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.A 

Utilizing Remote Sensing 

Data to Improve Our 

of Thematic Mapper 

Understanding of 

Estuarine Processes and Their 

Influence on the Productivity of 

Estuarine-Dependent Fisheries 

Final Report to the 

National Aeronautics and Space Administration 

Joan A. Browder, L. Nelson May, Alan Rosenthal 

Robert H. Baumann, and James G. Gosselink 

COASTAL ECOLOGY 

INSTITUTE 

Center for Wetland Resources Louisiana State University Baton Rouge, LA 70803-7503 
Utilizing Remote Sensing of Thematic Mapper 

Data to Improve Our Understanding of 

Estuarine Processes and Their 

Influence on the Productivity of 

Estuarine-Dependent Fisheries 

Final Report to the 

National Aeronautics and Space Administration 

Goddard Space Flight Center 

Greenbelt, Maryland 20771 

Joan A. Browder 

Southeast Fisheries Center 

National Marine Fisheries Service 

75 Virginia Beach Drive 

Miami, Florida 33149 

L. Nelson May 

Coastal Fisheries Institute 

Center for Wetland Resources 

Louisiana State University 

Baton Rouge, Louisiana 70803 

Alan Rosenthal 

Southeast Fisheries Center 

National Marine Fisheries Service 

75 Virginia Beach Drive 

Miami Beach, Florida 33149 

Robert H. Baumann 

Center for Energy Studies 

Louisiana State University 

Baton Rouge, Louisiana 70803 

James G. Gosselink 

Coastal Ecology Institute 

Center for Wetland Resources 

Baton Rouge, Louisiana 70803 

December 20, 1988 
This report should be cited as 

Browder, J. A, L N. May, Alan Rosenthal, R H. Baumann, and J. G. Gosselirik 1988. Utilizing 

Remote Sensing of Thematic Mapper Data to Improve Our Understanding of 13tuarine Processes 

and Their Influence on the Productivity of Estuarine-Dependent Fisheries. Final Report to the 

National Aeronautics and Space Administration. Center for Wetland Resources, Louisiana State 

University, Baton Rouge. 
Contents 

LISTOFFIGURES ............................................... V 

ABSTRACT .................................................... vii 

INTRODUCTION ................................................ 1 

METHODS ..................................................... 2 

Description of the New Model ..................................... 3 

Model Calibration .............................................. 7 

TM image processing ........................................ 7 

Study-site selection .......................................... 7 

Measurement of spatial-pattern statistics ........................... 8 

Calibrating the model ........................................ 10 

Simulation of study-site spatial patterns ............................ 11 

Analysis of brown shrimp catch data .............................. 12 

RESULTS ..................................................... 12 

Interface Length versus Disintegration Level of Study Sites ................. 12 

Evaluation of Simulations ......................................... 12 

Agreement of simulation and study-site interface ...................... 12 

Variation in simulation indices .................................. 14 

Water-body size groups ....................................... 14 

Visual evaluation ........................................... 16 

Model-parameter values ....................................... 16 

Simulated Site-Specific Interface Length versus DL ....................... 20 

Aggregated Interface Length versus Year .............................. 20 

Possible Impacts on Fisheries ...................................... 24 

DISCUSSION ................................................... 24 

SUMMARY .................................................... 26 

ACKNOWLEDGMENTS ........................................... 27 

REFERENCES .................................................. 28 

APPENDIX A Background and Methods ................................ 31 

APPENDIX B: Spatial-Pattern Statistics of Simulations Using All Combinations 

of W. G. and BC ................................................ 55 

... 

111 
APPENDIX C Spatial-Pattern Statistics of TM Scenes . . . . . . . . . . . . . , . . . . . . . . 59 

APPENDIX D: Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . 65 

APPENDIX E Comparison of Simulations with Th4 Scenes . . . . . . . . . . . . . . . . . . . 99 

APPENDIX F: Presimulation Predictions from the Knowledge Base . . . . . . . . . a 173 

APPENDIX G. Comparison of Presimulation Predictions to Simulation Values . . . . 181 

APPENDIX H Levels of Disintegration by USGS Topographic Map in 1956 and 1978 Data 

~ 

e . . 

Compiled by Leibowitz from Maps by Wicker with Annual Trend . . . . . , . . . . . . . . 187 

APPENDIX I: Shrimp Catch, 190-1987, in Barataria and Terrebonne-Timbalier Bays . 191 

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Figure 

List of Figures 

Page 

1 Snapshot view of simulated marsh disintegration at the 25%, 

50%, and 75% levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . a - . . 5 

2 Two simulated marshes at similar levels of disintegration, produced 

by different values of W, the interior-marshdecay weighting factor . . . . . . . . 6 

3 Map of Louisiana showing study area and corresponding U.S. Geological 

Survey topographic maps covering the area . . . . . . . . . . . . . . . . . . . . . . . . 9 

4 Interface length versus disintegration level . . . . . . . . . . . . . . . . . . . . . . . . 13 

5 Frequency distribution of cases by difference between number of 

water-body size groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 

6 Land-water image of a study site and its simulation showing 

disintegration level and model coefficients used in the simulation . . . . . . . . . . 17 

7 (A) Average G weighting factor and (B) average W weighting factor 

selected by the expert system . . . . . . . . . . . . . . . . . . - . . . . . . . . . . . . . . - . 18 

8 (A) Average W by G-class; (B) average border condition selected 

by the expert system, by marsh type and lobe . . . . . . . . . . . . . . . . . . . . . . . . 19 

9 Simulated interface versus disintegration level for 4 of the 70 cases 

showing modeling coefficients used in the simulation . . . . . . . . . . . . . . . . . . 21 

10 Frequency distributions of cases by simulation maximum interface, 

disintegration level at maximum interface, and maximum-interface 

DL minus 1985 DL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 

11 Aggregated simulation interface length, by lobe, expressed as a 

fraction of aggregated maximum interface, versus year . . . . . . . . . . . . . . . . . . 23 

V 
Abstract 

The land-water interface of coastal marshes may influence the production of estuarinedependent fisheries more than the area of these marshes. To test this hypothesis, we created a 

spatial model to explore the dynamic relationship between marshland-water interface and level 

of disintegration in the decaying coastal marshes of Louisiana’s Barataria, Terrebonne, and 

Timbalier basins. Calibrating our model with Landsat Thematic Mapper satellite imagery, we 

found a parabolic relationship between land-water interface and marsh disintegration. 

Aggregated simulation data suggest that interface in the study area will soon reach its maximum 

and then decline. We found a statistically significant positive linear relationship between brown 

shrimp catch and total interface length over the past 28 yr. This relationship suggests that 

shrimp yields will decline when interface declines, possibly beginning about 1995. 

PRECEDING PAGE BLANK NOT FILMED 

vii 
Introduction 

The loss of Louisiana's coastal wetlands at the average rate of about 100 km2/yr (Gagliano et al. 

1981) is a problem of national concern because of their importance to wildlife and fisheries. 

Louisiana's seafood production, the highest in the nation, is based on species dependent on 

estuaries and their associated wetlands, which provide food and shelter for young organisms 

(Boesch and Turner 1984). 

Both natural processes and human interference with these processes are responsible for 

the rapid wetland loss in Louisiana (Baumann et al. 1984). The leveeing of the Mississippi 

River has prevented the deposition of marsh-building sediment that could offset subsidence and 

sea-level rise (Kesel 1988). Drainage and navigation channels have altered the natural 

hydrologic processes that build coastal and interior marshes and stimulate marsh vegetation 

growth (Turner and Cahoon 1987). 

Despite the loss of wetlands and the known dependence of fishery species on wetlands, 

statistics indicate that Louisiana's fishery landings have been increasing. The increase in 

landings, not fully explained by an increase in effort (Nichols 1984)' has created a sense of false 

security that has delayed action to curb wetland loss. 

The production of fishery species may be more dependent on the land-water interface than 

on wetland acreage. Faller (1979), Dow (1982)' and Gosselink (1984) found statistically 

significant relationships between fishery production and land-water interface in neighboring 

areas. Zimmerman et al. (1984) noted that brown shrimp densities were highest in areas of 

high shoreline "reticulation." 

Using a stochastic computer model, Browder et al. (1984) provided a theoretical description 

of how the length of the land-water interface changes during marsh disintegration. They found 

that interface length increased in early stages of simulated marsh disintegration, reached a 

maximum when the marsh was roughly 50% water, and decreased thereafter. They further 

noted that the magnitude of maximum interface was variable and was affected by the spatial 

pattern of land and water-specifically the degree of clumping of water pixels to form water 

bodies. 

In the study reported here, we refined and expanded the Browder et al. (1984) model and 

calibrated it with Thematic Mapper (TM) imagery covering 70 marsh sites in coastal Louisiana 

(Appendix A). Then we used our model to simulate the complete cycle of marsh disintegration 
at each site and collected data on interface length. We used independent data to roughly 

convert interface length versus disintegration level to interface length loss versus time at each 

site. Then we tested total interface length from the 70 simulations for its ability to explain 

annual brown shrimp catch in estuaries adjacent to the study area. Finding a statistically 

significant relationship, we used it to estimate future shrimp production. We compared data 

from the TM imagery to simulated data from our model in order to evaluate Browder et al.’s 

general observations concerning the relationship of interface length to land loss and the spatial 

pattern of land and water. 

Methods 

The study can be viewed as consisting of four steps: model development, model calibration, 

model evaluation, and model extension. Model development consisted of refining the Browder 

et al. (1984) model for use with TM data. The model contains three adjustable parameters that 

were calibrated to the spatia1 patterns of land and water in 70 marsh sites in Louisiana, as 

indicated in TM imagery. The TM scenes were classified into land and water pixels, and several 

measures, or indices, of spatial pattern were obtained from each scene. We dleveloped an 

expert system that used these spatial-pattern indices to select the model-parameter values to 

simulate the history of marsh disintegration at each site. 

simulations of marsh disintegration--one for each scene--and recorded the history of interface 

length as a function of disintegration level (DL, water area as percentage total area) throughout 

each simulation. 

Then we made 70 ”best fit” 

We evaluated our simulations by several methods. We used regression arialysis to compare 

the spatial-pattern indices of the 70 simulations to those of the TM scenes. We compared the 

number of water-body groups in the simulation to those in the TM scene, by lobe and marsh 

type. We visually compared the TM scenes with our simulated scenes at the same stages of 

disintegration. Finally, we examined the model-parameter values selected by the expert system, 

comparing them by marsh type and lobe. 

We extended our model results to fshery production. First we determined the relationship 

between brown shrimp catch and simulated interface length in the study area for the past 28 yr. 

Finding a statistically significant relationship, we used it to estimate future shrimp catches based 

on simulated future interface length. 

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Description of the New Model 

Our new model simulates marsh disintegration by successively changing land pixels to water 

pixels. The relative probability that a land pixel will be converted to water at each iteration is 

governed by a function weighted by three adjustable parameters: interior disintegration (W), 

shoreline erosion (G), and border-condition (BC). The weighting parameters were based on 

Sasser et al.'s (1986) observation that two patterns of marsh disintegration occur in Louisiana. 

In one pattern, small, randomly spaced, gradually expanding water bodies develop in solid 

marshes. In the other, land disappears along the margins of major water bodies, as if lost to 

waves or other erosive forces. The model simulates the entire disintegration process, starting 

with solid land and ending with only open water. Each iteration represents passage of time, 

although time units are unspecified. 

The pixel to be disintegrated at each iteration is selected from a numbered list by a pair of 

randomly generated numbers (RN). The first makes a tentative selection by matching a number 

on the list; the second random number determines whether the tentative selection is eligible. 

The pixel is eligible if its total weight at that iteration (F& is greater than RNk max-Fij for 

that k (i and j = pixel coordinates and k = iteration). The relative probability that a specific 

pixel will be selected at iteration k (RP;,,k) is the ratio of the total weight oE that pixel to the 

sum of the total weights of all the land pixels: 

rc 

where r = number of rows, c = number of columns, 

W = weighting coefficient for each side of the pixel adjacent to water. Si,,k = number of pixel 

sides adjacent to water. G = weighting coefficient for pixels bordering a major water body, by 

border. B = a Boolean value (1 or 0) indicating whether the pixel is on a major water body, by 

marsh border. Border condition is the vector BC. BC, indicates which marsh borders (n, s, e, 

or w) are on major water bodies. (Note that throughout this paper, "side" refers to pirel 

3 
boundary and "border" refers to marsh boundary.) Once a pixel is converted to water, it is 

removed from the selection list, shortening it by one. Figure 1 gives a snapshot view of the 

progress of marsh disintegration in one simulation. The marsh is initially solid. By the time it 

is 25% disintegrated (DL = 25%), we see many small water bodies. Water areas are larger and 

are beginning to coalesce at 50% disintegration. Most water bodies are conn'ected by the time 

the marsh is 75% disintegrated. 

At each iteration of the model, counters keep track of the percentage of the total area that 

is water and the length of the land-water interface. Percentage water area is referred to 

throughout this discussion as the level of disintegration (DL). Land-water interface is measured 

in pixel-lengths--the length of one side of the square pixel. As measured, interface is 

homologous to the "join" statistic of Moran (1948) and is related to other spatial autocorrelation 

statistics indicating degree of clumping of the same pixel types (Upton and Fingleton 1985). By 

affecting the order of pixel disintegration, our model's weighting coefficients dletermine the 

degree of clumping of water pixels in simulated marshes. Figure 2 shows two marshes at similar 

stages of decay simulated by different interior-marsh-decay weighting coefficients. Note that 

water bodies are larger when W = 3,184 (bottom) than when W = 248 (top). (The erosion 

weighting coefficient was zero for both.) 

The new model differs from the Browder et al. (1984) model in several important details. 

In the original model, only pixels initiaZZy on a major water body had the G-weighting (B = 1). 

The G-effect was inconsequential in sensitivity tests, particularly as the size of the simulated 

marsh increased. In the new model, any pixel can eventually be assigned B =: 1, if it is 

connected to a designated water border by a continuous water path. The G-parameter now has 

a much greater effect. The new model allows flexibility in the initial identification of water 

borders, and up to four water borders can be set. The pixel-selection procedure of the new 

model is an improvement that made it practical to simulate marshes having as many pixels as 

the TM images of our study sites, 192 x 192. Appendix B presents the spatiall-pattern statistics 

of simulations using all combinations of W, G, and BC. The new model and all ancillary 

programs were written in C and executed on an AT&T PC-7300, a 16-bit, 10-MHz computer 

with a Unix-V operating system. 

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ORlGfNAL PAGE IS 

OF POOR QUALtTY 

Figure 1. Snapshot vicw of simulatcd marsh disintcgration at thc 25%. 50%. and 75% 

disintcgration lcvcls (pcrccntagc opcn watcr arcs). 

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Figure 2. Two simulated marshes at similar levels of disintegration, prodiiccd by dil'l'crcnt 

values of W, the interior-marshdecay weighting factor. 

ORIGINAL PAGE 1's 

OF POOR QUA'-'T'Y 

6 
Model Calibration 

TM image processing. We analyzed the TM scenes on the Fisheries Image Processing System 

(FIPS) of the National Marine Fisheries Service (NMFS) in Slidell, Louisiana, and a system 

operated by the Florida Department of Natural Resources in St. Petersburg. Both systems 

consisted of a minicomputer, color-image display device, and other hardware for processing 

remotely sensed digital data. The software was a modified version of the Earth Resources 

Laboratory Applications Software (ELAS) (Graham et al. 1984). 

The TM image we used was from a 2 December 1984 Landsat-5 overflight (Scene ID: 

50276016022). Covering most of the Mississippi deltaic plain, it was one of the few relatively 

cloud-free images of our study area (quads 1 and 2 in path 22 and row 40 of the World-Wide 

Reference System). 

ELAS modules PMGC (Georef constants-EROS format) and PMGE (Georef-EROS 

format) (Graham et al. 1984) were used to digitally rotate the images to fit a Universal 

Transverse Mercator projection with a north-south orientation. We used these modules to 

accumulate ground-control points, generate polynomial least-squares mapping equations, and 

resample the image with bilinear interpolation. Registration accuracies averaged 22-56 m. 

Resolution was the length of a TM pixel side, 30 m. Land and water pixels were classified by 

multiplying bands 4 and 5 (0.76-0.90 pm and 1.55-1.75 pm, respectively), rescaling to 0-255, and 

applying Pun’s (1981) global thresholding technique. 

Study-site se1ection. The study sites are located on two abandoned delta lobes of different ages. 

The early Lafourche lobe was an actively prograding delta within the last 1,800 yr. The late 

Lafourche lobe was an active distributary of the river within the last 600 yr. Chabreck (1972) 

distinguished four major types of Louisiana coastal marsh on the basis of vegetation: salt, 

brackish, intermediate, and fresh. Salt and brackish marshes are the most important marshes to 

estuarine-dependent fishery species and show a wide range of decay stages. For these reasons, 

we limited our study to these two more seaward marsh types. 

Site locations are within the areas represented by 21 U.S. Geological Survey 7.5-min 

topographic maps. We used these maps and a coastal habitat map (Chabreck and Linscombe 

1978), coupled with our extensive field experience, to distinguish brackish and salt marsh. We 

defined potential boundaries of study sites by dividing the area of the TM image corresponding 

I 7 
to each topographic map into four contiguous quarters measuring 192 x 192 pixels (5,760 IC 

5,760 m, roughly 33.18 km2). The intersection of the quarters corresponded 1.0 the center of 

the map. We selected 70 marsh sites: 38 salt (20 on one lobe, 18 on the other) and 32 

brackish (19 and 13 per lobe) (Fig. 3). 

Mmumnent of spatial-pa#ern indices. We generated 70 binary land-water ima.ges from the band4-x-band-5 images. To measure our spatial-pattern indices, we tabulated the following using 

ELAS command strings: (1) number of land and water pixels (to determine percentage water 

area = disintegration level [DL]); (2) number of water pixels by scan line and element column 

(to determine border condition); (3) number of land-water pixel-side contacts (interface length); 

(4) number of water pixels, excluding border pixels, with sides adjacent to zero, one, two, three, 

or four other water pixels (which we will refer to hereafter as the "side-adjacency" statistics); 

and (5) number of pixels in each water body (water-body size). Diagonal, or corner, contacts by 

water pixels were considered to connect two parts of the same water body. 

We tabulated interface length in a three-step process. First, we generated an intermediate 

image using the ELAS shoreline-length (SLIN) module (Graham et al. 1984). SLIN uses a 3-x3-pixel moving-window technique to classify each land pixel adjacent to water into 1 of 69 

shoreline categories (Dow, 1982; Dow and Pearson, 1982). Second, we used a look-up table to 

convert the SLIN image to an image file of six classes: land and water pixels and land shoreline 

pixels having one, two, three, or four sides adjacent to water. Our principal spatial-pattern 

index, interface, was determined by counting the land-pixel sides adjacent to water pixels. We 

determined the number of water-pixel sides adjacent to other water pixels with a similar 

technique to obtain the side-adjacency statistics, which were our other major indices of spatial 

pattern. Two processing changes were required: water pixels adjacent to land were defined as 

water shoreline pixels in SLIN-module processing; and a new look-up table was used to classify 

water pixels with zero, one, two, three, or four sides adjacent to other water pixels. The uraterbody classifier (WOD) of ELAS was used to determine water-body size. 

The length of an irregular shoreline is a function of measurement unit (Mandelbrot 1!%7). 

Our measurements of land-water interface and, possibly, other spatial-pattern indices are valid 

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only at TM resolution, 30 m. Appendix C presents the spatial-pattern statistics of the TM 

scenes. 

calibrating the model. An expert system, consisting of a knowledge base and decision proczss, 

selected the model parameters W, G, and BC to best approximate the spatial patterns of each 

study site. Selections were made by matching certain spatial-pattern indices of the imagery to 

those in a knowledge base built from simulations. Interface length, the five side-adjacency 

statistics, and a "target" border condition were the indices to be matched. The knowledge base 

showed how these variables changed in model executions as functions of W, (3, and BC. 'The 

decision process consisted of rules for selecting the best W-G-BC combination. 

The knowledge base was built by running simulations with all possible W and G 

combinations from the set [0, 4, 20, 60, 180, and 5401 for the six types of BC. (Throughout this 

report, BC is given six possible Boolean values: oo00, OOO1, 0011, 0101, 0111, and 1111, which 

show the specific spatial relationships of the borders, 0 indicating land and 1, water.) For BC = 

oo00, the set was extended to include W = 1,620 and 9,720. (In addition, power functions 

extrapolated to larger Ws.) Target BC is determined by comparing DL to the percentage water 

pixels (Pm) in each row or column forming the outer border of the marsh, as follows: 

Target BC, = 1 if P, > DL. (2) 

The spatial-pattern indices of simulated marshes at the same DL as the study site were used 

to obtain one or more weighted mean W for every G-BC combination. For each G-BC, there 

could be one or more W based on each spatial-pattern index. To calculate the mean, Ws were 

weighted by the number of water pixels of the index. For instance, if Adj-4 := 1,940, the weight 

given to the W obtained by matching this index was 1,940. The weight given to the W obtained 

by matching interface length was the sum of all water pixels. The weights used to calculate 

mean W were summed to calculate a "decision number" (DE) for each weighted mean W. DE 

was used to select the best W-G-BC from the many alternatives calculated for each site. 

Another criterion used to select the best W-G-BC was coefficient of variation of the 

weighted mean W (CV). CV was a useful criterion because low CV indicated a high degree of 

convergence of Ws estimated from all contributing spatial-pattern indices. 

BC was the main criterion used to select the best W-G-BC combination. If the target BC 

was not matched by a solution meeting other criteria, the solution having BC most similar to the 

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target was selected. In our 70 cases, BC usually matched target BC or differed by only one 

border. The decision algorithm selected the W-G-BC combination having, first, BC most similar 

to target BC; second, high DE (within at least 75% of the highest DE among all alternative WG-BC combinations); and third, lowest CV (see Appendix D, F). 

Simulation of study-site spatial paem. Once selected, model parameters were used to simulate 

the spatial pattern of each study site and the change in land-water interface with land loss. The 

land-water maps and spatial-pattern indices of the 70 simulated marshes were captured at the 

same levels of disintegration as corresponding study sites. In addition, interface length was 

recorded at each 5% level of disintegration as the simulation proceeded from solid land to open 

water. 

Analysis of brown shrimp catch data. To relate marsh-water interface to annual fishery catch 

data, we needed to estimate interface length as a function of time. Interface length in our 

model output was expressed as a function of DL, not time. Therefore, we needed an estimate 

of the time trend in DL We used data from Wicker’s 1956 and 1978 maps (1980) to estimate 

this trend. The data were compiled by Liebowitz (Louisiana State University, private 

communication, 1988), who provided us with water area for each topographic-map area 

corresponding to our study sites. We estimated average annual change in DL per topographicmap area by expressing water area in 1956 and 1978 as percentage total area and calculating the 

annual average of the difference. This assumed a linear trend in water area from 1956 to 1978, 

which we projected into the future. We aggregated the data for each site to obtain, for each 

lobe, an estimate of interface, by year, from 1956 until the future total loss of marsh and 

interface. (An in-depth comparison of 1956 and 1978 data from the Wicker maps is presented 

in Liebowitz and Hill [1988].) 

