calculation and use of the sensor scan angle enabled
use of the most reliable data for mapping/compositing
and (2} the cloud masking approach was an easily
implemented method to create multiscene AVHRR com-
posites. Furthermore, the cloud masking approach was
a useful tool for AVHRR projects such as this one, which
did not require an automated operation for image com-
positing.

LITERATURE CITED

Eggen-Mcintosh, Susan; Zhu, Zhiliang. 1992. Mapping
Mexico’s forest cover: an international perspective.
in: Greer, Jerry D., ed. Remote sensing and natural
resource management: Proceedings of the 4th For-
est Service remote sensing applications conference;
1992 April 6-11, Orlando, FL. Bethesda, MD: Ameri-
can Society for Photogrammetry and Remote Sens-
ing: 88-93.

ERDAS {Earth Resource Data Analysis System). 1991.
Field guide. Atlanta, GA: ERDAS tnc. 394 p.

Evans, David L.; Zhu, Zhiliang; Eggen-Mcintosh, Susan
[and others]. 1992. Mapping Mexico's forest lands
with Advanced Very High Resolution Radiometer.
Res. Note SO-367. New Orleans, LA: U.S. Depart-
ment of Agriculture, Forest Service, Southern Forest
Experiment Station. 4 p.

Hil, J.M.; Evans, D.L.; Turnipseed, DP; Harlow, C.A.
1985. Environmentat monitoring for coastal zone
management. RSIP-TR 302.85. Baton Rouge, LA:
Remote Sensing and Image Processing Laboratory,
Louisiana State University. 42 p.

Kidwell, Katherine B., comp., ed. 1991. NOAA polar
orbiter data user’s guide (TIROS-N, NOAA-6,
NOAA-7, NOAA-8, NOAA-9, NOAA-10, NOAA-11,
and NOAA-12}. Washington, DC: National Oceanic
and Atmospheric Administration: 1-1 to R-l +
appendix.


angles were overwritten in those pixel tocations by
clearer images or images with smaller scan angles.
First, all images were examined visually for the amount
and location of clouds and for the size of scan angies to
decide the order of overlay for each compositing period.
Then each image was classified according to the
amount of clouds, and the resulting cloud classification
was in turn used to mask the original image at cloud
locations. Finally, masked image files were combined to
create a composite in the overlay order as defined in the
above process. The following steps detail techniques
used in the compositing procedure.

1. Creating a Cloud Mask Image File

tmage files were evaluated on a display screen to
determine the amount and location of clouds. As stated
previously, the best image had the most complete cov-
erage of Mexico and the fewest clouds. At the same
time, AVHAR spectral channels were noted for the high-
est contrast in separating clouds from ground types.
The near-infrared and thermal channels provided the
best monochrome display for cloud identification.

Histogram matching was used if the general bright-
ness levels of compositing images were visibly different
due to temporal differences in spectral values. In this
process, the clearest image in a compositing period
was used as a model image. The histograms of spectral
Statistics of the other image files were compared to the
histogram of the model image file to create separate
look-up tables (tables that perform certain functions on
input image data} for each of the other images. Pixel
values of the other image files were mapped through
their respective took-up tables to adjust the resulting
spectral characteristics to that of the model image file.

Spectral signatures were extracted for cloud classes
using the spectral distance threshold option. Clouds
tended to be spectrally heterogeneous; thus, the
threshold was adjusted accordingly. Separate classes
were created for different illumination levels due to
cloud type and position (e.g., bright cloud tops or cloud
shadows). In extracting spectral signatures, as weil as in
subsequent classification, using the two channels
(near-infrared and thermal) was more effective than
using ail available spectral channels.

Asimple parallelepiped classification routine (ERDAS
1991) was used to separate cloud types from the rest of
the data with the spectral signatures. Because cloud
pixels were easy to identify on the computer screen,
using this classification routine permitted adjusting the
lower and upper spectral limits interactively while cloud
pixels were classified. The resulting classified cloud
mask had only cloud classes, with the rest of the pixels
set to zero.

2. Masking Original Image Fites

Where the images did not overlap, it was desirable to
preserve the clouds rather than leave open areas after
masking. This task was accomplished by changing the
values of the cloud classes in the mask file to zero,
which was the value for the rest of the masking pixels
not affected by clouds.

The edited cloud-mask image was used to eliminate
clouds from each of the origina! image files. The
masked image files had zero values in areas where
clouds had existed, whereas the rest of the data
remained unchanged.

3. Compositing and Smoothing

Masked image files from step 2 were superimposed
in the order determined by the visual evaluation in step
1. The image file with the largest amount of masked
cloud locations was overwritten by image data from
masked files that had progressively smaller amounts of
cloud cover. The image that had the least amount of
masked clouds was added last. The process resulted in
a composite image from which most of the cloud
patches were removed and replaced by good data
values from the overwritten images.

