 4, comparing two Landsat-5 TM images with the 7/28/99 Landsat-7 image. Although the peak noise levels are

comparable between the two sensors, the number of peaks and integrated noise level is considerably lower for Landsat-7.


bands 2–4 and 7, the low gain (higher order bits) corre-

sponds to the Landsat-5 eight-bit gain attribute. Thus, when

the high gain is selected, the sensor should be acquiring

observations with greater radiometric sensitivity, until ‘‘sat-

uration’’ (digital value of 255) is reached. For spectral

bands 1 and 5, the high-gain state replicated the Landsat-5

settings and for band 6, the TIR band, both gain settings

are telemetered which offers an equivalent temperature

precision of ± 0.2�C.To evaluate the impact of these gain attributes, we

created a simple numerical model of image information

content based on the gain settings of Landsat-5 and

Landsat-7 (high gain). We produced a large set of IFOVs

with a gaussian distribution of surface reflectance (r),with a specified mean (m), and standard deviation (s) for

each band. Values for m and s were taken from actual

Landsat imagery and are specific to land-cover type

(Table 2). Neglecting atmospheric effects, the DN value

for each IFOV in band k was calculated as:

DN ¼ E0rcosZ � bk

gkð4Þ

where E0 refers to the equivalent solar irradiance for band k,

Z is the solar zenith angle, and gk and bk refer to the gain and

bias for band k.

Fig. 4. (a) Integrated (wavenumber 20–128) Fourier amplitude spectra for Landsat-5 TM scenes and Landsat-7 ETM+ scene; (b) mean percent reduction in

integrated amplitude (noise) between Landsat-7 ETM+ and Landsat 5 TM.

Table 2

Mean (m) and standard deviation (s) of surface reflectance values used in

entropy model for deciduous forest and urban land-cover types

Land-cover type Band 1 Band 2 Band 3 Band 4 Band 5 Band 7

Forest mean (m) 0.015 0.020 0.015 0.339 0.140 0.048

Forest S.D. (s) 0.002 0.002 0.002 0.014 0.010 0.005

Urban mean (m) 0.066 0.077 0.102 0.150 0.164 0.128

Urban S.D. (s) 0.016 0.021 0.027 0.030 0.047 0.039


A convenient measure of information content is the

entropy of an image (Malila, 1985; Price, 1984). Following

the pioneering information theory of Shannon (1948)

entropy (E) can be defined in terms of information content as:

E ¼ �XNi¼0

pðiÞlog2 pðiÞ ð5Þ

where p(i) gives the probability of occurrence of a specific

numerical value i (e.g. a specific DN) in the range 0–N. In

practice, p(i) is simply the normalized value of the ith bin of

the image DN histogram. For the case of Landsat,

N = 28 = 256, and a histogram for which all values are

equally filled (flat histogram) yields E = 8, which is the

maximum possible information content. Alternatively, if all

Fig. 5. Simulated entropy per band (bits) using gain settings from Landsat-5 TM and Landsat-7 ETM+, and representative surface reflectance distributions for

(a) urban and (b) forest land-cover types.

Fig. 6. Entropy per band (bits) for Landsat-5 TM (dashed line) and Landsat-7 ETM+ (solid line) from data acquired during the simultaneous underfly on June 2,

1999 of the Washington, DC region.


values reside in one bin, E = 0, which is the lowest possible

information content (Price, 1984). Thus, E records the

number of bits required to actually characterize the image

signal, with the maximum possible entropy set by the level

of digitization of the data. Using the set of calculated DN

values for TM and ETM+ from Eq. (4), the entropy for each

set can be obtained using Eq. (5).

Given typical reflectance spectra for forest and urban

land-cover types (Table 2), the simulated ETM+ entropy

distributions show increased information content in all

bands, except bands 1 and 5 (Fig. 5). This finding fits the

specification of the instrument, since the high-gain state for

ETM+ bands 1 and 5 nearly replicate the gain of the

equivalent TM bands. Bands 2 and 3 record the greatest

improvement compared to Landsat-5 TM, with slightly less

than 1 bit of additional information content present within

ETM+ imagery. Since the standard deviation of surface

reflectance tends to scale with brightness, absolute values

of entropy tend to scale with target brightness as well. For

example, forest targets in the visible wavelengths use only

2–3 bits of the full 8-bit image, while forest targets in the

infrared use 4–5 bits.

