1.1 SCENE SELECTION
1.1.1 Season
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Figure 1. Big Bend study area on Florida's Gulf coast |
Our primary project area is in the Big
Bend region of the Florida Gulf coast (Figure 1).
Two scenes are required to cover the area, and two
seasons are required to fully classify the land cover.
Winter imagery is used to augment the spring
imagery for the distinction and classification of
deciduous vegetation. A total of four coordinated
scenes were acquired for the base years in the
analysis (Table 1). Supplementary analysis in the
time series requires contemporary spring scenes for
each additional year.
1.1.2 Water Level
Table 1. Landsat TM images and
Cedar Key water level at time of overpass |
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Landsat coordinates path/row |
Location | Date |
Cedar Key water level (msl) |
18/39 | Tallahassee, FL | 01/23/85 | 0.40 |
18/39 | Tallahassee, FL | 04/25/86 | 1.05 |
18/39 | Tallahassee, FL | 11/29/93 | 0.71 |
18/39 | Tallahassee, FL | 04/09/95 | 1.46 |
17/40 | Cedar Key, FL | 01/16/85 | 1.18 |
17/40 | Cedar Key, FL | 04/25/86 | 1.29 |
17/40 | Cedar Key, FL | 01/12/95 | 1.26 |
17/40 | Cedar Key, FL | 04/02/95 | 0.54 |
Due to a combination of factors such as cloud cover and scene availability, the imagery secured for the Big Bend does not entirely fit the C-CAP standards, but is the best available for the analysis. Some variability in water level is acceptable if two scenes per year are employed in the analysis. In the Big Bend we found that a complementary scene near MLW in the same year can facilitate the correction of differences caused by water level (Table 1). The 1986 south imagery exhibits high water level in both winter and spring, and consequently poses the greatest difficulty in identifying low elevation intertidal habitats.
1.1.3 Cloud cover
Cloud cover is the most limiting
parameter in scene selection. Listings may be
misleading, and previews of imagery should be
made whenever possible to ensure quality data
before purchase. Although it is advisable to
select entirely cloud-free imagery, in some cases
it may be necessary to establish only that the
critical study area is cloud-free.
1.1.4 Simultaneous coverage
When a study area is large and/or
encompasses more than one scene, simultaneous
coverage of the whole area is warranted. It is
possible, under ideal conditions, to secure
several images within days or at most within 3-4
weeks that cover the entire area. In the instance
where a study area falls between two scenes in
the same path, it is possible to order an image
that straddles the image boundaries, eliminating
the need to mosaic scenes together.
1.1.5 Specifications
Imagery obtained from the EROS Data
Center (EDC) and EOSAT is now provided in
CD-ROM format. The procedures that follow
begin with this format. Other formats may be
accommodated with minor adjustments in the
initial steps. All imagery for this project is
obtained in Space Oblique Mercator (SOM) and
with Nearest Neighbor resampling (NN). NN
resampling is recommended for studies involving
spectral analyses because it minimizes alterations
to the count values. Other resampling such as
cubic convolution may be preferred in studies
involving pattern recognition.
1.2 SCENE PREPARATION
The contents of the CDROM will
include 7 bands of TM along with summary and
header files. All 7 bands of data are extracted
from the CD and converted to a readable format
for image processing. The header file is also
extracted at this point.
The PCI procedures, CDEOSAT and
CDNLAPS, read data off the CD-ROM, create a
new file on disk, write the band data, and extract
header information which is written into an
orbital segment attached to the new file. The
orbital segment will be used in the rectification
of the imagery to the UTM coordinate system.
Other coordinate systems may be employed in a
similar manner.
1.2.2 Check header file
The header file should accompany the
scene and a readable copy of the header file is
made to disk. A sample header file may be
found in Appendix B. Information from the
header file is used in subsequent pre-processing
steps. Trailer files may or may not accompany
the imagery. Typically, the information desired
from this file is the time of overpass in
Greenwich Mean Time (GMT), which is noted
for use in the radiometric enhancement.
1.2.3 Visual check
All new imagery is previewed, band-by-
band, to determine if data lines are missing or
other anomalous features exist in the scene. The
visual review must be done at full-resolution.
Attention is focused on identifying lines or
blocks of missing data in each band for
subsequent "repair." Haze or cloud cover that
may render parts of the scene ineffective for
analysis is noted as well. In our case the visual
check is conducted in PCI Imageworks with
seven image planes in the display window. Each
portion of the image is displayed at full-
resolution, and each band is viewed separately to
identify missing data.
