An assessment of AVIRIS data quality for studying coniferous canopy chemistry was made. Seven flightlines of AVIRIS data were acquired over a transect of coniferous forest sites in central Oregon. Both geometric and radiometric properties of the data were examined including: pixel size, swath width, spectral position and signal-to-noise ratio. A flat-field correction was applied to AVIRIS data from a coniferous forest site. Future work with this data set will exclude data from spectrometers C and D due to low signal-to-noise ratios. Data from spectrometers A and B will be used to examine the relationship between the canopy chemical composition of the forest sites and AVIRIS spectral response

Swanberg, Nancy A.

English

NASA Technical Reports Server

AVIRIS DATA QUALITY FOR CONIFEROUS CANOPY CHEMISTRY 
NANCY A. SWANBERG, TGS Technology Inc., NASA Ames Research 
Center, USA 
ABSTRACT 
An assessment of AVIRIS data 
quality for studying coniferous canopy 
chemistry was made. Seven flightlines 
of AVIRIS data were acquired over a 
transect of coniferous forest sites in 
central Oregon. Both geometric and 
radiometric properties of the data were 
examined including: pixel size, swath 
width, spectra 1 posit ion and 
signal-to-noise ratio. A flat-field 
correction was applied to AVIRIS data 
from a coniferous forest site. Future 
work with this data set will exclude 
data from spectrometers C and D due to 
low signal-to-noise ratios. Data from 
spectrometers A and B will be used to 
examine the relationship between the 
canopy chemical composition of the 
forest sites and AVIRIS spectral 
response. 
INTRODUCTION 
The objective of this study is to determine whether or 
not AVIRIS data can be used to study coniferous canopy 
chemistry. This involves two steps: (1) an evaluation of 
AVIRIS data quality and ( 2 )  if in step one the data proves 
good enough, an examination of the relationship between 
canopy chemical composition and AVIRIS spectral response. 
The first step is the subject of this paper. 
DATA COLLECTION 
Seven flightlines of AVIRIS data were collected on 
August 1, 1988 over forest test sites in central Oregon. 
Together these zones form an east-west transect which 
exhibits a gradient of climate and fertility encompassing 
the western coast range, the interior coast range, the low 
elevation west cascades, the mid-elevation west cascades, 
the high cascade summit, the east slope cascades, and the 
interior high desert. AVIRIS data were acquired Over 1 3  of 
the total 14 sites distributed troughout the transect where 
ground data had been collected in previous studies. The 
site missed was in the western coast range. Average 
spectra from three-by-three pixel windows over three forest 
https://ntrs.nasa.gov/search.jsp?R=19890012793 2020-03-20T02:34:32+00:00Z
sites are shown in Figure 1. 
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250 
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150 
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0.4 0.6 0.0 1 1.2 1,4 1.6 1.0 2 2.2 2.4 
Wavelength (wn) 
Fig. 1. Nine pixel mean AVIRIS spectra from three forest 
sites: Casacde Head (top), Metolius (middle) I and 
Santiam Pass (bottom). 
METHODS AND RESULTS 
Both geometric and radiometric properties of the 
radiometrically corrected AVIRIS data received from JPL 
were examined. Pixel size in the along-track and 
across-track directions were measured on three flightlines. 
United States Geological Survey orthophoto quadrangles were 
used as the basis for comparison. Swath width was 
calculated as the average across-track pixel size 
multiplied by the number of pixels per line (614). These 
measurements and calculations for each flightline are shown 
in Table 1. 
Table 1. Mean pixel size and swath width of AVIRIS 
data 
Site Name Across-Track Along-Tzack Swath 
Mean Pixel Size Mean Pixel Size Width 
Cascade Head 1 8 . 3  m 
Trout Creek 17 .6  m 
Metolius 17.8  m 
18.9  m 11 ,236  m 
1 7 . 5  m 10,806 m 
16 .7  m 1 0 , 9 2 9  m 
LO3 
The average across-track pixel size ranged from 17.6 
to 18.3 m while in the along track direction it ranged from 
16.7 to 19.1. The swath width ranged from 10,806 to 11,236 
m. This variation is most likely due to differences in 
topographic relief between these flightlines. 
An atmospheric model, LOWTRAN 6 (Kniezys et al., 
1983), was used to evaluate AVIRIS spectral position. 
