In Cooperation with Minerals Management Service, Gulf of Mexico Outer 

Continental Shelf Region 

Influence of the Houma Navigation Canal on 

Salinity Patterns and Landscape Configuration 

in Coastal Louisiana 

Open-File Report 2008–1127 

U.S. Department of the Interior 

U.S. Geological Survey 
Influence of the Houma Navigation Canal on 

Salinity Patterns and Landscape 

Configuration in Coastal Louisiana 

By Gregory D. Steyer1

, Charles Sasser2, Elaine Evers3, Erick Swenson3, Glenn Suir4, and 

Sijan Sapkota5 

1

U.S. Geological Survey 

National Wetlands Research Center 

Coastal Restoration Field Station 

Baton Rouge, LA 70894 

2

Agricultural Center, School of Coast and Environment 

Louisiana State University 

Baton Rouge, LA 70803 

3

Coastal Ecology Institute, School of Coast and Environment 

Louisiana State University 

Baton Rouge, LA 70803 

4

IAP World Services, Inc. 

700 Cajundome Blvd. 

Lafayette, LA 70506 

5

U.S. Geological Survey 

National Wetlands Research Center 

Lafayette, LA 70506 

Open-File Report 2008–1127 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

DIRK KEMPTHORNE, Secretary 

U.S. Geological Survey 

Mark D. Myers, Director 

U.S. Geological Survey, Reston, Virginia 2008 

For product and ordering information: 

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS 

For more information on the USGS—the Federal source for science about the Earth, 

its natural and living resources, natural hazards, and the environment: 

World Wide Web: http://www.usgs.gov 

Telephone: 1-888-ASK-USGS 

Suggested citation: 

Steyer, G.D., Sasser, C., Evers, E., Swenson, E., Suir, G., and Sapkota, S., 2008, Influence of 

the Houma Navigation Canal on salinity patterns and landscape configuration in coastal 

Louisiana: an interagency collaboration: U.S. Geological Survey Open-File Report 2008-1127, 

190 p. 

Front cover image: The Houma Navigation Canal with 3-kilometer buffer zones on either sides of 

the Canal. The Houma Navigation Canal is located in Terrebonne Parish, Louisiana. Image 

source: 2000 Thematic Mapper imagery. Scale: 1:215,000. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

endorsement by the U.S. Government. 

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
Contents 

Introduction .................................................................................................................................1

Study Site Description................................................................................................................3

Salinity Assessment ...................................................................................................................6

Salinity Literature Review...........................................................................................................6 

Forcing Function Studies ........................................................................................................6 

Coastwide Patterns ...............................................................................................................11 

Local Patterns and Canal Effects..........................................................................................11 

Salinity Effects on Vegetation...................................................................................................19 

Historical Salinity Data Collection and Trend Analyses............................................................19 

Methods ................................................................................................................................19 

Results ..................................................................................................................................28 

Salinity Pulse Analysis .............................................................................................................44 

Long-Term Data Record........................................................................................................44

Short-Term Data Record.......................................................................................................45

Salinity Summary .....................................................................................................................70 

Landscape Metrics....................................................................................................................73 

Fragmentation Introduction ......................................................................................................73 

FRAGSTATS Method Development.........................................................................................74 

Marsh Fragmentation Classification System..........................................................................82 

Study Site Spatial Data ............................................................................................................82 

Geospatial Datasets..............................................................................................................82 

Photography Acquisition and Processing..............................................................................82 

Data Processing....................................................................................................................85 

Land-Water Analysis ................................................................................................................86 

Marsh Classification .................................................................................................................90 

Connectivity Assessment.......................................................................................................115 

Buffer Zone ............................................................................................................................115 

Vegetation Zone.....................................................................................................................119 

Buffer-by-Vegetation Analysis................................................................................................123 

Fresh Vegetation Type........................................................................................................123 

Intermediate Vegetation Type .............................................................................................124 

Brackish Vegetation Type ...................................................................................................125 

Salinity Vegetation Type......................................................................................................126 

Summary of Buffer-by-Vegetation Interactions....................................................................127 

Hotspots of Landscape Change.............................................................................................127 

Synthesis .................................................................................................................................133 

Salinity—Coastwide ...............................................................................................................133 

Salinity—Houma Navigation Canal Area................................................................................133 

Salinity and Connectivity ........................................................................................................134 

FRAGSTATS Method Assessment ........................................................................................135 

Landscape Assessments .......................................................................................................135

Acknowledgments ..................................................................................................................138 

References Cited.....................................................................................................................139 

Appendix 1: Salinity Level Plots ............................................................................................148

iv

Appendix 2: FRAGSTATS Classification Criteria and Thresholds .....................................184 
Glossary...................................................................................................................................185 

Figures 

1. Map of the major navigation channels in coastal Louisiana....................................................2 

2. Location map of the Houma Navigation Canal centerline and project area boundary ............4 

3. General vegetation zones for the Houma Navigation Canal study area over time..................5 

4. Schematic of an estuarine system illustrating the major pathways of freshwater and coastal 

ocean water inputs to the system..........................................................................................7 

5. Plots of measured monthly mean salinity and predicted monthly salinity .............................10 

6. Summary of saltwater intrusion model results for the Houma Navigation Canal ..................14 

7. Summary of saltwater intrusion model results for Bayou Petite Caillou................................15 

8. Map of salinity outlets, pathways along the Houma Navigation Canal, and various salinity 

stations ...............................................................................................................................17 

9. Chloride exceedance (days per year) from Houma water treatment plant............................18 

10. Map of salinity stations in the east Louisiana coastal marshes...........................................22 

11. Map of salinity stations in the west Louisiana coastal marshes..........................................23 

12. Plot of the relationship between salinity in grains per gallon and salinity in parts per 

thousand .............................................................................................................................25 

13. Plot of the relationship between salinity measured by the U.S. Army Corps of Engineers 

and the Louisiana Department of Wildlife and Fisheries ....................................................26 

14. Plot of surface salinity against bottom salinity for Louisiana Department of Wildlife and 

Fisheries discrete shrimp sampling program in area 1........................................................27 

15. Distribution of data series lengths for the salinity datasets used in the final analysis .........29 

16. Summary of dataset lengths by basin.................................................................................30 

17. Time series plot of daily and 90-day running mean salinity from Grand Terre station .......32 

18. Time series plot of daily and 90-day running mean salinity from Cameron station ............33 

19. Time series plot of daily and 90-day running mean salinity from Bayou Grand Caillou .....34 

20. Time series plot of daily and 90-day running mean salinity from Little Lake station ..........35 

21. Time series plot of daily mean salinity from Houma Navigation Canal at Crozier ..............36 

22. Average salinity for the period of record for the 44 stations used in the final analysis........39 

23. Summary of salinity trend analysis. ....................................................................................42 

24. Summary of salinity change in parts per thousand over the period from 1975–98 .............43 

25. Plot of salinity level data for Bayou Petite Caillou at Cocodrie............................................46 

26. Plot of salinity level data for Bayou Petite Caillou near Boudreaux Canal ..........................47 

27. Plot of salinity level data for Bayou Grand Caillou near Dulac............................................48 

28. Plot of salinity level data for Bayou Terrebonne near Bourg...............................................49 

29. Plot of salinity level data for the Houma Navigation Canal near Crozier.............................50 

30. Plot of salinity level data for the Gulf Intracoastal Waterway at Houma..............................51 

31. Map of stations used in the salinity pulse analysis .............................................................52 

32. Schematic of the three coast-inland transects in the Houma Navigation Canal..................56 

33. Time series plot of hourly and 25-hour filtered salinity from Bayou Terrebonne.................57 

34. Time series plot of hourly and 25-hour filtered salinity from Bayou Grand Caillou..............58 

35. Time series plot of hourly and 25-hour filtered salinity from the Houma Navigation Canal.59 

36. Time series plot of hourly and 25-hour water level from Bayou Terrebonne.......................60 

37. Time series plot of hourly and 25-hour water level from Bayou Grand Caillou ...................61 

38. Time series plot of hourly and 25-hour water level from the Houma Navigation Canal ......62 

v
39. Examples of spectral density estimates for water level.......................................................63 

40. Examples of spectral density estimates for salinity.............................................................64 

41. Time series plot of filtered discharge in Bayou Grand Caillou and the Houma Navigation 

Canal...................................................................................................................................66 