It is useful to examine the range and
histogram of count values in each band at this
time. Occasionally imagery is purchased with
offset or anomalous count values. A quick
evaluation now may save time later. Band 6, the
thermal band, is especially prone to such errors.
1.2.4 Line replacement and destriping
Missing lines are "repaired," and other
problems are resolved in each band before the
scene is rectified to the UTM coordinate system.
Once rectification has been conducted, errors will
no longer coincide with horizontal lines of data
and are virtually impossible to fix. Image line
replacement is a simple procedure that allows the
operator to fill-in missing lines with the line
above, below, or with an average of the two.
It also may be necessary to conduct
destriping of the image if a linear pattern is
prominent in the image. It is usually a more
significant problem with Landsat MSS than with
TM or SPOT, and affects dark objects such as
water in particular. However, we have run
several variations of a destriping procedure on
scenes with an 8 to 9-line repetitive stripe and
have concluded that the output image was not
improved over the original. Although we do not
conduct this procedure ourselves, it may be
advisable in other situations, and, if necessary,
must be attempted in this initial phase of pre-
processing, before rectification.
1.3 ORTHORECTIFICATION OF IMAGERY
Rectification of the imagery requires
several steps: identification of ground control
within the imagery, collection of ground control
points with a GPS unit, development of a
rectification model, reprojection of the imagery
using the model, and two accuracy checks.
Three terms are defined:
However, in some projects, a previously
rectified scene might be used as reference so that
all scenes are registered to the base scene. For
instance, a set of Landsat MSS scenes might be
registered to a NALC (North American Land
Characterization) scene. If the above or other
procedures are used where a polynomial
transform "rubber sheet" adjustment is
employed, it is usually preferable to co-register
scenes, then conduct the reprojection and
rectification on all of them. Co-registration
without rectification is never recommended.
1.3.1 Coordinate System
Rectification and reprojection of satellite
imagery to a standard coordinate system is
performed on all scenes in the project. Whether
you use UTM or another coordinate system, the
reprojection allows the determination of
geographic coordinates for features identified in
the analysis and facilitates integration with other
geographic data sets. The approach used in the
Big Bend project employs a PCI procedure,
SORTHO, which reprojects the image based on
satellite orbital information and a set of standard
ground control points. The results effectively
combine both inter-scene compatibility and
coordinate plane rectification. To achieve co-registration
without rubber sheeting, a common
set of ground control points is used for all
images. In this way the original data is rectified
and reprojected from the Space Oblique Mercator
(SOM) projection of the raw data to the
Universal Transverse Mercator (UTM) coordinate
system, with corresponding results in each
subsequent image rectification.
1.3.2 Ground control in image
Ground control points are identified in
the imagery as clearly visible point or right-angle
positions, which are also accessible by road.
Approximately twice as many points are
identified as are ultimately required for the
registration for several reasons. Some positions
are not retrievable in the field, not all positions
are identifiable in all imagery, and some
positions are held out as part of the registration
accuracy check. The operator examines a full-resolution
image display to select the ground
control. Preferred locations are corners at small
to intermediate right-angle road intersections in
rural or low-density developed areas. Highly
developed areas tend to give blurred intersections
and are difficult to re-locate in subsequent
imagery. The identification of visible and
distinct locations in the imagery which are also
accessible to a field team is a major challenge in
many regions. Alternatives to road intersections
and right angles may be selected based on other
landscape features. For example, the end of a
bridge over a stream may be suitable.
Consideration is given to the clarity of the
location and the possibility of re-identification of
the same point in earlier and later imagery.
Good judgement and careful selection ensures
accurate registration of the study area.
The set of selected positions are marked
on the imagery file with a vector which can be
displayed on a field laptop computer for
identification during the field reconnaissance. If
the technology is not available, a hardcopy is
printed of each potential ground control point in
the imagery, and these are bound with map
sheets to aid in field identification.
1.3.3 Ground control in field
Accurate ground control points are
collected for each image based on pre-identified
locations as described in section 3.2. A hand-
held Global Positioning System (GPS) unit is
employed with accuracy guaranteed 5-10 m.
