Comparison of relative radiometric normalization techniques

PHOTOGRAMMETRY & REMOTE SENSING

ELSEVIER

ISPRS Journal of Photogrammetry& Remote Sensing 5 1 (1996) 117- 126

Comparison of relative radiometric normalization techniques Ding Yuan a,*, Christopher D. Elvidge b ”Desert

Research Institute,

” Desert Research Institute

Biological

Sciences Centre,

and Nevada Agricultural

755 E.

Experiment

Flamingo

Station,

Reno, NV 89506-0220,

Road, Las Vegas, NV 89119,

Biological

USA

Sciences Centre, 70/O Dandini

Blvd.,

USA

Received I9 December 1994; accepted 7 August 1995

Abstract Relative radiometric normalization (RRN) is a procedure used to prepare multitemporal image data sets for the detection of spectral changes associated with phenomena such as land cover change. This procedure reduces the numeric differences between two images that have been induced by disparities in the acquisition conditions (e.g. sensor performance, solar irradiance, atmospheric effects) rather than changes in surface reflectance. We have applied seven empirical multitemporal radiometric normalization techniques to 1973 and 1990 Landsat MSS images acquired of the Washington DC. area. The results from the various techniques have been compared both visually and using a measure of the fit based on standard

error statistic. Both methods of comparison indicate that a linear regression technique using pixels from the two images which did not undergo spectral change produces the best results.

1. Introduction

Anthropogenic land cover changes are producing profound impacts on global biodiversity, terrestrial carbon stocks, soil fertility and erosion (Turner et al., 1993). The acceleration of land cover change during the past century closely tracks the expansion of human populations and the mechanization of land clearing. Because of the severity of the cumulative impacts of land cover change and the long time required for the recovery of landscapes, current rates of land cover change in many parts of the world are unsustainable. The scientific requirement of increased understanding of human impacts on terrestrial environments has renewed interest in the use of Landsat Multispectral Scanner (MSS) data for the analysis * Corresponding author.

of land cover change. A series of five Landsat MSS sensors were used to acquire earth observations over a 21-year period (1972-1992), forming the longest available set of repetitive satellite observations of the earth’s surface. Land cover changes generally alter the reflectance of the land surface, which can be detected using multitemporal Landsat data sets. The analysis of land cover change using multitemporal Landsat data is complicated by the presence of substantial radiometric differences between Landsat scenes (Markham and Barker, 1987). Landsat MSS had no on-board calibration system and, as a result, there are variations in the radiometric characteristics of MSS scenes due to differences in the performance of the individual sensors (e.g. Landsat 1 versus Landsat 5). There was also drift in the radiometric performance of the individual sensors over time. In addition, there were changes in the ground processing procedures between 1972

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D. ban, CD. Elvidge/ISPRS Journal of Photogrammetry & Remote Sensing 51 (1996) 117-126

and 1992 which resulted in radiometric differences between scenes present in the archives. Variations in solar illumination conditions, atmospheric scattering and atmospheric absorption result in differences in the at-sensor radiance values that are unrelated to the reflectance of the land surface. Given sufficient time and resources it would be possible to mode1 or calibrate each of these effects and generate multispectral datasets in physical measurement units (e.g. ground radiance or ground reflectance) for use in the analysis of land cover change. However, for large-scale projects, where change detection products are to be generated for the hundreds of scenes required to cover a continental size area, it would be advantageous to use an automated approach to perform a relative radiometric normalization of scenes in a single set of steps. Relative radiometric normalizations (RRN) use one image as a reference and adjust the radiometric properties of subject image(s) to match the reference (Hall et al., 1991). One advantage of these procedures is that the original radiometric condition of the reference image is retained, obviating the computational effort required to convert each image to units of radiance or reflectance. Over the years a wide range of algorithms have been used to perform RRNs. However, there has been very little effort to intercompare the various methods. As part of a broader research program to develop methods for analyzing land cover change using Landsat MSS data sets, we have compared seven of the prominent RRN algorithms. The results of the methods have been compared visually and quantitatively using the standard error of the RRN as a measure of the goodness of fit. 2. Background One approach to RRN is to remove the atmospheric haze difference between two images. A haze correction (HC) is typically accomplished by subtracting the digital count associated with the darkest materials present in a scene (Chavez, 1988). The concept is that the haze value will be equal to the DN count observed for a ground surface having zero reflectance. Another approach to multitemporal scene preparation is to convert the data in each scene to units