To compare the model predictions of information

content with actual Landsat imagery, entropy can be

calculated directly from actual image histograms using

Eq. (5). For this analysis, we used Landsat-5 and Land-

sat-7 data from Washington, DC, using scenes both from

different years, as well as imagery from the tandem

overflight of on June 2, 1999 (Table 1). Entropy was

calculated for seven of the most typical land-cover

categories within the scene, using 250–300 pixels per

class (Figs. 6 and 7). For Washington, DC, these land-

cover types included estuarine water, urbanized area,

residential area, grassland (dry), growing crops, wetlands,

and deciduous forest. In addition, entropy for larger areas

covering a mixture of the typical classes was also

calculated (DC mix in Figs. 6 and 7). For all ETM+

scenes, the VNIR SWIR bands were acquired in high-

gain mode, but the low-gain thermal band was used for

the entropy analysis.

Fig. 7. Entropy per band (bits) for Landsat-5 TM (acquired 7/11/96 — short dashed line; 7/14/97 — long dashed line) and Landsat-7 (acquired 7/28/99 — solid

line), for the Washington, DC region.


Results from the Landsat-5/Landsat-7 overflight of the

Washington, DC area (Fig. 6) indicate that ETM+ in high-

gain mode records up to 1 bit of extra information for

specific bands and land-cover types, a finding in excellent

agreement with the gain model (Fig. 5). Again, bands 2, 3,

and 7 showed a pronounced increase in information

content for most land-cover types, while band 4 also

showed an increase for crops and grassland. ETM+ bands

1 and 5 showed little change compared to Landsat-5 TM.

The overall trend is summarized in the graph titled DC

mix (Fig. 6).

Using Landsat TM/ETM+ data from different years

yields similar results (Fig. 7). Slight differences reflect

differing atmospheric and/or land-cover conditions

between the image acquisitions. The atmospheric effect

on entropy values can be well observed by comparing the

7/11/96 and 7/14/97 Landsat-5 TM plots for urban areas,

which should be relatively invariant from year to year

(Fig. 7). Although the entropy values should be nearly

identical, a higher aerosol load on 7/14/97 caused a loss of

information in the visible and near-infrared bands. It is an

interesting design implication that, despite advanced engin-

eering to improve gain, atmospheric contamination can

readily decimate such improvements. The creation of

robust atmospheric correction algorithms must be a pri-

ority in order to recover the full information content from

current and future sensors.

It should be noted that differences in spatial noise

quantified in Section 3.2 will also affect the actual informa-

tion content (entropy) of Landsat imagery. While noise is

(by definition) not scientifically useful, it does add vari-

ability to an image, increasing the information content.

Thus, the entropy values for Landsat-5 TM data shown in

Figs. 6 and 7 would actually be lower if the spatial noise

Fig. 8. Effect of improved radiometric information content on imaging forest properties. The four areas depict a forest stand 50 km southwest of Washington,

DC: (a) false color composite (5, 4, 3) of the area; (b) TM band 2 image of the area from July 11, 1996; (c) TM band 2 image from July 14, 1997; and (d)

ETM+ band 2 image from July 28, 1999. Despite the hazy conditions present in the area on July 28, 1999, the ETM+ image shows increased detail in the

forest structure.


were removed. Modeling suggests that removing an extra 1

DN of noise could lower entropy values by up to 0.5 bits

for dark targets. Therefore, the results presented in Figs. 5

and 6 must be considered as lower bounds on the actual

improvement in information content accomplished by

migrating to the ETM+ sensor.

4. Implications for land-cover characterization

The statistical comparisons presented above indicate that

the performance of the Landsat-7 ETM+ sensor represents a

substantial improvement over the earlier Landsat-5 TM. The

data are more accurately navigated, have a lower level of

noise, greater radiometric sensitivity, and appear to be

accurately (or at least consistently) calibrated. The real test,

however, is the extent to which these sensor improvements

translate to improved characterization of the terrestrial

environment. While a complete understanding of how Land-

sat-7 can be used to best advantage will take several years,

and is certainly beyond the scope of this paper, we present

here a few observations that might guide other researchers

using Landsat-7 data.

4.1. Radiometric precision and land-cover discrimination

Given the improved information content of ETM+, one

might hope that subtle land-cover features, previously

missed by the lower gain of the Landsat-5 sensor, could

be recorded. This should be particularly clear for dark

targets in bands 2 and 3 (e.g. forest, water), since these

bands show strong improvement in information content.