Using regression analysis, we compared 1960-1987 of the simulated interface time-series 

with unpublished brown shrimp catch data for Barataria, Timbalier, and Terrebonne bays for the 

same period (G. Davenport, NMFS, Miami, personal communication, 1988) to estimate a 

relationship between catch and interface. (Barataria Bay is associated with the late Lafourche 

lobe, and Timbalier and Terrebonne bays are associated with the early Lafourche lobe.) We 

predicted future shrimp catches from this relationship. Included as independent variables in the 

11 
analysis were local rainfall (R. Muller, Louisiana State University, personal communication., 

1988) and number of hours from April 9 through 30 in which temperatures were below 2OoC 

(Barrett and Gillespie 1975; B. Barrett, Louisiana Department of Wildlife and Fisheries, 

personal communication, 1988). Barrett and Gillespie (1975) suggested that salinity and the 

temperature variable affected brown shrimp catches. We used rainfall as an inverse surrogate 

for salinity. Lack of reliable effort data precluded inclusion of this variable in our analysis. 

Results are organized as (1) interface length versus disintegration level of study sites, (2) 

evaluation of simulations, (3) simulated site-specific interface length versus DI,, (4) aggregated 

interface length versus year, and (5) possible impacts on fisheries. 

Interface Length versus Disintegration Level of Study Sites 

A plot of interface length versus DL measured in the classified imagery of thc study sites (Fig. 

4) suggests that interface increased in the early stages of disintegration, reached a maximum 

when marshes were 30%-50% disintegrated, and decreased thereafter. Statistically significant @ 

e .l) parabolas were fit to separate data for salt and brackish sites. Most salt marsh sites were 

more than 50% disintegrated, whereas the DL of brackish sites ranged from low to high. DL 

and interface length did not differ significantly between early and late lobes, possibly because we 

excluded open-water areas of both lobes from our analysis. 

Evaluution of Simulations 

Following are the results of our evaluations of how well the simulations represented the spatial 

patterns of the study sites. Appendices E and G provide further specific cornlparisons in tabluar 

and graphic formats. 

Agrmmmt of simulation and study-site inte~~. Interface length in each simulation was obtained 

at the same DL as the TM scene it represented. Then the 70 simulation interfaces were 

regressed on the corresponding TM-scene interfaces. TM-scene interface explained 94% of the 

variation in simulation interface. The slope of the relationship was 1.06. The greatest 

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departures of simulation from TM-scene interfaces were in the highest values. In most 

departures, the simulation value was higher than the TM-scene value. Half the simulation 

interface lengths differed from corresponding TM-scene values by no more than lo%, and 86% 

differed by no more than 30%. The average absolute difference was 11.7%. 

Regression of simulation side-adjacency statistics on their TM-scene corollaries indicated 

highly significant relationships (F-stat. p < .001) for all but Adj-0, with 56%-99% of the 

variation in the simulation values explained by TM-scene values. R2's were 0.56 for Adj-1, 0.91 

for Adj-2, 0.80 for Adj-3, and 0.99 for Adj-4. Their slope coefficients varied hom 0.97 to 1.21. 

The poor fit of simulation Adj-0 to TM-scene Adj-0 probably was largely due to the usually low 

value and resultant extremely small influence of this spatial-pattern index in the decision 

process. 

Variation in simulation indices. Three replicate simulations with three sets of model-parameter 

values revealed the variation in simulation spatial-pattern indices caused by the random aspect 

of the model. CV averaged across all the spatial-pattern indices ranged from 4.9% to 19.3%. 

It was highest in the three replicate simulations where G = 540 and lowest hi those where G = 

0. The CV of Adj-0 was extremely high in the sets of replicate simulations in which G = 180 

(CV = 53%) and G = 540 )CV = 71%), probably because of the low value of Adj-0 (less than 

20 pixels in all cases). Average CV's for the other side-adjacency statistics ranged from 2.17% 

for Adj-4 to 7.34% for Adj-2. The CV of interface length averaged 6.28%. 

Watet-body size pups. Water-body size data for sites and simulations were difficult to compare 

because water bodies were few and their size range enormous. Rather than grouping them by 

even intervals, we defined breaks between size groups with the following consistently applied 

algorithm. In a list of water bodies sorted by size, a break was defined if the larger of two 

adjacent vater bodies was more than twice the size of the smaller. Upper arid lower boundaries 

were placed on water-body size groups for each marsh unit. Figure 5 summarizes the 

differences between number of water-body size groups in each case. The study sites and their 

simulations had the same number of water-body groups in 23 of 70 cases. In 59 cases, study 

sites and simulations differed by no more than one group. This was good agreement 

considering that the status of one pixel in a strategic location could determine whether two 

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SAMPLE > 3Or SIMULATED > 

0123 

n,, 

NUMBER OF WATERBODY 

SIZE GROUPS BY WHICH ONE 

EXCEEDS THE OTHER 

Figure 5. Frequrency distribution oE cascs by difference bctwecn numbcr of water-body sizc 

groups. Values on the abscissa indicate the numbcr of groups by which thc study 

site exceeds the simulation (values to the left OC zcro). 
clumps of pixels formed one water body or two. Usually, the study site had more groups than 

the simulated marsh. The average number per study site was 2.7, whereas the average number 

per simulated marsh was 2.3. Two groups were distinguished for most marshes. Typically, a 

small percentage of water pixels were distributed among many small water bodies, and the rest 

were in one large water body. For example, in one study site, 4.3% of the water pixels were in 

water bodies that included 0.003%-0.752% of the total water pixels, whereas '95.5% were in one 

water body. Water-body groups in the corresponding simulation were similar. Generally, when 

more than two water-body groups occurred, the additional ones were at the lower end of the 

size range. 

Visd evaluation. Visual comparisons suggested that the model often succeed,ed in simulating 

spatial patterns of the TM scenes, except when high G-values were used to simulate brackish 

marshes. The simulations did not appear to accurately represent those pattems of land arid 

water heavily influenced by underlying geologic features, such as ridgehwale topography 01: large 

lakes, nor man-made features such as canals and diked areas. Despite limitations, the model 

simulated the general patterns of most marshes well, and matched a few remarkably well. The 

marsh map in Figure 6 (bottom) was simulated with an interior-marsh-decay coefficient of 311, a 

shoreline-erosion coefficient of 540, and a BC of 0oO1. At a DL of 68.89%, it displays a spatial 

pattern of land and water very similar to that of the classified TM scene at the same decaly 

stage (Fig. 6, top). Interface length in the simulation map differed from that in the TM scene 

by 10.2%. 

Mutkl-Fr vuf~. Some generalizations can be made about the appropriate modelparameter values for simulating marsh disintegration. Lobe age did not appear to influence 

parameter values, whereas marsh type seemed to be an influencing factor. Based on the 

knowledge base and our criteria, the expert system gave salt marshes higher shoreline-erosion 

coefficients, more water borders, and lower interiordecay coefficients than it gave to brackish 

marshes (Fig. 7). W and G were inversely related in the expert system's selections (Fig. 8A). 

Our visual comparisons suggested that low-to-medium values of G (0-180) and moderately high 

values of W (about 200-400) matched the spatial patterns of brackish TM sa:nes best. 

Conversely, high G-values (180 and 540) gave the best match to salt marsh scenes. Because salt 

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Figure 6. 

ORIGINAL PAGE IS 

OF POOR QUALITY 

Land-water image of a study site (top) and its simulation (bottom) showing 

disintegration level (DL) and model coefficients (W, G, and BC) used in the 

simulation. (In BC, 0 = land and 1 = water.) 

17 
A. 

$ 100- 

0- 

2nn 

100 

0 

Salt Brackish 

Salinity 

B. 

*O0n 2oo 

Salt Brackish 

Salinity 

Late Early 

Lobe 

Late Early 

Lobe 

A-0-1 B-2 C-3-4 

Boirder class 

200100 0 

A-0-1 B-2 C-3-4 

Border class 

Figure 7. (A) Average G weighting factor (shoreline erosion) and (B) average W weighting 

factor (interiormarshdecay) selected by the expert system, by marsh type, lobe 

(early or late Lafourche), and border condition (BC). (Two extremely high 

outliers were excluded in cacluclating average W.) 

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2- 

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0. 

A. 

ORIGINAL PAGE IS 

OF POOR QUALITY 

B2m! 100 

0 r 

I 11 111 

G-CIZW 

B. 

L aJ 

3 I Earlylobe 

I Lailobe 

Brackish salt 

Salinity 

Figure 8. (A) Average W by G-class (Class 1, G = 0, 4, 20, or 60; Class 2, G = 180; Class 

3, G = 540). 

(B) Average border condition (BC) selected by the expert system, by marsh type 

and lobe. 

19 
marshes have more borders on major water bodies, shoreline erosion is more prevalent in them 

than is interior decay (Fig. 8B). Interior decay weighting coefficients selected by the expert 

system were highest for simulated marshes having the fewest water borders (F?g. 7B), which 

were primarily the brackish marshes (Fig. 8B). 

Simulated Site-Specific Integace Length versus DL 

We followed interface from 100% land to 100% water in each of the 70 simulations. Marly 

simulations were similar to those of Lost Lake NW, Mink Bayou SW, and Mink Bayou SE (Fig. 

9). Interface reached a maximum approaching 10,000 pixel-lengths (300 km) when the marsh 

was roughly 50% disintegrated. Interface in Pelican Pass SW (Fig. 9) followed a strikingly 

different path, reaching its unusually low maximum of 2,417 at a DL of only 111%. This is one 

of two simulations that differed markedly from the rest in reaching maximum interface at a low 

DL Both were simulated with G = 540 and BC = 1111. The distribution of maximum 

interface in the 70 simulations was bimodal, with a lower peak around 2,000-4,000 pixel-lengths 

and a higher one at 9,000 (Fig. 1OA). DL at maximum interface was between 45% and 60% in 

most simulated marshes (Fig. 10B). Based on the simulations, 37 sites had not yet reached the 

DL of maximum interface in 1985, whereas two were at maximum-interface DIL, and 31 were 

beyond it (Fig. 1OC). 

Aggregated Interjiace Length versus Year 

Aggregated 1985 simulation interface was 406,051 pixel-lengths (12,182 km)--82% of the 

aggregated maximum interface of 4%,%9 pixel-lengths (14,909 km). According to our estimates 

from Wicker’s (1980) map data, the average annual change in DL in the USGS-topographicmap areas of our study sites varied from 0.125% to 1.145% per year (Appendix H). Using 

these trends and the year of our TM image to relate DL to time, we transforimed our 

individual-site plots of interface versus DL to the lobe-aggregated plots of interface versus time 

in Figure 11. Our hindcasts (1956-1985) and predictions (1985+) of interface are plotted is 

fractions of total maximum interface. The interface curves do not reach 1.0 because all the 

simulated marshes will not reach their interface maxima concurrently. The 19:35 points are near 

the two maxima on the ascending side. These results suggest that total land-water interface in 

both deltaic areas has been increasing, but will soon begin decreasing. If the estimated linear 

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(X 1000) LOST LAKE NW (X 100) PELICAN PASS SW 

10 

8 

6 

4 

n 

ua 

c 

0 

2 

w 

J 0 

r 2 

9b water r32 \ W =23 

0 =180 

Bc=oo11 

1 I I I 

0 20 40 60 80 100 

1000) MINK BAYOU SW 

0 20 40 60 00 100 

DlSlNTEQRATlON 

t I I I 

0 20 40 60 00 100 

1000) MINK BAYOU SE 

% water =29 

0 20 40 60 80 100 

LEVEL (% WATER) 

Figure 9. Simulated interface versus disintegration level (DL) for 4 of the 70 cases showing 

the modeling coefficients (W, G, and BC) used in the simulation. 

21 
a 

W 

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0 

t3 

u, 

LL 

15- 

5 10- 

5- z - 

2ol 15 

- 

- - 

r -LL 

IO 

5 

0 d n 

5678 9 IO II 12 13 

3 20 

z 

15 

IO 

5 

0 n n n n 

r 

nnl 

000000000000 s,ao~coIc,~rr,cu~ - cu 

I I I I I I I I 

nn 

MAXIMUM INTERFACE LEVEL MINUS 1985 LEVEL 

OF DISINTEGRATION (%I 

Figure 10. Frequency distributions of cases by simulation maximum interface (A), 

disintegration level (DL) at maximum interface (B), and maximum-interface DL 

minus 1985 DL (C). Numbers on the abscissas are the upper values of each 

interval. 

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3W 

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trends in DL are accurate, the decline will begin about 1995. Sasser et al. (1986) reported an 

exponential rate of loss in our study area. On the other hand, recent observations by Liebowitz 

(personal communication) suggest that some large water areas that appeared between 1956 and 

1978 may not be growing. 

Possible Impacts on Fisheries 

We found a statistically significant (p < .OO01) relationship between brown shrimp catch and 

interface. The equation is as follows: 

Yp = -61.046 + 277.55 XI - 0.39198 X2 - 0.12948 X, (3) 

where Yp = catch per unit area, XI = interface length in pixel sides, X2 = rainfall in 

centimeters, and X, = hours from April 9 through 30 in which temperatures were below 20°C. 

The equation explained 49% of the variation in catch for 1960-1987 (Appendix I). Interface 

length alone explained 32%. The percentage variation in annual catch explained by the 

equation was high, considering that effort, usually a major factor influencing c:atch, was not 

included as an independent variable. Using our interface projections and assuming average 

conditions of the other independent variables, the equation predicts that brown shrimp catlches 

dependent upon Barataria, Timbalier, and Terrebonne bays may fall to zero within 75 yr 

(equation confidence limits 52 and 105 yr). Confidence limits do not include the error 

associated with predicted interface. 

Discussion 

Our model and expert system appear to have been successful in simulating general features of 

the spatial patterns of most study sites. The model was not designed to reprcduce the exact 

locations of land and water in each study site but, rather, the general charactexistics of the landwater pattern. Since the model is probabilistic, it produced a different pattern in every 

execution with the same W-G-BC combination. Necessary built-in restrictions such as having 

only six possible values of G and the same G for all water borders limited our versatility in 

matching spatial patterns. We could have matched the interface length of the TM Scenes :more 

closely had we not also matched the side-adjacency statistics. But selecting W-G-BC on the 

basis of both interface length and the side-adjacency statistics increased the probability that the 

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24 
trajectory of interface change with land loss during each simulation of marsh disintegration was 

realistic for the site. 

The statistically significant fit of a parabola to the plot of interface-length versus DL of our 

70 TM scenes (Fig. 4) supports Browder et al.3 (1984) first conclusion: In the progress of 

marsh decay, interface length increases initially, reaches a maximum, and then decreases. The 

scatter of points about the parabola (Fig. 4) supports their second conclusion: The magnitude 

of maximum interface (and, consequently, the trajectory of interface change with land loss) 

differs from marsh to marsh. Our expert system selected model-parameter values to simulate 

marsh disintegration at each site on the basis of spatial-pattern indices measured in the site’s 

TM scene. The site-specific parameters produced considerably different trajectories of interface 

change with land loss. Maximum interface varied from about 2,000 pixel-lengths (60 km) to 

over 13,000 (390 km) (Fig. lOA), and DL at maximum interface varied from about 10% to 70% 

(Fig. 10B) in the 70 simulations. Simulations with the Browder et al. (1984) model consistently 

reached maximum interface at a DL of about 50%. Apparently, the greater power of the Gweighting coefficient in our model gave it more flexibility in simulating interface trajectories. 

Nevertheless, our 70 simulation results were centered around a mean DL at maximum interface 

of 52.7% (S.D. = 9.95). 

In the plot of interface length versus DL with TM-scene data, DL at maximum interface 

was between 30% and 50%. This might appear to conflict with the site-specific simulation 

results, summarized in Figure 10B, which suggest that interfaces reach their maxima in most of 

the sites when they are 45%-60% disintegrated. But, when we plotted the simulated 1985 

interface lengths against DL, we found that these data, too, reached maximum interface at 30%- 

50% DL Apparently, a plot of interface length versus DL at many sites in different stages of 

disintegration does not precisely reflect the generalized shape of the curve of interface versus 

DL at the individual sites. 

The resolution of TM imagery seemed adequate for this analysis. Many water features 

were recognizable that would not have been noticeable in MSS imagery. Our model might be 

useful for roughly estimating the history of interface length with disintegration in other marshes, 

even in the absence of the detailed spatial data we acquired for our 70 sites. On the basis of 

our results, model coefficients could be set as follows: BC = observed water borders, G = our 

25 
mean or modal value for salt or brackish marsh (Fig. 7A), and W = our mea.n or modal value 

for that border class (Fig. 7B). 

Summary 

We demonstrated with TM imagery the general relationship of land-water interface length to 

stage of disintegration. We then simulated the disintegration of 70 specific marshes from 

hypothetical starting points of solid land, through their present states of disintegration, to total 

conversion to water. We used unpublished data from digitized maps (Wicker 1980) to quantify 

site-specific disintegration rates, and we hindcasted and forecasted land-water interface as a 

function of time. We then aggregated the site-specific data to produce an estimate of interface 

length, by year, on each lobe. The relationship with time may be tenuous because we assumed 

a linear trend based on only two points in time. Nevertheless, relating our results to time, even 

if only roughly, helps the reader comprehend the immediacy of the problem. 

We found a statistically significant relationship between a time series of fishery catch 'data 

and the length of the land-water interface. Others have found relationships between spatial 

data on fishery catch and interface. Our analysis may overestimate the importance of interface 

to brown shrimp production because the conversion of freshwater marsh to birackish marsh (or 

some other factor not included in our equation) might ameliorate the effect of interface loss in 

salt and brackish marsh. Nevertheless, the shape of our curve of interface over time, today's 

location on that curve, and our contribution to the mounting evidence re1atin.g fshery catches to 

interface length should be seriously regarded. 

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Acknowledgments 

We thank the following colleagues for their valuable help during this study: Andrew Kemmerer, 

Gerald Williamson, Rex Herron, Thomas Leming, Robert Muller, Barney Barrett, Scott 

Liebowitz, Kenneth Haddad, John Barker, Sharon Rickman, Ferron Risinger, Floyd Stayner, 

James Scurry, Deborah Fuller, Amy Karns, Mary Hater, Ann Slater, Robin Graham, Robert 

O’Neill, and three anonymous reviewers. This project was largely funded by a grant from the 

National Atmospheric and Space Administration under the Thematic Mapper Program. The 

Beaufort (NC), Mississippi, and Miami (FL) Laboratories of the Southeast Fisheries Center, 

NMFS, NOAA, also contributed substantially to this project. 

27 
References 

Barrett, B. B., and M. C. Gillespie. 1975. 1975 Environmental conditions relative to shrimp 

production in coastal Louisiana. Louisiana Wildlife and Fisheries Tech. Bull. 15. 

Baumann, R H., J. W. Day, Jr., and C. A Miller. 1984. Mississippi deltaic wetland survival: 

sedimentation vs coastal submergence. Science 2241093-1095. 

Boesch, D. E, and R E Turner. 1984. Dependence of fishery species on salt marshes: the 

role of food and refuge. Estuaries 1460-468. 

Browder, J. A, H. A Bartley, and K S. Davis. 1984. A probabilistic model of the relationship 

between marshland-water interface and marsh disintegration. Ecological Modeling 

29245-260. 

Chabreck, R. H. 1972. Vegetation, water and soil characteristics of the Louisiana coastal region 

(map). Louisiana State University, Agricultural Experiment Station Bull. 664, Baton 

Rouge. 

Chabreck, R. H., and G. Linscombe. 1978. Vegetative type map of the Louisiana coastal 

marshes. Louisiana Department of Wildlife and Fisheries, New Orleans. 

Dow, D. D. 1982 Software programs to measure interface complexity with remote-sensing data, 

with an example of a marine ecosystem application. NASA Report No. 219. NASA 

Earth Resources Laboratory, NSTL, Miss. 

I 

Dow, D. D., and R. W. Pearson. 1982. SLIN: a software program to measure interface length, 

NASA Report No. 208. NASA Earth Resources Laboratory, NSTL, Miss. 

Faller, K H. 1979. Shoreline as a controlling factor in commercial shrimp production. NASA 

Tech. Memo. 72-732 Earth Resources Laboratory, National Space Technologies 

Laboratory, NSTL, Miss. 

Gagliano, S. M., K J. Meyer-Arendt, and K M. Wicker. 1981. Land loss in the Mississippi 

River deltaic plain, Transactions of the Gulf Coast Association of Geological Societies 

3 1~295-30. 

Gosselink, J. G. 1984. The ecology of the delta marshes of coastal Louisiana: a communnty 

profile. FWS/OBS-84/09. Office of Biological Services, U.S. Fish and Wildlife Service, 

Slidell, La. 

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Graham, M. H., B. G. Junkin, M. T. Kalcic, R. W. Pearson, and B. R. Seyfarth. 1984. ELAS: 

Earth Resources Laboratory Applications Software. Vol. 2, ELAS User’s Guide. NASA 

Earth Resources Laboratory, NSTL, Miss. 

Kesel, R. H. 1988. The decline in the suspended load of the lower Mississippi River and its 

influence on adjacent wetlands. Environ. Geol. Water Sci. 11:271-281. 

Leibowitz, S. G., and J. M. Hill. 1988. Spatial analysis of Louisiana coastal land loss, In R. E. 

Turner and D. R. Cahoon, eds., Causes of Wetland Loss in the Coastal Central Gulf of 

Mexico, Vol. 2. OCS Study/MMS 87-0120. Minerals Management Service, New Orleans. 

Mandelbrot, B. B. 1967. How long is the coast of Britain? Statistical self similarity and 

fractional dimension. Science 56:636-638. 

Moran, P. A P. 1948. The interpretation of statistical maps. Journal of the Royal Statistical 

Society, Series B, 10:243-251. 

Nichols, S. 1984. Updated assessments of brown, white, and pink shrimp in the U.S. Gulf of 

Mexico. Stock Assessment Workshop 1984, Southeast Fisheries Center, National Marine 

Fisheries Service, NOAA, Miami, Fla. 

Pun, Y. T. 1981. Entropic thresholding, a new approach. Computer Graphics and Image 

Processing 16210-239. 

Sasser, C. E., M. D. Dozier, J. G. Gosselink, and J. M. Hill. 1986. Spatial and temporal 

changes in Louisiana’s Barataria Basin Marshes, 1945-1980. Environmental Management 

10671 -680. 

Turner, R. E., and D. R. Cahoon. 1987. Causes of wetland loss in the coastal central Gulf of 

Mexico. 3 vols. OCS Study/MMS 87-0119, 120, 121. Minerals Management Service, 

New Orleans. 

Upton, G., and B. Fingleton. 1985. Spatial Data Analysis by Example. Vol. 1. John Wiley, 

New York. 

Wicker, KM. 1980. Mississippi deltaic plain region ecological characterization: a habitat 

mapping study. FWS/OBS-79/07. U.S. Fish and Wildlife Service, Slidell, La., and 

Minerals Management Service, New Orleans. 

Zimmerman, R. J., T. J. Minello, and G. Zamora, Jr. 1984. Selection of vegetated habitat by 

brown shrimp, Penueus aztecus, in a Galveston Bay salt marsh. U.S. Fisheries Bull. 

82325-336. 

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Appendix A 

Background and Methods 

Louisiana’s Coastal Wetlands: Geological Background and Previous Remote Sensing Studies 

Methods 

Overview 

Model Specification 

Model Expansion, Refinement, and Sensitivity Testing 

Study Site Selection 

Image Processing and Analysis 

Measurement of Spatial-Pattern Statistics 

Description of the Expert System 

References 

Table Al. Look-up table used to classify water and land identified by the ELAS shoreline 

length module into water pixels and land pixels with zero, one, two, three, or 

four sides adjacent to water. 