42. Mean salinity pulse magnitude for stations in the Houma Navigation Canal.......................67 

43. Mean salinity pulse coast-inland decrease for stations in the Houma Navigation Canal ....68 

44. Mean salinity pulse duration, in days, for stations in the Houma Navigation Canal ............69 

45. Plot of salinity amplitude at Falgout Canal versus discharge through the Houma Navigation 

Canal...................................................................................................................................70 

46. Summary of salinity records in the Houma Navigation Canal area.....................................72 

47. Examples from the Dulac quadrangle of the classification scheme ....................................75 

48. Dulac digital orthophoto quarter quadrangle.......................................................................76 

49. Preliminary FRAGSTATS marsh fragmentation and configuration classification................81 

50. Salinity monitoring station, dredge history, and river mile locations....................................84 

51. Houma Navigation Canal project regions ...........................................................................88 

52. The 1958, 1968/69 and 1998 land and water classified images .........................................89 

53. Water percentage per grid tile for 1958, 1968/69, and 1998...............................................94 

54. Largest patch index per grid tile for 1958, 1968/69, and 1998............................................95 

55. Percentage of landscape difference per tile among 1958, 1968/69, and 1998...................96 

56. Largest patch index difference per tile among 1958, 1968/69, and 1998 ...........................97 

57. Cohesion difference per tile among 1958, 1968/69, and 1998 ...........................................98 

58. Patch density difference per tile among 1958, 1968/69, and 1998 .....................................99 

59. Landscape shape index difference per tile among 1958, 1968/69, and 1998...................100 

60. Clumpy difference per tile among 1958, 1968/69, and 1998 ............................................101 

61. Category classification for 1958, 1968/69, and 1998........................................................104 

62. Category difference per tile among 1958, 1968/69, and 1998..........................................105 

63. Configuration classification for 1958, 1968/69, and 1998 .................................................106 

64. Configuration difference per tile among 1958, 1968/69, and 1998 ...................................107 

65. Fragmentation classification for 1958, 1968/69, and 1998 ...............................................112 

66. Marsh degradation classification for 1958, 1968/69, and 1998.........................................113 

67. Map of buffers and vegetation zones within the Houma Navigation Canal.......................116 

68. Tile summary for each buffer by year, along with totals for each buffer zone...................117 

69. Average largest patch per tile for buffers in the project area ............................................118 

70. Average cohesion value per tile for buffers in the project area.........................................119 

71. A section of the 1998 land change image with a vegetation change layer........................120 

72. Percentage of tiles in each marsh class by vegetation type .............................................121 

73. Average largest patch per tile for vegetation zones within the project area......................122 

74. Average cohesion index per tile and vegetation zones.....................................................123 

75. Marsh classification by fresh vegetation zone, buffer, and years......................................124 

76. Marsh classification by intermediate vegetation zone, buffer, and years..........................125 

77. Marsh classification by brackish vegetation zone, buffer, and years. ...............................126 

78. Marsh classification by salt vegetation zone, buffer, and years. .......................................127 

79. Map of fragmentation difference per tile between 1958 and 1998 ....................................129 

Tables 

1. Summary of regression results to predict salinity in the Barataria system..............................9 

2. Results of the Kendall-Tau test on monthly mean salinity ....................................................12 

vi
3. List of stations used in the final analysis...............................................................................21 

4. Comparison of daily mean salinity versus daily 8 a.m. salinity readings...............................28 

5. Summary of dataset lengths, by basin, for the 44 salinity stations used for the final analysis

............................................................................................................................................30 

6. Summary statistics for the stations used in the final analysis ...............................................38 

7. Results of salinity trend analysis using time as the independent variable ............................41 

8. Results of salinity trend analysis using time as the independent variable from 1975 to 1998

............................................................................................................................................44 

9. Data availability for the salinity “spike” analysis in the Houma Navigation Canal for year 2003

............................................................................................................................................53 

10. Correlation between filtered salinity and water level...........................................................65 

11. Dulac sample area shift and scale analyses.......................................................................77 

12. FRAGSTATS classification system metric descriptions......................................................78 

13. Status of project-wide available photography .....................................................................83 

14. Area of total project landscape and feature classes for all project land-water images........87 

15. Total area lost and land loss rates at various spatial and temporal scales .........................87 

16. Project-scale landscape metrics .........................................................................................90 

17. Summary statistics of core class metrics............................................................................92 

18. Summary count of category and configuration classes.....................................................103 

19. Marsh classes and the classification system used for interpretation of aerial photography

..........................................................................................................................................108 

20. Summary of fragmentation class groupings for the three time periods.............................108 

21. Frequency and percentage of tiles within each region for the three project dates. .........110 

22. Average primary metric values by year and orientation to the Houma Navigation Canal. 111 

23. Change in classification from 1968 to 1998 for all 1958 class ‘1’ tiles ..............................114 

24. Explanation of buffer intervals ..........................................................................................115 

vii
Conversion of International System of Units (SI) used in this report to U.S. Units 

Unit Multiply By To obtain 

Length 

centimeter (cm) 0.3937 inch (in.) 

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi) 

meter (m) 1.094 yard (yd) 

Area 

hectare (ha) 2.471 acre 

square kilometer (km2) 247.1 acre 

square meter (m2

) 10.76 square foot (ft2) 

hectare (ha) 0.003861 square mile (mi2

) 

square kilometer (km2

) 0.3861 square mile (mi2) 

Volume 

liter (L) 33.82 ounce, fluid (fl. oz) 

liter (L) 0.2642 gallon (gal) 

cubic meter (m3

) 264.2 gallon (gal) 

cubic decimeter (dm3

) 0.2642 gallon (gal) 

cubic meter (m3

) 35.31 cubic foot (ft3) 

cubic meter (m3

) 1.308 cubic yard (yd3) 

Flow rate 

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s) 

Land loss/gain rate 

Square kilometer per year 

(km2

/yr) 

247.1 acre per year (acre/yr) 

Square kilometer per year 

(km2

/yr) 

0.3861 square mile per year (mi2

/yr) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 

°F=(1.8×°C)+32 

Vertical coordinate information is referenced to the insert datum name (and abbreviation) here, for instance, “North 

American Vertical Datum of 1988 (NAVD 88)” 

Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or gram per liter (g/L) 

Grains per gallon may be converted to parts per thousand (ppt) as follows: 

Grains per gallon=62.5×ppt 

viii
Influence of the Houma Navigation Canal on 

Salinity Patterns and Landscape 

Configuration in Coastal Louisiana 

By Gregory D. Steyer, Charles Sasser, Elaine Evers, Erick Swenson, Glenn Suir, and 

Sijan Sapkota 

Introduction 

Coastal Louisiana is a dynamic and ever-changing landscape. From 1956 to 2004, over 

297,000 ha of Louisiana’s coastal wetlands have been lost because of a combination of natural and 

human-induced activities (Barras, 2006). Subsidence, sea-level rise, freshwater and sediment 

deprivation, saltwater intrusion, the dredging of oil and gas canals, navigation canals, shoreline 

erosion, and herbivory are all contributors to wetland loss in Louisiana. Separating the influences 

of each of these factors is extremely difficult because of the wide range of spatial and temporal 

scales over which they occur. Direct impacts associated with an immediate physical conversion of 

habitat have been well described for coastal Louisiana (Boesch, 1982; Turner and Cahoon, 1988); 

however, the indirect impacts that are subtle and operate over longer time horizons (such as salinity 

intrusion) have been difficult to discern. 

The distribution of marsh vegetation is strongly affected by salinity (Fuller and others, 

1995), with changes in species composition occurring in response to salinity changes (Visser and 

others, 2002). Vegetation distributions are commonly used as an indicator of salinity regime, when 

salinity data are not available. Connectivity of the landscape can greatly influence vegetation 

distributions, since the degree of connectivity influences movement of water from primary sources 

(canals, bayous, lakes, bays) to interior estuarine marshes. Saltwater intrusion into marshes can be 

increased by the location of straight and deep canals that connect the coast with the interior marshes 

(Gosselink, 1984). Thus, the extent of connectivity, percentage of water in the marsh, and the 

configuration of water bodies within the marsh landscape are useful indicators to evaluate marsh 

conditions (Sasser and others, 2002). 