Edges of some areas where clouds had been
replaced by compositing images occasionally appeared
quite distinct despite the effort of matching histograms
from raw image files. To reduce the edge effect, the
following three steps were taken:

a. A convolution routine with a iow-pass filter was
applied to the composite image to suppress the distinc-
tive appearance of transition areas.

b. Boundaries of clouds in the cloud-mask image files
in step 2 were iow-pass filtered. The resulting cloud
boundary image was used to mask the filtered com-
posite image. The result was a masked image file that
contained only smoothed compositing edges.

c. The masked-edge image file was overlaid on the
first composite image file to create the final composite.

CONCLUSIONS

Good image data are the basis for a land cover map-
ping project that employs remote sensing. Often raw
image data require preprocessing treatments before
they can be effectively used. Such was the case in the
project to map the forests of Mexico conducted by the
SO-FIA unit. where cloud removal and image composit-
ing, as well as calculation of an accompanying sensor
scan-angle channel were needed. Overall, the work
conducted in this project demonstrated that: (1) the


United States
Department of
Agriculture
a Forest Service Research Note
Southern Forest $0-375
Experiment Station February 1994
Scan Angle Calculation and Image Compositing
for the Mexico Forest Mapping Project
Zhiliang Zhu
SUMMARY The AVHRR is a cross-track, scanning spectrometer

Data from the Advanced Very High Resolution Radi-
ometer (AVHRR) were used in a cooperative project,
sponsored by the U.S. Department of Agriculture, For-
est Service, Southern Forest Experiment Station, and
the United Nations, Food and Agriculture Organization
(FAQ), to map Mexico's forest cover types. To provide
satisfactory AVHRR data sets for the project, the sensor
scan angle needed to be calculated for data points in
ihe composite image data, and the clouds had to be
removed and composite image data created from indi-
vidual data sets. Techniques used to accomplish those
two tasks are described here. Concepts illustrated here
should be applicable to other similar projects.

INTRODUCTION

A mapping project was conducted in 1991 at the U.S.
Department of Agricuiture, Forest Service, Southern For-
est Experiment Station, Forest Inventory and Analysis
(SO-FIA} unit, to create a new forest map of Mexico. The
Project was part of a global forest assessment program
coordinated by the United Nations Food and Agriculture
Organization (FAO} and was linked with Mexico's nation-
wide forest survey effort.

Personnel at the SO-FIA unit assisted with the project
by acquiring and preparing remotely sensed and other
digital data and by providing computer support and
training in image processing techniques to Mexican sci-
entists. The Advanced Very High Resolution Radiometer
(AVHRR) was the primary data source. Results of the
project, most noticeably a new forest land cover map of
Mexico, have been reported previously (Eggen-
Mcintosh and Zhu 1992, Evans and others 1992).

located on polar-orbiting satellites of the National
Oceanic and Atmospheric Administration (NOAA). The
sensor's characteristics (e.g., large ground picture ele-
ment [pixel] size and frequent Earth revisiting cycle)
make the data particularly suitable to large area, land
cover studies such as the Mexico project. While stan-
dard AVHRR data can be received at almost any point
on Earth by a ground station, additional treatment (pre-
processing) is often necessary to make the data more
useful. For example, over a nominal scene (a scene is
the area on the ground that is covered by satellite image
data} of 2,048 by 3,600 kilometers (Kidwell 1991), clouds
are likely to be present somewhere on any given day.
With AVHRR’s daily coverage of the same Earth loca-
tion, there is the opportunity to create a composite
image with minimal cloud cover from multiple sets of
data from different dates. Furthermore, the sensor's
scan angle (3) is large (+ 55.4° cross-track}. Data at the
extremes of this scan angle are more susceptibie to
attenuation caused by the atmosphere, and pixels from
these areas become distorted (fig. 1). Thus, it is custom-
ary to include angular information for each pixet location
in the compositing process. Generally, AVHRR pre-
processing involves radiometric calibration, geo-
referencing, calculation of various sensor angular data,
and compositing of multitemporal data.

Ten AVHRR data sets, georeferenced to a common
Lambert azimuthal equal-area projection, were pro-
vided by the U.S. Geological Survey, EROS (Earth
Resources Observation System) Data Center, Sioux
Falls, South Dakota. Each data set included a local
zenith angle data channel. The large number of clouds
and claud shadows present in all of the data sets neces
sitated the use of a compositing approach to reduce the
total cloud coverage in the images of Mexico. Another

Southern Forest Experiment Station/T-10210 U.S. Postal Services Bldg., 701 Loyola Avenue, New Orleans, La. 70113
Forest Service, U.S. Department of Agricutture.
Serving Alabama, Arkansas, Louisiana, Mississippi, £. Oklahoma, Tennessee, E. Texas, Puerto Rico, U.S. Virgin Islands


task was to create a scan-angle channel to accompany
the resulting composite images. As noted above, the
angular information provided would serve as a refer-
ence for pixels that were scanned at large angles and
used in the resulting composites. The techniques that
were used to accomptish the two tasks are presented in
this paper. The data processing steps were carried out
within the ERDAS® (Earth Resource Data Analysis Sys-
tem) image processing software package. The con-
cepts described should be applicable to other similar
projects.