Table A2. Calculation of weighted mean W, DE, and CV from a G-BC look-up table in 

which G = 0, BC = oo00, and DL = 10.90%. 

Figure Al. The maximum extent of the influence of deltaic lobes of the Mississippi 

River on the present geomorphology of Louisiana’s coastal wetlands. 

PRECEDEMG PAGE BUNK NOT FILMED 

31 
Louisiana’s Coastal Wetlands 

Geological Background and Previous 

Remote-Sensing Studies 

The continuing disintegration of the coastal marshes of Louisiana is one of the major 

environmental problems of the nation. The average rate of loss for the last 20 yr has been 

approximately 104 km2b (Gagliano et al. 1981). At this rate, Louisiana’s coastal marshes will 

be gone in 145 yr. Prevailing evidence suggests that the marsh disintegration results from local 

imbalances between building processes, such as sedimentation and the growth and accumulation 

of dead vegetative matter, and destructive processes, such as sea level rise, crustal subsidence, 

erosion, and compaction (Gosselink 1984). Local elevation gradients within the marsh are so 

low that small changes in water level or land elevation can cause large changes in land and 

water area (Sasser 1977; Baumann 1980). Water management structures, navigation cuts and 

channels, and other alterations by man appear to accelerate the disintegration rate (Johnson and 

Gosselink 1982; Dozier 1983; Gosselink 1984, Turner et al. 1984). 

The problem of marsh loss in Louisiana is relevant to fihery management because Louisiana 

leads the nation in landings of fishery products, and most of the landed species are dependent 

upon estuaries and their associated tidal marshes. Coastal marshes contribute to estuarine food 

chains through the export of organic detritus, and the shallow, protected water of marshes 

serves as fish and shellfish nursery grounds, promoting survival and growth of the young. 

Remote-sensing studies by Faller (1979), Dow (1982), and Gosselink (1984) suggest that the 

abundance of fihery species is more strongly correlated with the length of the interface 

between land and water in the marsh (shoreline) than with actual area of marshland. 

Observations from a field study by Zimmerman et al. (1984) support this conclusion. 

Simulations from a theoretical computer model by Browder et al. (1984) suggested that landwater interface initially increases with marsh disintegration but reaches a maximum when the 

marsh is 50% water and decreases thereafter. The degree of change in interface with each 

incremental loss of marsh land and the maximum length of interface attained are a function of 

the order in which segments of land are converted to water and the resultant pattern of 

33 

PRECEDING PAGE BLANK NOT FILMED 
distribution of land and water. The more clustered the segments of land converted to water, 

the lower the rate of change and less the maximum interface. 

In evaluating the potential effect of marshland loss on fsheries, the first two critical factors 

to consider are (1) whether land-water interface in actual disintegrating marshes is currently 

increasing or decreasing, and (2) the magnitude of the change. 

This study used Landsat Thematic Mapper 0 data covering specific sample marshes in 

coastal Louisiana to (1) test conclusions from the Browder et al. (1984) model with regard to 

the stage in disintegration at which maximum interface occurs; (2) further explore the 

relationship between maximum interface and the pattern of destruction of land and water 

suggested by the model; and (3) determine the direction and degree of change in land-water 

interface in relation to land loss in actual marshes. 

Louisiana’s coastal marshes were ideally suited for this examination for several reasons. 

First, the large, contiguous expanses of marsh enabled us to sample large areis containing only 

wetlands. Second, this region has been the subject of many scientific investig<ations concerning 

ecological principles, geologic processes, and experimental use of remote-sensing techniques. 

Third, geologic changes are occurring very rapidly here, and fourth, Louisiana’s coastal marshes 

are the most extensive in the United States and support a high proportion of the total U.S. 

production of estuarine-dependent fish and shellfish. 

The coastal wetlands of Louisiana were formed as deltas of the Mississippi River and its 

tributaries. The large, heterogeneous expanse of deltaic wetlands along the Lnuisiana coast is 

very young geologically. This area was formed within the last 3,000-5,000 yr is a series of 

overlapping deltaic lobes of differing ages (Fig. Al). Instability is a characteristic of youthful 

geologic environments. Subsidence, a complex set of processes, has pronounced effects on 

wetlands near sea level. Isostatic adjustments in the form of crustal downwarping from 

sedimentary loading; tectonic processes that occur contemporaneously, such as folding, 

fracturing, flowing, and growth faulting; consolidation of underlying sediments due to the weight 

of natural features (e.g., natural levees); and differential compaction related to textural 

variability are among those natural processes involved in submerging this coastline. Human 

activities in the form of fluid withdrawals (hydrocarbons and water), marsh dewatering through 

reclamation processes, and sediment consolidation resulting from building structures on wet lands 

all exacerbate coastal submergence. The above subsidence factors, combined with eustatic sea 

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level rise, have given coastal Louisiana the fastest-submerging coastline in the United States 

(Hicks 1981). 

Submergence results in the "drowning" of marshes and their conversion to bay and lake 

environments. Sedimentation can balance the effects of submergence and, via the Mississippi 

River and its tributaries, has resulted in development of the Mississippi delta lobe. The 

geologic record indicates that, on the average, a major delta lobe complex will build and enlarge 

over approximately 1,OOO yr. This period is followed by one of abandonment characterized by 

wetland loss, which also lasts approximately 1,OOO yr. As one delta complex is being abandoned, 

another one is simultaneously building. Throughout at least the Holocene, the Mississippi 

deltaic plain has always concurrently had areas of development and abandonment. Presently, 

however, the leveeing of the Mississippi River and maintenance of its present course, combined 

with reductions in sediment loads (Tuttle and Combe 1981) and debouchment of sediment at 

the edge of the continental shelf, have resulted in widespread wetland loss. The construction of 

ship channels, pipeline canals, and access canals for hydrocarbon exploration and production has 

both contributed to and accelerated these losses. Acceleration occurs through the effect of 

these structures on salinity distributions and sediment deposition. For instance, canals promote 

saltwater intrusion, which results in the death of brackish-water marsh vegetation and thus 

retards the accumulation of organic soils. Spoil banks associated with canals prevent deposition 

of sediment on the marsh surface and reduce exchanges of water and materials between the 

marsh and open water. The natural geological process of wetland deterioration, which would 

otherwise take place over several centuries, appears now to have been compressed into several 

decades. 

Chabreck (1WZ) distinguished four major types of Louisiana coastal marshes on the basis of 

vegetation: fresh, intermediate, brackish, and saline. Several investigators have found significant 

differences between these marsh types in various soil, water quality, and other parameters, 

thereby supporting Chabreck's classification. Gosselink et al. (1979) found considerable 

differences in the length of land-water interface per unit area among the four marsh types in 

the neighboring Chenier Plain (marginal Mississippi deltaic plain) of southeast Texas and 

southwest Louisiana. 

Sasser et al. (1986) used photo interpretation of aerial photographs 

computer-based geographic information system (GIS) to detect changes 

in 

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combination with a 

the percentage water 

35 
within wetlands on the late Lafourche delta lobe. They found a pattern of general degradation 

in wetland area: marshes were degrading into various densities of shallow water bodies. Of the 

marsh and natural levee area, 91% was solid or less than 10% water in 1945. By 1956, oiily 

77% of the marsh was less than 10% water; by 1%9, only 46% was; and by 1980, only 28%. 

They noted two patterns of disintegration. In one, small, randomly spaced water bodies 

developed within solid marshes and gradually grew into larger water bodies. [n the other, land 

was lost along the margins of major water masses, as if by mechanical wave attack or erosion. 

The first pattern seemed to be the more important. 

Rosen (1980) in his study of Chesapeake Bay, concluded that shorelines with low tidal 

ranges have higher rates of erosion than areas with higher tidal ranges, possibly because higher 

tidal ranges form beaches of higher elevation. On these beaches, storm surges are less likely to 

reach the elevation of fastland (bluff or dune) material to augment erosion, and wave energy is 

distributed over a greater distance in the course of a tidal cycle. The tidal range in Chesapeake 

Bay varies from 0.36 to 1 m over a distance of 120 km. The tidal range in the north-central 

Gulf of Mexico is approximately 0.6 m. 

Leibowitz and Hill (1988) used digital habitat maps for 1956 and 1978 from the U.S. Fish 

and Wildlife Service (Wicker 1980) to quantify change in coastal marshes during the 22-yr 

period and to evaluate various possible causes of the change. Their study covered our two 

study areas--the late Lafourche lobe and the early Lafourche lobe (referred to as Terrebonne in 

their study). Water, wetland, and upland could be distinguished in the data, which were 

classified according to the Cowardin et al. (1979) system. Boundaries between saline and 

freshwater zones were also defined on the basis of vegetation. Liebowitz and Hill classified 

each map cell on the basis of a comparison of 1956 and 1978 habitat maps as follows: areas 

that were fresh in 1956, but saline in 1978; areas that changed from saline to fresh between 

1956 and 1978; and areas that remained saline during the 22 yr. They also identified the cells 

in each habitat category that changed from land to water during the 22 yr. Their results 

revealed a 37% net area change from salt to fresh on the late Lafourche lobe and a 16% net 

area change from fresh to salt on the early Lafourche lobe. The highest rate of land loss on 

the late Lafourche lobe was 27% and occurred in the fresh-to-salt area. The highest rate of 

land loss on the early Lafourche lobe was 16% and occurred in the fresh-to-fresh area. By 

statistical comparisons, they ruled out saltwater intrusion as a reason for land loss on the early 

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Lafourche lobe, but concluded that it could be a cause of land loss on the late Lafourche lobe. 

The highest rates--47%-55%--occurred in the mud flat and beacwdunelreef habitats. Loss rates 

in fresh and saline marsh averaged approximately 18%. Loss from shoreline erosion accounted 

for only 2.1% (early Lafourche) and 3.2% (late Lafourche) of all land loss. Thus, the major 

form of land loss for all three regions was the conversion of land to inland open water (lakes, 

ponds, or bays). 

Several studies have used TM and Landsat MSS imagery with collateral data, such as fish 

abundance and vegetative biomass, to examine the role of coastal wetlands in estuarine food 

chains and the production of estuarinedependent fish and shellfish. These studies were 

supported by the development of software routines used to determine shoreline density (Faller 

1977) and shoreline length (Faller 1977; Dow and Pearson 1982), to identify water bodies 

(Butera 1982a), and to measure the distance between land-cover classes (Butera 1982b). Faller 

(1979) found a strong correlation between shrimp yields and shoreline density in subareas of the 

Louisiana coastal zone. Dow (1982) expanded Faller’s (1979) approach and developed 

predictive equations that related the abundances of selected species of fiih and shellfish to 

shoreline-length estimates for subareas of Apalachicola Bay, Florida. The findings of both 

authors suggest that abundances of some fish and shellfish could be influenced by the density 

and length of the marshland-water interface. Butera and Seyfarth (1981) and Butera et al. 

(1984) used water-body identification, distance measures, shoreline density, and vegetative 

biomass estimates to quantify organic carbon export into nearby water bodies. 

37 
Methods 

Overview 

We expanded an existing model (Browder et aL 1984) so that it could simulate marshes of 

substantial size, used actual marshes to calibrate the weighting factors of the models, and then 

used the model to simulate the disintegration over time of each sample marsh. Model 

calibration was accomplished by quantifying the spatial-pattern statistics of the sample marshes 

and matching them to the spatial-pattern statistics expected from simulated marshes, basedl on a 

series of simulations in which W, G, and the number of water borders (BC) were varied. 

The process consisted of nine steps: (1) expansion, refinement, and sensitivity testing of the 

model; (2) selection of sample sites; (3) analysis of imagery; (4) measurement of spatial-paittern 

statistics; (5) development of a knowledge base and an expert system; (6) calibration of the 

model to the sample marshes; (7) simulation of the disintegration patterns of the sample 

marshes; (8) evaluation of simulation results; and (9) interpretation. 

Model sp)k&ion. The model used in this study is the second generation of a stochastic spatial 

computer model introduced by Browder et al. (1984). In the initialization of the model, marsh 

dimensions are defined in terms of the numbers of rows and columns of pixells. Each pixel can 

exist in one of two states, land (emergent vegetation) or water. Initially, all the pixels are land 

and the marsh is solid. One land pixel is converted to water at each iteration. The actual pixel 

converted is determined by a random number generator linked to a probability function that 

incorporates two weighting factors that approximate the natural processes of interior marsh 

decay (the W factor) and shoreline erosion (the G factor). The W factor determines 

disintegration probability on the basis of the number of sides that the pixel is bordered by water. 

The G factor governs the probability that the pixel will disintegrate if it borders the main water 

body. The probability weight of each pixel is calculated by the equation: 

Fijk = 1 + W Sq,k + GI Big + Gz BI~ + G3 Bjij + G4 Bdj. (1) 

where W = weight coefficient for each side adjacent to water; S = number of sides adjacent to 

water; G = weight coefficient for pixels adjacent to a major outside water body; and B = a 

Boolean value (1 or 0) indicating whether the pixel is adjacent to a major outside water body. 

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The probability weight of a given pixel changes throughout the simulation, depending on what 

happens to other pixels, particularly those adjacent to it. 

In having the weighted probability function approximate natural processes of interior marsh 

decay (W) and erosion due to tidal action or wind-induced turbulence along the edge of major 

water bodies (G), we did not assume that marsh loss is a random process, but merely that it 

could be simulated by a weighted, randomly driven function. 

The model simulates the entire process of disintegration, starting with solid land and ending 

with solid water. Each iteration represents the passage of time, although the units of time are 

not specified. 

At each iteration of the simulation, a counter keeps track of the percentage area 

represented as water, referred to throughout this discussion as the "level of disintegration," and 

the length of the land-water interface. The latter is expressed in terms of pixel-lengths, the 

length of one side of the square pixel; therefore, measuring interface length consisted of 

counting the number of "joins" between land pixels and water pixels. Thus, interface, as we 

measured it, is exactly homologous to the "black-white join" (J), the spatial autocorrelation 

parameter that Moran (1948) introduced into the literature of quantitative geography. Upton 

and Fingleton (1985) described the common relationship between the join statistic and other 

spatial-pattern parameters, such as that of Cliff and Ord (1973), and defined the cross-product 

statistic, R, which is equal to 2 x J. 

Upton and Fingleton (1986) provide an intricate set of equations for calculating R, the 

expected value of R (EW]), and the variance of the expected value. E(R) assumes a random 

distribution of black and white (or land and water) cells. R departs from E(R) to the extent 

that like cells are clumped (R < EP]) or uniformly distributed (R > E[R]). They provide 

simpler equations for calculating J, E(J), and var E(J) for cases in which the area is regularsided and square in configuration (their equations for the R statistics are more general). In our 

simulations, we were able to determine J simply by keeping a running total of the number of 

land-water joins created at each conversion of a land pixel to a water pixel. A method related 

to counting was used to determine the number of land-water joins in satellite images classified 

as land and water. Our observations indicate that, for a square area with regular sides, E(J) is 

approximately equal to one-half the number of land-water joins of an area of the same 

39 
dimensions having a checkerboard pattern of distribution of land and water. This can be 

calculated as follows: 

E(J) = 2 N2 - 4 N 

where N = the number of rows = the number of columns. 

The weighting factors affect the order of disintegration of marsh land pixds and the 

resultant distribution of land and water in the simulated marsh. The higher the values of the 

weighting factors, the more clumped the water pixels. By affecting the spatial distribution of 

water pixels, the weighting factors determine interface length in simulated marshes. Taking 

advantage of this relationship, the approach we took to simulating the disintegration of actual 

marshes was to use spatial pattern, as expressed by level of disintegration, interface length, and 

other spatial pattern statistics of the actual marshes, compared to those from simulated marshes, 

to select W and G weighting factors for the model. The other spatial-patterns statistics that 

were used were numbers of water pixels with zero, one, two, three, or four sides adjacent to 

other water pixels, and numbers of water pixels on each of the marsh's four borders. The 

distribution of water pixels by size of water clusters at the current (Le., December 1984) level of 

disintegration was used to test the fit of the simulated marsh to the actual miirsh. Comparison 

of simulated marshes to actual marshes in general suggests that the function will work well for 

simulating reticulated marshes, such as those on the Gulf coast, although it might not work well 

for marshes with a more dendritic pattern of land and water, such as those along the U.S. 

Atlantic coast. 

Model Expansion, Refinement, and Sensitivity Testing 

The first phase in the study was improving the model. Our improvements were guided by a 

series of sensitivity tests: (1) tests of the effects of the W and G weighting fixtors, varied 

separately; (2) tests of the effect of marsh geometry (Le., length, relative to width); and (3) tests 

of the effect of marsh size, in terms of number of pixels. 

In the original version of the model, only the pixels initiaZZy on the major outside water body 

had the G weighting (B = 1). The G effect was inconsequential in sensitivity tests with the 

original model, particularly as the size of the marsh simulated was increased. On the basis of 

this observation, the model was revised so that any pixel, regardless of original location, could 

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eventually be assigned B = 1. The G factor in the present version of the model has a much 

greater effect than that in the earlier version. 

Other sensitivity tests indicated that the geometry of the marsh @e., ratio of length to 

width) affected the trajectory of change in interface relative to W and G and greatly 

complicated the process of examining interface as a function of W and G and the number of 

water borders to the marsh @e., simulation results differed depending upon whether a water 

border was the long or the short border). We decided to work with square marshes, both 

simulated and actual, in order to avoid this complication. 

To eliminate another complicating variable--scaling--we decided to simulate marshes of the 

same size (same number of pixels) as our sample sites. We determined that it would be 

practical to simulate marshes up to 192 x 192 pixels, although not with replication. A site 

represented by 192 x 192 pixels covers 33.18 km2 and is approximately one quarter of the area 

covered by a 7.5-minute U.S. Geological Survey topographic map. 

Increasing the size of the simulated marsh necessitated streamlining the algorithm for 

weighting disintegration probability and converting land pixels to water pixels. In the original 

algorithm, each pixel, identified by its x,y coordinate, was repeated on the list the same number 

of times as its probability factor (F in equation 1). Each item on the list had a unique number, 

and the pixel selected was the one that corresponded to the random number at that iteration, 

providing that it had not already been converted to water at a previous iteration. All 

Occurrences of pixels that had been newly converted to water were cleared from the list at five 

periodic intervals throughout the simulation. The process got slower and slower as the need for 

purging the list approached. This algorithm was too slow and awkward to be scaled up in the 

same form. In our revision, each pixel appears on the numbered list only once, but its 

probability factor is listed with it. Two random numbers are associated with each selection. 

The first random number makes a tentative selection, and the second determines whether the 

pixel is eligible. Eligibility depends on whether the pixels’s probability factor is larger than the 

random number. The selection process continues, with two new random numbers generated 

each time, until the selection of an eligible pixel is made. Of course, the first random number-- 

the one that makes the tentative selection--is a uniform random number from 0 to 1 that is 

multiplied by the largest probability factor on the list. A pointer system keeps track of the 

41 
pixels on the list and eliminates from the list the pixel that has been converted to water at each 

iteration. 

The model and all ancillary programs were written in C and were execute:d on an ATkT 

PC-7300, a 16-bit computer that has a Unix-V operating system. 

Study Site Selection 

The study sites are located in salt and brackish marsh areas on two abandoned delta lobes of 

the Mississippi River, the early lafourche and the late Lafourche. The early Lafourche lobe was 

an actively prograding lobe within the last 1,800 yr; the late Lafourche lobe vias active as a main 

distributary of the river within the last 600 yr. On each lobe we selected sites that 

corresponded to the boundaries of five contiguous U.S. Geological Survey 7.5-minute 

topographic maps. Areas defined by each topographic map were divided into four contiguous 

quarters, each encompassing an area 192 elements wide and 192 scan lines long on the Tpd 

image. The intersection of the four quarters was aligned to correspond to the center point of 

each topographic map. Each area corresponding to a quarter area of the 10 topographic maps 

was a potential sample site. After excluding sites with upland vegetation and sites for which no 

cloud-free TM images were available, we had 72 samples to use in the study: 40 salt marsh 

sites (20 on each lobe) and 32 brackish marsh sites (19 on the early Lafourche lobe and 13 on 

the late Lafourche lobe). Salt and brackish marshes were distinguished by means of the ITS. 

Fish and Wildlife SeMce habitat maps (Cowardin et al. 1979). 

Because of small errors in TM imagery, pixels are neither exactly square rior exactly the 

same size; therefore, it was necessary to eliminate several pixels on the outer boundaries of 

imagery corresponding to each topographic map in order to have a 192 x 192 image. Our 

sample images therefore do not provide complete coverage of the area--small strips at the 

boundaries of the topographic maps are missing. Selecting square samples (samples having the 

same number of rows and columns of pixels) greatly simplified the analyses of this study in 

several ways. First, we had fewer alternatives to consider in sensitivity analysis and constructing 

look-up tables. Second, we could use simpler and less time-consuming equations for estimating 

spatial autocorrelation statistics. The quarter was the largest square unit into which a 

topographic map could be evenly divided that could be simulated with practicality in the same 

dimensions by our computer model on available dedicated hardware. 

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Image Processing and Analysis 

TM scenes were analyzed on the Fisheries Image Processing System (FIPS) maintained by 

NMFS in Slidell, Louisiana. FIPS uses a Sperry-Univac V77/600 mini-computer, color image 

display device, and other hardware to process remotely sensed digital imagery. The software is a 

modified version of the Earth Resources Laboratory Applications Software (ELAS) (Graham et 

al. 1984). 

The TM image acquired for the project represented one of the few relatively cloud-free 

images covering southern Louisiana (quads 1 and 2 in path 22 and row 40 of the World-Wide 

Reference System). The Landsat overflight occurred on 2 December 1984 (Scene ID: 50276- 

16022) and covers most of the Mississippi deltaic plain. 

TM images of the sites were georeferenced to fit a Universal Transverse Mercator 

projection with a north-south orientation. The ELAS modules PMGC and PMGE (Graham et 

al. 1984) were used to accumulate ground control points, generate polynomial least-squares 

mapping equations, and resample the image using the bilinear interpolation technique. The 

average registration accuracies ranged from 22 to 56 m. 

Land and water were distinguished in the TM images by first generating a product image 

from bands 4 and 5 and then applying the global thresholding technique developed by Pun 

(1981). 

Measurement of Spatial-Pattern Statistics 

We generated 72 binary land-water images from the product images of the salt and brackish 

marsh sites. Sequential ELAS commands set up for batch processing were used to measure the 

following spatial-pattern parameters in each image: (1) total numbers of land and water pixels; 

(2) total numbers of water pixels by scan line and by element column; (3) the length of the 

land-water interface, expressed as the total number of land-water joins; (4) total numbers of 

water pixels with sides adjacent to zero, one, two, three, and four other water pixels; and (5) 

water-body size frequencies. In determining the total number of water pixels with sides adjacent 

to other water pixels, we excluded the pixels at the boundary of the sample to avoid biasing the 

distribution of pixels toward those having less than four sides adjacent to water. 

The total number of land-water joins in each image was tabulated using a three-step process. 

First, an intermediate image was generated using the ELAS shoreline-length (SLIN) module 

43 
(Graham et al. 1984). SLIN uses a 3-x-3 moving window technique to classifj each land ]pixel 

adjacent to water into one of 69 shoreline categories (Dow 1982; Dow and Pearson 1982). 

Second, we used a look-up table (Table Al) to convert the SLIN image into an image file 

comprising six classes: (1) land; (2) water; and (3) shoreline pixels with one, two, three, or four 

sides adjacent to water. Finally, we determined the total number of land-water joins in each 

sample site by enumerating the number of land pixels sides bordering water pixel sides. 