Our initial observations confirm this view. ETM+ band 2

images of forest cover from Central and Western Virginia

converted to at-sensor radiance and stretched to a uniform

radiance range indicate both less noise and slight improve-

Fig. 9. Tropical forest cover estimation using the panchromatic (15-m resolution) band. (a, b) ETM+ image subsets of Manaus Brazil using (a) 30-m bands 5, 4,

and 3, and (b) 15-m resolution merge of all reflective bands and panchromatic band, as described in text; (c) forest (green) maximum-likelihood classification

using image (a) as input; (d) forest (green) maximum-likelihood classification using image (b) as input. Classification (d) appears to detect smaller forest stands,

resulting in � 3% greater forest cover estimate.


ment in the ability to image subtle brightness variations

within the forest cover compared to TM (Fig. 8). The

improvement suggests that Landsat-7 may be able to

recover subtle variations attributable to vegetation pheno-

logy and health that are currently being missed by Landsat-

5. It also points to the importance of increasing the bit-depth

of future Landsat-type imagery, with 12-bit data now being

standard for the industry.

4.2. Panchromatic band

Unlike previous Landsat sensors, ETM+ includes a high-

resolution (15-m resolution) panchromatic band. Due to the

limitations of the scanning architecture of ETM+, obtaining

sufficient energy for15-m resolution required a wide band-

pass for the panchromatic band, effectively integrating the

visible and near-infrared spectrum. Two important roles for

the panchromatic band include precision coregistration of

images and developing more accurate classifications of land

cover. The first application simply requires that known

ground-control points (GCPs) be registered to the 15-m

panchromatic band rather than the 30-m multispectral

bands. Assuming a < 1 pixel RMS error associated with

image warping, use of the panchromatic band should enable

precision image-to-image registration with an accuracy of

< 15 m. This represents a substantial improvement over the

current TM system, and should be invaluable for calculating

accurate change detection estimates (Townshend, Justice,

Gurney, & McManus, 1992).

The second opportunity, improving land-cover clas-

sification, relies on using the 15-m panchromatic band as

an added input to the classification process. The finer

spatial resolution should change the calculated area of

individual classes in proportion to the degree of spatial

fragmentation. To evaluate this possibility, we have com-

pared forest/nonforest classification of fragmented tropical

forest in Manaus Brazil, using (a) just the 30-m resolution

reflective ETM+ bands and (b) both the 30-m bands and

the 15-m resolution panchromatic band. A spatial–spectral

image merge of ETM+ reflective bands (30 m) with

panchromatic band (15 m) was performed prior to the

classification process using a principal component (PC)

transformation. The original 30-m resolution data were

supersampled to 15-m resolution. After performing a

standard PC on the multispectral data, the panchromatic

band was substituted for the first PC, and then transformed

back to the original feature space. The back-transformed

data layers, with 15-m resolution, were then used as inputs

for a maximum-likelihood classification.

The two classifications were performed on a portion of

the L1G image of the Manaus area (Fig. 9). The 4� 4 km

area covers the urban center of Manaus city and includes

patches of tropical forest (mostly forest regeneration). Com-

paring the 30- and 15-m resolution classification, it is

apparent that the panchromatic band allows the detection

of small forest patches missed in the coarser, 30-m resolu-

tion class map. The total area of forest cover in the image

subset is 3% greater when calculated with the 15-m resolu-

tion data. While this appears insignificant when working at

the local scale, it could be important when monitoring forest

status at regional to global scales.

5. Conclusions

Our analyses show that ETM+ data continues the her-

itage of Landsat-5 TM, and, in some ways, substantially

improves on it. In particular, the ability to automatically

navigate pixels to within 50 m of the actual ground location

will promote the use of Landsat for large-area analysis, and

applications requiring multidate imagery such as change

detection. The improvements in ETM+ radiometric informa-

tion content, while more subtle, will likely be useful for

mapping spatial variability within vegetated landscapes. The

decreased levels of noise and increased radiometric pre-

cision inherent in ETM+ imagery paves the way for future

improvements in next-generation Landsat-type sensors.