Positioning of
Field plans should include 5-10
minutes per station for data collection and site
documentation to facilitate identification of the
positions in subsequent imagery. We
recommend a minimum of 2-3 readings per
position, and 24-30 positions per image. The
ground control should be well-distributed across
the image and at locations which are visible in
imagery over a period of 5-10 years. As some
GPS readings turn out to be poor quality, we
recommend collecting multiple readings at each
location. Accurate and complete field notes
accompanying the collection of GPS ground
control facilitate the identification of the
positions in subsequent imagery. A description
including county, road, and intersection names,
distance from nearby intersections, directional
designators, and photographs are helpful in future
relocations. We have also concluded that the
occupation of an intersection corner,
appropriately designated (as in "southwest
corner"), is better than occupying the center of a
road intersection as has been previously
practiced.
Other techniques exist for the collection
of ground control points and may be applied
where resources are limited. For instance, low-cost,
non-differential , non-P-code GPS receivers
can be used, providing positional accuracy of 30-100
m. Map accuracy in will be reduced from
the 1:25,000 achieved in this project to 1:50,000
or more. Alternative approaches include
digitization of positions from mylar 7.5 minute
quadrangles, or the selection of similar positions
from digital data sets including vectors, digital
orthophotographs, and other imagery. Each
approach carries with it inherent errors, of which
the analyst should be aware. At the very least,
the operator should know the error range for the
input coordinates prior to the analysis. This
will determine an acceptable RMS error range
and whether or not the intended and final map
accuracy will be achieved. The same caution
applies to the selection of alternative adjustment
techniques.
The PCI software in use for this project
has a field version of GCPWORKS (under our
investigation), which incorporates the selection
of ground control in the imagery and the
collection of GPS ground control
simultaneously, on a field laptop computer.
Although this does not eliminate the need to
carefully select quality and well-distributed
control within the image and in the field, it does
consolidate the work, and allows the operator to
evaluate the quality of the ground control as it is
being collected. The on-site development of a
rectification model eliminates costly returns to
the field in the event some ground control proves
useless.
The GPS unit employed in this project
has a stated accuracy of < 10 m, which met our
mapping needs with TM and MSS. Higher
resolution imagery such as SPOT and IRS may
benefit from even higher accuracy ground control,
available with newer GPS units at 1-5 m.
Although 24-30 positions are collected per
image, not every position is used in the actual
rectification. In addition to the elimination of
poor quality positions, other ground control
points are not easily relocated in the imagery and
are not used. Eight to ten positions are used to
rectify the imagery, and another 10-12 are set
aside to be used in the accuracy check.
1.3.4 Create model
A model is created with the ground
control points. It is preferable to use a low-order
polynomial transform to reduce distortion in the
final image, particularly at scene edges or over
large water bodies. The PCI procedure we use
makes the conversion between the two systems
without introducing distortion to the resulting
image as is normally found in polynomial
adjustments. We prefer this approach as it gives
consistent spatial positioning across the image.
Other registration packages are available and may
be applied with varying results depending on the
quality of the ground control and the amount of
distortion introduced in the polynomial function.
User-entered coordinates
Imagery is brought to display on the
monitor for the collection and identification of
ground control points. These ground control
points are part of the original set selected from
the imagery at full-resolution display and
subsequently collected in the field. They are
spatially well-distributed over the extent of the
image and show promise of being stable and
easily identifiable in a time series. The GPS
coordinates collected at these locations show
little variation in repeat readings.
During the identification of ground
control, watch the RMS (root mean square) error
for each ground control point and the total RMS
error in both x and y. Examine the scatter plot
of the ground control to help identify positions
which are in error. Within reason, it is possible
to adjust the location of the ground control in the
imagery to achieve optimal positioning and
distortion-free rectification.
We have found with 10 m accuracy in
the GPS and 30 m resolution in the imagery that
the RMS of each individual position and the
total RMS need not exceed 0.5 pixels (15 m).
At the same time, consider that excessive
adjustment of point positions to produce
significantly lower RMS readings will introduce
errors in the final adjustment. An RMS of 0.5
pixels indicates that the position is + 15 m or
1/2 pixel from the optimal location. Under the
circumstances, it is reasonable to expect a total
RMS of 0.3-0.6 pixels, or 10 - 20 m, best fits
the accuracy achievable with the given data. We
have found that substantially forcing the model
to lower errors introduces regional distortion into
the final image.
Model
The collection of ground control points
in the imagery leads to the development of a
model to adjust the full image to the new
coordinate system. See Chapter 2 for detailed
steps in PCI. If not running GCPWORKS, and
SMODEL, follow appropriate steps for software
application.
