The purpose of the project is to refine and validate a probabilistic spatial computer model through the analyses of thematic mapper imagery. The model is designed to determine how the interface between marshland and water changes as marshland is converted to water in a disintegrating marsh. Coastal marshland in Louisiana is disintegrating at the rate of approximately 40 sq mi a year, and an evaluation of the potential impact of this loss on the landings of estuarine-dependent fisheries is needed by fisheries managers. Understanding how marshland-water interface changes as coastal marshland is lost is essential to the process of evaluating fisheries effects, because several studies suggest that the production of estuarine-dependent fish and shellfish may be more closely related to the interface between marshland and water than to acreage of marshland. The need to address this practical problem has provided an opportunity to apply some scientifically interesting new techniques to the analyses of satellite imagery. Progress with the development of these techniques is the subject of this report

Baumann, Robert H.

Browder, Joan A.

Gosselink, James G.

May, L. Nelson, Jr.

Rosenthal, Alan

English

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https://ntrs.nasa.gov/search.jsp?R=19860005242 2020-03-20T16:43:54+00:00Z
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The TM Project
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"Utilizing remote sensing of thematic mapper data
to improve our understanding of estuarine processes
and their influence on the productivity of
estuarine-dependent fisheries"
Joan A. Browder
Alan Rosenthal
Miami, Fla., Unit of Beaufort, N. C., Laboratory
Southeast Fisheries Center
Miami, Fla. 33149
L. Nelson May, Jr.
Coastal Fisheries Institute
Center for Wetland Resources
Louisiana State University
Baton Rouge, La. 70803
Robert H. Baumann
Center for Energy Studies ^^;	 ,,!!	 S
Louisiana State University 1,^	 D
Baton Rouge, La.	 70803 EC,^:,./oti
James G. Gosselink
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Sll F,:Coastal Ecology Institute :;'
B1w4wCenter for Wetland Resources
Louisiana State University 6
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uiUBaton Rouge, La.	 70803 ^' t
First Semi-Annual Progress Report
Decett>er 10, 1985
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SENSING OF THLM P TIC dAFPES GAZA TO IMPROVE
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lUABINE EROCESS ES AND	 Uuclas
THEIR INFLUENCE 
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Introduction
The purpose of the project is to refine and validate a probabilistic
spatial =tputer model through the analyses of thematic mapper imagery.
The model is designed to determine how the interface between marshland and
water changes as marshland is converted to water in a disintegrating marsh.
Coastal marshland in Louisiana is disintegratinr at the rate of approxima-
tely 40 sq mi a year (Gagliano et al. 1981), and an evaluation of the
potential inpact of this loss on the landings of estuarine-dependent
fisheries is needed by fisheries managers. Understanding how marshland-
water interface d.-uxges as coastal marshland is lost is essential to the
process of evaluating fisheries effects, because several studies suggest
that the production of estuarine-dependent fish and shellfish may be more
closely related to the interface between marshland and water than to
acreage of marshland (Faller 1979, Dow 1982, Zimmerman et al. 1984). The
need to address this practical problem has provided an opportunity to apply
some scientifically interesting new techniques to the analyses of satellite
imagery. Our progress with the developpment of these techniques is the sub-
ject of our first report.
The study group consists of two research teams. The first, located at
the Southeast Fisheries Center in Miami (Miami. Unit of the Beaufort, N. C.,
Laboratory) consists of Joan Browder and Alan Rosenthal. The second,
located at Louisiana State University in Baton Rouge, La., consists of
Nelson May, Robert Baumann, and James Gosselink. Image analyses is being
performed an the Fisheries Image Analyses System at the Slidell, La.,
Laboratory of the Mississippi Laboratories of the Southeast Fisheries
Center.
1
Following are summaries of work acomplishsd to date and immediate
future plans of the two research teams.
Background
The model simulates a disintegrating marsh, starting with a solidly
vegetated % arsh and ending with total open water. Disintegration proceeds
one pixel at a time, the specific pixel converting to water at each itera-
tion determined by a probability function linked to a random number genera-
tor. Weighting factors in the probability function allow us to weight the
probability that a pixel will be converted at the next interaticn based on
the number of sides an which it is exposed to water or based on its posi-