of ground reflectance factors using radiative transfer codes. Although radiative transfer codes are readily available (e.g. Kniezys et al., 1988, or Tanre et al., 1990), the use of these codes to derive surface reflectance factors with the accuracies suitable for digital change detection requires additional data inputs in order to constrain the range of possible solutions. This may include input of vertical radiosonde profiles of atmospheric temperature and relative humidity or aerosol backscatter from sunphotometer measurements. Given the logistical and personnel time required to acquire these ancillary datasets and the general dearth of such datasets for use with historical Landsat scenes, the use of radiative transfer codes for the correction of image datasets is at present not a suitable route to take in projects attempting to derive products from large numbers of Landsat scenes. An alternative method for deriving ground reflectance factors from Landsat imagery is through the use of bright and dark ground targets of known reflectance (Marsh and Lyon, 1981). Widely known as the empirical line method, this approach requires the availability of large homogeneous targets and the use of field or laboratory spectroradiometers to characterize the reflectance of the targets. As with the radiative transfer model approach, the empirical line method is not widely applicable to projects involving large numbers of scenes. In recognition of the difficulties associated with acquiring suitable data to constrain radiative transfer codes or the expert identifications of spectral control sets from the ground or from the images, some researchers started looking for new empirical multitemporal normalization techniques utilizing spectral control sets extracted by objective formulations. Jenson (1983) presented a normalization technique based on simple regression (SR). Hall and Badhwar (1987) and Hall et al. (1991) developed a normalization technique using dark and bright (DB) control sets extracted from the images by Kauth-Thomas (KT) greenness-brightness scattergrams (Kauth and Thomas, 1976; Kauth et al., 1978; Crist and Cicone, 1984). Schott et al. (1988) and Salvaggio (1993) presented a normalization technique using the pseudoinvariant features (PI), basically urban features, extracted from the data using TM band 3 to band 4 ratio and band 7 data. Yuan and Elvidge (1993)

D. Yunn, CD. Elvidge/ISPRS

Journal of Photogrammet~

Table 1 Summary of empirical linear normalization techniques Methods

uk ak

=

akxk

+

bk

h

HC

Ykmin -Xk,,,,nun

MM

Ykmn

MS

yk - &fk

SR

yk - akxk

DB

jYid”- akyLdd)

-

-_(llC) yk

119

uk = a,& + bk, where xk is the digital Value (DN) of band k in image X on date 1, Uk is the normalized DN of band k on date 1 and ak, bk are normalization constants for band k. 3.1. Study area

akxk,,

PI

NC

& Remote Sensing 51 (19%) 117-126

-XIX) akxk

and Elvidge et al. (1995) proposed a controlled regression technique utilizing a no-change (NC) pixel set identified using the scattergrams obtained from near-infrared bands. 3. Methods For the purpose of evaluating the available relative radiometric normalization techniques, seven variations of RRNs (Table 1) were applied to a Landsat MSS pair covering the Washington D.C. area. These seven techniques include: (1) haze correction (HC); (2) minimum-maximum (MM) normalization; (3) mean-standard deviation (MS) normalization; (4) simple regression (SR) normalization (Jenson, 1983); (5) dark set-bright set (DB) normalization (Hall et al., 1991); (6) pseudo-invariant feature (PI) normalization (Schott et al., 1988; Salvaggio, 1993); and (7) no-change set (NC) regression normalization (Yuan and Elvidge, 1993; Elvidge et al., 1995). The objective of empirical linear spectral normalization is to rectify subject image X to reference image Y through a linear transformation. This linear transformation can be represented by a straight line passing through the ‘centre’ of the scattergram of X and Y images, presumably represented by the ‘true’ no-change pixels - pixels that have not changed in terms of radiometric properties, or for application purpose, in terms of land cover types. The common form for linear radiometric rectification is