Known atmospheric absorption features were compared to 
those from a flat-field AVIRIS spectrum. This comparison 
is shown in Figure 2 .  where a fifty pixel average spectrum 
from a beach and LOWTRAN 6 output from the mid-lattitude 
summer model of solar spectral irradiance reflected by a 
hypothetical fifty percent reflector are plotted. 
i'i 
WRVELENGTH I U M l  
Fig. 2 .  Solar spectral radiance (uW/cm squared-nm-sr) 
from LOWTRAN (top) and an AVIRIS flat-field 
(bottom) showing the relative positions of 
atmospheric water and carbon dioxide 
absorption features. 
As can be seen in Figure 2. the atmospheric water 
absorbtion features at 0.942, 1.135, 1.389 and 1.876 
micrometers (Coulson, 1975) as well as atmospheric carbon 
dioxide features at 1.4, 1.6 and 2 . 0  micrometers (Liou, 
1980) are properly located within the appropriate 10 
nanometer AVIRIS spectral band. 
Visual inspection of the data revealed a noise 
pattern, present in all channels and especially noticable 
in homogeneous areas, that made the image look like 
corrugated cardboard. This periodic noise was evident in 
the Fourier domain as noise spikes, but was not filtered 
out prior to the following analysis of the signal-to-noise 
ratio. Sudden shifts in scene brightness were also evident 
in several flightlines, but did not OCCUK between imaging 
of the forest study site and the beach site used in this 
analysis. 
The ratio of mean radiance to standard deviation of 
the radiance over  a homogeneous surface was used to 
estimate the ratio of signal-to-noise for each AVIRIS 
spectral band. Assuming the beach was a relatively 
homomgeneous s u r f a c e  an  estimate of the siqnal-to-noise 
ratio for each AVIRIS spectral band was obtained by 
computing the mean radiance of the fifty beach pixels and 
dividing it by the standard deviation of those fifty 
pixels. This is a "best case" estimate of the 
signal-to-noise ratio for this flightline since beach sand 
is a highly reflective target as compared to the rest of 
the scene, but it does provide an upper bound. 
To obtain a signal-to-noise estimate for forested 
sites of interest in this study a similar method was 
employed. Nine adjacent pixels were extracted over the 
forest test site from the same flightline as the beach and 
for each channel the mean was divided by the standard 
deviation. In this case the assumption of homogeneity is 
not as good as for the beach and will lower the estimate of 
signal-to-noise. To minimize this effect a dense stand 
with a high leaf area index was chosen to provide as 
homogeneous a target as possible. This estimate served as 
a lower bound on the signal-to-noise ratio for forest 
targets on this flightline. Estimates of signal-to-noise 
ratios for both targets in all bands are shown in FigUKe 3 .  
The signal-to-noise ratio is lower in nearly all 
channels for data taken over the forest test site than for 
data from the beach (as would be expected from their 
different albedos). Excluding the atmospheric water 
absorption bands centered at 1.38 and 1.87 micrometers, the 
greatest difference between the two estimates is in 
spectrometers C and D. In spectrometer C between 1.55 and 
1.68 micrometers the mean signal-to-noise ratio as 
calculated from the beach AVIRIS data is 20.8 while the 
mean signal-to-noise ratio for the forest data is 7.7. In 
spectrometer D between 2.08 and 2.26 micrometers the 
estimates for the signal-to-noise ratio axe 14.6 and 2.3 
for the beach data and the forest data, respectively. 
A flat-field approach (Roberts et al., 1986) was used 
to correct for atmospheric effects in data from the forest 
site. The spectrum from the beach was divided into the 
forest spectrum to produce the spectrum shown in Figure 4. 
This spectrum looks like a typical vegetation spectrum with 
some exceptions: the flat region centered at approximately 
1.2 micrometers, the noisy portion between approximately 
2.0 -2.4 micrometers, and the decending values between 0.4 
105 
- 0.55 micrometers. 
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Z 
2;; 
30 
20 
10 
0 
2.2 2.4 2 1.2 1.4 1.6 1.0 
Wavcltngth (wn) 
1 0.4 0.6 0.8 
Fig. 3 AVIRIS signal-to-noise ( S / N )  estimates for a 
flat-field (top) and forest site (bottom) 
2.1 , 
2 -  
1.9 - 
1.0 - 
1.7 - 
1.6 - 
1,5 - 
1.4 - 
1.3 - 
1.2 - 
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1 -  