Ten major Federal navigation canals—up to 13.7 m deep and 304.8 m wide —have been 

constructed in coastal Louisiana since the mid-1800s (fig. 1; Good and others, 1995). These 

channels are partly responsible for the severity of coastal wetland loss that Louisiana is 

experiencing via direct/primary losses and by indirect/secondary losses including saltwater 

intrusion, hydrologic disruption, and shoreline erosion. Louisiana Department of Natural 

Resources (LDNR) has estimated that approximately 23,100 ha of coastal wetland habitat have 

been lost via shoreline erosion of the 10 major Federal navigation canals, with erosion losses 

ranging from 145 percent to greater than 300 percent of the original direct loss of 

approximately6,880 ha associated with construction (Good and others, 1995). Maintenance 

dredging and the deepening of existing channels to accommodate increased and larger vessel traffic 

also increase the movement of more saline waters farther inland (Wang, 1988), further exacerbating 

the problem. Because of the adverse impacts of canals (direct excavation, saltwater intrusion, 

hydrologic disruption), there is a need to better quantify the secondary impacts of navigation canals 

as well as gain a better understanding of the patterns of change and wetland loss in order to 

minimize future impacts. 

1
Figure 1. Major navigation channels in coastal Louisiana. 

The objective of this project is to perform an evaluation of the extent that Outer Continental 

Shelf (OCS) waterways and navigation canals have contributed to changes in salinity and wetland 

landscape patterns in coastal Louisiana. Our approach includes (1) assembling and synthesizing all 

available salinity data, salinity management studies, and salinity models for coastal Louisiana; (2) 

conducting temporal and spatial analyses of available salinity data to determine changes in those 

variables relative to pre- and post-navigation canal construction and subsequent deepening events; 

and (3) relating salinity changes to hydraulic connectivity and associated habitat changes over time. 

The last element of our approach includes developing a marsh water configuration classification 

and fragmentation index to classify marsh within the study area into multiple categories based on 

estimates of percentages of marsh and water, configurations of water bodies within the marsh, and 

connectivity of water bodies with selected navigation canals related to the (OCS). This approach 

will help to identify breakup patterns of interior marshes over time and the relationship between 

fragmentation, hydrologic connectivity, and salinity intrusion via straight, deep channels. 

2
Study Site Description 

The study site selected for analysis in this project was the Houma Navigation Canal (HNC) 

area (fig. 2). The canal study site is located in the Terrebonne basin bordered on the north by the 

Gulf Intracoastal Waterway (GIWW); the east by Bayou Terrebonne; the west by Minors Canal, 

Lake de Cade, and Bayou du Large; and extends south to the lower regions of the Bayou Sauveur 

and Lake Quitman quadrangles (USGS). Construction of the HNC began in 1958 and was 

completed by 1962. The canal dimensions were originally 4.6 m deep by 45.7 m wide; however, 

in 1974, the lower reaches near Terrebonne Bay were enlarged to 4.5 m deep by 91.5 m wide. The 

entire length of the HNC is maintenance dredged approximately every 10 years and spot dredged in 

intervening years. The canal is used for many purposes including navigation for the oil and gas 

industry, commercial shipping, and commercial and recreational fishing. 

The Houma Navigation Canal study site contains swamp forest and fresh, intermediate, 

brackish, and saline marshes. Based on historical vegetative change, this area has shown a trend of 

increasing salinity over time (fig. 3), much of which is attributed to the construction of the canal. 

The study site was chosen on the basis of the availability of adequate data (historical salinity data 

and photography) for analysis. Moreover, this site provides a broad spectrum of wetland habitats 

and physical characteristics representing the Deltaic Plain of coastal Louisiana. 

3
Figure 2. The Houma Navigation Canal and project area in coastal Louisiana.

4
Figure 3. General vegetation zones for the Houma Navigation Canal study area in coastal Louisiana 

data provided by the U.S. Geological Survey with original data from O’Neil (1949), Chabreck and other1988, 1997), and Linscombe and others (2001). 

5
Salinity Assessment 

Salinity Literature Review 

Forcing Function Studies 

Water levels and open-water salinity in coastal estuaries are generally controlled by a 

balance between the freshwater input generated from precipitation and runoff and the saltwater 

mixing into the coastal estuaries through the coastal boundary layer. The coastal boundary is in 

turn influenced by exchange with the deeper shelf and Gulf of Mexico waters as well as with the 

freshwater plume from the Mississippi River. These exchanges are bidirectional with significant 

transfers of mass and momentum and also with chemical and geological constituents occurring 

between the shelf and the estuary (Wiseman, 1986). 

Meteorological forcing in estuaries along the northern Gulf of Mexico can be considered in 

terms of (1) exchange between the estuarine waters and the waters in the coastal zone and (2) local 

forcing within the estuary proper. Baumann (1987) attributed the changes in the spatial and 

temporal salinity patterns in the Barataria Estuary to three basic factors: (1) the seasonal 

evapotranspiration and precipitation regime, (2) Mississippi River discharge, and (3) seasonal 

water level cycle. At shorter time scales (a few days), the along-estuary wind stress drives an 

estuarine-shelf exchange, whereas at longer time scales, Ekman convergence/divergence driven by 

the alongshore wind stress drives estuarine-shelf exchanges (Schroeder and Wiseman, 1986). 

Work by Kjerfve (1975) in Caminada Bay, La., demonstrated that the diurnal tidal influence, in 

addition to the wind forcing, can be important in controlling the internal dynamics of these 

systems. The most pronounced effect of wind forcing on the central northern Gulf of Mexico 

systems is the difference between a northerly and a southerly wind. Strong winds from the south 

“push” water towards the coast, forcing water into the estuaries, raising water levels about 0.3–0.5 

m above normal. Conversely, winds from the north force water out of the estuaries, depressing the 

water levels 0.3–0.5 m below normal. The “set up” of water usually develops as a cold front 

approaches the area from the west and the southerly winds pile water along the coast; after the front 

passes the winds shift to a more northerly direction. This situation results in a rapid drop in the 

estuarine water levels as described for the Chandeleur-Breton Sound (Hart and Murray, 1978) and 

for Fourleague Bay (Perez and others, 2000). These events result in substantial fluxes of water into 

and out of estuarine systems and can have dramatic effects on the salinity distribution within the 

system. Thus, the salinity signal in these estuarine systems is fairly complex. A schematic 

detailing the major forcing functions discussed above is shown in figure 4. 

Orlando and others (1993) described the factors influencing salinity in 26 estuarine systems 

in the northern Gulf of Mexico. The Mississippi-Atchafalaya discharge dominates the input in 

Louisiana and in the central portion of the gulf, while the western (Texas) and eastern (Mississippi 

to Florida) portions of the gulf are more heavily influenced by local river flow. In general, the Gulf 

of Mexico is characterized by low precipitation in Texas, higher and uniform precipitation in the 

central gulf, and a distinct wet-dry season in Florida. Evapotranspiration exhibits a fairly uniform 

pattern across the central and eastern gulf. Texas and southern Florida are characterized by high 

evapotranspiration. The surplus shows an overall pattern in which there is a rainfall deficit 

(evaporation exceeds precipitation) in the western part of the gulf (and southern Florida) and a 

rainfall surplus (precipitation exceeds evaporation) in the central portion of the gulf. The overall 

result is that some of the estuaries in the northern gulf have the highest freshwater input per unit of 

estuarine volume (Ward, 1980). Orlando and others (1993) concluded that high Mississippi River 

6
flows reduced the salinities in the lower portion of the estuaries in the central gulf because of 

advection of Mississippi River water into the estuaries. During times of high river flow, the local 

precipitation is unimportant. Conversely, at times of low Mississippi River flow, the salinities in 

the lower bays increase, and local precipitation becomes more important. The MississippiAtchafalaya River flow serves as a primary or secondary source of fresh water to a large proportion 

of the Louisiana estuaries. 

Figure 4. Schematic of an estuarine system illustrating the major pathways of 

freshwater and coastal ocean water inputs to the system. 

7
The salinity of the estuaries around the Gulf of Mexico shows a fairly large range of values 

as a result of the freshwater input distribution. In general, the central gulf estuaries exhibit lower 

salinities because of the effect of the Mississippi River discharge. The south Texas estuaries and 

the south Florida estuaries exhibit the highest salinities as a result of the general pattern of rainfall 

deficits in these locations. The data from Orlando and others (1993) were analyzed by Swenson 

(2003) to detect the salinity changes that occur in the Gulf of Mexico estuaries with changes in 

freshwater input for high and low freshwater input years. The data show a general trend of salinity 

decreases of 2–5 parts per thousand (ppt) for high freshwater input years and increases of 5–7 ppt 

for low freshwater input years. There were a few exceptions to the overall pattern, which were to 

be expected, since the data were limited in spatial and temporal extent. 