We selected Landsat MSS scenes from the Washington DC. area for June 25, 1990 and July 8, 1973 for our research. The data sets were preprocessed by the U.S. Geological Survey EROS Data Centre for the U.S. EPA North American Landscape Characterization (NALC) project (Lunetta et al., 1993). The NALC preprocessing included geometric registration and resampling the data to a common 60-m grid in a Universal Transverse Mercator projection. The mean-square error for the registration and resampling is within l/2 a pixel. Our initial examination of the two scenes indicated that they were very different from each other radiometrically (Fig. 1) even though the images were processed identically. The 1990 data were contrast stretched, then the 1990 contrast stretch was applied to the 1973 image. Relative to the 1990 image, the 1973 image has a duller appearance, with less contrast than the 1990 image. The 1973 image has a blueish tinge to it, as if there were more haze present. It is not possible to say from this observation alone that the 1973 data had more haze or not, because we are dealing with uncalibrated data. One area of light cirrus cloud cover can be observed slightly to the left of the 1973 image centre. The 1990 image contains substantial cloud cover and associated shadow. 3.2. Examination of the scatter-grams Figs. 2 and 3 show the 1990 versus 1973 full scene scattergrams for bands 1 and 4. The scattergrams for bands 2 and 3 are quite similar to bands 1 and 4 and are therefore not presented. Large numbers of pixels fall onto the same locations in the densest regions of the scattergrams, therefore an exponential stretch was applied to visually enhance the relative magnitude of pixel concentrations for elements of the scattergram matrix. The regions having the densest numbers of pixels are shown in white, surrounded by zones of gray and black, indicating declining numbers of pixels.

D. Yuan, CD. Elvidge/ISPRS

Journal of Photogrammetry & Remote Sensing 51 (19%) I1 7-126

M

.I

mM E

D. Yuan, C.D. Elvidge/lSPRS

Journal of Photogrammetry & Remote Sensing 51 (19%) 117-126

121

128 -

96 si‘ % 5 7 -0 c 8

96 .

.1 Dl

E? isi ;

64

64.

Z r# 32 .

32

OIL.. 0

1..

.

32 Bond

‘.

.

.

1.

64 96 1 (1973)

*.

1

64

32

128

96

128

Bond 4 (1973) NC Regression Line

Fig. 2. Full scene scattergram of 1990 versus 1973 digital number values for band I (0.5 to 0.6 pm).

Areas outside the data-cluster, having no pixels present, are white. Fig. 2 shows the scattergram for band 1 (0.5 to 0.6 pm) data from 1973 and 1990. The scattergram shows a single dense data-cluster containing land and water pixels. The 1990 cloud and cloudcontaminated pixels have anomalously higher 1990 DN values and are pulled away from the land-water data-cluster along the 1990 data axis. There are a large number of 1990 cloud pixels with saturated DN values in band 1 (DN = 127). The fact that the 1973 image had more haze or atmospheric scattering present than the 1990 image is illustrated by the larger offset from the origin to the dense land-water data-cluster along the 1973 DN axis relative to the 1990 DN axis. This haze condition was repeated to a lesser degree in bands 2 and 3. Fig. 3 shows the scattergram for band 4 (OS-l.1 pm) in the near-infrared region. In this wavelength region water is much darker than the land surface, resulting in two dense data-clusters: a compact datacluster for water near the origin, and an elliptical data-cluster for land surface pixels near the centre of the scattergram. Clusters of 1990 cloud and cloud-contaminated pixels rise along the 1990 axis from both the water and land surface dense dataclusters. In addition, there are data lobes protruding

.. ... ..

Regression

Envelope

-

Distribution

Centers

-+i+

-

Fig. 3. Full scene scattergram of 1990 versus 1973 digital number values for band 4 (0.9 to 1.1pm).

below the land data-clusters, resulting from shadow and shadow-contaminated pixels present in the 1990 dataset. 3.3. Empirical normalization techniques