1.3.5 Satellite image rectification
The CD-ROM Format
1.2.1 Download raw imagery
Ideally, the rectification and reprojection
will inherently produce co-registered images. We
use rectification and projection procedures which
result in sufficient accuracy to produce co-
registered scenes. The reprojection is critical in
that it produces images in a standard projection
such as UTM or state plane.
10 m requires differential, P-code,
or comparable receivers for accurate
rectification of Landsat TM or SPOT imagery.
Standard non-differential units do not produce the
necessary sub-pixel accuracy.
Table 2. Scene size and coordinates for georeferencing segment | ||
Landsat TM Big Bend | Scene Location | |
File Information | Tallahassee | Cedar Key |
Path/Row | 18/39 | 17/40 |
Initial file size | 6967 pixels/5965 lines | 6967 pixels/5965 lines |
Final file size (x/y) | 7556 pixels/5412 lines | 7916 pixels/7112 lines |
Pixel size (x/y in meters) | 28.5/28.5 | 28.5/28.5 |
Upper left coordinates (UTM) | 98560 E, 3408400 N | 210287 E, 3296110 N |
Lower right coordinates (UTM) | 313906 E, 3254158 N | 435893 E, 3093418 N |
The size and initial boundaries for the two scenes in the study (Figure 2 and Figure 3) are shown in Table 2 with UTM zone 17 coordinates for the upper left and lower right corners. The north image is cut at the Florida/Georgia border, approximately - 1100 lines (Figure 3). A new file of matching size is created for each image in the series. A matching georeference segment is established. Details are provided in Chapter 2.
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In the event new regions are processed under these instructions, the designation of an output file size and geographic extent will depend on project needs, although it is recommended that full, or close to full scenes are pre-processed to maximize their subsequent utility. The calculation of file size is determined by the maximum and minimum eastings and northings of the raw image and the pixel size. Pixels in Landsat TM are 28.5 x 28.5 m, in MSS the pixel size is 57 x 57 m. Image rectification is conducted with the model segment created earlier, and the output applied to the newly-created and georeferenced image file discussed in the preceding paragraph.
1.3.6 Accuracy check
Accuracy checks involve both geographic or map
accuracy, and co-registration or inter-scene
accuracy. It is important to recognize that
multiple scenes may meet one criteria and not
the other. Two scenes may be co-registered to
within a pixel, but have significant geographic
error. Conversely, scenes may meet a standard
map accuracy in a selected coordinate system,
but have systematic errors that result in poor co-
registration between scenes. Mis-registration
may result from poor models or the use of high
order polynomials. Evaluation of both map
accuracy and image co-registration is necessary
subsequent to image reprojection.
UTM or coordinate system accuracy
Image rectification is not complete
without accuracy checks. If check points were
not entered and evaluated as in GCPWORKS
and SMODEL above, it is necessary at this point
to objectively locate 8-12 check points in the
rectified image. The evaluation will give a
measure of the map accuracy, the percentage of
the image within the standard x' m. Obviously
the evaluation varies according to the resolution
of the imagery, the accuracy of the ground
control, and the intended accuracy for the
particular study. The measure obtained tells the
user the expected positional accuracy of the
majority of features in the image.
The check points are part of the larger
set of ground control established with GPS as
described in 1.3.2 and 1.3.3.
Visually locate
each ground control check point with the cursor
in the rectified image. Note the image
coordinates at the location, and evaluate how far
the position is from the known x,y (UTM)
coordinates. It may be difficult to conduct this
step objectively. Try evaluating the portion of a
pixel from which your visual position strays
from the coordinates at which it visually should
be. In our case, the goal was to accurately map
90% or more of the map features within 20 m of
their known location, so we evaluate each
position relative to our objective.
The accuracy check of the image to the
UTM coordinate system meets our needs and is
acceptable. Clearly, with only 12 check points,
only one position per x/y component can exceed
the established tolerance. It is worth conducting
a careful check at this point to ensure good
geographic correlation of data. If there is a
geographic error in the image, it helps if you
have indicated in which direction it shifted with
a simple ± after each x/y offset.
Inter-image accuracy
The second accuracy check is conducted
on imagery which will be used in inter-scene
comparisons. It has been a common practice to
rectify only one image, and then register all other
images to the base scene, or to co-register all
scenes to a master and apply the rectification for
that scene to all others. While the standard
approach provides a solution to multiple-image
registration, every image in the present study is
rectified individually to UTM coordinate system.
According to this approach, each subsequent
image is rectified using the same set of ground
control points, and is visually checked against
the original, or first-rectified base image as
follows.