The inverse relationship between Mississippi River discharge and Louisiana coastal 

salinities was first pointed out by Geyer (1950). Barrett (1971) and Gagliano and others (1973) 

further described this inverse relationship by using linear statistical models. Wiseman and others 

(1990a) used autoregressive moving average (ARMA) models to analyze the relationship between 

weekly discharge of the Mississippi River and Louisiana coastal salinities. The river discharge 

portion of the models accounted for 30–50 percent of the variance of the observed salinity data 

along the coast. The results were consistent with a conceptual model in which Mississippi River 

discharge alters coastal salinities, which in turn propagate up-estuary and westward along the coast 

(Wiseman and others, 1990b). Swenson and Turner (1998) developed empirical statistical models 

to explain the seasonal isohalines in the Barataria estuary system by using coastal water levels, 

Mississippi River discharge, and local (New Orleans) precipitation from 1980 through 1995. The 

models were able to explain approximately 50 percent of the variance of the observed data (table 1) 

and reproduced the observed monthly salinity pattern over the 1999–2000 drought period quite 

well (fig. 5). 

Swenson and others (2004) investigated the role of climate drivers on salinity in the 

Louisiana coastal zone. They concluded that the extremely high salinities observed during the 

1999–2000 drought period were the result of prolonged drought coupled with low Mississippi 

River discharge and low coastal water levels. The combination of these events resulted in a time 

period characterized by the highest salinities on record. The duration of the high salinities 

observed during the 1999–2000 time period for the Barataria basin was characterized by salinities 

greater than 30 ppt at Grand Terre for 12 months, salinities greater than 20 ppt at St. Marys Point 

for 10 months, and salinities greater than 7.5 ppt at Little Lake for 8 months. This is a fourfold 

increase in the high salinity levels observed in this system. Systems throughout the Louisiana 

coastal zone were characterized by much higher than normal salinities during this time period, 

dramatically illustrating the important role of freshwater input (rainfall as well as river flow) in 

determining Louisiana coastal salinities. 

8
Table 1. Summary of regression results to predict salinity in the Barataria system 

using Mississippi River discharge, precipitation at Louisiana climate region 9, and 

detrended coastal water levels. 

[LDWF, Louisiana Department of Wildlife and Fisheries] 

LDWF S315: Grand Terre

 Overall model, R-square = 0.72 

 Linear portion, R-square = 0.48 

Variable Estimate F-value Probability > F 

 Intercept 19.99 

 Mississippi discharge -0.00029 544.38 0.0001 

 Region 9 precipitation -0.2761 99.56 0.0001 

 Grand Isle water level -0.0329 83.50 0.0001 

 1 month previous salinity +0.5466 354.00 0.0001 

LDWF S317: St. Marys Point

 Overall model, R-square = 0.74 

 Linear portion, R-square = 0.41 

Variable Estimate F-value Probability > F 

 Intercept 9.66 

 Mississippi discharge -0.00024 277.17 0.0001 

 Region 9 precipitation -0.3806 88.96 0.0001 

 Grand Isle water level -0.0065 22.74 0.0001 

 1 month previous salinity +0.6297 330.94 0.0001 

LDWF S326: Little Lake

 Overall model, R-square = 0.63 

 Linear portion, R-square = 0.16 

Variable Estimate F-value Probability > F 

 Intercept 2.916 

 Mississippi discharge -0.00007 47.25 0.0001 

 Region 9 precipitation -0.1522 11.55 0.0009 

 Previous month salinity +0.8728 145.63 0.0001 

 2 months previous salinity -0.4220 7.37 0.0075 

 3 months previous salinity +0.2511 6.96 0.0094 

9
 

Figure 5. Plots of measured monthly mean salinity (solid blue line) and predicted 

monthly salinity (red line with open circles) for Louisiana Department of Wildlife and 

Fisheries (LDWF) station 315, LDWF station 317, and LDWF station 326 in the Barataria 

system. The following models were used: station 315: salinity = 19.99 - 0.00029 Q - 

2761P - 0.0329 WL - 0.5466 S-1; station 317: salinity = 9.66 - 0.00024 Q - 0.3806 P - 

0.00655 WL +0.6297 S-1; and station 326 : salinity = 2.92- 0.00007 Q - 0.1522 P + 

0.8728 S-1 - 0.4220 S-2 + 0.2511 S-3, where Q = total monthly Mississippi River 

discharge (m3

s-1), P = total monthly precipitation (cm), WL = detrended water level at 

Grand Isle (cm), S-1 = salinity (ppt) of previous month, S-2 = salinity (ppt) 2 months 

previous, and S-3 = salinity (ppt) 3 months previous.

10
Coastwide Patterns 

Salinity intrusion into historically fresher wetland areas has been identified as a likely cause 

of large-scale coastal wetland change, deterioration, and subsequent conversion to open water. In 

order to determine whether salinities were increasing throughout the coast, several studies of 

salinity trends in Louisiana coastal marshes and estuaries have focused on broad-scale coastwide 

patterns and trends. Gagliano and others (1970) provided a comprehensive summary of salinity 

statistics of available data for coastal Louisiana from 1946 to 1968 but did not offer a 

comprehensive analysis of the data. Wiseman and Swenson (1988) and Wiseman and others 

(1990b) reported on a study with the objective of analyzing existing data throughout coastal 

Louisiana to determine if there were any long-term trends in estuarine salinity levels. They were 

specifically interested in determining changes related to natural and human-induced factors. 

 Hourly and daily salinity data from the U.S. Army Corps of Engineers (USACE) and 

Louisiana Department of Wildlife and Fisheries (LDWF) over the period 1946–85 were averaged 

to obtain monthly data to remove tidal aliasing in the daily values. The data were first investigated 

by using a linear model that used time and an annual harmonic to remove the seasonal effects. 

Many of the test results had to be discarded since much of the data lacked normality. Wiseman and 

Swenson (1988) reanalyzed the seasonal data by using the Kendall-Tau, a nonparametric test used 

by Hirsch and others (1982) to assess water quality parameters. The Kendall-Tau approach (which 

does not require fulfilling normality or linearity assumptions) tests for the presence or absence of a 

trend. If a significant trend was observed, then the magnitude of the trend was estimated by using 

the Kendall seasonality slope estimator (Hirsch and others, 1982). Although significant trends 

were identified within individual estuaries, many of them showed a significant decrease in salinity; 

however, no consistent pattern of increasing, decreasing, or static salinity trends was noted for the 

overall coast (table 2). Wiseman and Swenson (1988) suggested that the lack of trend was likely 

due to the differences in record length and strong cyclical climate, river discharge, and relative sea 

level variations which may make weak trends hard to detect. Nevertheless, the authors state that 

“extensive coastal degradation and land loss documented over time periods of the same order of 

our data records. If this were caused by a trend in the salinity regime, the present analysis should 

have identified the trend”(Wiseman and Swenson, 1988, p. 121). Similarly, in a coastwide analysis 

of 17 LDWF stations originally located in brackish or saline marshes, Fuller and others (1990) 

found no broad-scale pattern on increasing salinity, but to the contrary, several of the individual 

datasets showed decreasing trends in salinity. Again, Fuller and others (1990) identify short data 

records (most were less than 5 years in duration) as being a major obstacle in detecting trends. 

They also suggest that, because the stations were in salt or brackish marshes, the areas may have 

already been subjected to saltwater intrusion and, additionally, that saltwater intrusion may not be a 

broad phenomenon but rather a more local occurrence. 