The seven empirical temporal spectral normalization techniques to be tested are listed in Table 1, along with a summary of their formulations. The following paragraphs provide brief descriptions of each technique. (1) Haze correction (HC) normalization. The simple haze correction assumes that pixels having zero reflectance on both subject and reference images should have the same minimum DN values. The HC is essentially an offset correction, with the gain term set at 1.O. The HC normalization coefficients are ak = 1,

bk =

Yk,r,,, -

xk,,,,

are the haze values in band where xk,,, and Yk,, k in images X and Y, respectively. Haze values were selected as the DN value threshold required to isolate the darkest 0.1% of the image pixels. This thresholding procedure was employed because

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122

Journal of Photogrammetry & Remote Sensing 51 (19%) 117-126

simply taking the lowest DN value in each band yielded ‘haze’ corrections of O-l DN. (2) Minimum-maximum (MM) normalization. This method normalizes the subject image so that it will have the same minimum and maximum DN values as those of the reference image in all bands. The normalization coefficients for the minimummaximum method are ak = YklW’ - Ykuin ,

bk = )&.,, - akxkti

xk.ux - %in

where x&,, Xk,,,, y&, and yk, are the minimum and maximum DN values of band k for two dates. The MM values for the two dates of imagery were selected as the DN thresholds required to isolate the upper and lower 0.1% of the image data. (3) Mean-standard deviation (MS) normalization. This method normalizes image X such that subject image X and reference image Y have the same mean and standard deviation in all bands. Suppose zk and Tk are the means, sq and syk are the standard deviations of xk and yk, respectively, then the MS normalization coefficients are then derived as 'Vk

ak=-,

bk = vk - a,&.

(4) Simple regression (SR) normalization (Jenson, 1983). In the SR method, the subject image is regressed against the reference image using a least-squares regression. The SR normalization coefficients are solved from the least-squares regression equation a& -

bk)2 =

min

where the summation runs the whole scene. To solve this equation, one obtains the normalization coefficients as ‘.t, Vk ak = ;, bk =~k-LIk~k ‘XkY

where s,,,, = -

(xk

-

xkj2

(xk

-

x,>(Yk

and S&j’k

=

-

--Ibj

-_(dl

YY - YK’ ak = -_(b)_ $d) ’ ‘k

bk =

jTr’- akzd).

k

The dark set and bright set for the DB method were empirically defined in our comparison study by dark set = (greenness ( 2 and brightness I 40)

‘xk

Q=z(yk-

(5) Dark set-bright set (DB) normalization (Hall et al., 1991). Instead of using single minimum and maximum values as in MM normalization, the DB method uses the average of a set of dark pixels to replace the actual minimum value in the scene, and uses the average of a set of bright pixels to replace the actual maximum value in the scene. The dark sets and the bright sets are extracted from the subject and the reference images through KT transformation (Kauth and Thomas, 1976; Hall et al., 1991). Theoretically the dark set should represent water pixels of great depth, and the bright set should represent objects of high reflectivity, such as concrete blocks and rock outcrops. The normalization line is determined by four parameters: $‘, FLb’,$@, @, the means of the dark set and bright set of two images. The DB normalization coefficients are

-

Lk)

where ‘scene’ denotes the set of pixels in the image, whereas Iscene! denotes the number of pixels in the scene.

and bright set = (greenness 5 16 and brightness 5 120} where greenness and brightness were computed using the formula given by Kauth et al. (1978). Since each pixel represents an area of about 60 x 60 m2 for the MSS image, it is often difficult to locate pixels of none vegetation contamination from ‘true’ concrete blocks or rock outcrops. Therefore our greenness threshold for the bright set was higher than that for the dark set. Notice also that the dark set and bright set can be different for the subject and reference images if land cover had changed. (6) Pseudo-invariant (PI) normalization (Schott et al., 1988; Salvaggio, 1993). Assume existence of pseudoinvariant objects that had not experienced any significant change from date 1 to date 2 in terms of reflectivity. Some of the pseudoinvariant objects maybe taken as roads, urban area, and industrial cenn-es. The pseudoinvariant features are extracted by analyzing the infrared to red ratio of the subject and reference images to identify pixels having low green vegetation cover, and a NIR threshold to eliminate

D. Yuan, CD. Elvidge/lSPRS

Journal of Photogrammetry & Remote Sensing 51 (1996) 117-126

water pixels. Assume the means and standard deviations of the selected pseudoinvariant sets for the two dates to be: Z$), $“, sir) and sj$). The PI normalization coefficients are ,_p ak=(pi),

bk =

sx,

-(pi)

Yk

-

-(pi)

ukxk

.