tion in the marsh.
The probability function is:
F=1+WS+GB
where F - frequency of a given pixel an the selection list (relative
probability), W - weight given per side bordered by water, S - number of
sides bordered by water, G - weight given to pixels initially bordering the
main water body, and B = a Boolean character equal to 1 or B that indicates
whether a pixel borders the main water body. When W - 0 and G - 0,
selection is random except that boundary pixels have a slightly lower
probability of disintegrating throuc1mout the simulation because they are
assumed to have one side bordered by permanent land. F is calculated for
each pixel at each iteration.
Early modeling results (Bro der et al. 1985) indicated that the
marshland-water interface reaches its maximmim in an area vim the spatial
composition of the marsh is about 50 percent land and 50 percent water but
2
that the magnitude of maximura interface changes inversely with the
wei,pting factor of the probability function that converts segments of land
to water. The strength of the weighting factor is reflected in the spatial
distribution of land and water. With increasing weighting factor, the
degree of clustering of water pixels is greater and water bodies became
larger and less scattered.
We plan to refine and, validate our model by measuring and couparing
the spatial distribution of land and water in simulated marshes and actual
marshes in Louisiana, adjusting model coefficients so that spatial patterns
in simulated marshes more nearly approocimate those in the actual marshes,
and placing actual marshes on disintegration curves (from zero to 100$ con-
version of land to water) produced from model simulations. The successful
matching of model probability functions to patterns of land and water in
actual marshes should not only quantify the relationship between magnitude
of maximum interface and patterns of land and water in the actual marshes
but also confirm the model's prediction that maximum interface always
occurs at about 50 percent land loss.
A critical phase of the study is the development of quantitative para-
meters that are descriptive of the spatial distributia, of lard and water.
The development and testing of such parameters has been a major activity
of the NMFS team for the past six months. Software development to measure
both marshland-water interface and the parameters describing land and water
parameters has been the major activity at LSU during its three m nths of
involvement (since September).
LM'S Activities
The behavior of the model was tested beyond the limits of the original
3
tests discussed in the proposal by extending the weighting factor for sides
adjacent to water (W) up to 36 (originally we tested it only through 5).
We found that the stage in disintegration at %hich maximum interface occurs
remained arouml 50 percent (Fig. 1, Table 1), but that maximum interface,
expressed as percent maximum possible interface, decreased from about 51
percent at W - 0 (the random case) to 23 percent at W - 36 (Fig. 2, Table
1).
Algorithms were developed to measure spatial patterns. Four parame-
tens which have been implemented are: (1) frequency distribution of water
pixels by water-cluster size; (2) frequent' distribution of water clusters
by size (in terms of water pixels); (3) frequency distribution of water
pixels by number of sides bordered by other water pixels; and (4) percent
pixels as water by distance from main water body (in terms of pixel row).
There are actually two versions of parameters one and two above: one
including all water clusters and the other including only those water
clusters connected to the main water body.
In early tests with 20 repetitions of the sane weighting factor, we
found that parameter means appeared to reflect differences in the weighting
factor. Means and confidence limits from these early tests are shown in
Figures 3 through 6. In these simulations, the marsh size was 20 rows
(pixels) by 50 columns (pixels). Pixels on each of the four edges of the
marsh could have a maximum of three sides adjacent to water. Parameters
were measured when the marsh was 50 percent water.
The frequency distribution of water pixels by water body niece is dhown
for four different weighting factors in Figure 3. When W - 0, the rwxkm
case, mere than half the water pixels are contained in clusters of 1 to 25
4
i
water pixels. The number of water pixels contained in clusters of 1 to 25
pixels decreases and the distribution moves to the riot as W increases.
The frequency distribution of water pixels in terms of number of sides
bordered by other water pixels is shown for weighting factors (W) from 0 to
12 in Figure 5. The number of water pixels bordered on three or four sides
by other water pixels increases with increasing wei ghting factor.
The number of water pixels with distance from a man water body (in