The total number of water pixel sides adjacent to other water pixels was tabulated using a 

modification of the technique used to count land-water joins. Two changes in the processing 

sequence were required: (1) water pixels adjacent to land were defined as shoreline pixels 

during processing with the SLIN module and (2) an additional processing step with a new lookup table was required to correctly classify water pixels with zero, one, two, three, or four sides 

adjacent to other water pixels. 

As Hutchinson (1957) originally pointed out and first Richardson (1961) and then 

Mandelbrot (1%7) elaborated upon, the length of an irregular shoreline is, to some extent, a 

function of measurement unit. Our measurements of land-water joins and, possibly, the other 

spatial-pattern statistics, are valid only at the resolution of the TM imagery, the 30-x-30-m pixel. 

Future measurements cannot be compared to ours unless the same measurement unit is used. 

Description of the Expert System 

An expert system was developed to select the model parameters--W, G, and BC--that would best 

approximate the spatial patterns of each study site. The expert system consisted of a knowledge 

base and a decision process. The knowledge base indicated how each of the spatial pattern 

indice-interface length and the four side-adjacency statistics-varied as functions of W, G, and 

BC (border condition). The decision process consisted of the rules for selecting the best W-GBC combination. 

To build the knowledge base for the expert system, we ran simulations with all possible W 

and G combinations from the set [0, 4, 20, 60, 180, and 5401 for six types of study-site border 

conditions: 0 = no water border, 1 = 1 water border, 2 = 2 adjacent water borders, 3 = 2 

opposite water borders, 4 = 3 water borders, and 5 = 4 water borders. For border condition 0, 

the set was extended to include W = 1620 and 9720. Each simulation contribluted information 

to 21 tables. Each table contained interface length and side-adjacency inform ation collected at 

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a 5% increment of DL (level of disintegration, or water area as percentage total area). We 

compiled 21 tables (one for each increment of DL) for each value of G and for each border 

condition (a total of 6 x 5 = 30 sets of 21 tables). For border condition 0 (no water border), 

only one set of 21 tables was compiled, since G must equal zero. For each of the other data 

sets, there were 21 tables for each G value. 

The following statistics from each study site were used in the decision process: DL; 

interface length; and Adj-0, Adj-1, Adj-2, Adj-3, and Adj-4 (number of water pixels having 0, 1, 

2, 3, or 4 sides adjacent to water). Target BC was an additional factor in the decision process. 

The DL of the study site was used to determine which tables were accessed. The tables of 

the nearest DL's on either side of the study-site DL were accessed. For instance, if the DL of 

the image was 32%, then the tables for DL's 30% and 35% were accessed. Interpolation 

between levels was then used to produce, for every G value and border condition, a table of 

values of spatial-pattern indices for each of the six values of W for the specific DL of the study 

site. 

Then, for each G value and border condition, the study-site interface and side-adjacency 

values were compared with values for the spatial-pattern index in the tables prepared for the 

specific disintegration level. If the study-site value for a spatial-pattern index was within the 

range of values for that index on a particular table, exact matching or interpolation between 

values was used to estimate W on the basis of that index, given the G value and border 

condition of that table. If the value of a given index from the study site was not within the 

range of values for that index in a table, then W could not be estimated from that particular 

index and table. 

We usually obtained several estimates of W from a given G-BC table. A weighted mean W 

for the specific G-value and border condition was obtained from these. In cases where a 

parabolic relationship between the parameter and W occurred, more than one estimate of W 

was sometimes obtained for the same index and table. In such cases, each estimate was used 

alternatively in calculating a weighted mean until we had calculated all possible weighted means 

from the indices. For instance, interface might yield W = 2, 4; Adj-0, W = 180, 193; and Adj-3, 

W = 300. Then 2 x 2 x 1 weighted mean W's were calculated. One would involve 2, 180, and 

300, another 2, 193, 300, another 4, 180, 300, and another 4, 193, 300. Weighting was a 

function of the number of water pixels involved in each parameter estimate of W. The value of 

45 
the parameter was the estimate of the number of pixels involved in the estimate of W from that 

spatial-pattern index. Weighted mean Ws were calculated as follows: 

Weighted Mean W = Sum (Wi Vi) 1 Sum (Vi) (3) 

where Wi = the estimate of W from statistic i, and Vi = the number of pixels involved (index 

value), statistic i. Only the water pixels of the spatial-pattern indices involved in the specific 

calculation of the weighted mean W were summed. As mentioned above, if the index value 

from the sample was not within the range of values for that index in a particular table, an 

estimate of W based on that index could not be obtained. 

The coefficient of variation (CY) of each weighted mean W also was calculated, as folllows: 

CV = (Variance)I/2 1 Weighted Mean W (4) 

In addition, the sum of the water pixels used in calculating the weighted mean W was retained 

as a "decision number" (DE) for later use in the selection process. Table A2 lists the weights, 

the Ws, and the calculations of weighted mean W, DE, and CV. 

By the above procedure, the expert system estimated many W-G-BC combinations for each 

study site. Weighted mean Ws, CV's, DES, and their corresponding Gs and BC's were stored 

in solution files specific to each study site. The file was sorted by DE and CV. 

The first step in selecting the best model parameters to simulate the spatial patterns of a 

study site was to define a "target" BC. Target BC was the estimated BC of the study site. To 

make this estimate, the expert system compared the proportion of water pixels on each border 

to the proportion of water pixels in the marsh as a whole. Those borders having a higher 

proportion of water pixels than the entire site were assumed to be influenced by a major water 

body at the border. The BC estimates were confirmed by visual examination of black-and-white 

photographs of binary land-water images of the sites. In a few cases, estimates were changed 

on the basis of the visual examination. 

Once a target BC was selected, the solution file specific to the spatial pattern indices of 

that study site was searched for the "best" weighted mean W, specific to calculated G, for that 

BC. If a solution having the target BC was found in the group of solutions with the highest 

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DE, it was selected as the best solution. If more than one solution in the group of solutions 

with the highest DE had the target BC, then the one with the lowest CV was selected. If a 

solution having the target BC could not be found within the group having the highest DE, then 

the expert system sought a solution with the target BC among all solutions having DE within 

75% of the highest DN. The solution having the target BC, the largest DE, and the lowest 

coefficient of variation was selected. If a solution having the target BC was not found in either 

of the above groups, then solutions having alternative BC were considered. First, solutions with 

BC having no more than one border different from that of the target BC were considered. 

Then, solutions having no more than two borders different from that of the target BC were 

considered. We usually found a solution having the target BC or no more than one border 

different from that of the target BC. 

47 
References 

Adams, R. D., and R. H. Baumann. 1980. Land-building in coastal Louisiana: emergence of 

the Atchafalaya Bay delta. Center for Wetland Resources, Louisiana State University, Baton 

Rouge. 

Baumann, R. H. 1980. Mechanisms of maintaining marsh elevaion in a subsiding environment. 

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Browder, J. A, H. A Bartley, and K S. Davis. 1984. A probablilistic model of hte relationship 

between marshland-water interface and marsh disintegration. Ecological bAodelling 29: 245- 

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Butera, M. K. 1982a. Identification of water bodies using remotely sensed multispectral 

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NASA Tech. Memo. No. TM-84672, NASA Earth Resources Laboratory, NSTL, Miss. 

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Butera, M. K, A. L Frick, and J. k Browder. 1984. A preliminary report on the assessment 

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cliff, A. D., and J. K Ord. 1973. Spatial Autocorrelation. Pion, London, England. 

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Dow, D. D. 1982. Software programs to measure interface complexity with remote-sensing 

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thesis, Louisiana State University, Baton Rouge. 

Faller, K H. 1977. A procedure for dteection and measurement of interfaces in remotelyacquired data using a digital computer. NASA Tech. Report No. TR R-472. Lyndon B. 

Johnson Space Center, Houston, Tex. 

Faller, K H. 1979. Shoreline as a controlling factor in commercial shrimp production. NASA 

Tech. Memo. 72-732. NASA Earth Resources Laboratory, NSTL, Miss. 

Gagliano, S. M., K J. Meyer-Arendt, and K M. Wicker. 1981. Land loss in the Mississippi 

River detlaic plain. Transactions of the Gulf Coat Association of Geological Societies 

31:295-300. 

Gosselink, J. G. 1984. The ecology of the delta marshes of coastal Louisiana: a community 

profile. FWS/OBS-84/09. Office of Biological Services, U.S. Fish and Wildlife Service, 

Washington, D.C. 

Gosselink, J. G., C. C. Cordes, and J. W. Parsons. 1979. An ecological characterization study 

of the Chenier Plain coastal ecosystem of Louisiana and Texas. FWS/OBS-78/9 through 

78/11 (3 vols.). Office of Biological Services, U.S. Fish and Wildlife Service. 

Graham, M. H., B. G. Junkin, M. T. Kalcic, R. W. Pearson, and B. R. Seyfarth. 1984. ELAS: 

Earth Resources Laboratory Applications Software. Vol. 2, ELAS User’s Guide. NASA 

Earth Resources Laboratory, NSTL, Miss. 

Hicks, S. D. 1981. Long-period sea-level variations in the United States through 1978. Shore 

and Beach 4926-29. 

Hutchnison, G. E. 1957. A Treatise in Limnology. Vol. 1, part 1. John Wiley, New York. 

Johnson, W. B., and J. G. Gosselink. 1982. Wetland loss directly associated with canal 

dredging in the Louisiana coastal zone. Pp. 60-72 in D. E Boesch, ed., Proceedings of the 

Conference on Coatal Erosion and Wetland Modification in Louisiana: Causes, 

49 
Consequences, and Options. FWSlOBS-SU59. Office of Biological SeMts, U.S. Fish and 

Wildlife SeMce, Washington, D.C. 

Leibowitz, S. G., and J. M. Hill. 1988. Spatial analysis of Louisiana coastal :land loss. In R. E. 

Turner and D. R. Cahoon, eds., Causes of Wetland Loss in the Coastal Ckntral Gulf of 

Mexico. OCS Study/MMS 87-0119. Minerals Management Service, New Orleans. 

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Processing 16210-239. 

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Geology 34:45-59. 

Sasser, C. E. 1977. Distribution of vegetation in Louisian coast1 marshes as response to tidal 

flooding. Master’s thesis, Louisiana State University, Baton Rouge. 

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changes in Louisiana’s Barataria Basin marshes, 1945-1980. Environmental Management 

10671-680. 

Turner, R. E., K. L McKee, W. B. Sikora, J. P. Sikora, I. A Mendelssohn, EL Swenson, C. 

Neill, S. G. Lei’bowtz, and E Pedrazini. 1984. The impact and mitigation of man-made 

canals in coastal Louisiana. Nat. Sci. Tech. 6497-504. 

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the Mississippi River. Pp. 334-348 in R. D. Cross and D. L Williams, e&., Proc. National 

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New York. 

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50 
Wicker, K. M. 1980. Mississippi deltaic plain region ecological characterization: a habitat 

mapping study. A User’s Guide to the Habitat Maps. FWS/OBS-79/07. Office of 

Biological Services, U.S. Fish and Wildlife Service, Slidell, La. 

Zimmerman, R. J., T. J. Minello, and G. Zamora, Jr. 1984. Selection of vegetated habitat by 

brown shrimp, Penaeus uztecrcs, in a Galveston Bay salt marsh. U.S. Fisheries Bull. 82325- 

336. 

I 

c 

51 
Table A1 . Look-up table used to classify water and land identified by the ELAS 

shoreline length (SLIN) module into water pixels and land pixels with zero, 

one, two, three, and four sides adjacent to water. 

SLIN Class SLIN Class SLIN Clas S 

output code output code OUtpLlt code 

0 

1 

2 

3 

4 

5 

6 

7 

8 

9 

10 

11 

12 

13 

14 

15 

16 

17 

18 

19 

20 

21 

22 

23 

5 

0 

0 

1 

0 

0 

1 

1 

0 

1 

1 

0 

2 

1 

1 

1 

1 

1 

1 

2 

2 

1 

1 

2 

24 

25 

26 

27 

28 

29 

30 

31 

32 

33 

34 

35 

36 

37 

38 

39 

40 

41 

42 

43 

44 

45 

46 

47 

2 

1 

1 

2 

2 

2 

2 

2 

2 

2 

2 

1 

2 

2 

2 

2 

2 

2 

2 

2 

2 

2 

3 

3 

48 

45, 

50 

5 1. 

52! 

5 ‘I 

54 

5 5 

56 

55’ 

5 8: 

59 

60 

61 

62 

63 

64 

65 

66 

67 

68 

69 

70 

71 

J 

3 

3 

2 

2 

2 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

3 

4 

4 

4 

4 

4 

ND 

ND 

Key to Class Codes: 0 = land pixel with zero sides adjacent to water. 

1 = land pixel with one sides adjacent to water. 

2 = land pixel with two sides adjacent to water. 

3 = land pixel with three sides adjacent to water. 

4 = land pixel with four sides adjacent to water. 

5 = water pixel. 

52 
I 

I Table A2. Calculation of weighted mean W, DE, and CV from a G-BC 

look-up table in which G = 0, BC = 0000, and DL = 10.90%. 

I 

R 

B 

I' 

Index 

Name Weight W W Weight 

Interface 4,011 60 240 , 660 

Adj-0 108 53 5,724 

Adj-1 805 256 206,080 

Adj-2 1 , 257 121 152,097 

Adj-3 1,109 53 58 , 777 

Adj-4 680 156 106,080 

7,970 769,418 

Weighted mean W = 97 

Decision Number (DE) = 7,970 

CV = 64.91% 

(Number of border pixels = 52) 

(C Adj weights = 3,959) 

i 

II 

I 

53 
S P(#UOIIIIIWI 200-1.0 01. Pnvnt 

Figure Al. The maximum extent of the influence of deltaic lobes of the Mississippi River on the 

present geomorphology of Louisiana's coastal wetlands (modified from Adams anld 

Baumann 1980). 

ORIGINAL PAGE: IS 

OF IPOOR QUALtTY 

54 
Appendix B 

Spatial-Pattern Statistics of Simulations Using 

All Combinations of W, G, and BC 

Table B1. Interface length (number of pixels) vs. W, G, and border condition (BC) at 50% 

disintegration level (DL), from simulations of 192-x-192-pixel marshes. 

Table B2. Disintegration level (DL) at maximum interface for simulations of 192-x-192 

pixel marshes with varying W, G, and border conditions. 

55 
Table B1. Interface length (number of pixels) vs. W, G, and border 

condition (BC) at 50% disintegration level (DL), from 

simulations of 192-x-192-pixel marshes. 

Border Condition 

W G 0000 0001 001 1 01 01 0111 1111 

0 0 36,695 

4 0 24,946 

20 0 18,243 

60 . 0 14,357 

180 0 10,921 

540 0 8,113 

0 4 

4 4 

20 4 

60 4 

180 4 

540 4 

0 20 

4 20 

20 20 

60 20 

180 20 

540 20 

0 60 

4 60 

20 60 

60 60 

180 60 

540 60 

0 180 

4 180 

20 180 

60 180 

180 180 

540 180 

0 540 

4 540 

20 540 

60 540 

180 540 

540 540 

34,573 33,427 33,412 31,888 30,754 

24,801 24,630 24,505 24,243 24,300 

18,516 18,115 18,392 18,264 18,296 

14,434 14,300 14,595 14,385 14,384 

10,726 10,959 10,443 11,053 11,272 

7,672 7,957 8,183 8,156 8,254 

31 , 208 26,080 28,635 20,395 16,088 

23,401 21,440 22,500 19,311 16,768 

17,924 17,271 17,444 16,867 16,612 

14,273 13,863 14,167 13,766 13,747 

10,937 11,055 10,719 10,999 10,586 

7,940 8,039 7,706 8,058 8,428 

25,752 15,520 20,946 9,667 6,998 

20,540 15,743 18,578 11,001 8,120 

16,791 14,475 16,031 12,348 10,069 

13,777 13,008 13,366 11,792 10,904 

10,771 10,227 10,343 9,927 9,561 

7,789 7,679 7,732 7,895 8,075 

16,113 6,001 9,302 3,836 2,946 

15,994 6,850 10,708 4,251 3,282 

13,866 9,012 11,416 5,402 3,914 

12,481 9,622 11,457 6,687 5,722 

9,919 8,821 9,644 7,551 6,054 

7,789 7,556 7,524 7,404 6,622 

5,961 2,468 3,831 1,939 1,846 

7,093 2,534 4,121 1,979 1,922 

8,826 3,168 5,530 2,205 1,946 

8,513 5,826 7,248 3,693 2,772 

6,877 5,408 6,890 4,572 3,900 

9,322 4,650 7,169 2,737 2,220 

0000 = no water borders, 0001 = one water border, 0011 = two 

adjacent water borders, 0101 = two opposite water borders, 0111 = 

three water borders, 1111 = four water borders. 

57 

PRECEDING PAGE BLANK NOT FILMED 
Table B2. Disintegration level (DL) at maximum interface for 

simulations of 192-x-192 pixel marshes with varyinlg W, 

G, and border conditions. 

Border Condition 

W G 0000 0001 001 1 01 01 01 11 1111 

0 0 49.56 

4 0 47 24 

20 0 49.99 

60 0 45.10 

180 0 52.40 

540 0 52.55 

0 4 

4 4 

20 4 

60 4 

180 4 

540 4 

0 20 

4 20 

20 20 

60 20 

180 20 

540 20 

0 60 

4 60 

20 60 

60 60 

180 60 

540 60 

0 180 

4 180 

20 180 

60 180 

180 180 

540 180 

0 540 

4 540 

20 540 

60 540 

180 540 

540 540 

46.51 45.35 44.74 4:3.98 42.64 

47.42 48.99 48.76 4'7.17 47.71 

49.90 SO. 24 -- 4'7.35 50.55 

49.66 49.53 47.99 5'1 .21 48.13 

47.59 50.33 50.53 5'7.19 55.88 

57.06 44.78 46.68 5:2.37 54.76 

45.11 49.76 46.42 512.42 48.42 

47.78 49.98 49.18 51 -18 50.76 

52.63 50.43 51.19 4'7.46 48.57 

47.39 52.37 51.41 48.01 48.37 

49.70 49.25 54.64 51.47 51.36 

48.21 45.57 42.41 4'3.38 49.13 

49.02 57.92 50.40 49.56 40.26 

47.17 54.83 51.14 51.94 51.91 

50.50 47.74 52.20 5.4.46 49.21 

52.80 50.51 54.88 4'3.13 53.12 

50.49 50.81 50.47 4'3.37 44.85 

47.00 49.67 54.79 46.17 50.40 

57.35 59.07 57.26 49.01 36.41 

55.18 57.84 57.12 53.10 33.12 

48.04 55.26 56.49 59.55 53.95 

49.80 49.30 51.49 5.5.46 56.67 

51.01 47.94 54.02 50.60 51.47 

51.84 52.39 56.04 48.08 44.68 

56.58 49.25 55.69 3'1 .70 11.22 

59.41 55.14 -- 39.76 13.02 

59.55 59.64 61.17 44.34 17.11 

50.65 62.83 50.22 6i3.04 15.17 

52.96 62.62 49.95 6'1 .40 47.89 

54.74 47.00 57.95 56.73 53.37 

0000 = no water borders, 0001 = one water border, 0011 = two 

adjacent water borders, 0101 = two opposite water borders, 0111 = 

three water borders, 1111 = four water borders. 

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Appendix C 

Spatial-Pattern Statistics of TM Scenes 

Table C1. Values of the spatial-pattern indices measured in the TM imagery. 

59 
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Appendix D 

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Simulation Results 

Table D1. W, G, and BC selected by the expert system to simulate each Th4 scene. 

Table D2. Values of spatial-pattern indices from 70 simulations. 

Table D3. Simulation values for interface length and disintegration level compared with TM 

scene disintegration levels. 

Table D4. Coefficients of variation from three sets of three replicate simulations using the 

same values of W, G, and BC. 

Figure D1. "Best-fit" simulations showing interface as a function of disintegration level 

for 70 marsh sites in coastal Louisiana. 

Figure D2. Coefficients of variation of spatial-pattern indices from three sets of three 

repeated simulations with the same W, G, BC, and DL values. 

65 
Table D1. W, G, and border condition (BC) selected by the expert 

system to simulate each TM scene, with disintegration 

level (DL), target BC, decision number (DE), and CV. 

(DE is expressed as percentage total water pixels [TWPI . 

Target 

Quad Quarter DL W G Bc BC DE/TWP cv 

Late Lafourche, salt 

Leeville Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Caminada Pass Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Pelican Pass Nw 

NE 

SE 

sw 

Mink Bayou Nw 

Bay Tambour Nw 

42.94 

34.93 

39.91 

46.74 

26.59 

24.54 

29.44 

29.57 

62 09 

82.87 

99.27 

58.02 

51.18 

76.55 

88.72 

84.38 

98.96 

82.60 

86.68 

97.24 

Early Lafourche, salt 

Grand Bayou 

du Large 

Lake La 

Graisse 

Central Isles 

Dernieres 

Cocodrie 

Dog Lake 

Nw 53.83 

NE 67.26 

SE 40.44 

SW 83.72 

NW 91 .13 

NE 99.28 

Nw 75.77 

NE 90.05 

SE 95.18 

sw 93.53 

Nw 33.43 

NE 68.89 

SE 85.79 

SW 54.72 

Nw 62.97 

NE 30.74 

SE 42.38 

SW 36.40 

272 

188 

237 

8 

244 

24 

248 

261 

58 

13 

1 

28 

31 

17 

107 

13 

8 

88 

16 

38 

30 

29 

14 

0 

113 

10 

26 

43 

16 

33 

115 

31 1 

69 

233 

44 

21 3 

120 

123 

67 

PRECEDING PAGE BLANK NOT FILMED 

60 

0 

4 

180 

180 

180 

0 

180 

180 

540 

540 

540 

180 

540 

540 

180 

180 

540 

540 

540 

540 

540 

180 

540 

540 

180 

180 

540 

540 

540 

180 

540 

540 

60 

540 

180 

180 

540 

001 1 

0111 

001 1 

001 1 

01 01 

001 1 

0111 

0001 

0111 

0111 

001 1 

001 1 

0111 

001 1 

0111 

0111 

0111 

0111 

001 1 

1111 

001 1 

001 1 

001 1 

001 1 

0111 

0111 

1111 

0111 

0111 

1111 

01 01 

0001 

001 1 

001 1 

001 1 

01 01 

001 1 

0001 

195.1 

197.2 

194.4 

196.8 

197.8 

197.4 

197.8 

193.5 

190.6 

0101 197.8 

198.0 

197.6 

190.0 

198.2 

197.9 

197.6 

197.9 

198.0 

197.8 

0111 197.9 

198.4 

197.9 

197.0 

0001 98.1 

198.0 

197.8 

197.1 

197.8 

0101 198.0 

0111 197.9 

193.9 

197.2 

195.6 

196.0 

0001 198.3 

193.7 

198.0 

0101 197.1 

42 

43 

42 

128 

46 

115 

58 

45 

9 

78 

225 

32 

41 

84 

53 

74 

56 

35 

76 

49 

40 

36 

95 

21 1 

55 

71 

26 

70 

26 

43 

72 

55 

15 

30 

65 

32 

56 

61 
Table D-1. (cont.) 

Target 

Quad Quarter DL W G Bc Bc DE/TWP cv 

Late Lafourche. brackish 

Lake Bully 

Camp 

I Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

~ 

Golden Meadow 

Bay 

NW 33.69 62 180 

NE 47.84 244 180 

SE 22.21 130 60 

SW 50.02 255 60 

NW 30.91 129 180 

NE 60.69 160 540 

SE 35.56 308 60 

SW 38.95 3,184 0 

SE 81.53 8 180 

SW 52.26 1 540 

NW 32.93 100 60 

NE 58.71 33 540 

SW 27.66 116 0 

Early Lafourche, brackish 

Lost Lake NW 32.65 23 180 

NE 47.32 245 180 

SE 21.81 111 60 

SW 50.78 290 20 

Lake Mechant NW 64.05 4 180 

NE 29.91 118 60 

SE 44.91 86 540 

SW 67.06 17 540 

Bayou Sauveur NW 8.72 325 0 

NE 10.90 93 0 

SE 22.23 133 180 

SW 24.31 35 540 

Lake Quitman NE 48.20 116 540 

SE 32.41 20 180 

SW 31.85 121 180 

Dulac NE 9.63 701 0 

SE 66.80 10,947 0 

Montegut SE 47.48 289 180 

SW 27.16 404 0 

0111 

0001 

001 1 

1111 

001 1 

001 1 

001 1 

0000 0001 

0111 

001 1 

1111 

0011 0001 

001 1 

001 1 

0001 

0111 

0111 

001 1 

001 1 

001 1 

001 1 

0000 

0000 

001 1 

0001 

0001 

001 1 

0011 0111 

0000 

0000 

01 01 

0000 

197.8 

198.0 

192.1 

197.8 

197.7 

197.5 

193.8 

198.9 

197.9 

98.0 

196.9 

198.2 

198.0 

198.4 

198.3 

196.7 

195.8 

196.4 

194.1 

190.5 

197.8 

198.8 

197.4 

197.5 

198.4 

196.3 

197.9 

197.4 

199.2 

195.3 

197.6 

--- 

90 

52 

88 

41 

45 

14 

47 

53 

58 

807 

80 

43 

82 

131 

53 

66 

61 

132 

86 

35 

81 

78 

61 

45 

141 

75 

110 

52 

39 

59 

70 

-- 

Key to BC: 0 = land border, 1 = water border. For czxample, 01 01 

indicates two opposite water borders. 