Acknowledgments

The authors thank Dr. Phillipe Teillet and Jennifer

Seiferts for making Landsat-5/Landsat-7 underfly imagery

available. This work was supported by NASA grant NAG

53454 to the Landsat Science Team. Two anonymous

reviewers are thanked for providing constructive comments.

References

Bernard, A. (1997). Earth Sciences Data Information System (ESDIS)

Level 1 Product Generation System (LPGS) System Design Specifica-

tion. NASA Goddard Spaceflight Center, 510-4SDS/0196.

Chavez, P. S. (1996). Image-based atmospheric corrections — revisited and

improved. Photogrammetric Engineering and Remote Sensing, 62,

1025–1036.

Crippen, R. E. (1989). A simple spatial filtering routine for the cosmetic

removal of scan-line noise from Landsat TM P-tape imagery. Photo-

grammetric Engineering and Remote Sensing, 55, 327–331.

Goward, S. N., Haskett, J., Williams, D., Arvidson, T., Gasch, J., Lonigro,

M., Reeley, M., Irons, J., Dubayah, R., Turner, S., Campana, K., &

Bindschadler, R. (1999). Enhanced Landsat capturing all the Earth’s land

areas. EOS Transactions, American Geophysical Union, 26, 289–293.

Goward, S. N., &Williams, D. L. (1997). Landsat and Earth systems science:

development of terrestrial monitoring. Photogrammetric Engineering

and Remote Sensing, 63, 887–900.

Helder, D. L., Quirk, B. K., & Hood, J. J. (1992). A technique for the

reduction of banding in Landsat Thematic Mapper images. Photogram-

metric Engineering and Remote Sensing, 58, 1425–1431.

Malila, W. A. (1985). Comparison of the information contents of Landsat

TM and MSS data. Photogrammetric Engineering and Remote Sensing,

51, 1449–1457.

Markham, B. L., & Barker, J. (1985). Spectral characterization of the Land-

sat Thematic Mapper sensors. International Journal of Remote Sensing,

6, 697–716.

Poros, D. J., & Peterson, C. J. (1985). Methods for destriping Landsat


Thematic Mapper images — a feasibility study for an online destriping

process in the Thematic Mapper Image Processing System (TIPS).

Photogrammetric Engineering and Remote Sensing, 51, 1371–1378.

Press, W. H., Flannery, B. P., Teukolsky, S., & Vetterling, W. T. (1988).

Numerical recipes in C (p. 735). New York: Cambridge Univ. Press.

Price, J. (1984). Comparison of the information content of data from the

Landsat-4 Thematic Mapper and the Multispectral Scanner. IEEE

Transactions on Geoscience and Remote Sensing, GE-17, 272–280.

Shannon, C. E. (1948). A mathematical theory of communication. Bell

System Technical Journal, 27, 623–656.

Sheffner, E. J. (1994). The Landsat program: recent history and prospects.

Photogrammetric Engineering and Remote Sensing, 60, 735–744.

Teillet, P. M., Barker, J., Markham, B. L., Irish, R. R., Fedosejevs, G., &

Storey, J. C. (2001). Radiometric cross-calibration of the Landsat-7

ETM+ and Landsat-5 TM sensors based on tandem data sets. Remote

Sensing of Environment, 78 (1–2), 39–54.

Townshend, J. R. G., Justice, C. O., Gurney, C., & McManus, J. (1992).

The impact of misregistration on the detection of changes in land-

cover. IEEE Transactions on Geoscience and Remote Sensing, 30,

1054–1060.

Vogelmann, J. E., Choate, M. J., Helder, D., Merchant, J. W., & Bulley, H.

(1999). Effects of Landsat Thematic Mapper radiometric and geometric

calibrations on selected land cover analyses. In: Proceedings of the

Fourteenth William T. Pecora Memorial Remote Sensing Symposium.

(pp. 143–153). Bethesda MD: American Society for Photogrammetry

and Remote Sensing. (CD-ROM).

Vogelmann, J. E., Helder, D., Morfitt, R., Choate, M., & Merchant, J.

(1998). Characterization of Landsat Thematic Mapper radiometry

and geometry for land cover analysis. In: IGARSS ’98, Proceedings

of the International Symposium on Geoscience and Remote Sensing,

Seattle, WA, IEEE Geoscience and Remote Sensing Society (CD-

ROM, unpaginated).


Documents

Landsat-7 ETM+ as an observatory for land cover Initial radiometric and geometric comparisons