Local Patterns and Canal Effects 

Several studies have provided detailed analyses of spatial and temporal salinity patterns on 

the local level. For instance, Van Sickle and others (1976) reported an increasing salinity trend at 

the St. Marys Point station in the Barataria basin from 1961 to 1974; however, Fuller and others 

(1990) did not detect a significant trend at the same site for the time period of 1973–80 because of 

an increasing salinity trend during 1973–77 and a decreasing trend during 1978–80. Also for the 

Barataria basin, analyses by Byrne and others (1976) suggested that salinities in the basin prior to 

the dredging of the Barataria Waterway in 1962 were lower than post dredging activities. Using 

most of the same stations and longer data records, Wiseman and others (1990a) concluded that 

there was a negative (decreasing) trend in mean salinity at the mouth of Barataria Bay and that 

11
there was no trend in the upper reaches of the bay. Significant increases in salinity trends were 

found, however, at the lower end of Bayou Lafourche and in Lake Pontchartrain in southeast 

Louisiana (Wiseman and others, 1990a). Increases in salinities in Lake Pontchartrain also were 

described by Sikora and Kjerfve (1985), who analyzed a 36-year record (from 1946 to 1982). 

Table 2. Results of the Kendall-Tau test on monthly mean salinity. The station, 

years of record, the significance level (alpha), the trend (estimated from the Kendall 

slope estimator) in parts per thousand per year, and the expected change over the 

period of record are listed. 

[Datasets with short records are in bold since these results are questionable. Three-digit stations are those of the Louisiana Department of 

Wildlife and Fisheries (LDWF), and five-digit stations are those of the U.S. Army Corps of Engineers (USACE). These results are from 

Wiseman and Swenson (1988). ppt/yr, parts per thousand per year] 

 

Station 

Years of 

record Alpha 

Trend 

(ppt/year) 

Change over 

period of 

record (ppt) 

719 (Cameron) 17.4 0.006 -0.449 -7.8 

70675 (Mermentau River) 31.6 0.000 -0.010 -0.3 

76800 (Vermilion Lock W on the Gulf 

Intracoastal Waterway) 19.6 0.000 -0.024 -0.5 

76720 (Vermilion Lock E on the Gulf 

Intracoastal Waterway) 29.1 0.025 0.039 1.3 

88850 (Cypremort Point) 24.3 0.012 -0.067 -1.6 

64380 (Bayou Teche at Charenton) 24.4 0.000 -0.009 -0.2 

03720 (Wax Lake Outlet) 10.0 0.012 0.000 0.0 

64450 (Charenton Drainage Canal) 29.0 0.000 -0.008 -0.3 

88600 (Eugene Island) 8.5 0.068 0.131 1.2 

03780 (Morgan City) 18.0 0.000 0.000 0.0 

76343 (Houma Navigation Canal at 

Crozier) 20.3 0.000 -0.012 -0.2 

76320 (Gulf Intracoastal Waterway at 

Houma) 30.8 0.000 0.002 0.1 

76403 (Bayou Terrebonne at Bourg) 19.1 0.003 -0.009 -0.2 

82203 (Larose) 22.9 0.093 -0.003 -0.1 

82350 (Leeville) 21.8 0.000 0.156 3.4 

315 (Grand Terre) 26.3 0.000 -0.242 -6.4 

85683 (North Shore of Lake 

Pontchartain) 3.8 0.002 1.992 7.8 

85650 (Little Woods) 31.9 0.000 0.086 2.8 

221 (Bay Gardene) 12.3 0.010 -0.344 -4.2 

85700 (The Rigolets pass) 24.0 0.061 0.039 0.9 

253 (Sable Island) 4.8 0.095 0.870 4.2 

Most of the studies described above were designed to describe and evaluate salinity patterns 

throughout the coast; however, only a few studies have investigated trends in historical salinity 

records as they relate to waterways or navigation canals and the effects they may have on salinity 

intrusion, wetland change, and wetland loss. It has been estimated that navigation canals, via 

direct and indirect actions, are responsible for 20–50 percent of total wetland loss in coastal 

12
Louisiana (Johnson and Gosselink, 1982; Boesch and others, 1994). Primary losses that are due to 

Federal navigation channel construction have been estimated approximately at 23,500 ha to 38,900 

ha and secondary loss (bankline erosion, saltwater intrusion) estimated at 110,900 ha since the late 

1800s (Good and others, 1995). Wang (1988) developed a two-dimensional, laterally averaged, 

semi-implicit hydrodynamic model coupled with a salt flux model to investigate the salinity 

distribution for various channel dimensions. The study was of limited scope in that it only 

investigated two constructed channels (the Calcasieu Ship Channel [CSC] and the Houma 

Navigation Canal) and one natural channel (Bayou Petite Caillou) and was based only on data 

collected during one season of the year (late fall to early spring) under low to moderate freshwater 

inflow conditions. Despite these limitations, Wang (1988) concluded that, under similar external 

forcings, salt water moves further inland in larger, deeper channels than in smaller, shallower 

channels and also that channel deepening changes the salinity distribution pattern and the degree of 

saltwater intrusion. For instance, model results show that, by doubling the Houma Navigation 

Canal’s depth to 13.2 m, the 5 ppt isohaline moves from 45 km to 80 km inland (fig. 6), whereas 

with a doubling of the depth in Bayou Petite Caillou, a smaller natural channel, to 6 m, the 5 ppt 

isohaline moves only from 20 km to 23 km inland (fig. 7; Wang, 1988). 

13
Figure 6. Summary of saltwater intrusion model results (adapted from Wang, 1988) for 

the Houma Navigation Canal in coastal Louisiana. The top panel shows the salinity 

distribution under the present conditions, and the bottom panel shows the predicted 

salinity distribution that would result from a doubling of the channel depth. 

14
Figure 7. Summary of saltwater intrusion model results (adapted from Wang, 1988) for 

Bayou Petite Caillou in coastal Louisiana. The top panel shows the salinity distribution 

under the present conditions, and the bottom panel shows the predicted salinity 

distribution that would result from a doubling of the channel depth. 

15
Saltwater intrusion via the Houma Navigation Canal (HNC) into the fresh and brackish 

marshes of the Terrebonne basin and the secondary impacts associated with increased salinity are 

of great concern yet very difficult to quantify. Construction of the HNC contributed to the direct 

conversion of 744 haof wetland habitat to open water and spoil banks (Good and others, 1995; T. 

Baker Smith, Inc., 2002; and U.S. Army Corps of Engineers, 2003). Since construction, significant 

erosion has taken place within the HNC (channel widths exceeding 305 m in some areas). The 

erosion, coupled with the direct connectivity of existing natural waterways with the HNC, has 

provided greater opportunities for saltwater intrusion into the Terrebonne marshes and the Houma 

Water Treatment Plant (HWTP) (U.S. Army Corps of Engineers, 2003). Figure 8 illustrates the 

natural pathways through which water flowed prior to the construction of the HNC and all major 

outlets (blow-outs and connections to other canals) that provided direct saltwater pathways from 

the Gulf of Mexico via the HNC. 

Long-term salinity (chloride) records have been kept at the HWTP since the 1940s with 

records indicating periods when chlorides exceed 250 mg/L (approximately 0.4 ppt, fig. 9). Data 

collected after the construction of the Houma Navigation Canal (HNC) show an increase in the 

number of days in which the 250 mg/L limit was exceeded, with a higher occurrence during the 

months of September, October, and November due to the low Atchafalaya River discharge and 

frontal passage events (T. Baker Smith, Inc., 2002). A study by Swarzenski (2003) of the surface 

water hydrology of the Gulf Intracoastal Waterway (GIWW) indicated that the GIWW is a source 

of fresh river water to many coastal Louisiana wetlands. Through prevailing hydraulic gradients, 

this navigation channel captures and distributes water from the lower Atchafalaya River to areas in 

coastal Louisiana 48–80 km east and west of Morgan City. The flow in the GIWW increases with 

increasing stage of the lower Atchafalaya River. Analysis of salinity data indicated that salinity in 

the HNC at Dulac and at Cypremort Point decreased as the Atchafalaya River stage in Morgan 

City increased, with monthly averaged values remaining below 3 ppt whenever monthly averaged 

river stage equaled or exceeded 1.5 meters North American Vertical Datum-1988 (NAVD88) 

(Swarzenski, 2003). In a salinity intrusion/channel deepening/canal lock study for the HNC 

related to the proposed Morganza to the gulf project, a model was created to evaluate the effects 

that deepening the HNC would have on salinities at the HWTP and on the operation of the 

proposed Houma Navigation Canal Lock near Dulac (U.S. Army Corps of Engineers, 2003). 