The maximum possible value of 100 DE/TWP is slightly less than 200. 

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8 

Table D3. Simulation values for interface length and disintegration 

level (DL) compared with TM scene disintegration levels, 

with deviation and percentage deviation. 

Maximum Sim. DL at Sim. Sign 

Interface Maximum 1985 of 

Quad Quarter Length Interface TM DL Diff. 

( Sim . -TM 

Late Lafourche, salt 

Leeville Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Caminada Pass Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Pelican Pass Nw 

NE 

SE 

sw 

Mink Bayou Nw 

Bay Tambour Nw 

Early Lafourche, salt 

Grand Bayou Nw 

du Large NE 

SE 

sw 

Lake La Nw 

Graisse NE 

Central Isles Nw 

Dernieres NE 

SE 

sw 

Cocodrie Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Dog Lake Nw 

9,329 

10,681 

10,156 

8,638 

8,905 

10,200 

9,923 

9,543 

7,087 

2,188 

2,582 

4,110 

6,232 

3,444 

3,273 

5,086 

4,608 

3,113 

3,265 

2,417 

4,010 

4,180 

9,196 

2,514 

3,430 

4,738 

4,442 

2,388 

2,187 

2,443 

10,496 

8,108 

5,122 

9,624 

4,473 

9,375 

9,662 

9,737 

51.8 

47.6 

49.3 

63.8 

51.1 

55.6 

49.5 

56.6 

60.4 

33.2 

42.6 

67.1 

65.7 

60.2 

72.6 

59.6 

63.4 

57.1 

59.0 

10.9 

60.1 

61.6 

59.3 

54.8 

67.0 

50.6 

53.1 

54.7 

60.3 

13.3 

50.6 

46.7 

59.8 

47.9 

59.4 

53.4 

45.5 

51.8 

42.0 + 

34.0 + 

39.0 + 

46.0 + 

26.0 + 

24.0 + 

29.0 + 

29.0 + 

62.0 - 

82.0 - 

99.0 - 

58.0 + 

51 .O + 

76.0 - 

88.0 - 

84.0 - 

98.0 - 

82.0 - 

86.0 - 

97.0 - 

53.0 + 

67.0 - 

40.0 + 

83.0 - 

91 .o - 

99.0 - 

75.0 - 

90.0 - 

95.0 - 

93.0 - 

33.0 + 

68.0 - 

85.0 - 

54.0 - 

62.0 - 

30.0 + 

42.0 + 

36.0 + 

73 
Table D-3. (cont.) 

Maximum DL at Sign 

Quad Quarter Length Interface DL Diff. 

Interface Maximum 1 !385 C) f 

Late Lafourche. brackish 

Lake Bully 

Camp 

Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

Golden Meadow Bay 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

SE 

sw 

SE 

sw 

sw 

Early Lafourche, brackish 

Lost Lake Nw 

NE 

SE 

sw 

Lake Mechant NW 

NE 

SE 

sw 

Bayou Sauveur Nw 

NE 

SE 

sw 

Lake Quitman NE 

SE 

sw 

Dulac NE 

SE 

Montegut SE 

sw 

7,055 

9,203 

10,787 

9,410 

10,033 

6,393 

9,142 

4,459 

4,496 

2,582 

9,835 

4,218 

12,284 

9,934 

9,469 

10,869 

9,346 

7,717 

11,116 

5,717 

3,302 

9,362 

13,189 

8,925 

10,567 

9,602 

9,235 

9,378 

7,600 

3,425 

8,576 

8,937 

61.7 

54.4 

57.4 

49.6 

52.6 

57.8 

55.8 

44.6 

44.6 

42.6 

46.6 

56.8 

50.3 

57.3 

51.5 

52.2 

45.2 

63.2 

53.2 

59.0 

62.3 

48.1 

48.7 

48.7 

58.0 

48.7 

55.3 

49.2 

49.5 

41.2 

46.7 

48.8 

33.0 

47.0 

22.0 

50.0 

30.0 

60.0 

35.0 

38.0 

81 .O 

52.0 

32.0 

58.0 

27.0 

32.0 

47.0 

21 .o 

50.0 

64.0 

29.0 

44.0 

67.0 

8.0 

10.0 

22.0 

24.0 

48.0 

32.0 

31 .O 

9.0 

66.0 

47.0 

27.0 

74 

I 

1 

I 

1 

I 

I 

I 

I 

1 

I 

I 

1 

I 

I 

I 

I 

1 

I 

I 
Table D4. Coefficients of variation (CV) from three sets of three 

replicate simulations using the same values of W, G, and BC. 

Spatial Set 

Pattern Mean 

Index 1 2 3 

Interface 5.02 10.72 3.11 6.24 

Adj-0 3.95 71.25 53.29 42.83 

Adj-1 8.26 7.75 2.69 6.23 

Adj-2 5.57 14.80 1.66 7.34 

Adj-3 2.57 10.93 5.37 6.29 

Adj-4 4.02 0.54 1.96 2.17 

Mean 4.90 19.33 11.35 

Set 1: W = 275, G = 0, BC = 1111, DL = 29.44 

Set 2: W = 111, G = 540, BC = 1000, DL = 88.72 

Set 3: W = 337, G = 180, BC = 1010, DL = 47.48 

75 
Figure D1. "Best-fit" simulations showing interface as a function of disintegration level for 70 

marsh sites in coastal Louisiana. The @ symbol indicates the coordinates of the 

comparable TM scene for each. Quadrangles are presented in the same order as in 

Table D1. 

76 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 
8 Figure D1-1. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Leeville quadrangle. 

77 

I 
MINK BAYOU 

(X looo) NW (X 1000) NE 

% water = 27 

do 20 40 60 80 100 

X 

3 

& 

Y 

w (X 1000) 

y 10 

li 

K 

W 

h I 

6 

sw 

- 

9b water =30 

0 20 40 60 80 100 

0 20 40 60 80 100 

(X 1Ooo) SE 

10 

8 

6 

4 

2 

0 

- 

W water=29 

0 20 40 60 80 100 

OlSlNTEGRATlON LEVEL (9b WATER) 

Figure D1-2. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Mink Bayou quadrangle. 

I 78 

I 

I 

1 

I 

1 

I 

I 

1 

I 

I 

I 

1 

I 

I 

I 

I 

1 

I 

1 
NW 

CAMINADA PASS 

I/ % water =62 \ 

0 2w 0 20 40 60 80 100 

W=58 

G=180 

Z=Olll 

h (XlOoo) : 5- 

a 

LL 

K 

e4 z 

sw 

(X 100) 

24r 

NE 

12 -' 

% water =83 

W =13 

8 -- 

0 20 40 60 80 loo 

12 '1 

4 

% 

Z=Ol11 

water 

G=540 

W=13 =83 0 

I I I I I 

0 20 40 60 80 loo 

(X 1000) 

3 

SE 

2 

1 9b water = 58 

t3=540 

c 

Bc=OOll 

01 

20 40 60 80 100 0 20 40 60 80 100 

DISINTEGRATION LEVU (% WATER) 

9b water=- 

Figure D1-3. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Caminada Pass quadrangle. 

79 
BAY TAMBOUR 

(X 1000) NW (X 1000) NE m 

I 4 

2 

e 

P 

- 

4 

- 

% water =5 1 

4 oq 20 40 80 80 100 

0 20 40 

- 

% water =77 

0 20 40 60 80 100 

(X 1OOo) SE 

4- 

G=540 ' 

0 20 40 00 80 100 

DISINTEGRATION LEVEL (% WATER) 

Figure D1-4. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Bay Tambour quadrangle. 

1 

1 

1 

i 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

1 

80 
n 

I 

P 

9 

A 

W 

X 

h 

PELICAN PASS 

(X 1000) - 

NW 

4 - 

% water =99 

W=8 

G=180 1 

0 

0 20 40 60 80 100 

K E 20 

z 

16 

12 

8 

4 

0 

% water=97 

W =38 

G=540 

0 20 40 60 80 100 

NE 

- 

9b water=83 

\ a=o111 I 

20 40 80 80 100 

(X 1000) SE 

% water = 87 

I Bc=OOll 

20 40 60 80 100 

DlSlNlEGRATlON LEVEL (9b WATER) 

Figure D1-5. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Pelican Pass quadrangle. 

81 
n 

I 

b 

0 

z 

W 

d 

GRAND BAYOU DU LARGE 

(X 1000) NW (X 1000) NE 

- 

\ 96 water=54 

W=30 

G =540 

iS=OOll 

c 

I I I I Io 

20 

15 

$0 .- 

L 

20 40 60 80 100 0 

\ % water =84 

w=o I 0 = 540 

T Bc=OOll 

0 20 40 60 80 100 

- 

% water=67 

(X 1000) 

8 c 

20 40 60 80 100 

SE 

I I 

% water =40 

W=14 

G=l8O 

8- 

8- 

4- 

2 

Bc=OOll 

0 

0 20 40 60 80 100 

\ 

8- 

4- 

% water =40 

W=14 

G=l8O 2 

Bc=OOll 

0 20 40 60 80 100 

DISINTEGRATION LEVEL (% WATER) 

Figure D1-6. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Grand Bayou du Large quadrangle. 

82 ~ 

I 

1 

I 

1 

I 

I 

1 

1 

1 

I 

I 

1 

1 

I 

1 

1 

I 

1 

I 
LAKE LAGRAISSE 

(X 1000) NW (X 1000) NE - 

4r 

5 

1 

0 I I I I 

9b water=91 

W=113 

G=540 

Be=0111 

I w=10 

G=180 

BC=Olll 

4 

m 

I I I I 

0 20 40 60 80 100 0 20 40 60 80 100 

DISINTEGRATION LEVEL (# WATER) 

Figure D1-7. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Lake La Graisse quadrangle. 

83 
CENTRAL ISLES DERNIERES 

(X 1000) NW (X 100) NE - 5 

4 

3 

2 

n 

r c1 

0 

z 

w 

W 

5 

a 

% 24 

U 

(X 100) 

% water=76 

20 40 60 80 100 

LA 

Q: E 20 

z 

16 

12 

8 

4 

0 

sw 

12 - 

% water=Z)O 

0 20 40 60 80 100 

(X 100) - 24 - 

20 

- 16 

12 

I water=94 8 - 

w=33 

0 =540 

- BC=ll11 4 

20 40 60 80 100 

SE 

I water=95 

W=16 

- 0 =540 

Bc=0111 

20 40 60 80 100 

DISINTEGRATION LEVEL (I WATER) 

Figure D1-8. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Central Isles Dernieres quadrangle. 

84 

1 

I 

I 

I 

I 

I 

I 

1 

1 

1 

I 

1 

1 

I 

1 

1 

1 

1 

I 
I 

I 

COCODRIF 

(X 1000) NE (X 1000) NW 

a 

'X 1000) sw 

% water =55 

W = 233 

2 G=6O 

3=0011 

0 20 40 60 80 100 

0 I 1 I I 

(X 1000) SE 

9b water=88 

W=69 

G = 540 

Bc=o011 

I' I I I 

0 20 40 60 80 100 

DISINTEGRATION LEVEL (# WATER) I 

P 

I 

8 

I Figure D1-9. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Cocodrie quadrangle. 

85 
DOG LAKE 

(X 1000) NW 

sr 

% water =63 

w=44 

1 G =540 

Bc=0011 

0 I I I I 

0 20 40 60 80 

% watw==38 

Bc=ooo1 

0 20 40 60 80 100 

(X 1000) NE 

10 - 

8- 

6- 

% water =:3 1 

4- 

2 z= 0 10 1 

0 

0 20 40 0 0 80 100 

(X 1000) SE 

10 - 

9b watew=42 

0 20 40 610 80 100 

DISINTEGRATION LEVEL (9b WATER) 

I 

I 

I 

1 

1 

I 

1 

I 

1 

1 

I 

1 

Figure D1-10. Simulated interface versus disintegration level, with TM scene coordinates, for the 1 

Dog Lake quadrangle. 

86 
LAKE BULLY CAMP 

1000) NW (X 1000) NE 

8- 10 

% water=34 % water=48 

0 20 40 60 80 100 0 20 40 60 80 100 

tX 

c 

-I 

W 

1000) 

12r 

SE 

0 20 40 60 80 100 0 20 40 60 80 100 

DISINTEGRATION LEVEL (9b WATER) 

Figure D1-11. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Lake Bully Camp quadrangle. 

87 
GOLDEN MEADOW FARMS 

(X 1OOo) NW (X 1000) NE 

10 - 

% watert31 % water ::6 1 

80 100 

sw (X 1000) SE Y 

w (XlOOo) 

z 

a 

z 

s- @ 

W 

c 

% water =38 

0 20 40 60 80 100 

DISINTEGRATION LEVEL (% WATER) 

10 - 

8- 

6- 

% watw=:36 

W = 308 

4- 

2 0=60 

Bc=OO11 

0 

0 20 40 60 80 100 

Figure D1-12. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Golden Meadow Farms quadrangle. 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

I 

1 

I 

1 

1 

I 

I 

1 

1 88 
I 

I 

8 

B 

t 

e 

z 

(r 

I 

8 

i 

1 

(X 1000) "r 

BAY L'OURS 

sw (X 1OOo) SE 

G=540 

0 

0 

Y,,, 

20 40 60 80 100 

51- 

DISINTEGRATION LEVEL (9b WATER) 

I Figure D1-13. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Bay Lours quadrangle. 

89 
THREEBAYOUBAY 

(X 1000) sw (X 1000) SE 

10 

9b water=59 9b water =:I3 

20 40 60 80 100 0 20 40 6 0 80 100 

DISINTEGRATION LEVEL (9b WATER) 

Figure D1-14. Simulated interface versus disintegration level, with TM scene ccordinates, for the 

Three Bayou Bay quadrangle. 

90 
GOLDEN MEADOW 

sw 

% water=28 

0 20 40 60 80 100 

DISINTEGRATION LEVEL (96 WATER) 

1 Figure D1-15. S,,nulated interface versus disintegration level, with TM scene coordinates, for the 

Golden Meadow quadrangle. 

91 
LOST LAKE 

(X 1000) (X 1000) 

J 

W 

E (X a 

Y 

W 

0 

< 

li 

U 

W 

I- z 

-/ 

% Water== 

W=23 

G=180 

iz=oo11 

10 

a 

6 

4 

2 

% water ai47 

I I I I 

20 40 80 80 100 0 20 40 00 80 100 

1000) sw (X 

% *=Sl 

\ 

0 20 40 60 80 100 

\ w=290 

G=20 

Bc*lll 

0 20 40 60 80 100 

SE 

0 20 40 

DISWTEQRATION LEVEL (% WATER) 

Figure D1-16. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Lost Lake quadrangle. 

92 

I 

I 

I 

1 

1 

I 

1 

1 

I 

1 

I 

1 

I 

(I 

1 

I 

I 

1 

I 
(X 1000) - 8 

6 

4 

n 

I 

c 

z 

W 

A 

a2 

wo X - 

a 

Y 

NW 

LAKE MECHANT 

% water =64 

w=4 

G=180 

Bc=OO11 

~ 

0: 

W 

c 

E 

3 

2 

1 

0 

0 

Figure D 

\ 

(X 1 

40 I4 

ia 

a 

0 

4 

2 

20 40 60 80 100 

8 (X 1000) 

< 4- L 

sw 

NE 

20 40 60 80 100 

K)) SE 

9b water=45 

20 40 60 80 100 

K)) SE 

9b water=45 

.17. Simulatec 

20 40 60 80 100 0 20 40 60 80 100 

10 

DISINTEGRATION LEVEL (% WATER) 

lliterface versus disintegration level, with TM scene coordinates, for the 

Lake Mec hant quadrangle. 

93 
n 

I 

c 

Q 

2 

w 

J 

A 

w 

E 

(X 1000) 

10 r 

NW 

BAYOU SAUVEUR 

(X 1000) 

14r 

NE 

9b water =Q 

0 L 

0 

I I I I 

20 40 60 80 100 

sw 

% water 1 1 

% waterr24 

w=35 

00540 

E=ooo1 

I I I I 

0 20 40 60 80 100 

DISINTEGRATION 

(X 1Ooo) lor SE 

% wator-:22 

0 20 40 60 80 100 

LEVEL (% WATER) 

Figure D 1 - 18. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Bayou Sauveur quadrangle. 

94 

I 

1 

I 

1 

I 

1 

1 

I] 

I 

1 

I 

1 

I 

4 

1 

1 

I 

1 

1 
(X 1000) sw 

n 

I 

L 

W 

IL 

w 

c 

2 

a 

z 

LAKE QUITMAN 

(X 1000) NE 

% water =48 

W=l16 

G=540 

BC=OOOl 

I I I I 

0 20 40 60 80 100 

9b water =32 

0 

Figure D1- 

20 40 60 80 100 

lb watorr32 

w=20 

3 V G=180 

0 20 40 60 80 100 

DISINTEGRATION LEVEL (% WATER) 

Simuted interface versus disintegration level, with TM scene coordinates, for the 

Lake Quitman quadrangle. 

95 
DULAC 

(X 1000) NE 

8r 

% water= 10 

00 100 

0 

Y 

w (X 1000) 

Z 4r 

SE 

% wrtor=d7 

1 W 10,047 

0 I I I I 

0 20 40 eo eo 100 

Figure D1-20. Simulated interface versus disintegration level, with TM scene ccordinates, for the 

Dulac quadrangle. 

96 
L 

L 

MONTEGUT 

sw tx 1000) SE 

% water=27 

W=404 

G=O 2 

Bc=oooo 

0 I I I I 

0 20 40 60 80 100 

\ % water =47 

W=289 

G=180 

BC=0101 

1 I I I 1 

0 20 40 60 80 100 

DISINTEGRATION LEVEL (W WATER) 

1 Figure D1-21. Simulated interface versus disintegration level, with TM scene coordinates, for the 

Montegut quadrangle. 

E 

I 

97 
2or 

70- 

60- 

50- 

40- 

30 - 

20 - 

IO- - 

0. 

n 

- 

‘W= 275 

G=O 

IBC = 1111 

IDL = 29.440/0 

‘rN = 337 

G = 180 

13C = I010 

IDL = 47.480/0 

nnlL w= 111 

G = 540 

13C = 1000 

I) L 3 8 8.72 Yo 

I A-O A-l A-2 A-3 A-4 

SPATIAL PATTERN STATISTIC 

Figure D2. Coefficients of variation of spatial-pattern indices from 3 sets of 3 repeated 

simulations. The 3 repetitions of each set had the same W, G, BC arid DL values. I = 

total interface; A-0 to A-4 refer to the number of water pixels in each 192 x 192 scene 

that had 0 to 4 sides adjacent to other water pixels 

I 98 
II 

8 

I 

1 

I 

@ 

I 

Appendix E 

Comparison of Simulations with TM Scenes 

Table El. TM image and simulation values for interface length, with deviation and 

percentage deviation. 

Table E2 TM image and simulation values for side-adjacency 0, with deviation and 

percentage deviation. 

Table E3. TM image and simulation values for side-adjacency 1, with deviation and 

percentage deviation. 

Table E4. TM image and simulation values for side-adjacency 2, with deviation and 

percentage deviation. 

Table E5. TM image and simulation values for side-adjacency 3, with deviation and 

percentage deviation. 

Table E6. TM image and simulation values for side-adjacency 4, with deviation and 

percentage deviation. 

Table E7. TM image and simulation values for border water pixels, with deviation and 

percentage deviation. 

Table E8. Results of simple linear regression of simulation spatial-pattern indices on 

corresponding TM-image spatial-pattern statistics. 

Figure El. Comparisons of spatial patterns of marsh disintegration in TM scenes with 

"best-fit" simulations at the same disintegration level. 

Figure E2 Comparisons of pond size distributions of TM scenes with the "best-fit" 

simulations at the same disintegration level. 

Figure E3. Comparison of interface as a function of disintegration level for the 70 Th4 

scenes and for simulations at the same disintegration level. 

Figure E4. TM imagery interface and side-adjacency statistics (number of pixels) 

compared with simulations at the same disintegration level. 

99 
Table El. TM image and simulation values for interface length, with 

deviation and percentage deviation. 