The pseudoinvariant feature set for the PI method is defined by band 4 band 2 5 1 and band 4 > 30 1 This is similar to the one used by Schott et al. (1988) for TM data. (7) No-change regression (NC) normalization (Yuan and Elvidge, 1993; Elvidge et al., 1995). This method constructs a normalization line using a subset of the image data where there has been no significant reflectance changes. In the scattergram of subject versus reference images (Figs. 2 and 3), the no-change pixels are located in a narrow central belt of no-change. The trend of the no-change belt is mainly due to the effects associated with sensor, illumination, atmospheric and phenological conditions. The width of the no-change belt is associated primarily with the phenological variations in vegetation in the area and other random factors. Thus if the no-change subset NC is identified, one can solve the least-squares equation PI set =

Q = c(yk

- a& -

bk)* =

min

NC

to obtain the normalization coefficients

where $@ and -_(nc) y, are the means, p) = -vi-v.

-

1

WI

c

(xk -

$@)*

NC

and ,(nc) =

rkyt

1 c (xk - F’“‘)(y, - $@) INC( NC

are the sample variance and covariance for subset NC on two dates. INCJ is the number of pixels in set NC. Yuan and Elvidge (1993) and Elvidge et al. (1995) proposed using NC envelopes in the scattergrams of infrared bands to determine the NC set.

123

The no-change set for NC method is defined by NC set = (I y3 - aJox3 -

b3,,

and (~4 - a$x4 -

1 < HVW3 b%l <

HVW4}

where ~3~’bjO, a+, and b% are the initial estimates for as, b3, u4 and b4 through locating the centres of land and centres of water from band 3 and band 4 scattergrams, HVWs and HVW4 are the corresponding half vertical widths of the no-change regions in the scattergrams given that the half perpendicular width (HPW) of the no-change region equals 10 DN (Elvidge et al., 1995). The HVW and HPW have a simple relation: HVW = HPW J1+;;1. Our assumption is that the majority (> 50%) pixels in the images did not experience significant land cover change between the two dates represented by the reference and subject images. In particular, spectral centres, or distribution peaks in the scattergram, for land and water pixels should be dominated by no-change land pixels and no-change water pixels, respectively. Those two centres together can be used to estimate the trend (no-change line) of the no-change belt, and the pixels lying within a short distance of the initial no-change line can be considered as no-change pixels. The distribution peaks in the scattergram can be detected automatically using an appropriate algorithm. The actual width of the nochange belt can be determined empirically using the criteria that more than 50% pixels are inside the nochange belt. In our experiment, setting HPW = 10 DN generated a no-change belt consisting of more than 50% of the pixels in the images. 4. Results The gains and offsets determined by each RRN algorithm are given in Table 2. Each set of gains and offsets were applied to the 1973 dataset and the resulting images are shown in Fig. 1 using the same contrast stretch in each case. Visual inspection of the images indicates that the NC normalization gave the closest match to the 1990 reference dataset. Visual inspection suggests that the minimum-maximum, mean and standard deviation, and pseudo-invariant feature normalization yielded poor matches to the 1990 reference dataset.

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Journal of Photogrammetry & Remote Sensing 51 (1996) 117-126

Table 2 Washington D.C. scene normalization coefficients for the tested normalization techniques Band 1

Method

HC MM MS SR DB PI NC

Band 3

Band 2

al

bl

a2

b

a3

b3

a4

1 2.188 3.530 0.875 1.319 2.396 0.797

-11 -40.590 -91.550 -1.498 -18.838 -58.596 -2.816

1 2.000 2.907 0.904 1.216 1.977 0.960

-7 -21.000 -44.848 3.612 -6.152 -31.239 -2.254

1 1.653 1.580 1.211 1.162 2.024 1.295

-6 -11.220 -17.362 0.153 -3.183 -34.746 -4.925

1 1.429 1.366 1.121 1.061 2.037 1.215

b4

-1 - 1.860 -7.996 4.554 0.533 -29.980 -0.304

should be a positive MSE for each band, even for an ideal RRN. Note that the HC normalization had virtually the same MSE as the RAW 1973 dataset and that the MM, MS, and PI normalizations had higher MSEs than the RAW 1973 dataset. The quantitative MSE results are in line with the qualitative visual evaluation of the Fig. 1 images.