Model results showed that increasing the channel depth to 5.5 m (6.1 m actual depth,)would 

increase salinities on average by 0.0024 ppt, and increasing the depth to 6.1 m (7 m actual depth) 

would increase salinities by 0.0054 ppt, both figures fairly insignificant considering the variability 

of salinities in the channel (U.S. Army Corps of Engineers, 2003). The depth increases would yield 

a mean HWTP closure rate of 47 and 48 days per year, respectively, a slight increase from a mean 

closure of 46 days from 1981 to 2002 (U.S. Army Corps of Engineers, 2003). 

16
Figure 8. Map of salinity outlets, pathways along the Houma Navigation Canal 

(HNC), and various salinity stations within the project area in coastal Louisiana. 

The light blue lines represent preconstruction natural and constructed waterways, 

the medium blue lines represent post-construction canals, and the yellow circles 

represent salinity outlets along the HNC. 

17
Figure 9. Days per year that chlorides measured greater than 250 mg/L at the Houma 

Water Treatment Plant on the Intracoastal Canal near its confluence with the Houma 

Navigation Canal in coastal Louisiana (T. Baker Smith, Inc., 2002). The completion date 

for the Houma Navigation Canal is indicated by the arrow below the x-axis. 

Salinities in Lake Pontchartrain, a brackish-water estuary in southeast Louisiana, have 

increased in the past decades with the primary cause often cited as the construction of the 

Mississippi River-Gulf Outlet (MRGO), a 120-km-long deep-water channel connecting the lake to 

the Gulf of Mexico. In a preconstruction study, Rounsefell (1964) predicted a salinity increase in 

the lake of 4–5 ppt depending on the amount of freshwater flow entering the lake. Sikora and 

Kjerfve (1985), however, conducted a study using a three-decade salinity record in the lake to 

describe the salinity pattern, determine the effects of the MRGO, and determine which forcing 

functions controlled salinity in the lake. They found no significant trend in salinity mainly as a 

result of large variability in the salinity range (0–15 ppt). In the analysis, an 11-year periodicity 

also was found “probably caused by synoptic meteorologic/hydrologic forcing functions” (Sikora 

and Kjerve, 1985, p. 175) but the authors noted that the 36-year record was insufficient to make 

broad statements regarding these cycles with confidence. Salinities did increase in the lake after 

completion of the MRGO by 0.2 ppt at Pass Manchac in the west to 2.0 ppt at The Rigolets in the 

east. These increases were less than predicted, but no changes in the variation of the salinity pattern 

were found (Sikora and Kjerfve, 1985). 

In the Chenier Plain of coastal Louisiana, there have been many alterations to the natural 

hydrology since the late 1800s, leading to significant losses of wetland habitat through saltwater 

intrusion (Louisiana Department of Natural Resources, 2002). For example, the Calcasieu Ship 

18
Channel (CSC), which runs from the Gulf Intracoastal Waterway (GIWW) south through Calcasieu 

Lake and into the Gulf of Mexico near Cameron, has been maintained for navigation since 1874. 

Original dimensions were 1.5 m deep and 24.4 m wide, with several enlargement events occurring 

in 1903, 1937, and 1968 bringing the current dimensions to 12.2 m deep and 122 m wide at the 

bottom (Wang, 1988; Louisiana Department of Natural Resources, 2002). Historically there was a 

shoal at the mouth of the Calcasieu River which retarded saltwater intrusion into the system, but 

after the initial dredging in 1870 and subsequent widening and deepening of the CSC there has 

been a shift to more saline habitats in the basin (Louisiana Department of Natural Resources, 

2002). Several other channels are maintained in the Chenier Plain, including the Sabine River, 

Mermentau River, and Freshwater Bayou Canal. 

Salinity Effects on Vegetation 

Salinity is a major stressor on wetland plants that strongly influences vegetation 

establishment and spatial distribution. Salinity effect on wetland vegetation is a constant issue in 

coastal Louisiana because processes and activities including subsidence, storm events, river 

channelization, and canal construction can facilitate redistribution of salt water within the estuaries, 

changing salinity levels in established marshes. The widely different ranges of tolerance to salinity 

by wetland plant species are evident in the Louisiana coastal landscape by the presence of broad 

bands of vegetation community types more or less parallel to the coast, which is a response to the 

decreasing salinity gradient from the coast inland. Spartina alterniflora dominates most saline 

marshes in Louisiana and is found in areas with an average salinity of about 15 ppt (Chabreck, 

1972). The less saline brackish marsh is dominated by S. patens, occurring at an average salinity of 

8.5 ppt (Chabreck, 1972) but with growth inhibited at salt concentrations greater than about 5 ppt 

(Pezeshki and Delaune, 1993). In freshwater marshes dominated by Panicum hemitomon or 

Sagittaria latifolia, salinity increases cause a species shift to more salt-tolerant plants such as 

Eleocharis spp., Scirpus sp., and S. patens or cause a dramatic loss to open water. Experiments 

under both controlled settings and field conditions confirmed that P. hemitomon has little tolerance 

of salt and grows only in a nearly salt-free environment (McKee and Mendelssohn, 1989; Flynn 

and others, 1995; Howard and Mendelssohn, 1999a, b); however, different genotypes of this 

species have shown different salinity resistance thresholds (Hester and others, 1998). In recently 

developing marshes in the Atchafalaya River Delta, freshwater vegetation species that have low 

salinity tolerance colonized new land. A study by Holm and others (2001) pointed out the potential 

importance of salinity in the temporal and spatial distribution of plants on delta land, concluding 

that periodic salinity incursions negatively impact the development of S. latifolia marshes in the 

Atchafalaya River Delta. Incursions of higher salinity water periodically affect newly colonized 

land and affect the composition and productivity of colonizing plant species. 

Historical Salinity Data Collection and Trend Analyses 

Methods 

Historical salinity data in coastal Louisiana have been collected from as many long-term 

stations as possible in order to analyze long-term coastal salinity trends and salinity levels during 

pre- and postconstruction periods, as well as between dredging events, for navigation canals. 

Datasets were collected from several sources including the USACE, Louisiana Department of 

19
Natural Resources (LDNR), the U.S. Environmental Protection Agency STORET database, the 

brown marsh database, and personal data files. At present, during the course of this study, over 

125 historical data files containing salinity information for the coast were gathered and cataloged; 

however, not all files contained useable data. The data obtained were in various formats and were, 

therefore, converted to a standard file format. Data were converted to and reported as salinity in 

parts per thousand, and common start and end dates were added for all datasets. The length of 

salinity record (in years) and daily minimum, mean, and maximum salinity values were calculated 

for all datasets, in addition to long-term trend analyses performed via linear regression. Salinity 

baseline readings for each station were identified via habitat classification overlays, and the 

frequency and duration of salinity spikes were calculated for all datasets. 

Data were processed and inventoried from a total of 125 stations. Many of these stations 

contained only limited salinity data (a few observations over a period of several years) and were 

not used for the final analysis. The final dataset was composed of 44 stations, which covered the 

time period from 1948 through 2003. A list of the stations is presented in table 3, and the station 

distribution is shown in figures 10 and 11. 

The data files were obtained in digital form either directly through the Internet or in the 

form of a data compact disk. These files were transferred to a desktop computer for analysis by 

using Statistical Analysis System software (SAS, 1990a, b, c) or Microsoft EXCEL® (Microsoft 

Corp., 1994). Because all of the data were in time series format (hourly, daily, weekly, or 

monthly), the same basic techniques were used for all sites. The levels of quality assurance/quality 

control (QA/QC) were variable and inconsistent among the various datasets. Therefore, the 

datasets were inventoried and checked for data quality and consistency when needed. Several of 

the datasets obtained from the brown marsh study conducted by Swenson and others (2004) had 

already been through QA/QC procedures and were ready for use upon receipt. 

20
 [LDWF, Louisiana Department of Wildlife and Fisheries; USACE, U.S. Army Corps of Engineers] 

Table 3. List of stations used in the final analysis. 