METHODS

Deriving the Sensor Scan Angle

As illustrated in figure 1, the sensor scan angle, 8, is
the angie through which the sensor oscillates from the
nadir position to either side in a cross-track direction.
The ground area represented by an AVHRR pixel at or
near the nadir position is nominally set at 1.1 kilometers
on each side of the square area. When resampled, this
dimension is often set to 1 kilometer. As the sensor
swings to a large scan angie (e.g., an angle greater than

+ 35°}, the area captured by the instantaneous field of
view (IFOV or 6) becomes greater and deviates from the
standard pixel size and shape. Therefore, it is neces-
sary to derive the size of the sensor scan angle for
pixels in a composite data set. The tocal zenith angle
supplied with the AVHRR data for the Mexico project
was used to derive the sensor scan angle for each pixel.

In figure 2, B is the sensor scan angle with an abso-
lute value range between 0 and 55.4°, 2 is the local
zenith angle with an absolute value range between 0
and 69° (Kidwell 1991), A, is the equatorial radius of the
Earth, and A,,, is the nominal altitude of the satellite.
Given the trigonometric relationships observed in the
diagram in figure 2, and that sin (180° A) = sin A, the
following equation is obtained:

sin
Re

sin av
Ra + Asat a}

The inverse of equation 1 is:

Re)

B = aresin [sina
Re tAsat

(2)

RTELUITE

‘GROUND COVERAGE OF @
WHEN BIS LARGF (ONE
PUCEL)

Eig crouno coversse of ©

#0 AT NADIR (ONE PIXEL?

‘ro

ACTUAL SCAN LINE

Figure 1.—Retationship between the satellite scan angle and ground pixet size (after Hill and
others 1985). In the diagram, 6 = satellite scan angie, and O instantaneous
field of view (FOV) angle. One pixel is the area covered by one 0). Note that the
pixe/ at nadir (the point on the ground directly beneath the satellite} is a square:
pixels at a farge B become distorted.

Zhiliang Zhu is a research forester, Forest Inventory and Analysis unit, U.S. Department of Agriculture, Forest Service, Southern Forest Experiment
Station, Starkville, MS 39759.


SATELLITE

SATELLITE
SCAN ANGLE
4B)

Local ZEMTH
ANGLE (A)

SOLAR ZCNITH
ANGLE

| 27 —erumcieat Pont

Figure 2.—Angutar relationstyps of satellite to Earth and Sun (after Kidwel!
1991). Nadir is the point on the ground directly beneath the
satellite, R, is the equatorial radius of the Earth, and A... is the
nominal attitude of the satellite

A value of 90° was usually added to the local zenith
angle by the EROS Data Center to put it in a range of 21
to 159°. Therefore, when equation 2 was entered in
ERDAS to salve for B, 90° was subtracted from prior to
calculation.

The value of degree (* ) and the arcsin function were
not supported by ERDAS. Thus, the following trig-
onometric equations were used:

180)
degree{*) = radian (1180) _)
(3)
where x is equal to 3.1416, and
_*_)
arcsin X — arctan. ©j7#—————~"
v 1-x2 (4)

where X is any given value.

Substitution of equations 3 and 4 into equation 2
gives equation 5, which was used in ERDAS to calculate
the sensor scan angle:

mc th SLE) a

= (arctan sn ye? (Fy Aue) 180
1 Ik 9017 R y 7
Ch nT. ar a 6)

Given the A value, and by using A, ~ 6,378 km and A,a,
for the NOAA sateltites =- 850 km, equation 5 can be
solved for B.

The above process created 1 sensor scan-angie
channel for each of the 10 AVHRR data sets. The scan-
angle channel was then appended to the regular data
set and included for use in the second task (i.e., remov-
ing clouds and creating the composite data sets). Thus,
pixels in a resulting composite included five spectral
channel data points as weil as the corresponding scan
angle.

During the compositing process, scan-angle vaiues
were used as one of the factors for consideration in
selecting pixels from different AVHRR dates. The inclu-
sion of scan-angle data also provided an opportunity for
users, among other things, to identify potential atmo-
spheric effects and to adjust area statistics represented
by classified pixels.

Cloud Removal and Compositing

Among the 10 available 1990 AVHRR data sets of
Mexico, 3 were acquired in March, 4 in May and June,
and 3 in December. To preserve spectral differences
due to the different vegetation growing seasons, com-
positing was done using acquisition dates close to the
dates of the original image files. Therefore, three com-
posites were created: one based on the three sets of
March images, one on the four sets of May and June
images, and one on the three sets of December images.

The generai strategy for creating a composite from
severat original image files was to overlay and overwrite
the image files in a hierarchical order based on the
amount of clouds (including cloud shadows) present in
the images, as wel! as on the relative sizes of scan
angles. images with heavy cloud cover or larger scan