The inter-scene accuracy check consists
of selecting 3 to 4 areas within the image to be
displayed, one by one, at full resolution with
selected bands from each image. Two methods
are used to check inter-scene registration: (1) a
simultaneous display of the same band from both
scenes, using color as a reference to examine
horizontal and vertical linear features, (2) a flicker
state between the two scenes with the cursor at a
fixed, well-defined location. Both methods may
be applied regardless of the software in use.
In the first method single bands, usually
band 4 or 3, from each scene are displayed
simultaneously in a red-green-blue (RGB)
display. Offsets will be obvious in either the x or
y direction as bands of color on either side of a
vertical or horizontal roadway
(Plate 1).
The first evaluation gives a quick check on overall
alignment of the two images.
In the second approach the bands from
the base image are displayed simultaneously
with the same bands from the new image in a
multiple-image plane display and set to flicker
state. Three to five right-angle road intersections
which display true horizontal and vertical
direction within the image are evaluated in each
of the selected full-resolution windows. Each
intersection is evaluated and recorded in a table
(see Chapter 2.2.7).
Plate 2
illustrates the position of the
cursor at a right angle road intersection in the
base image and evaluation of the cursor position
relative to the intersection in the newly-rectified
image. The cursor is placed at the center of the
intersection in the base image, as if two
imaginary lines were drawn N/S and E/W,
taking into consideration the mixed pixel effect.
The display is flickered to the newly-registered
image, and the location of the intersection is
compared in half pixel increments. Roads and
intersections at non-right angles to the pixel/line
orientation are never used in this evaluation. See
Plate 3
for examples of intersections preferred for
evaluation. The second method provides the
operator with a detailed evaluation of inter-scene
compatibility and the direction of offsets, if any.
Expected inter-scene registration is ±
one pixel. A trend of greater than one pixel in x
or y requires re-examination of the ground
control point segment and a repeat of the whole
rectification process. Consistent offsets or
regional trends of greater than one pixel suggest
poor quality registration and require re-
rectification of the imagery with a thorough
evaluation of the ground control point
coordinates, positioning, and land cover changes
between the imagery dates.
Again, good judgement, and a second
opinion may help to eliminate operator errors or
unforeseen problems. All imagery in the current
project must meet the ± one pixel inter-scene
accuracy before additional processing can be
conducted. The goal is consistently met and
presents no real problem with this particular set
of imagery. We encourage other projects to
develop similar methods to achieve high inter-scene
positional registration.
1.4 RADIOMETRIC AND ATMOSPHERIC CORRECTIONS
Several band enhancements and
corrections are applied to the rectified imagery to
normalize the dn (digital number) values,
facilitating direct spectral comparisons between
imagery bands and a comparable set of values as
input to indices and clustering programs.
Radiometric calibration, conversion to reflectance
or solar correction, and atmospheric correction
are conducted on every image. The adjustments
rely heavily on data contained within the header
file, including gain, bias, and solar zenith angle.
Programs have been written in-house to facilitate
the first two adjustments. The atmospheric
adjustment still relies on a visual/manual
assessment of dark water values, although it may
also be automated eventually.
1.4.1 Radiometric correction
The digital counts in the image are
transformed to reflectance using the calibration
that comes with the files and the equations and
constants of Price (1987), and Markham and
Barker (1985):
where
Eo is the solar constant (Price, 1987;
Markham and Barker, 1985), r is the normalized
earth-sun distance,
1.4.2 Atmospheric correction
The atmosphere introduces two forms of
path radiance into the signal, radiance from
Rayleigh or molecular scatter, and radiance from
aerosols or haze. These can be removed
simultaneously using dark object subtractions.
However, improvements in atmospheric
correction offers advantages in treating
atmospheric and Rayleigh corrections separately.
If no dark water exists in the scene, the Rayleigh
correction is critical.
Rayleigh radiance is removed before the
dark object subtraction. While not critical, when
dark water is present, removal of the Rayleigh
path radiance permits pixel by pixel correction of
aerosols for water pixels and allows better control
on the adjustment for aerosols. The Rayleigh
term is determined using standard equations and
coefficients. Models such as LOWTRAN (Air
Force) can be used for the solution.