21

Table 3

Agency

Station 

number Station description

Latitude 

(degree)

Longitute 

(degree)

Start 

year

End 

year

Total 

year

LDWF

LDWF

LDWF

LDWF

LDWF

LDWF

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

USACE

315 Marine lab at Grand Terre

317 St. Marys Point

326 Little Lake

518 Sister Lake

619 Cypremort Point

719 Cameron

1386 Mississippi River at Braithewaite

1420 Mississippi River at Port Sulphur

1480 Mississippi River at Venice

1575 Southwest Pass at mile 9.2

1670 Southwest Pass at east jetty

67890 Vermilion River at Intracoastal City

70600 Mermentau River at Lacassine Refuge

70675 Mermentau River at Catfish Point Structure (North)

70750 Mermentau River at Catfish Point Structure (South)

73472 Calcasieu River saltwater barrier channel (East)

76060 Inner Harbor Canal near Seabrook Bridge, New Orleans

76303 Bayou Petit Caillou near Boudreaux Canal

76305 Bayou Petit Caillou at Cocodrie

76320 Gulf Intracoastal Waterway at Houma

76323 Bayou Grand Caillou near Dulac

76343 Houma Navigation Canal near Crozier

76403 Bayou Terrebonne near Bourg

76480 Wax Lake East Drainage Area at Control Structure

76592 Freshwater Canal at Freshwater Bayou Lock (North)

76600 Schooner Bayou (Inland Waterway) at Control Structure (East)

76680 Schooner Bayou (Inland Waterway) at Control Structure (West)

76720 Gulf Intracoastal Waterway at Vermilion Lock (East)

76800 Intracoastal Waterway at Leland Bowman Lock (West)

76873 Intracoastal Waterway at Gibbstown

76880 Intracoastal Waterway at Calcasieu Lock (East)

81050 Bayou Blue 1 Mile at Grand Bayou Canal

82203 Bayou Lafourche at Larose

82250 Bayou Lafourche at Golden Meadow Floodgate (North)

82300 Bayou Lafourche at Galliano

82750 Bayou Barataria at Barataria

82875 Bayou Barataria at Lafitte

85600 Lake Pontchartrain at Midlake near New Orleans, La

85800 Mississippi River-Gulf Outlet at Shell Beach, La

85850 Breton Sound near Gardner Island

88410 Grand Isle at Coast Guard station

88550 Atchafalaya Bay near Eugene Island

29.2744

29.4231

29.5156

29.2531

29.7136

29.8272

29.2744

29.4667

29.2667

29.2667

28.9000

29.7833

30.0000

29.8500

29.8500

30.2500

30.0167

29.3833

29.2333

29.5833

29.4500

29.5500

29.5536

29.6391

29.5500

29.7500

29.7500

29.7833

29.7833

29.9333

30.0833

29.5167

29.5667

29.3417

29.3667

29.7333

29.6667

30.1833

29.8500

29.6883

29.2476

29.3861

-89.9422

-89.9506

-90.1842

-90.9217

-91.8781

-93.3336

-89.9422

-89.6833

-89.3500

-89.3500

-89.4167

-92.1333

-92.7667

-92.8500

-92.8500

-93.2167

-90.0167

-90.6167

-90.6500

-90.7000

-90.7000

-90.7000

-90.6052

-91.3185

-92.3000

-92.2500

-92.2500

-92.1833

-92.1833

-93.0833

-93.2833

-90.4000

-90.3833

-90.2456

-90.2500

-90.1167

-90.1000

-90.1167

-89.6833

-89.4034

-89.9863

-91.3978

1973

1961

1981

1967

1974

1967

1961

1933

1975

1988

1971

1971

1947

1949

1951

1968

1957

1948

1970

1946

1948

1961

1962

1991

1975

1947

1947

1949

1947

1951

1951

1987

1949

1991

1961

1967

1955

1971

1961

1987

1992

1990

2005

2005

2005

2005

2005

1994

2000

1982

1978

1995

1989

1982

1981

1982

1981

1982

1994

1981

1982

1981

2001

1993

1981

1993

1981

1981

1982

1981

1981

1981

1981

1999

1981

1996

1985

2002

1988

1981

1965

1993

1997

1997

32

44

24

38

31

27

39

49

3

7

18

11

34

33

30

14

37

33

12

35

53

32

19

2

6

34

35

32

34

30

30

12

32

5

24

35

33

10

4

6

6

8
Figure 10. Map of salinity stations in the east Louisiana coastal marshes. Stations with five-digit numbe

Corps of Engineers; stations with three-digit numbers are those of the Louisiana Department of Wildlife a

22
23

Figure 11. Map of salinity stations in the west Louisiana coastal marshes. Stations with five-digit numbers a

Corps of Engineers; stations with three-digit numbers are those of the Louisiana Department of Wildlife and F
The following general procedures were used in the analysis: 

a. Data Input

 The data from the USACE were processed in order to strip the headers from text 

 files to create time series data files that could be used in subsequent analysis. 

b. Data Inventories

 The data inventory included identifying the total observations in each dataset, 

 sampling frequency, and spatial distribution of stations. 

c. Data Conversion

 The following relationship (Grasshoff and others, 1999) was used to calculate 

 salinity from chloride: 

 Salinity (ppt) = 1.80655 x chloride (g/L). 

The relationship between temperature, conductivity, and salinity was used to convert 

conductivity readings to salinity. Algorithms for the calculation are given in Grasshoff and others 

(1999). The authors also stated that there are differences in the salinity calculated by using various 

algorithms on the order of 0.02 ppt. Discrepancies of this small magnitude are considered 

negligible for the present study since the data were recorded from instruments that have accuracies 

on the order of +0.2 ppt. 

The relationship between salinity expressed in grains per gallon (a unit used by the rice 

farmers in western Louisiana) and parts per thousand was investigated by using data downloaded 

from the USACE Lock Operations site. The results (fig. 12) yielded the following equation: 

 Salinity in ppt = 0.016 x salinity in grains per gallon. 

24
Figure 12. Plot of the relationship between salinity in grains per gallon and salinity in 

parts per thousand (ppt) in coastal Louisiana. The results of a regression analysis are 

indicated on the figure. 

d. QA/QC Information

Salinity data values greater than 30 ppt were considered to be suspect. In most cases the 

high salinity values (usually >60 ppt) were coupled with either high (50°C) or low (-5°C) 

temperature readings, indicating an equipment malfunction. These values were set to “missing.” 

The salinity data from the different agencies were compared whenever possible. Swenson 

and Turner (1998), for example, presented data to show that the salinity from LDWF and Louisiana 

Department of Health and Hospitals were comparable. A comparison between USACE and LDWF 

data is shown in figure 13. The relationship is quite reasonable considering that, although the data 

were collected at the same general site on the same day, the samples were not simultaneous. 

Routine salinity data collected by LDWF as part of their shrimp monitoring program were 

used to observe salinity stratification in the open water bodies. Figure 14 presents relationships 

between surface and bottom salinity for the open water in the Pontchartrain-Borgne basin and 

Barataria basin. These data indicate that there is very little stratification in the open-water areas. 

Barrett (1971) stated that stratification in the open waters was rare because of the mixing of the 

water column that was due to shallow depth, tidal actions, winds, and heavy boat traffic. 

Stratification does occur, however, in the deeper bayous and dredged channels such as the Inner 

Harbor Navigation Canal in Lake Pontchartrain (Swenson, 1980), the Houma Navigation Canal 

(Wang, 1988), and the Calcasieu Ship Channel (Wang, 1988). 

25
e. Computation of daily means

Since the data were collected at different sampling frequencies (hourly, daily), they were 

averaged to put all observations on a common interval. All of the data could be merged on a daily 

basis since all sampling intervals were equal to or less than a day. There was some concern over 

the potential incompatibility of using daily means computed from hourly data with daily 

observations. Swenson and Swarzenski (1995) compared daily means computed from hourly data 

and daily data. The results (table 4) indicate that the daily 8 a.m. readings were equivalent to the 

daily means computed from hourly data. Byrne and others (1976) reported similar results for a 

comparison of daily water levels against daily mean water levels (from 3-hour data) in the 

Barataria system. 

f. Salinity Trend Analysis

Linear regressions were run on all salinity datasets by using computed daily mean salinity 

values. Slopes were converted to indicate increases or decreases of salinity in parts per thousand 

per year. The trends were then multiplied by the period of record to obtain the total salinity change 

at the station. 

g. Merging of All Datasets by Date

Once all of the data computations were completed, all datasets were merged by date. 

h. Data Filtering

In order to identify the large-scale changes (on the order of a season), the daily data were 

filtered by using a 90-day running mean. 

Figure 13. Plot of the relationship between salinity measured by the U.S. Army Corps 

of Engineers (USACE) and Louisiana Department of Wildlife and Fisheries (LDWF) for 

three stations in the Louisiana coastal marshes.