2.2 2.4 2 0.4 0.6 0.0 1 1.2 1.4 1.6 1.0 
Wavclenqth (wn} 
Fig. 4. Ratio of an AVIRIS forest spectrum to a flat-field 
spectrum 
106 
DISCUSSION AND CONCLUSIONS 
Site coverage of 1 3  out of 1 4  sites, spatial 
resolution of better than 2 0  m, and spectral position of 
within one AVIRIS bandwidth are all expcted to be 
sufficient for this study of coniferous canopy chemistry. 
Excluding the atmospheric water absorption bands the 
signal-to-noise ratio in spectrometer A and B as bounded by 
even the low estimate in Fiqure 3 .  is above t e n  f o r  n e a r l y  
all bands and should be adequate for this work. AIS data 
of similar spectral resolution for this same forest site 
exhibited signal-to-noise ratios greater than t e n  in all 
channels and correlated well to canopy chemistry in a 
previous study (Swanberg and Peterson, 1987). 
The mean signal-to-noise ratio in spectrometers C and 
D, however, fall somewhere in the range 7.7 - 20.8, and 2.3 - 14.6, respectively. If one believed that the true 
signal-to-noise ratio for the forest site were actually 
closer to the upper bound the data could be considered 
useable; however, it is probably closer to the lower bound. 
The lower bound, calculated from data of the forest site, 
is an underestimate, but all efforts were made to use as 
homogeneous a forest target as possible and any lack of 
homogenity probably accounts for only a small percentage of 
the noise in this estimate, 
Although the results of  flat-field correction were 
encouraging they exhibit a number of problems. The flat 
region at 1.2 micrometers can be explained by the 
discrepancy in that spectral region between the beach 
spectrum and the LOWTRAN model output. The noisy region 
between 2.0 - 2.4 micrometers can be attributed to the 
AVIRIS signal-to-noise ratio in spectrometer D. The 
decending values between 0.4 - 0.55 micrometers can not 
easily be explained. This technique and additional methods 
of atmospheric corection will need to be examined before 
data from several flightlines can be compared. 
FUTURE WORK 
Since the signal-to-noise ratios in spectrometers C 
and D are unacceptabely low, future work with these data 
will be restricted to data from spectrometers A and B. 
Fourier filters will be constructed for each flightline to 
reduce periodic noise and methods to normalize data from 
different flightlines with respect to the dark current 
offsets will be investigated. Atmospheric corrections will 
be invesigated to corect for differences in elevation as 
well as possible differences in atmospheric composition 
between sites, Finally, the relationship between canopy 
chemical composition, as characterized in previous studies, 
and AVIRIS spectral response at each site will be examined. 
10 7 
ACKNOWLEDGEMENTS 
The author thanks J. Leipner at NASA Ames Research 
Center for her help running the LOWTRAN model and making 
plots for this paper. The author also thanks J. Dungan 
and P. Curran at NASA Ames Research Center for their 
helpful discussions. Funding for this research was 
provided by the Earth Science and Applications Division, 
Land Processes Branch, Terrestrial Ecosystems Program, NASA 
Headquarters. 
REFERENCES 
Coulson, K.L. 1 9 7 5 .  - -  Solar and Terrestrial Radiation. 
Academic Press, New York, New York, pp. 265-267.  
F.X., E.P. Shettle, W.O. Gallery, J.H. Chetwynd, Jr., 
L.W. Abreu, J.E.A. Selby, S.A. Clough, R.W. Fenn 
1983.  Atmospheric Transmittance/Radiance:Computer 
Code LOWTRAN 6. Air Force Geophysics Laboratory, 
Environmental Research Paper, No. 846 ,  2 0 0  pp. 
Liou, K. 1 9 8 0 .  An Introduction to Atmospheric Radiation. 
Academic Press, New York, NewYork, PP 60-63.  . - -  
Roberts, D.A., Y.. Yamaguchi, and R.J.P. Lyon 1 9 8 6 .  
Comparisons of Various Techniques for Calibration of 
AIS Data, Proceedings of the Second Airborne Imaqing 
Spectrometer Data Analysis Workshop, JPL 86-35,  
Swanberg, N.A. and D.L. Peterson 1 9 8 7 .  Using the the 
Aixbone Imaging Spectrometer to Determine Nitrogen 
Content in Coniferous Forest Canopies, Proceedings of 
- the International Geoscience Remote Sensing 
Symposium, p. 981 .  
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pp .21-3 0. 
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AVIRIS data quality for coniferous canopy chemistry

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