pixel lengths, or rows) is shown in Figure 5. With G set at 3, pixels in
the first row (the row bordering the main water body) have a hider proba-
bility of disintegrating than pixels in the other mows. The result is that
the front row has a hider number of water pixels at 50 percent disin-
tegration than do the other rows. When W is increased first to 2 and then
to 4, the second raw also has a higher number of water pixels than the
other rows. otherwise, the number of water pixels in each raw does not
differ with distance.
Work Scheduled for December 1985 to June 1986
Further systematic tests of variation in spatial pattern will be
conducted the spatial pattern parameters in future weeks. We are presently
developing algorithms for measuring spatial aut000rrelation. When
colleted, the algoritlms for measuring spatial patterns will be converted
to FORTRAN 77 for inclusion in the ELAS software on the PIPS system in
Slidell.
Our modeling wrung to date has been on a Hswlitt Padcnrd 86-B mierooane-
pater. We are rapidly reaching the memory limits of this eaqxzter, even
though we have not yet expanded the size of the simulated marsh, as we
intend to do. In our recent worst (beyond that covered by this rot= ), we
5
P"
A.
are interfacing the HP with a Burroughs 6808 minicomputer by transfering
files over a modem in order to increase execution speed. Our working unit
soot, will receive an AT&T Unix-PC microoatputer, which will be dedicated
almost exclusively to this project and will give us greater capacity to
expand both the size of the marsh and the number of parameters measured.
To summarize, activities in Miami during the next six months will con-
gist of:
• development of techniques for measuring spatial aut000rrelation
• measurement of spatial patterns on marshes simulated with alter-
native weighting factors
• conversion of spatial pattern parameters to FORTRAN 77 for inclu-
sion in ELA.S
• eonverson of the model program to C for execution on the AT&T Unix
microomputer
• expansion of the size of the simulated marsh (in amber of pixels).
LSU Activities
Work performed during the first three months of the project was eon-
eentrated in two main areas: (1) activities associated with the start-up
of the project, such as meetings with co--investigators and acquisition of
magnetic tapes, computer disk packs,topographic maps, and other supplies
needed for the project, and (2) work with a contractor to begin some cost=
modifications of image-processing software required for the project.
Image-prvoessing for the project is being performed at the WA
Slidell Computer Couplex, Slidell, Luuisiana, where the Southeast Fisheries
Center maintains the Fisheries Image Processing ft-stem (PIPS). The system
consists of a Sperry-Univac V77/600 ocaputer and associated hardmm and is
equipped with a modified version of the Earth Resources Laboratory
6
Appications Software (MM) (Graham at al. 1984). ELAS is a
image-processing package developed by NRSk for processing digital data from
satellite rsnote sensors. Software modifications involved adapting
existing capabilities in the latest version of ELAS to operate in the FIPS
version of ELAS used in this study.
Adaptation of the Water Body (WBM) Nodule
The water body module (WBOD) is designed to categorize a user-
specified class—for exanple, open water—into three classes: open bodies;
small, closed bodies; and large, closed bodies. Information tabulated fran
repeated nuts of the module will be used with other ELAS routines to deter-
mine the size-frequency distribution of water bodies from Landsat TM images
of the Louisiana coastal marshes. Correct operation of the module was
verified by running WBX an a series of test files with known size-
frequency distributions of water bodies. The FIPS version of WBM became
fully operational in mid-October.
Adaptation of the Shoreline Length (SLIN) Module
Adaptation of the shoreline length (SUN) module to operate in FIPS
has just been completed. The SLIM module will be used to measure the
length of the shoreline in land-water images derived from classifications
of Lardsat TM data. Algorithms that preceded the development of SLIN
(Faller 1979) were not symmetrical; that is, the reversal of lard and water
classes in a given image did not yield the same length measurement (Dow and
Pearson 1982). SLIN uses a different algorithm to measure shoreline,
avoiding the problem in the older software.
The lengths of boundaries between two adjacent land-cover types, such
7
r,
as land and water, are difficult to measure accurately in raster image@.
SLSN has a tendency to underestimate or overestimate the true shoreline
length in a given area. The magnitude and direction of the bias is related
to several factors (Dow and Pearson 1982):
sites of the pixels composing the map
level of reticulation in the shoreline
preprocessing tedmiquea used to produce the