Interface Length 

Quad Quarter Image Sim, Deviation % Deviation 

Late Lafourche. salt 

Leeville Nw 9,093 

NE 10,025 

SE 10,118 

sw 6,115 

Mink Bayou Nw 7,166 

NE 5,519 

SE 8,420 

sw 7,937 

Caminada Pass Nw 6,778 

NE 1,341 

SE 101 

sw 3,884 

Bay Tambour Nw 5,818 

NE 3,047 

SE 2,245 

sw 3,515 

Pelican Pass NW 341 

NE 2,433 

SE 2,100 

sw 385 

Early Lafourche, salt 

Grand Bayou 

du Large 

Lake La 

Graisse 

Central Isles 

Dernieres 

Cocodrie 

Dog Lake 

Nw 

NE 

SE 

sw 

Nw 

NE 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

3,431 

3,621 

7,174 

1,111 

2,079 

292 

3,439 

1,605 

748 

745 

7,430 

6,457 

3,681 

9,692 

4,612 

8,114 

7,774 

6,482 

9,133 

9,935 

9,811 

7,780 

7,241 

6,359 

8,681 

8,163 

6,989 

1,323 

190 

3,946 

5,884 

3,057 

2,255 

3,774 

534 

2,395 

2,357 

442 

3,797 

4,048 

7,978 

1,678 

2,039 

492 

3,548 

1,315 

71 9 

842 

9,375 

7,116 

3,240 

9,279 

4,397 

8,129 

9,434 

8,615 

40 

-90 

-307 

1,665 

75 

840 

261 

226 

21 1 

-1 8 

89 

62 

66 

10 

10 

259 

193 

-38 

257 

57 

366 

427 

804 

567 

-40 

200 

109 

-290 

-29 

97 

1,945 

659 

-441 

-41 3 

-21 5 

15 

1,660 

2,133 

0.44 

-0.90 

-3.03 

27.23 

1.05 

15.22 

3.10 

2.85 

3.11 

-1 -34 

88.12 

1.60 

1.13 

0.33 

0.45 

7.37 

56.60 

-1 -56 

12.24 

14.81 

10.67 

11.79 

11 -21 

51 -04 

-1.92 

68.49 

3.17 

-1 8.07 

-3.88 

13.02 

26.18 

10.21 

-11 -98 

-4.26 

-4.66 

0.19 

21 -35 

32.91 

101 

PRECEDING PAGE BLANK NOT FfLMm 
Table El. (cont. ) 

Interface Length 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche. brackish 

Lake Bully 

CmP 

Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

Golden Meadow 

Bay 

Nw 5,689 

NE 9,158 

SE 6,157 

sw 9,502 

Nw 6,870 

NE 5,700 

SE 8,163 

sw 4,954 

SE 3,572 

sw 1,755 

SE 7,393 

sw 4,136 

sw 9,367 

Early Lafourche, brackish 

Lost Lake Nw 

NE 

SE 

sw 

Lake Mechant Nw 

NE 

SE 

sw 

Bayou Sauveur NW 

NE 

SE 

sw 

Lake Quitman NE 

SE 

sw 

Dulac NE 

SE 

Montegut SE 

sw 

6,093 

9,209 

7,227 

8,645 

6,070 

6,782 

4,570 

3,050 

3,578 

5,847 

5,580 

4,892 

6,983 

5,981 

6,545 

3,708 

2,710 

8,315 

6,787 

Mean 5,283 

5,831 

9,099 

8,350 

9,398 

8,050 

6,239 

8,567 

4,430 

3,308 

2,484 

9,304 

4,108 

10,187 

7,570 

9,387 

8,247 

9,253 

7,697 

9,797 

4,844 

3,223 

3,997 

6,316 

6,737 

6,319 

9,468 

7,261 

8,002 

3,445 

2,853 

8,551 

7,439 

5,801 

142 

-59 

2,193 

-1 04 

1,180 

539 

404 

-524 

-264 

729 

1,911 

-28 

820 

1,477 

178 

1,020 

608 

1,627 

3,015 

274 

173 

41 9 

469 

1,157 

1,427 

2,485 

1,280 

1,458 

143 

236 

652 

51 8 

-263 

2.50 

-0.64 

35.62 

-1 -10 

17-18 

9.46 

4-95 

-10.58 

-7.39 

41 -54 

25.85 

8.75 

-0.68 

24.24 

1.93 

14.11 

7.03 

26.80 

44.46 

6.00 

5.67 

11 -71 

8.02 

20.74 

29.17 

35.59 

21 -40 

22.26 

-7.09 

5-28 

2.84 

9.61 

12.29 

102 
Table E2. TM image and simulationvalues for side-adjacency 0, with 

deviation and percentage deviation. 

Side-Adjacency 0 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche. salt 

Leeville Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Caminada Pass Nw 

NE 

SE 

sw 

Bay Tambour Nw 

NE 

SE 

sw 

Pelican Pass Nw 

NE 

SE 

sw 

Mink Bayou Nw 

Early Lafourche, salt 

Grand Bayou Nw 

du Large NE 

SE 

sw 

Lake La Nw 

Graisse NE 

Central Isles Nw 

Dernieres NE 

SE 

sw 

Cocodrie Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Dog Lake Nw 

114 

159 

146 

54 

101 

117 

178 

103 

80 

3 

0 

114 

61 

18 

24 

28 

1 

7 

13 

0 

40 

37 

109 

17 

17 

0 

31 

6 

10 

3 

106 

40 

32 

69 

29 

113 

95 

67 

21 

17 

16 

353 

658 

237 

18 

18 

33 

76 

12 

106 

111 

74 

7 

70 

1 

11 

45 

7 

114 

73 

298 

266 

2 

1 

58 

19 

11 

17 

40 

4 

10 

7 

57 

25 

26 

45 

103 

-93 

-1 42 

-1 30 

299 

-74 

120 

-1 60 

-85 

-47 

73 

12 

-8 

50 

56 

-1 7 

42 

0 

4 

32 

7 

66 

36 

189 

249 

-1 5 

1 

27 

13 

1 

14 

-66 

-36 

-22 

-62 

28 

-88 

-69 

-22 

-81.58 

-89.31 

-89.04 

-73.27 

-89.89 

-82.52 

-58.75 

24.33 

0.00 

-7.02 

81 -97 

311.11 

-70.83 

150.00 

0.00 

57.14 

246.15 

0.00 

553.70 

102.56 

137.50 

97.30 

173.39 

14.65 

-88.24 

0.00 

87.10 

216.67 

10.00 

466.67 

-62.26 

-90.00 

-68.75 

-89.86 

-77.88 

-72.63 

-32.84 

96.55 
Table E2. (conk.) 

Side-Adjacency 0 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche. brackish 

Lake Bully 

Camp 

Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

~ Golden Meadow 

Bay 

NW 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

SE 

sw 

SE 

sw 

sw 

46 

101 

122 

78 

81 

80 

86 

47 

42 

34 

108 

60 

182 

Early Lafourche. brackish 

I Lost Lake Nw 

NE 

SE 

sw 

Lake Mechant Nw 

NE 

SE 

sw 

Bayou Sauveur NW 

NE 

SE 

sw 

Lake Quitman NE 

SE 

sw 

Dulac NE 

SE 

Montegut SE 

sw 

I 

62 

107 

126 

75 

69 

90 

37 

23 

82 

160 

79 

36 

69 

64 

63 

82 

30 

112 

67 

I Mean 66 

71 

9 

44 

13 

41 

20 

19 

3 

107 

386 

34 

85 

45 

199 

16 

54 

10 

41 2 

39 

67 

134 

24 

69 

45 

148 

24 

252 

36 

10 

1 

19 

14 

68 

25 

-92 

-78 

-65 

-40 

-60 

-67 

-44 

65 

352 

-74 

25 

-1 37 

137 

-91 

-72 

-65 

343 

-51 

30 

111 

-58 

-91 

-34 

112 

-45 

188 

-27 

-72 

-29 

-93 

-53 

2 

54.35 

-91 -019 

-63.93 

-83.33 

-49.38 

-75.00 

-77.91 

-93.62 

154.76 

1,035.29 

41 -67 

-68.52 

-75.27 

220.97 

-85.05 

-57.14 

-86.67 

-56.67 

497.10 

81 -08 

482.61 

-70.73 

-56.88 

-43.04 

311 -11 

-65.22 

293.75 

-42.86 

-87.81 

-96.67 

-83.04 

-79.10 

105.15 

104 
Table E3. TM image and simulation values for side adjacency 1, with 

deviation and percentage deviation. 

Side-adjacency 1 

Quad Quarter Image Sim. Deviation B Deviation 

Late Lafourche. salt 

Leeville Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Caminada Pass Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Pelican Pass Nw 

NE 

SE 

sw 

Mink Bayou Nw 

Bay Tambour Nw 

Early Lafourche, salt 

Grand Bayou 

du Large 

Lake La 

Graisse 

Central Isles 

Dernieres 

Cocodrie 

Dog Lake 

Nw 

NE 

SE 

SW 

Nw 

NE 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

439 

71 3 

519 

253 

403 

343 

61 1 

489 

344 

20 

1 

254 

233 

92 

73 

106 

11 

93 

64 

4 

114 

159 

477 

50 

54 

3 

131 

33 

18 

12 

493 

234 

153 

41 0 

159 

449 

403 

331 

665 

802 

760 

1,174 

658 

904 

737 

698 

667 

131 

11 

538 

747 

433 

164 

448 

34 

206 

302 

40 

51 0 

534 

1,077 

83 

148 

22 

382 

143 

73 

74 

846 

435 

229 

599 

480 

71 0 

798 

803 

105 

226 

89 

241 

921 

255 

561 

126 

209 

323 

111 

10 

284 

51 4 

341 

91 

342 

23 

113 

238 

36 

396 

376 

600 

33 

94 

19 

251 

110 

55 

62 

353 

201 

76 

189 

321 

261 

395 

472 

51 -48 

12.48 

46.44 

364.03 

63.28 

163.56 

20.62 

42.74 

93.90 

555.00 

1,000.00 

111.81 

220 - 60 

370 - 65 

124.66 

322.64 

209.09 

121 -51 

371.87 

900.00 

347.37 

237.97 

125.79 

66.00 

174.07 

633.33 

191 -60 

333.33 

305.56 

516.67 

71.60 

85.90 

49.67 

46.10 

201 -89 

58.13 

98.01 

142.60 
~ Table E3. (cont.) 

Side-adjacency 1 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche. brackish 

Lake Bully 

CmP 

Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

Golden Meadow 

Bay 

NW 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

SE 

sw 

SE 

sw 

sw 

276 

562 

41 3 

469 

41 4 

367 

51 5 

243 

254 

106 

469 

21 8 

709 

Early Lafourche. brackish 

Lost Lake Nw 

NE 

SE 

sw 

Lake Mechant NW 

NE 

SE 

sw 

Bayou Sauveur NW 

NE 

SE 

sw 

Lake Quitman NE 

SE 

sw 

Dulac NE 

SE 

Montegut SE 

sw 

Mean 

329 

550 

496 

442 

325 

379 

168 

122 

291 

556 

323 

199 

348 

302 

365 

298 

115 

479 

423 

290 

678 

662 

805 

676 

761 

51 2 

683 

319 

443 

138 

831 

533 

909 

1,088 

672 

821 

620 

1,137 

857 

526 

440 

430 

776 

678 

903 

773 

989 

797 

31 1 

182 

670 

578 

561 

402 

100 

392 

207 

347 

145 

168 

76 

189 

32 

362 

31 6 

200 

759 

122 

325 

178 

81 2 

478 

358 

318 

139 

220 

355 

704 

425 

687 

432 

13 

67 

191 

155 

271 

145.65 

17.79 

94.92 

44.14 

83.82 

39.51 

32.62 

31.28 

74.41 

30.19 

77.19 

145.62 

28.21 

230.70 

22.18 

65.52 

40.27 

249.85 

126.12 

213.10 

260.66 

47 . 77 

39 . 57 

109.91 

353.77 

122.13 

227.48 

118.36 

4.36 

58.26 

39.88 

36.64 

172.33 

106 
Table E4. TM image and simulation values for side-adjacency 2, with 

deviation and percentage deviation. 

Side-adjacency 2 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche, salt 

Leeville Nw 2,124 

NE 2,315 

SE 2,470 

sw 1,495 

Mink Bayou Nw 1 , 686 

NE 1,316 

SE 1,873 

sw 1,867 

Caminada Pass NW 1,669 

NE 41 8 

SE 34 

sw 952 

Bay Tambour Nw 1,506 

NE 773 

SE 539 

sw 833 

Pelican Pass Nw 67 

NE 579 

SE 500 

sw 100 

Early Lafourche, salt 

Grand Bayou 

du Large 

Lake La 

Graisse 

Central Isles 

Dernieres 

Cocodrie 

Dog Lake 

Nw 845 

NE 886 

SE 1 , 688 

sw 254 

Nw 475 

NE 67 

Nw 809 

NE 432 

SE 186 

sw 173 

Nw 1,692 

NE 1,454 

SE 81 3 

sw 2,256 

Nw 1,084 

NE 1,893 

SE 1 , 758 

sw 1,560 

1,853 

2,048 

1,947 

1,026 

1 , 455 

969 

1,743 

1,635 

1,396 

169 

21 

653 

1,013 

478 

451 

669 

82 

482 

41 8 

80 

61 8 

701 

1,277 

89 

41 3 

57 

669 

224 

120 

163 

1,949 

1,397 

659 

1,822 

869 

1,663 

1,932 

1,712 

107 

-271 

-267 

-523 

-469 

-231 

-347 

-1 30 

-232 

-273 

-249 

-1 3 

-299 

-493 

-295 

-88 

-1 64 

15 

-97 

-82 

-20 

-227 

-1 85 

-41 1 

-1 65 

-62 

-1 0 

-1 40 

-208 

-66 

-1 0 

257 

-57 

-1 54 

-434 

-21 5 

-230 

174 

152 

-12.75 

-1 1.53 

-21.17 

-31.17 

-1 3.70 

-26.37 

-6.94 

-1 2.43 

-1 6.36 

-59.57 

-38.24 

-31 -41 

-32.74 

-38.16 

-16.33 

-1 9.69 

-16.75 

-16.40 

-20.00 

22 . 39 

-26.86 

-20.88 

-24 . 35 

-64.96 

-13.05 

-14.93 

-17.31 

-48.15 

-35.48 

5.78 

15.19 

-3.92 

-18.94 

-1 9.24 

-1 9.83 

-12.15 

9-90 

9.74 
I Table E4. (cont.) 

Side-adjacency 2 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche, brackish 

Lake Bully 

C-P 

Golden Meadow 

Farms 

Bay L’Ours 

Three Bayou 

Golden Meadow 

Bay 

Nw 1,259 

NE 2,039 

SE 1,363 

sw 2,124 

Nw 1,530 

NE 1,181 

SE 1,813 

sw 1,085 

SE 820 

sw 387 

SE 1,670 

sw 974 

sw 2,118 

Early Lafourche, brackish 

Lost Lake Nw 1,352 

NE 2,069 

SE 1,644 

sw 1,912 

Lake Mechant NW 1,449 

NE 1,613 

SE 1,197 

sw 823 

Bayou Sauveur Nw 771 

NE 1,201 

SE 1,309 

sw 1,245 

Lake Quitman NE 1,608 

SE 1,421 

sw 1,574 

Dulac NE 791 

SE 666 

Montegut SE 1,828 

sw 1,521 

Mean 1,226 

1,126 

1,800 

1,732 

1,845 

1,691 

1,258 

1,751 

853 

51 7 

123 

1,887 

749 

2,078 

1,227 

1,863 

1,677 

1,853 

976 

1,998 

946 

474 

820 

1,244 

1,330 

1,067 

1,880 

1,167 

708 

502 

1,674 

1,577 

1,098 

1,577 

-1 33 

-239 

369 

-279 

161 

77 

-62 

-232 

-303 

-264 

21 7 

-225 

-40 

-1 25 

-206 

33 

-59 

-473 

385 

-251 

-349 

49 

43 

21 

-1 78 

272 

-254 

3 

-83 

-1 64 

-1 54 

56 

-1 27 

-10.56 

-11 -72 

27.07 

10.52 

6.52 

-13.14 

-3.42 

-21 -38 

-36.95 

-68.22 

12.99 

-23.10 

-1 -89 

-9.25 

-9.96 

2.01 

-3.09 

-32.64 

23.87 

-20.97 

-42.41 

6.36 

3.58 

1.60 

-14.30 

16.92 

-17.88 

0.19 

-1 0.49 

-24.63 

-8.43 

3.68 

-14.28 

I 

I 

1 

I 

:I 

1 

1 

I 

‘1 

1 

1 

1 

‘I 

< 1 

1 

I 

I 

I 108 
Table E5. TM image and simulation values for side-adjacency 3, with 

deviation and percentage deviation. 

Side-adjacency 3 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche. salt 

Leeville Nw 2,964 

NE 2,473 

SE 2,901 

sw 2,061 

Mink Bayou Nw 2,067 

NE 1,289 

SE 1,977 

sw 2,192 

Caminada Pass Nw 1,998 

NE 41 1 

SE 25 

sw 705 

Bay Tambour Nw 1,788 

NE 1,112 

SE 826 

sw 1,364 

Pelican Pass Nw 161 

NE 932 

SE 839 

sw 160 

Early Lafourche, salt 

Grand Bayou 

du Large 

Lake La 

Graisse 

Central Isles 

Dernieres 

Cocodrie 

Dog Lake 

Nw 

NE 

SE 

sw 

Nw 

NE 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

1,151 

1,145 

1,839 

367 

853 

143 

1,268 

601 

258 

334 

2,005 

2,596 

1,387 

3,511 

1,769 

2,394 

2,535 

1,967 

3,295 

3,279 

3,440 

721 

2,218 

730 

2,845 

2,678 

2,041 

280 

53 

582 

1,153 

471 

831 

796 

261 

763 

41 1 

134 

542 

726 

937 

158 

740 

302 

832 

360 

21 0 

226 

2,741 

2,931 

1,165 

3,758 

954 

2,542 

3,026 

2,545 

109 

331 

806 

539 

-1,340 

151 

-559 

868 

486 

43 

-1 31 

28 

-1 23 

-635 

-641 

5 

-568 

100 

-1 69 

-428 

-26 

-609 

-41 9 

-902 

-209 

-1 05 

159 

-436 

-241 

-48 

-1 08 

736 

335 

-222 

247 

-81 5 

148 

491 

570 

11 -17 

32.59 

18.58 

-65.02 

7.31 

-43.37 

43.91 

22.17 

2.15 

-31.87 

112.00 

-17.45 

-35.52 

-57.64 

-41.64 

-18.13 

-51 -01 

-16.25 

0.61 

62.11 

-52 . 91 

-36.59 

-49.05 

-56.95 

-12.31 

111.19 

-34.39 

-40.10 

-18.61 

-32.34 

36.17 

12.90 

7.04 

6.18 

19.37 

29.39 

-16.01 

-46.07 
Table E5. (cont.) 

Side-adjacency 3 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche. brackish 

Lake Bully 

Camp 

Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

Golden Meadow 

Bay 

Nw 2,079 

NE 2,845 

SE 1,578 

sw 3,373 

Nw 2,179 

NE 1,836 

SE 2,538 

sw 1,775 

SE 963 

sw 51 3 

SE 2,108 

sw 1,218 

sw 2,184 

Early Lafourche. brackish 

Lost Lake Nw 2,074 

NE 2,848 

SE 1,820 

sw 3,078 

Lake Mechant Nw 1,836 

NE 1,957 

SE 1,471 

sw 894 

Bayou Sauveur NW 791 

NE 1,030 

SE 1,559 

sw 1,592 

LakeQuitman NE 2,326 

SE 1,881 

sw 1,948 

Dulac NE 883 

SE 854 

Montegut SE 2,657 

sw 2,103 

Mean 1,617 

1,246 

3,402 

2,224 

3,610 

2,186 

2,072 

2,872 

1,731 

503 

269 

2,807 

664 

3,029 

1,008 

3,512 

2,152 

3,545 

594 

3,003 

1,089 

394 

942 

1,139 

1,833 

829 

3,208 

897 

2,283 

1,024 

1,265 

3,087 

2,417 

1,609 

-833 

557 

646 

237 

7 

236 

334 

-44 

-460 

-244 

779 

-554 

845 

1,056 

664 

332 

467 

1,046 

-1,242 

-382 

-550 

151 

109 

274 

-763 

882 

-984 

335 

141 

41 1 

430 

31 4 

-8 

-40.07 

19.58 

40.94 

7.03 

0.32 

12.85 

13.16 

-2.48 

-47.77 

-47.56 

36.95 

-45.49 

38.69 

-51.40 

23.32 

18.24 

15.17 

-67.65 

53.45 

-25.97 

-55.93 

19.09 

10.58 

17.58 

-47.93 

37.92 

-52.31 

17.20 

15.97 

48.13 

16.18 

14.93 

-3.50 

110 

I 

I 

I 

I 

1 

I 

1 

I 

1 

I 

1 

I 

1 

1 

d 

1 

I 

I 

I 
Table E6. TMimage and simulation values for side-adjacency 4, with 

deviation and percentage deviation. 

Side-adjacency 4 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche. salt 

Leeville NW 9,856 

NE 6,860 

SE 8,369 

SW 13,064 

Mink Bayou Nw 5,326 

NE 5,744 

SE 5,975 

sw 6,026 

Caminada Pass NW 18,306 

NE 29,050 

SE 35,792 

SW 18,845 

Bay Tambour NW 14,891 

NE 25,719 

SE 30,541 

SW 28,120 

Pelican Pass NW 35,491 

NE 28,221 

SE 29,910 

SW 34,833 

Early Lafourche, salt 

Grand Bayou NW 17,371 

du Large NE 22,053 

SE 10,349 

SW 29,643 

Lake La NW 31,517 

Graisse NE 35,641 

Central Isles NW 25,018 

Dernieres NE 31,419 

SE 33,918 

SW 33,245 

Cocodrie Nw 7,765 

NE 20,598 

SE 28,656 

SW 13,522 

Dog Lake NW 19,788 

NE 6,218 

SE 10,514 

sw 9,106 

9,551 

6,534 

8,299 

13,460 

5,004 

5,778 

5,397 

5,584 

18,074 

29,187 

35,763 

19,006 

15,202 

26,191 

30,542 

28,413 

35,356 

28,283 

30,161 

34,822 

17,550 

22,217 

10,845 

29,645 

31,565 

35,459 

25,227 

31,723 

33,935 

33,236 

6,287 

20,194 

28,964 

13,490 

20,321 

5,937 

9,381 

7,990 

111 

-305 

-326 

-70 

396 

-322 

34 

-578 

-442 

-232 

-29 

137 

161 

31 1 

472 

1 

293 

-1 35 

62 

251 

-1 1 

179 

164 

496 

2 

48 

-1 82 

209 

304 

17 

-9 

-1,478 

-404 

308 

-32 

533 

-281 

-1,133 

-1,116 

-3.10 

-4.75 

-0.84 , 

3.03 

-6.05 

0.59 

-9.67 

-7 . 34 

-1 -27 

0.47 

-0.08 

0.85 

2.09 

1.84 

0.00 

1.04 

-0.38 

0.22 

0.84 

-0.03 

1.30 

0.74 

4.79 

0.01 

0.15 

-0.51 

0.84 

0.97 

0.05 

-0.03 

-1 9.03 

-1 -96 

-0.24 

-4.52 

-10.78 

-1 2.26 

1.08 

2.69 
Table E6. (cont.) 

Side-adjacency 4 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche, brackish 

Lake Bully 

C-P 

Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

Golden Meadow 

Bay 

Nw 8,482 

NE 11,744 

SE 4,481 

SW 11,989 

Nw 6,931 

NE 18,357 

SE 7,862 

SW 11,049 

SE 27,359 

SW 17,837 

SE 7,410 

SW 18,775 

sw 4,802 

Early Lafourche, brackish 

Lost Lake Nw 

NE 

SE 

sw 

Lake Mechant NW 

NE 

SE 

sw 

Bayou Sauveur Nw 

NE 

SE 

sw 

Lakewitman NE 

SE 

sw 

Dulac NE 

SE 

Montegut SE 

sw 

Mean 

8,031 

11,576 

3,689 

12,874 

19,400 

6,710 

13,303 

22,310 

1,239 

966 

4,721 

5,748 

13,105 

8,035 

7,490 

1,468 

22,739 

12,077 

5,661 

35,792 

8,680 

11,405 

2,951 

11,537 

6,281 

17,993 

7,320 

11,360 

27,790 

17,852 

5,743 

19,110 

4,006 

8,059 

11,034 

2,752 

12,138 

19,940 

4,663 

13,462 

22,755 

959 

71 4 

3,885 

5,730 

11,475 

8,185 

6,598 

1,460 

22,312 

11,578 

5,303 

35,763 

198 

-339 

-1,530 

-452 

-650 

-364 

-542 

31 1 

431 

15 

-1,667 

335 

-796 

28 

-542 

-937 

-736 

540 

-2,047 

159 

445 

-280 

-252 

-836 

-1 8 

-1,630 

-892 

-8 

-427 

-499 

-358 

-227 

150 

2.33 

-2.89 

-34.14 

-3.77 

-9.38 

-1 -98 

-6.89 

2.82 

1.58 

0.08 

1.78 

-22.50 

-16.58 

0.35 

-4.68 

-25.40 

-5.72 

-30.51 

1.20 

2.00 

-22.60 

-26.09 

-1 7.71 

-0.31 

-1 2.44 

1.87 

-11.91 

-0.55 

-1.88 

-4.13 

-6.32 

-4.44 

2.78 

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Table E7. TM image and simulation values for border water pixels, 

with deviation and percentage deviation, 

Number of border water pixels 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche. salt 