1.4.3 Aerosol correction
The aerosol correction is performed
using subtraction from bands 1-4. Because the
Big Bend region has black-water lakes and
rivers, water can be found that has negligible
reflectance in all bands. A dark-object
subtraction is used, with the reflectance of the
darkest water being the value subtracted. The
correction is either constant or decreases slightly
with wavelength (Chavez, 1989). The dn value
selected should be that corresponding to the
lowest value that has a significant number of
pixels. In bands 3 and 4 the dark water area
should be the same region. Identifying dark water
in the blue and green bands may be difficult. If a
suitable area is not present, extrapolation from
bands 3 and 4 may be necessary.
The foregoing values represent pixels in
the darkest water area of a scene. The 5 and 13
values are probably artifacts of the sensor (or
boats). A value of 8 would be appropriate for
dark water subtraction.
Normalization of marsh for bands 3 and
4 is determined in the image overlap. By
convention, we use values of zero to define
missing data. A simple model is applied to all
bands to restore zeroes to a value of one within
the image bitmap. The non-image area
surrounding the scene is eliminated from this and
the index calculations by the application of an
"image-only" mask, detailed in Chapter 2.3 and
2.4.1.
1.5 INDICES
Analysis includes the calculation of a
vegetation index, wetness index, temperature,
and water reflectance. Each index provides a
means to compare a particular feature between
different scenes.
1.5.1 Vegetation Index
Subsequent analysis uses band ratioing as a
surrogate measure of biomass. The vegetation
index is a ratio of TM bands 3 and 4. The
normalized difference vegetation index (NDVI) is
a quantification of green biomass. It is not
meaningful for water.
where 4 and 3 are near-infrared and red bands,
respectively, and R = reflectance after aerosol
correction. A weight of 0.01 for the ratio
denominator will scale NDVI by 100, such that
an NDVI of 1.0 produces a count of 100.
1.5.2 Wetness Index
The wetness index is a measure of the
wetness in the soil observable through the
canopy and is particularly effective in
distinguishing tidal influence in the coastal
marsh zone and in areas with thin vegetation
canopy. It is calculated based on the inversion of
a procedure used to delineate open or standing
water. We find that the gradient provided with
the inverse, the "wetness index", shown here is
effective in delineating the extent of tidal flooding
in the coastal marshes.
1.5.3 Temperature
Temperature is calculated from the
thermal band, TM band 6. The contrast in water
temperatures is particularly noteworthy in
Florida gulf coastal waters during the winter
season, when it is possible to observe the source
and redistribution patterns of the relatively
warmer waters of the Floridan aquifer. We
convert radiance of TM band 6 to Celsius + 5 .
Resulting values include freezing temperatures,
which may occasionally occur in the region.
1.5.4 Water Reflectance
Water reflectance is calculated as the
difference of bands 2 and 4.
EQUATION 1. REFLECTANCE CALIBRATION
is the band; the radiance, L, is
determined by:
EQUATION 2. RADIANCE
L = G * N + BIAS
is the solar zenith angle at
the image center, N is the digital count, G is the
calibration slope, and BIAS is the calibration
offset for zero radiance. A scale factor of 500 is
applied to bands 1, 2, 3, 4, 5, and 7 to convert to 1
byte per pixel (0-255). A scale factor of 100 is
applied to band 6. More sophisticated
computations for reflectance exist. These involve
additional terms in equation 1 for atmospheric
transmission, and are being considered for
implementation.
8 8 9 8 9
8 9 9 9 9
9 9 9 8 9
9 10 9 13 10
8 5 8 7 8
EQUATION 3. NDVI (NORMALIZED
DIFFERENCE VEGETATION INDEX)
NDVI =
R(4) - R(3)
R(4) + R(3)
EQUATION 4. WETNESS INDEX
WETNESS = R (5) - R (2)
EQUATION 5. TEMPERATURE IN CELSIUS
Celsius + 5 =
(1260.56/ln(60.776/(L(6)/100)+1)-268)
EQUATION 6. WATER REFLECTANCE
Water reflectance = R (2) - R (4)
Plate 1.
Misregistration and acceptable inter-scene registration for 1986 and 1995.
Plate 2.
Right-angle road intersection for inter-scene registration check.
Plate 3.
Preferred locations for inter-scene accuracy check, Chassahowitzka, FL, 1995.
Plate 4.
PCI procedure flow chart.
Coastal and Marine Program >
St. Petersburg Coastal and Marine Science Center >
Research by Theme >
Gulf of Mexico Tidal Wetlands >
Image Processing Methods - OFR 97-287 >
Chapter 1
U.S. Department of the Interior,
U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center
http://coastal.er.usgs.gov/wetlands/ofr97-287/chapter1.html
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