land-water nap from Lardsat imagery.
Thus, experimental techniques nay have to be devised to correct for the
biases in shoreline lengths derived from Iandsat images of the Mississippi
deltaic plain.
Work Scheduled for December 1985 to June 1986
delivery of the lardsat TM data tapes for the project
conversion of the Landsat TM data to an E[AS format
development of an experiment to quantify the magnitude and
direction of the biases in shoreline-length measurements
derived from the SLIM module
selection of sample sites in the Mississippi deltaic plain by
marsh type (fresh, brackish, salt) and delta lobe, with consi-
deration of cloud cover and quality of the 7M imagery
measurement of percentage area of open water, shoreline
lengths, maxizmz{n shoreline lengths, and determination of the
distribution of water body sizes for each sample rite
analysis of U. S. Fish and Wildlife Service Habitat Maps
(Wicker at al. 1980) to determine historical changes in land-
water ratios and shoreline lengths between 1956 and 1978.
References
Brander, J. A., H. A. Bartley, and K. S. Davis. 1985. A probabi-
listic model of the relationship between zaarshland-rater inter-
face and marsh disintegration. Zoological Modelling 29: 245-260.
8
9Dow, D. D. 1982. Software pmogrns to measure interface ootplexity
with remote-sensing data, with an example of a marine ecosystem
application. NASA Rep. 219. National Aeronautics and Space
Administration, Earth Resources Laboratory, National Space
Technology Laboratories, NSTL, MS, 25 pp.
Dow, D. D., and R. W. Pearson. 1982. SLIM: a software program to
measure interface length. NASA Rep. 208. National Aeronautics
and Space Administration, Earth Resources Laboratory, National
Space Tedmlogy Laboratories, NUM, MS, 19 pp.
Faller, K. H. 1979. Shoreline as a controlling factor in cam ercial
shrimp production. NASA Rep. 24-72-732. National Aeronautics
and Space Administration, Earth Resources Laboratory, National
Space Technology Laboratories, NM, MS, 33 pp.
Gagliano, S. M., K. J. Meyer-Arendt, and K. M. Wicker. 1981. Land
loss in the Mississippi River deltaic plain. Trans. Gulf Coast
Assoc. Geol. Soc. 31: 295-300.
Graham, M. H., B. G. Jurkin, M. T. Kalcic, R. W. Pearson, and B. R.
Seyfarth. 1964. ELAS: Earth Resources Laboratory Applications
Software, Volume II, ELAS User's Guide. National Aeronautics
and Space Administration, Earth Resources Laboratory, National
Space Technology Laboratories, NSTL, MS, 428 pp.
Wicker, K. M., J. B. Johnston, M. W. Young, and R. M. Rogers. 1980.
The Mississippi deltaic plain region habitat mapping study. 464
maps. U. S. Fish and Wild. Serv., Office of Biol. Serv.
FWS/OBS/79-07.
Zinmerman, R. J., T. J. Minello, and G. Zamora, Jr. 1984. Selection
of vegetated habitat by brawn shrimp, Penaeus aztecus, in a
Galveston Bay salt marsh. U. S. Fish. Bull. 0:'	 36.
Table 1.	 Mears and 95 percent confidence limits of percent lard loss at
maxirnan interface and maxitnan interface expressed as percent
maximan possible interface for various w*i.O* N famare %W)
(W, n-20).
W
Percent Land Loss
LCI
	 Mean	 Lcl
Maximm Interface (t)
ICI	 Mean	 UCI
0 49.40 50.69 51.98 50.94 51.39 51.85
1 47.55 49.16 50.77 41.41 42.04 42.66
2 46.20 48.08 49.95 37.99 38.71 39.43
3 48.22 50.15 52.08 35.92 36.60 37.29
4 45.57 47.73 49.83 33.80 34.45 35.08
5 49.23 51.36 53.49 33.00 33.58 34.15
6 48.49 50.41 52.32 31.39 31.92 32.45
7 47.61 49.94 52.26 30.79 31.54 32.30
8 46.61 49.51 52.41 30.03 30.60 31.17
9 49.81 52.04 54.27 29.45 30.15 30.85
10 45.75 48.20 50.65 29.24 29.86 30.48
11 46.50 48.74 50.36 28.57 29.29 30.02
12 47.08 49.46 51.84 28.16 28.76 29.36
16 46.89 49.17 51.45 26.56 27.31 28.07
20 48.17 50.40 52.62 25.32 25.97 26.62
24 46.55 49.61 52.67 24.91 25.63 26.33
28 47.86 50.67 53.48 23.78 24.47 25.15
32 47.43 49.88 52.33 23.30 23.f.j 24.56
36 46.55 50.74 54.92 22.34 22.91 23.47
I
LIST OF FIGURES
1. Percent land converted to water at maximxm interface versus
weighting factor (W). Dot indicates mean and vertical line
indicates confidence range (nr20).
2. Maxicnsn interface as percent maximan possible interface versus
weighting factor (W). Dot indicates mean and vertical line
indicates confidence range (rt20).
3. Frequency distribution of water pixels by water cluster site
(in ter;& of number of pixels) at 50 percent disintegration.
Tcp of bar indicates mean and vertical line indicates con-
fidence range (n=20).
4. Frequency distribution of water pixels by number of sides bor-
dered by other water pixels at 50 percent disintegration.
5. Number of water pixels by distance from water (in pixel
lengths, or rows) at 50 percent disintegration.
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Utilizing remote sensing of Thematic Mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

Utilizing remote sensing of Thematic Mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

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