Leeville Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Caminada Pass Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Pelican Pass Nw 

NE 

SE 

sw 

Mink Bayou Nw 

Bay Tambour Nw 

Early Lafourche. salt 

Grand Bayou 

du Large 

Lake La 

Graisse 

Central Isles 

Dernieres 

Cocodrie 

Dog Lake 

Nw 

NE 

SE 

sw 

Nw 

NE 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

Nw 

NE 

SE 

sw 

332 

355 

308 

304 

21 9 

236 

237 

225 

493 

646 

741 

51 8 

388 

504 

704 

654 

751 

61 1 

629 

751 

31 6 

51 4 

444 

530 

679 

744 

675 

706 

698 

71 3 

264 

475 

583 

402 

385 

203 

31 7 

386 

443 

195 

251 

497 

439 

426 

110 

289 

679 

705 

733 

503 

640 

570 

71 1 

708 

748 

703 

61 8 

764 

51 0 

541 

471 

61 9 

71 9 

756 

764 

727 

739 

764 

461 

436 

597 

493 

532 

453 

459 

321 

111 

-1 60 

-57 

193 

220 

190 

-1 27 

64 

186 

59 

-8 

-1 5 

252 

66 

7 

54 

-3 

86 

-1 1 

13 

194 

27 

27 

89 

40 

12 

89 

21 

41 

51 

197 

-39 

14 

91 

147 

190 

142 

-65 

33.43 

-45.07 

-18.51 

63.49 

100.46 

80.51 

-53.59 

28.44 

37.73 

9.13 

-1 -08 

-2 . 90 

64.95 

13.10 

0.99 

8.26 

-0, 40 

13.94 

-1 -75 

1.73 

61 -39 

5.25 

6.08 

16.79 

5.89 

1.61 

13.19 

2.98 

5.87 

7.15 

74.62 

2.40 

22.64 

38.18 

72.24 

44.80 

-8.21 

-16.84 

113 
Table E7. (cont.) 

Number of border water pixels 

Quad Quarter Image Sim. Deviation % Deviation 

Late Lafourche, brackish 

Lake Bully 

CmP 

Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

Golden Meadow 

Bay 

NW 

NE 

SE 

sw 

NW 

NE 

SE 

sw 

SE 

sw 

SE 

sw 

sw 

276 

344 

230 

406 

261 

553 

294 

159 

61 7 

387 

375 

501 

202 

Early Lafourche, brackish 

Lost Lake Nw 

NE 

SE 

sw 

Lake Mechant NW 

NE 

SE 

sw 

Bayou Sauveur NW 

NE 

SE 

sw 

Lake Quitman NE 

SE 

sw 

Dulac NE 

SE 

Montegut SE 

sw 

187 

294 

265 

340 

534 

267 

380 

550 

42 

106 

203 

140 

31 2 

244 

301 

29 

221 

349 

237 

Mean 404 

61 7 

357 

431 

758 

435 

51 9 

462 

92 

694 

495 

758 

501 

129 

453 

347 

584 

555 

554 

464 

465 

524 

41 

76 

422 

283 

408 

456 

449 

37 

363 

473 

123 

492 

341 

13 

201 

352 

174 

-34 

168 

-67 

77 

108 

383 

0 

-73 

266 

53 

31 9 

21 5 

20 

197 

85 

-26 

-1 

-30 

21 9 

143 

96 

21 2 

148 

8 

142 

124 

-114 

87 

123.55 

3.78 

87.39 

86.70 

66.67 

57.14 

12.48 

27.91 

102.13 

0.00 

-6.15 

-42.14 

-36.14 

142.25 

18.03 

120.38 

63.24 

3.75 

73.78 

22.37 

-4.73 

-2.38 

-28.30 

107.88 

102.14 

30.77 

86.89 

49.17 

27.59 

64.25 

35.53 

-48.10 

29.10 

114 

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Table E8. Results of simple linear regression of simulation spatial 

pattern indices on corresponding image spatial pattern 

indices. 

Interface lencrth 

Sim = 154.8 + 1.069 Image R2 = 94.12 

(.4250) (.0001) P of F-stat = .0001 

Adi-0 

Sim = 73.23 - 0.0739 Image R2 = 013 

(.0005) (-7667) P of F-stat = ,7667 

Adi-1 

Sim = 212.4 + 1.204 Image R2 = 56.14 

(-0001) (.0001) P of F-stat = -0001 

Adi-2 

Sim = -88.01 + 0.9680 Image R2 = 90.72 

(.0929) (.0001) P of F-stat = -0001 

Adi-3 

Sim = -325.7 + 1.213 Image R2 = 79.67 

(.0113) (.0001) P of F-stat = ,0001 

Ad?-4 

Sim = -640.3 + 1.026 Image R2 = 99.77 

(.0001) (.0001) P of F-stat = .0001 

Note: Values in parentheses are T-stat probability 

levels. 

115 
Figure El. Comparisons of spatial patterns of marsh disintegration in TM scenes (upper images) 

with "best-fit" simulations at the same disintegration level (lower images). 

Quadrangles are presented in the same order as in Table D 1. 

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W = 272 G = 60 DL = 42.94 BC = 0011 W = 188 G = 0 DL = 34.93 BC = 0111 

Figurc El-1. Leeville, NW quadrangle. Figurc El-2. Lecvillc, NE quadrangle. 

117 

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W = 237 G = 4 DL = 39.91 BC = 0011 

Figure El-3. Leeville, SE quadnnglc. 

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W = 8 G = 180 DL = 46.74 BC = 0011 

Figurc El-4. Leevillc, SW quadrangle. 

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W = 244 G = 180 DL = 26.59 1SC = 0101 

Figurc E1-5. Mink Bayou, NW quatlranglc. 

W = 24 C = 180 DL = 24.54 IlC = 0011 

Figurc El-6. Mink Bayou, NE cluadranglc. 

119 
W = 248 G = 0 DL = 29.44 BC = 0111 

Figurc E 1-7. Mink Bayou, SE quadrnnglc. 

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W = 261 G = 180 DL = 29.57 BC = 0001 

Figurc E1-8. Mink Bayou, SW quatlrnnglc. 

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W = 58 C = 180 DL = 62.09 BC = 0111 

Figure E1-9. Caminada Pass, NW quadranglc. 

W = 13 C = 540 DL = 82.87 BC = 0111 

Figure E 1 - 10. Caminada Pass, NE quadrangle. 

121 
Figure El-11. Caminada Pass, SE quadrangle. 

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W = 28 G = 540 DL = 58.02 BC = 0011 

Figure El-12. Caminada Pass, SW quadrangle. 

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Figure E1-13. Bay Tambour, NW quadrangle. 

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W = 17 C = 540 DL = 76.55 ISC = 0011 

Figurc E1-14. Bay Tambour, NE quadranglc. 
Figurc E1-15. Bay Tambour, SE quadranglc. Figurc El-16. Bily Tambour. SW quadranglc. 

124 

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W = 8 G = 180 DL = 98.96 BC = 0111 

Figure El-17. Pelican Pass, NW quadrangle. 

W = 88 G = 530 DL = 82.60 BC = 0111 

Figurc E 1- 18. Pclican Pass, NE quadranglc. 

125 
W = 16 G = 540 DL = 86.68 BC = 0011 W = 38 G = 540 DL = !J7.24 BC = 1111 

Figurc E1-19. Pclican Pass, SE quadranglc. 

126 

Figurc E1-20. Pclican Pass, SW qundnnglc. 

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w = 30 G = 540 DL = 53.83 BC = 0011 W = 29 G = 540 DL = 67.26 BC = 0011 

FiWeE1-21. Grand Bayou Du Large, NW quadranglc. Figurc El-22. Grand Bayou Du Largc, NE quadrmglc. 

ORIGINAL PAGE IS 

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127 
W = 14 G = 180 DL = 40.44 BC = 0011 W = 0 G = 540 DL = 83.72 1)C = 0011 

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ORIGINAL PAGE IS 

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W = 113 G = 540 DL = 91.13 I3C = 0111 

Figure El-25. Lake LaGraisse, NW quadrangle. 

129 

W = 10 G = 180 DL = 99.28 BC = 0111 

Figurc El-26. Lakc LaCraissc, NE quadrangle. 
I W = 26 C = 180 DL = 75.77 BC = 1111 W = 43 G = 540 DL = 90.05 I1C = 0111 

Figurc El-27. Ccnual Islcs Dcrnicrcs, NW quadranglc. Figurc El-28. CcnUal Islcs Dcrnicrcs, NE quadrangle. 

130 

ORIGINAL PAGE IS 

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W W = 33 G = 540 DL = 93.53 BC = 1111 = 16 G = 540 DL = 95.18 BC = 0111 

Figure E1-29. Central Isles Dernicrcs, SE quadrangle. Figurc E1-30. Central Islcs Dcrnicrcs, SW quadranglc. 

131 
W = 115 G = 180 DL = 33.43 BC = 0101 

Figure El-31. Cocodrie, NW quadrangle. 

132 

W = 311 C = 540 DL = 68.89 BC = 0001 

Figure El-32. Cococfric, NE quadrangle. 

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W = 69 G = 540 DL = 85.79 BC = 0011 

Figure El-33. Cocodrie, SE quadrangle. 

w = 233 G = 60 DL = 54.72 ISC = 0011 

Figurc El-34. Cocodric, SW quadrangle. 

133 
ORIGINAL PAGE IS 

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W = 44 G = 540 DL = 62.97 BC = 0011 W = 213 G = 180 DL = 30.74 BC = 0101 

Figure El-35. Dog Lake, NW quadranglc. Figurc E 1-36. Dog Lakc, NE quadrangle. 

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W = 120 G = 180 DL = 42.38 BC = 0011 

Figure El-37. Dog Lake, SE quadrangle. 

W = 123 (3 = 540 DL = 36.40 BC = 0001 

Figurc El-38. Dog Lakc, SW quadranglc. 

135 
ORIGINAL PAGE IS 

OF POOR QUALKY 

W = 62 G = 180 DL = 33.69 BC = 0111 W = 244 G = 180 DL = 17.84 BC = 0001 

Figure El-39. Lake Bully Camp, NW quadrangle. 

136 

Figure E1-40. Lakc Bully Camp, NE quadrangle. 

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W = 130 G = 60 DL = 22.21 DC = 0011 

Figurc El-41. Lake’B~lly Camp, SE quadrangle. 

W = 255 CI = 60 DL = 50.02 DC = 1111 

Figurc El-42. Lakc Bully Camp, SW quadranglc. 

137 
ORIGINAL PAGE IS 

OF POOR QUALmY 

W = 129 G = 180 DL = 30.91 BC = 0011 W = 160 G = 540 DL = 450.69 BC = 0011 

Figure E1-43. Golden Meadow Farms, NW quadrangle. Figurc E1-44. Goldcn Mcdow Farms, NE quadranglc. 

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W = 308 G = 60 DL = 35.56 BC = 0011 W = 3184 G = 0 DL = 38.95 BC = 0000 

Figurc E1-45. Golden Meadow Farms, SE quadranglc. Figurc El-46. Goldcn Mcadow Farms, SW quadranglc. 

139 
ORIGINAL PAGE IS 

OF POOR QUALtTY 

W = 8 G = 180 DL = 81.53 IlC = 0111 

Figurc E1-47. Bay L'Ours, SE quadrmglc. 

__-- . -- .. 

W = 1 (; = 540 DL = 5;!.26 IIC = 0011 

Figurc E148. Bay L'Ours, SW quadranglc. 

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ORIGINAL PAGE IS 

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W = 100 G = 60 DL = 32.93 BC = 1111 

Figure E1-49. Three Bayou Bay, SE quadranglc. 

141 

W = 33 G = 530 DL = 58.71 BC = 0011 

Figure El-50. Thrcc Bayou Bay, SW quadranglc. 
W = 116 G = 0 DL = 27.66 BC = 0011 

Figurc El-51. Goldcn Mcadow, SW quadranglc. 

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W = 23 G = 180 DL = 32.65 I3C = 0011 W = 245 G = 180 DL = 47.32 I1C = 0001 

Figure El-52. Lost Lake, NW quadrangle. Figurc €1-53. Lost Lakc, NE quadmnglc. 

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W = 111 G = 60 DL = 21.81 BC = 0111 W = 290 G = 20 DL = 50.78 BC = 0111 

Figure El-54. Lost Lake, SE quadrangle. Figure El-55. Lost Lake, SW quadrangle. 

144 
W = 4 G = 180 DL = 64.05 BC = 0011 W = 118 G = 60 DL = 29.91 BC = 0011 

Figurc El-56. Lakc Mcchant, NW quadranglc. Figurc El-57. Lakc Mccliant, NE quadrangle. 

145 
ORIGINAL PAGE IS 

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W = 86 G = 540 DL = 44.91 BC = 0011 W = 17 G = 540 DL = 67.06 1)C = 0011 

Figure E1-58. Lake Mechant, SE quadrangle. Figurc E1-59. Lake Mcchant, SW quadranglc. 

146 
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W = 325 G = 0 DL = 8.72 DC = 0000 

Figurc El-60. Bayou Sauveur, NW quadranglc. 

W = 93 G = 0 DL = 10.90 1SC = 0000 

Figurc El-61. Bayou Sauvcur, NE quadrangle. 

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OF POOR QUALWY 

W = 133 G = 180 DL = 22.23 BC = 0011 W = 35 G = 540 DL = 24.31 nC = 0001 

Figure El -62. Bayou Sauveur, SE quadranglc. Figure El-63. Bayou Sauveur, SW quadrangle. 

148 

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W = 116 G = 540 DL = 48.20 BC = 0001 

Figure El-64. Lake Quitman, NE quadrangle. 

149 
ORlGlNAL PAGE IS 

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W = 20 G = 180 DL = 32.41 BC = 0011 W = 121 G = 180 DL = 31.85 BC = 0011 

Figurc El-65. Lake Quitman, SE quadrangle. Figurc El-66. Lakc Quiman, SW quadranglc. 

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W = 701 G = 0 DL = 9.63 BC = 0000 W = 10947 G = 0 DL = 66.80 BC = 0000 

Figurc E1-67. Dulac, NE quadrangle. Figurc E 1-68. Dulac, SE quadranglc. 

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W = 289 G = 180 DL = 47.48 BC = 0101 W = 404 G = 0 DL = 27.16 1SC = 0000 

Figure E1-69. Montegut, SE quadrangle. Figurc E1-70. Montcgut, SW quadranglc. 

152 

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Figure E2. Comparisons of pond size distributions of TM scenes with the "best-fit" simulations at 

the same disintegration level. Water-body size is the frequency distribution of waterbody classes (in pixel units) expressed as percentage of total number of water pixels. 

Frequency is the number of pixels in a water-body class, expressed as a percentage of 

the total number of water pixels. Each mirror image histogram shows one TM scene 

and its representative simulation. Quadrangles are grouped by age of delta lobe and 

salinity (as in Table D 1). 

153 
Late Lafourche, salt 

Leeville 

Nw NE 

0 B ""cm il:"I 

50 

100 Sim. 100 Sim. L 

100 Pond Size 100 0 Pond Size 0 

sw 

E a 

100 Sim. L e 1@E7Fl 0 100 

Pond Size 

SE 

100 Sim. L 

0 100 

Pond Size 

Mink Bayou 

NW NE 

0 il:'" 

Bo 5 

100 Sim. 100 Sim. L 

100 Pond Size 100 0 Pond Size 0 

sw SE 

0 100 0 100 

Pond Size Pond Size 

Figure E2-1. Pond size distributions for Leeville and Mink Bayou quadrangles. 

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Late Lafourche, salt 

Caminada Pass 

Nw NE 

clWrnI [l:; 

2 

100 Sim. 100 Sim. ct" 

100 Pond Size 100 0 Pond Size 0 

sw SE 

$lo:"il:; 2 

100 Sim. 100 Sim. ct" 

0 100 0 100 

Pond Size Pond Size 

Bay Tambour 

Nw NE 

~100~1 I-/'",/ 

100 Sim. Sim. ct t 100 c4 

100 Pond Size 100 0 Pond Size 0 

sw ;*i@l 8 0 f:;

% 

100 Sim. 100 Sim. ct; 

0 100 0 100 

Pond Size Pond Size 

Figure E2-2. Pond size distributions for Caminada Pass and Bay Tambour quadrangles. 

155 
Late Lafourche, salt 

Pelican Pass 

Nw NE 

0 ^F(;; 

go F 

100 Sim. 100 Sim. LL 

100 Pond Size 100 0 Pond Size 0 

sw SE 

$111;; 

!!l 

100 Sim. 100 Sim. LL 

% 

Le 

0 100 0 100 

Pond Size Pond Size 

Figure E2-3. Pond size distributions for Pelican Pass quadrangle. 

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Early Lafourche, salt 

Grand Bayou du Large 

Nw NE 

Im/n1 Il;"II( 

OO 

iiT 

100 Sim. 100 Sim. c4 

3 

& 

100 Pond Size 100 0 Pond Size 0 

sw 

5 

21m"" y)l 

100 Sim. 100 Sim. LE 

0 100 0 100 

Pond Size Pond Size 

Lake La Graisse 

Nw NE 

Cloo"1 i1;;

3 

100 Sim. 100 Sim. & 

100 Pond Size 100 0 Pond Size 0 

Figure E2-4. Pond size distributions for Grand Bayou du Large and Lake La Graisse quadrangles. 

157 
1 Early Lafourche, salt 

Central Isles Dernieres 

Nw 

5 

Sim. 

!? 

100 LL lEEE3I 0 Pond Size 100 

SW 

5 

E 

100 Sim. Lt ?? lOE!EI 0 100 

Pond Size 

Cocodrie 

Nw 

c4 1001 Sim. I 

100 Pond Size 0 

SW 

6 

~ c4 Ox 1E!!31 0 Sim. 100 $ 

100 

Pond Size 

NE 

2 

F 

100 Sim. LL lE!!F3 0 Pond Size 100 

SE 

6 

1 iT 

100 Sim. LL l;l 0 100 

Pond Size 

NE 

6 

E 

100 Sim. & ?? 1Ez51 0 Pond Size 100 

SE 

6 

1 iT 

100 Sim. LL e l:: 0 100 

Pond Size 

Figure E2-5. Pond size distributions for Central Isles Demieres and Cocodrie quadrangles. 

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Dog Lake 

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Nw NE 

5100WI y;l 5 

100 Sim. 100 Sim. r.t 

100 Pond Size 100 0 Pond Size 0 

SW SE 

so Q1OOil:; i!. 

100 Sim. 100 Sim. L 

0 100 0 100 

Pond Size Pond Size 

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Figure E2-6. Pond size distributions for Dog Lake quadrangle. 

159 
Late Lafourche, brackish 

Lake Bully Camp 

Nw NE 

0 *lOOI’*c;) 

3 

E 100 Sim. 100 Sim. 

100 0 Pond Size 100 Pond Size 0 

sw SE 

clOOm [l:; 

3 

E 100 Sim. 100 Sim. 

0 100 ~~~ 0 100 

Pond Size Pond Size 

Golden Meadow Farms 

Nw NE 

clWI’; 8 10 [l;p-7-J 

F 

100 Sim. 100 Sirn. L 

100 Pond Size 100 0 Pond Size 0 

sw SE 

clOOml 8 10 il:;l F 

100 Sim. 100 Sirn. CL 

0 100 0 100 

Pond Size Pond Size 

Figure E2-7. Pond size distributions for Lake Bully Camp and Golden Meadow Farms quadrangles. 

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Late Lafourche, brackish 

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Bay L'Ours 

sw SE 

$0 QlWW i1;; 

100 Sim. 100 Sim. r.t 

100 Pond Size 100 0 Pond Size 0 

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Three Bayou Bay 

sw SE 

QlWM1 il:;l 

100 Sim. 100 Sim. 

100 Pond Size 0 100 0 Pond Size 

Golden Meadow 

sw 

100 

0 100 

Pond Size 

Figure E2-8. Pond size distributions for Bay L'Ours, Three Bayou Bay and Golden Meadow quadrangles. 

161 
I Early Lafourche, brackish 

Lost Lake 

Nw 

5 

100 Pond Size 0 

SW 

1001 Sim. LL 1 

0 100 

Pond Size 

Lake Mechant 

Nw 

100 Pond Size 0 

SW 

5 

I 1 

iT 

100 Sim. L4 'Ezzd 0 100 

Pond Size 

NE 

x loot TM 1 

E Sim. 

100 

L !i 100 0 

o~ Pond Size 

SE 

5 

3 

100 Sim. rz O" 1EzE3 0 

100 ' 

Pond Size 

NE 

100 Pond Size 0 

SE 

s 

3 

100 Sim. iE Ox lOEEE33 0 100 

Pond Size 

Figure E2-9. Pond size distributions for Lost Lake and Lake Mechant quadrangles. 

162 

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Early Lafourche, brackish 

Bayou Sauveur 

Nw NE 

ClWp--l il:"i 

Sim. 100 Sim. 

E 

100 E4 

100 Pond Size 100 0 Pond Size 0 

SW SE 

clOOml so il:;l 2 

100 Sim. 100 Sim. rE 

0 100 0 100 

Pond Size Pond Size 

Lake Quitman 

I 

NE 

x 1001 

1001 Sim. 

lo( Pond Size 0 

sw SE '*m1 0 ij1;;1 

Sim. 100 Sim. 

Pond Size 

100 

0 100 0 Pond Size 100 

Figure E2-10. Pond size distributions for Bayou Sauveur and Lake Quitman quadrangles. 

163 

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5 0. 

TM scenes 

HBraddsh marsh X Salt marsh 350- 

300. 

5 250. 

5 

A2 

- 

p 200. 

8 150. c - 

= 100. 

5 0. 

o,.,.,. ,.,.,.,.,.,. 0 10 20 30 40 50 60 70 80 90 100 

DL (YO open water) 

TM scenes: Brackish y = 34.92 + 11 .lx - 0.205~2 + 0.0009~~; R2 = 0.32 

Sal y = 23.77 + 13.26x - 0.246x2 + 0.001 x3: R2 a 0.83 

Slmul~tlona 

aaraddshmarsh saltnrarsh 3501 

Y a 

0 10 20 30 40 50 60 70 80 90 100 

DL (Yo open water) 

Regression: 

Simulations: Brackish y = 8.29 + 15.8~ - 0.319~~ + 0.0016~~; R2 = 0.37 

Sal y = -11.2 + 16.67~ - 0.311~~ + 0.001Sx3; R2 = 0.87 

Figure E3. Comparison of interface as a function of disintegration level for the 70 TM scenes and 

for simulation at the same disintegration levels. 