Table 3 provides a summary of the number of pixels involved in each RRN and the mean square error (MSE) calculated for each RRN using the bottom half of the scene. The southern half of the image was used in the MSE calculation in order to avoid the inclusion of large numbers of cloud pixels. This measure was taken simply because all RRN methods, except the NC method, do not work well if clouds are present. No hindrance of clouds is one of the distinctive advantages of the NC method. The MSE is defined by MSE=

Band4

5. Discussion The RRNs can be further evaluated relative to their ability to yield useful results in the presence of clouds, shadows, and statistical outliers. Because clouds and cloud shadows are not generally present in the same locations from date 1 to date 2, their presence will tend to confound full scene methods such as the SR, MM, and MS. In addition, clouds and cloud shadows can readily contaminate the pixel sets extracted to the DB and PI normalizations. Only the NC method takes preventive measures eliminating

1 c (& - Y!J2. lscenel Scene

Using the MSE as a statistical measure of the goodness of fit, the effectiveness of the RRNs can be ranked as: NC > DB > SR > HC > MS > MM > PI. Because of land cover changes and changes in the reflectance characteristics of agricultural fields between the two dates, it is to be expected that there Table 3 Sampling fractions and MSEs of the tested normalization techniques Method

RAW HC MM MS SR DB

PI NC

Control set in 1973 image

Control set in 1990 image

Size

%

Size

%

7927228 7927228 7927228 7927228 7927228 1072339(D) 74667(B) Total: 1147006 65117 6154810

100 100 100 100 100

7927228 7927228 7927228 7927228 7927228 1232688(D) 105900(B) Total: 1338568 104980 6154810

100 100 100 100 100

14.47 0.82 77.64

16.89 1.32 77.64

MSEl

MSE2

MSE3

MSE4

Average MSE

11.73 6.65 12.63 15.04 7.53 5.74

10.06 10.30 13.82 17.31 10.57 7.14

12.38 16.56 15.49 11.06 9.40 9.80

14.46 15.10 15.35 11.61 10.64 9.89

12.16 12.15 14.32 13.76 9.54 8.14

11.13 4.86

13.99 6.49

17.15 7.55

24.71 8.34

16.75 6.81

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Journal of Photogrammetry & Remote Sensing 51 (1996) 117-126

cloud and shadow in the selection of the pixels to be used in the normalization procedure. Distinctions between the seven methods can be observed based on the percent of the pixels involved in generating the normalization statistics. Methods that use the full scene contents to generate statistics (HC, MM, MS, and SR) tend to be more readily affected by statistical outliers. Methods that employ statistically drawn subsets of the image data would seem to have an advantage in this regard. However, methods such as DB and PI that employ low percentages of the image data, extracted from atypical cover types, did not perform well. It appears that any method that relies on statistics generated from a small percentage of the image datasets, using atypical cover types, runs the risk of having a normalization that only works well for the small image areas used in the normalization procedure. The NC methods uses a high percentage of image dataset and avoids the use of pixels which changed spectrally from date 1 to date 2. As a result, the errors in the radiometric normalization are widely distributed across all major spectral classes. 6. Conclusions Compared with other relative radiometric normalization methods, the no-change (NC) method has the following advantages. (I) Cloud/shadow/ snow effects are reduced compared to other methods. (2) Because a large percentage of the total number of image pixels are used, normalization errors are distributed among different landcover types. (3) The necessity of identifying bright and dark radiometric control pixels is eliminated. (4) The speed of the normalization procedure is accelerated by reducing human intervention compared with DB and PI methods, though it may not necessarily reduce the time of computation compared with HC, MM, MS and SR methods. The NC procedure is designed to be applied to imagery acquired under similar solar illumination geometries and similarphenological conditions. The basic assumptions of the NC method is that land cover for a large portion of the land surface covered has not changed between the subject and reference images. In addition, the procedure requires the presence of both land and water pixels in the scene. Considering the

