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Produced by the NASA Center for Aerospace Information (CASI)
4700",

Sami_amwal Progrew Rsport t86"1 001

to M.- / 76 y,'^;

f` The National Aeronautics and Space Administration

on

The TM Project

A/ks#- p^^ER S -s ^^

"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 /o EC,^:,. ti

James G. Gosselink

Cn

co nC^,

Sll F,: Coastal Ecology Institute :;'

Center for Wetland Resources B1w4w

Louisiana State University 6

F % Baton Rouge, La. 70803 ^' t uiU

First Semi-Annual Progress Report

Decett>er 10, 1985

<ol

(E86-10013

LIZING

N86-147 11

NA.. &-C^-17E406)

UgI REHOTE 

SENSING OF THLM P

TIC dAFPES GAZA TO IMPROVE

OUR UNDERSTANDING OF E

c lUABINE EROCESS ES AND Uuclas

THEIR INFLUENCE 

co THE PfiOLUCTI e) O 1 7 ? G3/43 00013

INational MariuG Fisberies Set v

i

c
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 approximately 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 marshlandwater 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 subject 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 iteration determined by a probability function linked to a random number generator. 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 position 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$ conversion 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 parameters 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 parametens 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
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 probability 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 disintegration 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 mierooanepater. 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 congist of:

• development of techniques for measuring spatial aut000rrelation

• measurement of spatial patterns on marshes simulated with alternative weighting factors

• conversion of spatial pattern parameters to FORTRAN 77 for inclusion 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 eoneentrated 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 userspecified 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 determine 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 sizefrequency 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 consideration 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 landwater ratios and shoreline lengths between 1956 and 1978.

References

Brander, J. A., H. A. Bartley, and K. S. Davis. 1985. A probabilistic model of the relationship between zaarshland-rater interface and marsh disintegration. Zoological Modelling 29: 245-260.

8
9

Dow, 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 confidence range (n=20).

4. Frequency distribution of water pixels by number of sides bordered 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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