165 
Figure E4. TM imagery interface and side adjacency statistics (number of pixels) compared with 

simulations at the same disintegration level. The side adjacency statistic refers to the 

number of sides of a water pixel that border water and may equal 0-4. 

166 

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SIMUT I 

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12000 + 

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loo00 + 

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Booo+ 

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6ooo+ 

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Qooo+ 

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2OOo+ 

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1 

Plot of SIMILRT*IMERY. Legend: R = 1 obs, B = 2 oh, etc. 

FI 

FI 

a FI 

FI BFI IW 

FIB CI 

FIclcyI 

FI 

CI 

FI 

FI 

FI 

BcIn 

B fle 

BFI 

FI 

FI 

m 

a 

FI 

FIFI 

I 

IB 

IC 

O+ 

I 

IFI 

: -___+-__c___- 

0 2OOo 40oo 6ooo wx)o 1w 1m 

IMRY 

Figure E4-1. Total interface (number of pixels). 

167 

I 
Plot of SIm)lrIM60. Legend: 4 = 1 obs, B = 2 obs, etc. 

FI 

FI 

A 

A 

A 

A 

FI 

A 

SImS I 

450 + 

I 

1 

I 

I 

400+ 

I 

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I 

350+ 

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300+ 

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250+ 

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m+ 

1 

I 

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I 

150 + 

I 

I 

I AA A 

100 + 

I A 

la 

I A aeA a 

I W 

I A AA AA 

I A8 a MA AAW AA 8 

18 AB A A m MA A 

I 

I a 

50+ a Aa A 

I a AA a a 

o+e A a IIA 

t 

0 20 40 60 80 100 120 140 160 180 200 

II 

A 

Figure E4-2. TM scenes (SIMO) versus simulations (IMGO) with side adjacency = 0. I 

I 

168 

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SIHl I 

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loa, + 

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I &lo+ 

I 

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I 

I 

600+ 

I 

Plot of SIHl+IffiL Legend: R = 1 obs, B = 2 oh, etc. 

A 

A 

A 

R 

R R R 

R R 

RR 

RAW R 

R a RR 

AFI AFI A A 

II 

A R 

FI 

F1 

A CIA A F1 

FI 

FI 

I F1 F1A 

I RF1 

I F1 

I FI R 

I FIR AA 

400+ 

I A 

I 

I FI A1 I 

I 

I a 

200+ 0 

I AA 

I FIFIFI FI 

I 

I WFI 

I 

o+ FI 

I 

0 

im 

100 200 300 100 so0 600 700 800 

IS1 

Figure E4-3. TM scenes (SIMI) versus simulations (IMG1) with side adjacency = 1. 

169 
SI# I 

2250+ 

I 

I 

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I 

m+ 

I 

I 

I 

I 

1750 + 

I 

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I 

I 

1500 + 

I 

I 

I 

I 

1250 + 

I 

1 

I 

I 

loo0 + 

I 

I 

I 

I 

m+ 

I 

I 

I 

I 

500+ 

I 

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I 

250+ 

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I 

I 

O+ 

I 

Plot of SI#Wl62. Legend: = 1 obs, B = 2 oh, etc. 

a 

a 

a 

aa 

a aa 

aa a 

a flB 

a a 

ma 

m 

Fylcl 

a 

a 

aa 

a 

ma 

aa 

a a 

aa 

an 

B 

a 

a 

ma 

a 

a1 

a a CIA 

a a 

m 

a 

a a 

m aa a 

a 

a 

0 560 lobo ls00 2oiM 25iO 

Figure E4-4. TM scenes (SIM2) versus simulations (IMG2) with side adjacency = 2. 

170 

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51113 I 

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4OOot 

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3500 t 

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jooot 

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I 

I 

I 

m+ 

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I 

I 

I 

lsoo + 

I 

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lo00 + 

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I 

5Oot 

I 

I 

I 

O+ 

I 

ia 

Plot of SIH3*IR3. Legend: a = 1 oh, 8 = 2 obs, etc. 

L 

a 

a 

aa a 

a 

a L 

a la 

aa 

a 

L 

a La 

a 

L 

L 

L 

a 

a 

a L 

L 

a 

aa aa II 

A AAA 

a 

am 

am 

B aa 

a 

LM 

I L 

L L 

A 

m 

a 

a 

a 

aa 

a 

m 

I163 

Figure E4-5. TM scenes (SIM3) versus simulations (IMG3) with side adjacency = 3. 

171 
Plot of SIM*IW. Legend: A = 1 oh, B = 2 obs, etc. 

SIn4 I 

I 

40000+ 

I 

I 

I 

I 

35ooo+ 

I 

I 

I 

I 

JOOOO+ 

I 

I 

I 

I 

m+ 

I 

I 

I 

I 

20000+ 

I 

I 

I 

I 

lsooo + CI 

I 

I M 

I m 

~ I ma 

I aa 

I ea 

5OOo+ mR 

I m 

l loo00 + a l 

I M 

I cFw# 

I RCI 

I 

ICIB 

O+ 

I 

a 

Re 

Q 

A 

B 

A 

m 

m aB 

a 

m 

M 

B 

Ma 

Figure E4-6. TM scenes (SIM4) versus simulations (IMG4) with side adjacency = 4. 

172 

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Appendix F 

Presimulation Predictions from the Knowledge Base 

Table F1. Presimulation values of the spatial-pattern indices, predicted from the knowledge 

base. 

8 

1 

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Presimulation Predictions from 

the Knowledge Base 

The expert system's estimate of the model parameters W, G, and BC to "best" simulate 

the disintegration of a specific marsh was derived from separate estimates based on each of the 

spatial-pattern indices. For instance, the W-estimate is a weighted mean of Ws based on matching 

each of several spatial-pattern indices from the imagery to those in a knowledge base. The 

knowledge base consists of sets of look-up tables that relate spatial-pattern index values for a given 

DL to W, G, and BC. The best W-G-BC estimate was determined from the knowledge base in the 

manner described in Appendix A. Given a W-G-BC estimate, it was possible to work backward 

through the selection process (relating a given W, G, and BC to spatial-pattern index values) and 

use the expert system to "predict" the spatial pattern indices for a given DL that would result from 

a simulation of marsh disintegration using that particular W-G-BC estimate. The presimulation 

predictions of spatial-pattern indices €or the 70 cases of our study are presented in Table F1. The 

significance of presimulation predictions of the spatial-pattern indices is that they can potentially 

be used to test and fine tune W-G-BC estimates produced by the expert system. Alternative 

decision algorithms could be tested for their relative ability to simulate the spatial-pattern indices 

of a given study site. A comparison of presimulation to simulation values of the spatial pattern 

indices is presented in Appendix G. 

PRECEDiNG PAGE UNK NOT FiiMED 

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Appendix G 

Comparison of Presimulation Predictions to Simulation Values 

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Table G1. Percentage deviation of simulation values from presimulation-predicted values 

of the spatial-pattern indices. . 

Table G2. (A) Mean percentage deviation and (B) mean absolute percentage deviation of 

simulation from image values and pre-simulation prediction from simulation 

values of spatial-pattern indices. 

181 

PRECEDING PAGE BLANK NOT FILMED 
Comparison of Presimulation Predictions 

to Simulation Values 

Given the random aspect of the model, we could not expect perfect correspondence of 

presimulation-predicted and simulation values of the spatial pattern indices. No two simulations 

were alike, even when the same W-G-BC values were used. Table G1 shows the percentage 

deviation of simulation values from presimulation predictions (both at the same DL, which was the 

DL of the study site in the imagery). Average values are given at the bottom of the table. The 

largest average percentage deviation is in Adj-0, which we know to be the most variable index from 

one simulation to another. 

Average percentage deviations are summarized in Table G2, which also gives average 

absolute percentage deviations, which provide a slightly different picture. Since deviations can be 

in either the positive or the negative direction, they tend to cancel each other out in averaging 

procedures. This is why average percentage absolute deviations are also shown. 

In addition to comparisons of simulation to presimulation-predicted values, comparisons of 

simulation to image values are also given in Table G2. Note that percentage absolute deviations 

of simulation from image values are notsubstantially higher--and, in fact, are in some cases 

lower--than deviations of simulation from presimulation-predicted values. 

183 

PRECEDING PAGE BLANK NOT FILMED 
Table G1. Percentage deviation of simulation values from presimulation- I predicted values of the spatial-pattern indices. 

Percentage Deviationa 

Interface 

Quad Quarter Length Adj-0 Adj-1 Adj-2 Adj-3 Adj-4 Pixels 

Late Lafourche, salt 

Leeville Nw 

NE 

SE 

sw 

NE 

SE 

sw 

Caminada Pass Nw 

NE 

SE 

sw 

Bay Tambour Nw 

NE 

SE 

sw 

Pelican Pass Nw 

NE 

SE 

sw 

Mink Bayou Nw 

Early Lafourche, salt 

Grand Bayou Nw 

du Large NE 

SE 

sw 

Lake La Nw 

Graisse NE 

Central Isles Nw 

Dernieres NE 

SE 

sw 

Cocodrie Nw 

NE 

SE 

SW 

NE 

SE 

sw 

Dog Lake Nw 

-3,21 10.53 

-1.09 -19.05 

-3.45 60.00 

7.07 -21.73 

4.37 -5.58 

-3.23 -28.00 

2.13 -30.77 

5.94 -28.26 

-5.37 7.04 

100.00 33.33 

5.68 -15.87 

1.80 -17.16 

1.06 -10.57 

11.63 -12.50 

10.45 -24.73 

58.93 -85.71 

2.75 -35.29 

10.61 -15.09 

-0.59 8.00 

23.12 75.00 

2.62 -16.18 

8.82 -23.96 

4.30 -18.58 

2.01 5.98 

11.91 -66.67 

100.00 -75.00 

-2.37 -28.40 

-9.81 18.75 

-0.42 -21.43 

1.76 -20.00 

-2.09 -63.64 

-1 0.84 0.00 

-4.85 -22.22 

-0.97 -28.75 

6.20 -35.00 

5.88 -10.00 

18.43 13.33 

1.17 19.05 

-4.86 

-1.96 

-1.55 

19.67 

-0.90 

4.87 

-2.77 

4.96 

16.40 

57.14 

4.87 

1.63 

10.18 

23.08 

6.25 

-8.04 

26.36 

21.21 

-6.43 

-1.80 

1.39 

13.38 

10.92 

5.71 

4.76 

-5.68 

5.15 

35.19 

15.63 

-2.53 

1.64 

-1 6.73 

-9.10 

-5.88 

0.00 

-0.25 

6.22 

-5.68 

184 

-2 63 

0.59 

-6.53 

17.93 

-2.74 

9.99 

-4.44 

-0.31 

3.64 

-5 0 59 

250.00 

16.40 

3.37 

-2.05 

19.63 

14.36 

46.43 

8.56 

8.57 

25.00 

15.30 

9.36 

12.91 

8.54 

21.47 

39.02 

4.37 

-1 9.42 

-4.00 

33.61 

3.51 

-0.57 

-7.96 

-4.96 

5.72 

0.79 

7.63 

2.76 

-2 e 72 

-1.65 

-2.36 

-0.28 

2.78 

4.89 

5.27 

3.76 

-1 1.95 

341.67 

7.98 

9.08 

-0.21 

16.22 

4.46 

186.31 

11.55 

4.05 

25.23 

-0.52 

-0.73 

19.02 

-1.99 

-1 2.22 

9.52 

313.70 

3.61 

-1 3.46 

-12.86 

10.24 

5.75 

-4.15 

-9.55 

-1.91 

5.53 

2.42 

13.04 

12.02 

2.38 

0.57 

2.42 

-1.93 

-0.20 

-2.37 

2.94 

-2.58 

-1.26 

0.20 

-0.11 

-0.62 

-0 e 77 

-0.04 

-0.58 

-0.61 

-0.50 

-0.33 

-0.35 

-0.14 

-0.36 

-0 e 93 

-1.53 

0.01 

-0.43 

-0.66 

-0.02 

0.29 

0.07 

-0.20 

-2.65 

0.72 

0.94 

2.03 

-0.18 

-1.35 

-4.58 

-4.39 

-8.85 1 

20.37 

16.74 

5.07 I 

0.00 

-1.16 

-170g1 0.70I 

5.27 

-2.27 

-2.52 

-0.28 

0.00 

1.30 

1 .15 

0.791 

-0 0 55 

1.51 

-0.28 

-0.40 

1.68 

0.14 

0.00 

-1.28 1 

-1.13 

-5.84 

-6.45 I 

-0.56 

2.03 

3.22 

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Table G1. (conk. ) 

Percentage Deviationa 

Interface 

Quad Quarter Length Adj-0 Adj-1 Adj-2 Ad]-3 Adj-4 Pixels 

Late Lafourche, brackish 

Lake Bully 

Camp 

Nw -1.50 

NE -4.12 

SE -3.23 

sw 1.59 

Nw 4.97 

NE 5.02 

SE -1.96 

SW -1 0.58 

SE -0.75 

sw -0.21 

SE -4.39 

sw 3.84 

sw -5.77 

-5.33 

-50.00 

-12.00 

18.18 

-26.79 

-23.08 

-93.62 

-28.19 

-29.17 

-25.44 

-8.16 

18.75 

3.76 

-1.31 

-3.92 

-8.42 

5.79 

3.12 

6.22 

31.28 

13.88 

3.76 

-10.26 

2.70 

-11.75 

-2.71 

-1 -05 

-5.56 

-1.09 

-1.71 

8.33 

2.61 

-0.23 

-21.38 

8.39 

-1 5.75 

-3.63 

17.95 

-5.24 

-1.66 

-1.88 

0.54 

2.88 

6.01 

9.28 

-3.72 

-2.48 

-1 4.02 

-5.28 

2.70 

3.59 

0.27 

0.52 

1.73 

2.97 

-0.92 

-3.80 

-1.25 

1.84 

2.81 

0.14 

0.12 

1.90 

-0.51 

6.54 

0.98 

5.62 

1.89 

0.53 

-1 .81 

0.19 

1.09 

-42.14 

-0.57 

0.00 

0.66 

-3.47 

-5.84 

Golden Meadow 

Farms 

Bay L'Ours 

Three Bayou 

Golden Meadow 

Bay 

Early Lafourche, brackish 

Lost Lake Nw 3.62 

NE -0.90 

SE 0.61 

sw -8.32 

Lake Mechant Nw 8.21 

NE -4.02 

SE 3.13 

sw 1.93 

Bayou Sauveur Nw -3.15 

NE -3.51 

SE 5.41 

sw 3.76 

Lake Quitman NE 6.92 

SE 0.53 

sw 2.81 

Dulac NE -3.45 

SE 

Montegut SE -4.27 

sw -2.86 

-13.85 

-11 .ll 

-16.92 

-37.50 

8.99 

-1 8.75 

15.52 

-4.97 

-20.00 

-36.11 

-19.64 

-1 9.57 

-50.00 

2.86 

-36.84 

-28.57 

11.13 

-2.89 

-3.41 

-12.18 

12.46 

-12.28 

1.94 

3.29 

-6.52 

-3.60 

-0.29 

5.99 

4.60 

0.20 

4.87 

-9.59 

1.15 

-2.21 

2.88 

-6.41 

3.50 

-3.01 

4.88 

4.41 

-2.03 

-1 -03 

5.47 

9-21 

4.91 

-0.43 

0.00 

-4.58 

5.00 

1.97 

4.98 

-7.92 

-2.94 

3.95 

2.93 

3.41 

2.28 

2.71 

16.98 

4.80 

14.94 

1.47 

10.08 

5.89 

-1.64 

-0.08 

-3.61 

4.58 

-0.98 

1.52 

-0 e 52 

-0.08 

3.12 

5.00 

-7.46 

-2.52 

-4.57 

-0.22 

-3.27 

1.53 

0.00 

3.27 

0.17 

4.92 

2.88 

-3.53 

-3.14 

28.13 

46.15 

-0.71 

3.66 

11.17 

0.66 

0.67 

-0.89 

-7.50 

35.71 

-1 2.50 

1.21 

-1 1.62 

-7.00 

2.20 

-4.69 

-2 26 

2.47 

1.98 

-1.87 

2.50 

Mean 5.07 -15.40 3.39 7.56 14.52 -0.14 0.76 

I 

I 

I 

185 
Table G2. (A) Mean percentage deviation and (B) mean absolute 

percentage deviation of simulation from image values 

and presimulation predictions from simulation values 

of spatial-pattern indices. 

A. Percentage deviation 

Mean Stand. Dev. 

Spatial Simulation Pre-sim-pred Simulation Pre-sim-pred 

Pattern from from from from 

Index Image Simulation Image Simulation 

Interface 12.28 18.80 5.07 18.91 

Adj-0 105.15 393.28 -1 5.40 28.62 

Adj-2 -14.28 19.28 7.56 31.80 

Adj-3 -3.50 39.44 14.52 59.38 

Adj-4 -4.44 8.67 -0.14 2.32 

Border 

water pix 29.10 44.09 0.76 9.62 

Adj-1 172.34 191.04 3.39 12.21 

B. Percentage absolute deviation 

Mean Stand. Dev. 

Spatial Simulation Pre-sim-pred Simulation Pre-sim-pred 

Pattern from from from from 

Index Image Simulation Image Simulation 

Interface 14.54 8.29 17.08 17.72 

Adj-0 189.14 25 e 68 359.97 19.76 

Adj-1 172.34 8.46 191.04 9.38 

Adj-2 19.21 11.72 14.30 30.50 

Adj-3 32.43 18.10 22.37 58.38 

Adj-4 5.59 1.63 7.96 1.64 

Border 

water pix 38.13 4.40 36.44 8.58 

1 

I 

1 

1 

I 

I 

1 

1 

1 

PI 

1 

1 

I 

1 

1 

1 

I 

1 

I 
Appendix H 

Levels of Disintegration by USGS Topographic Map 

in 1956 and 1978 Data Compiled by Leibowitz (LSU) from 

Maps by Wicker (1980) with Annual Trend 

Table H1. Levels of disintegration in 1956 and 1978 for the areas covered by each U.S. 

Geological Survey topographic map corresponding to our study sites, and annual 

trend in DL. 

187 
Table H1. Levels of disintegration (DL, water area as percentage 

total area of site) in 1956 and 1978 for the areas 

covered by each U.S. Geological ,Survey topographic map 

corresponding to our study sites , and annual trend in 

DL . 

Year 

Quadrangle 1956 1978 

Annual 

Trend 

Late Lafourche. salt 

Leeville 20.348 34.866 

Mink Bayou 16.195 32.606 

Caminada Pass 74.764 78.267 

Bay Tambour 68.500 74.506 

Pelican Pass 88.324 91.086 

Early Lafourche, salt 

Grand Bayou 

du Large 62.358 65.693 

Lake LaGraisse 93.909 96.826 

Central Isles 

Dernieres 84.760 87.623 

Cocodrie 50.914 63.774 

Dog Lake 38.994 52.395 

Late Lafourche, brackish 

Lake Bully Camp 4.294 26.711 

Golden Meadow 

Farms 16.421 41.627 

Bay L'Ours 40.193 49.211 

Three Bayou Bay 24.009 40.360 

Golden Meadow 7.502 16.180 

Early Lafourche, brackish 

Lost Lake 29.838 35.709 

Lake Mechant 31.240 54.635 

Bayou Sauveur 10.813 25.621 

Lake Quitman 31.498 44.509 

Dulac 13.314 32.425 

Montegut 9.038 33.700 

-0.659 

-0.745 

-0.159 

-0.273 

-0.125 

-0 151 

-0.132 

-0.130 

-0.584 

-0.609 

-1 .018 

-1.145 

-0.409 

-0.743 

-0.394 

-0.266 

-1.063 

-0.673 

-0.591 

-0.868 

-1.121 

Note: Mean annual trend, late Lafourche lobe = -0.567% 

Calculated by the authors from original data compiled by a 

Liebowitz (LSU, private communication, 1988) from maps by Wicker 

(1980). 

Mean annual trend, early Lafourche lobe = -0.563% 

PRECEDING PAGE BLANK NOT FILMED 

189 
Appendix I 

Shrimp Catch, 1960-1987, in Barataria 

and Terrebonne-Timbalier Bays 

Table 11. Shrimp catch in Barataria Bay and Terrebonne-Timbalier bays, annual rainfall, 

and number of hours between April 9 and 30 when temperatures were below 

20°C. 1960-1987. 

191 

PRECEDING PAGE BLANK NOT FILMED 
Table 11. Shrimp catch (pounds) in Barataria Bay (area = 57,709 

acres) and Terrebonne-Timbalier bays (130,101 acres), 

annual rainfall (inches), and number of hours between 

April 9 and April 30 when temperatures were below 2OoC, 

by year, from 1960 through 1987. 

Catch Rainfall 

Year Barataria Terr-Timb Barataria Terr-Timb Hours 

1960 3,145,172 4,415,118 

1961 1,331,299 2,887,287 

1962 1,407,457 2,507,703 

1963 3,043,799 5,476,282 

1964 1,386,736 2,565,840 

1965 3,625,101 4,283,412 

1966 3,356,306 3,697,933 

1967 4,947,377 6,376,487 

1968 5,538,431 6,759,208 

1969 5,343,343 7,030,628 

1970 6,101,020 6,549,845 

1971 6,243,131 9,276,749 

1972 6,015,595 6,900,478 

1973 3,703,811 5,848,112 

1974 5,159,804 3,935,266 

1975 2,503,492 5,311,733 

1976 7,070,085 8,781,531 

1977 5,480,952 11,011,931 

1978 4,084,009 8,985,448 

1979 3,637,205 6,377,519 

1980 3,340,586 3,638,863 

1981 5,185,351 8,845,254 

1982 5,564,216 8,248,135 

1983 5,488,142 5,937,343 

1984 6,028,856 6,968,363 

1985 3,965,987 4,217,895 

1986 8,532,259 4,736,575 

1987 6,463,704 5,046,182 

56.2 

73.4 

40.4 

69.3 

72.2 

60.6 

81 .9 

65.3 

50.8 

56.9 

55.7 

59.4 

62.2 

77.9 

56.2 

71.4 

47.7 

63.9 

65.1 

72.8 

76.8 

50.7 

67.0 

61.8 

61.8 

68.4 

49.4 

70.1 

46.4 

78.6 

37.0 

51.4 

62.3 

59.0 

72.6 

61 .7 

52.0 

58.0 

72.1 

64.5 

70.9 

73.4 

62.7 

72.6 

58.4 

79.6 

61 .l 

75.3 

74.6 

54.6 

67.7 

63.5 

64.5 

70.1 

59.7 

65.9 

32 

148 

37 

19 

100 

0 

34 

0 

0 

4 

27 

11 

0 

137 

18 

95 

0 

9 

49 

0 

92 

0 

80 

205 

86 

61 

7 

103 

Note: Barataria Bay is associated with the late Lafourche lobe, 

and Terrebonne and Timbalier bays are associated with the early 

Lafourche lobe. The rainfall station used for the Barataria area 

was Houma, and the station used for the Timbalier-Terrebonne area 

was primarily Golden Meadow, supplemented by Galiano and Paraday. 

Shrimp catch data were compiled from catch-by-area data of the 

National Marine Fisheries Service, Miami, Florida (G. Davenport, 

private communication, 1988). Rainfall data were provided by 

Robert Muller of Louisiana State University (private communication, 

1988). Hours of temperatures below 2OoC were obtained from Barrett 

and Gillespie (1975) and B. Barrett of the Louisiana Department of 

Wildlife and Fisheries (private communication, 1988).