125

large area covered by Landsat scenes (185 x 185 km) these conditions will generally be met. Acknowledgements This research was conducted with assistance from the United States Environmental Protection Agency, Environmental Monitoring Systems Laboratory in Las Vegas, Nevada through Cooperative Agreement #CR816826-02. Although the research described in this article has been supported by the U.S. EPA, it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. References Chavez, P.S., Jr., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing Environ., 24: 459-479. Crist, E.P. and Cicone, R.C., 1984. Application of the Tasseled Cap concept to simulated Thematic Mapper data. Photogramm. Eng. Remote Sensing, 50(4): 343-352. Elvidge, C.D., Yuan, D., Weerackoon, R.D. and Lunneta, R.S., 1995. Relative radiometric normalization of Landsat Multispectral Scanner (MSS) data using an automatic scattergramcontrolled regression, Photogramm. Eng. Remote Sensing, 61(10): 1255-1260. Hall, EC. and Badhwar, G.D., 1987. Signature-extendable technology: global space-based crop recognition. IEEE Trans. Geosci. Remote Sensing GE-25(I). Hall, EC., Strebel, D.E., Nickeson, J.E. and Goetz, S.J., 1991. Radiometric rectification: toward a common radiometric response among multidate, multisensor images. Remote Sensing Environ., 35: 1l-27. Jenson, J.R. (Editor), 1983. Urban/suburban land use analysis. In: Manual of Remote Sensing, 2nd ed. American Society of Photogrammetry, Vol. 2, pp. 1571-1666. Kauth, R.J. and Thomas, G.S., 1976. The Tasseled Cap - a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proc. Symp. Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, IN, pp. 4B4l-4B5 I. Kauth, R.J., Lambeck, RF., Richardson, W., Thomas, G.S. and Pentland, AI?, 1978. Feature extraction applied to agricultural crops as seen by Landsat. The LACIE Symposium Proceedings of the Technical Session, pp. 705-722. Kniezys, F.X., Shettle, El?, Gallery, W.O., Chetwynd, J.H., Abreu, L.W., Selby, J.E.A., Clough, S.A. and Fenn, R.W., 1988. Atmospheric Transmittance/Radiance: Computer Code

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LOWTRAN-7, AFGL-TR-88-0177, Air Force Geophysis Lab, Hanscom AFB, MA. Lunetta, R.S., Lyon, J.G., Sturdevant, J.A., Dwyer, J.L., Elvidge, C.D., Fenstermaker, L.K., Yuan, D., Hoffer, S.R. and Weerackoon, R., 1993. North American Landscape Characterization (NALC) Research Plan. U.S. EPA/6OO/R-931135,419 pp. Markham, B.L. and Barker, J.L., 1987. Radiometric properties of U.S. processed Landsat MSS data. Remote Sensing Environ., 22: 39-7 1. Marsh, SE. and Lyon, R.J.P., 1981, Quantitative relationships of near-surface spectra to Landsat radiometric data. Remote Sensing Environ., 10: 241-261. Salvaggio, C., 1993. Radiometric scene normalization utilizing statistically invariant features. Proc. Workshop Atmospheric Correction of Landsat Imagery, Defense Landsat Program Office, Torrance, CA, pp. 155-159.

Schott, J.R., Salvaggio, C. and Volchok, W., 1988. Radiometric scene normalization using pseudoinvariant features. Remote Sensing Environ., 26: l-6. Tame, D., Deroo, C., Duhaut, P., Herman, M. and Mocrette, J.J., 1990. Description of a computer code to simulate the satellite signal in the solar spectrum: the 5S code. Int. J. Remote Sensing, 2(4): 659-668. Turner, B.L. II, Moss, R.H. and Skole, D.L., 1993) Relating Land Use and Global Land-Cover Change, International GeosphereBiosphere Program, Report No. 24. Yuan, D. and Elvidge, CD., 1993. Application of relative radiametric rectification procedure to Landsat data for use in change detection. Proc. Workshop Atmospheric Correction of Landsat Imagery, Defense Landsat Program Office, Torrance, CA, pp. 162-166.