26
Figure 14. Plot of surface salinity against bottom salinity for Louisiana Department of 

Wildlife and Fisheries (LDWF) discrete shrimp sampling program in area 1 (Lakes 

Pontchartrain and Borgne) and area 3 (Barataria basin). The results of a regression 

analysis are indicated on each plot. 

27
Table 4. Regression analysis of daily mean salinity (calculated from hourly values) 

versus daily 8 a.m. salinity readings in coastal Louisiana. 

[Adapted from Swenson and Swarzenski (1995); daily 8 a.m. reading and daily mean from hourly data; LDWF, Louisiana Department of 

Wildlife and Fisheries] 

 

Agency Station n Intercept Slope R-square 

LDWF 315 5,800 -0.16 1.00 0.95 

LDWF 317 5,460 -0.21 1.01 0.95 

LDWF 323 1,271 -0.21 1.01 0.97 

LDWF 325 3,004 -0.01 0.99 0.98 

LDWF 326 1,874 -0.08 1.05 0.97 

LDWF 416 2,065 0.00 1.00 0.96 

LDWF 518 2,180 -0.16 1.00 0.95 

Results 

A summary of the distribution of dataset lengths in years and the percent of the data records 

in each basin is shown in figure 15 and table 5. The main interest in this study was the availability 

of long-term data records to compare with the landscape changes. A summary of the percent of all 

data records within a basin by length of record is shown in figure 16. With the exception of the 

Atchafalaya and Mississippi River basins (which are not being considered in this study), all basins 

considered have datasets with lengths of at least 25 years for at least 20 percent of their data 

records. The basins with a large portion of their data records being 30 years or more are 

Terrebonne, Vermilion, and Calcasieu. Thus, there are sufficient data in all of the basins of interest 

to investigate the long-term changes. 

28
Figure 15. Distribution of data series lengths for the salinity datasets from the 44 

stations in coastal Louisiana used in the final analysis. The top panel shows the 

distribution of all datasets as a function of dataset length. The bottom panel shows the 

distribution of all of the datasets by basin. 

29
Table 5. Summary of dataset lengths, by basin, for 44 salinity stations in coastal 

Louisiana. The number of stations of a given length and the total number of stations and 

percent of total stations within each basin also are indicated. 

Record Basin 

length 

year 

Mississippi 

River 

Pontchartain 

and Borgne Barataria Terrebonne Atchafalaya Vermilion Mermentau Calcasieu

00–05 0 0 1 1 1 0 0 0 

05–10 4 2 0 0 2 1 1 1 

10–15 0 0 1 1 0 1 0 1 

15–20 0 0 1 1 0 0 0 0 

20–25 1 1 1 0 0 0 0 1 

25–30 0 0 1 1 0 2 4 3 

30–35 0 0 3 2 0 0 0 0 

35–40 0 0 1 0 0 0 0 0 

40–45 0 1 0 0 0 0 0 0 

45–50 0 0 0 0 0 0 0 0 

50–55 0 0 0 1 0 0 0 0 

Total 5 4 9 8 3 4 5 6 

Percent 

of total 11.4 9.1 20.5 18.2 6.8 9.1 11.4 13.6 

0

20

40

60

80

100

Mississippi

Pontchartrain

Barataria

Terrebonne

Atchafalaya

Vermilion

Mermentau

Calcasieu

Basin

Percent

>5 years >10 years >15 years >20 years >25 years >30 years

Figure 16. Summary of dataset lengths by basin in coastal Louisiana. The percentage 

of the total number of data records with a given basin by data record length is indicated. 

30
Examples of long-term daily mean coastal salinity from Grand Terre (Barataria basin) and 

Cameron (Calcasieu basin) are presented in figures 17 and 18. The overall patterns at both sites are 

similar, exhibiting seasonal fluctuations as well as large-scale fluctuations (usually from frontal 

passage influence). Daily salinity plots from Bayou Grand Caillou and Little Lake are shown in 

figures 19 and 20. The patterns at these locations are typical of a more inland location and are 

characterized by a more pronounced seasonal change from low salinities (0–2 ppt) to moderate 

salinities (5–10 ppt). The presence of short duration pulses or “spikes” up to 15–20 ppt also are 

apparent. These spikes are the result of meteorological forcing (strong frontal passages). The 

greater frequency of salinity spikes after 1976 at Bayou Grand Caillou is coincident with a change 

in vegetation communities from fresh to intermediate. The high salinities associated with the large 

scale drought during 1999–2000 are also quite evident on the filtered plots from Grand Terre, 

Bayou Grand Caillou, and Little Lake. 

Figure 21 shows data from the Houma Navigation Canal at Crozier. The pattern in this 

figure is typical of an inland station on a large channel and is characterized by low salinities (0–1 

ppt) most of the time with short duration, high salinity (approximately 15 ppt) spikes from 

meteorological forcing (strong frontal passages). The comparison of these salinity spikes between 

navigation channels and natural channels and the potential impact on the surrounding marshes are 

also of interest in this study and will be examined further in the next section. 

31
Figure 17. Time series plot of daily mean salinity (top panel) and 90-day running mean 

filtered data (bottom panel) from the Louisiana Department of Wildlife and Fisheries 

(LDWF) continuous monitoring station at Grand Terre, La. (station 315). Hourly data 

were used in this analysis. 

32
Figure 18. Time series plot of daily mean salinity (top panel) and 90-day running mean 

filtered data (bottom panel) from the Louisiana Department of Wildlife and Fisheries 

(LDWF) continuous monitoring station at Cameron, La. (station 719). Hourly data were 

used in this analysis. 

33
Figure 19. Time series plot of daily mean salinity (top panel) and 90-day running mean 

filtered data (bottom panel) from the U.S. Army Corps of Engineers (USACE) monitoring 

station in Bayou Grand Caillou, La. (station 76323). Daily data were used in this analysis.

34
Figure 20. Time series plot of daily mean salinity (top panel) and 90-day running mean 

filtered data (bottom panel) from the Louisiana Department of Wildlife and Fisheries 

(LDWF) continuous monitoring station in Little Lake, La. (station 326). Hourly data were 

used in this analysis. 

35
Figure 21. Time series plot of daily mean salinity from the U.S. Army Corps of Engineers 

(USACE) monitoring station in the Houma Navigation Canal at Crozier, La. (station 

76343). Daily data were used in this analysis. 

36
The basic statistics for each of the stations are presented in table 6, and the station means are 

plotted in figure 22. The coastal waters have a mean salinity of 15–20 ppt, which decreases fairly 

rapidly as you move inland. The inland water bodies in the eastern part of the State tend to have 

higher salinity than do the inland water bodies in the western part of the State. The estuaries in the 

western part have limited exchange with the coastal waters compared to the estuaries in the eastern 

part of the State. In addition, a large portion of the western part of the State (e.g., Grand Lake and 

White Lake basins) is managed (by a series of locks and water control structures) with the goal of 

maintaining fresh water for agriculture (Gammill and others, 2001). 

37
Table 6. Summary statistics for coastal Louisiana stations used to analyze salinity, 1951–2005. 

[ppt, parts per thousand]

Salinity (ppt)

Station 

number

Latitude 

decimal

Longitude 

decimal

Start 

year

End 

year

Elapsed 

years Mean

Standard 

deviation Minimum Maximum

315 29.27444 -89.94222 1961 2005 44 20.9 5.7 2.5 38.0

317 29.42306 -89.95056 1973 2005 32 13.8 6.3 0.3 31.4

326 29.51556 -90.18417 1981 2005 24 3.6 3.7 0.0 25.2

518 29.25306 -90.92167 1967 2005 38 11.0 5.6 0.0 31.6

619 29.71361 -91.87806 1974 2005 31 4.0 3.3 0.0 18.7

719 29.82722 -93.33361 1967 1994 27 15.1 6.1 0.1 29.8

1386 29.87829 -89.95331 1988 1996 8 0.2 0.1 0.1 1.0

1420 29.46667 -89.68333 1975 1997 22 0.1 0.2 0.0 2.1

1480 29.26667 -89.35000 1991 1998 7 0.4 0.5 0.0 2.9

1575 29.04360 -89.31780 1988 1995 7 0.7 0.7 0.0 4.9

1670 28.90000 -89.41667 1990 1999 9 3.9 3.7 0.0 17.8

67890 29.78333 -92.13333 1987 1999 12 3.4 6.8 0.0 43.0

70600 30.00000 -92.76667 1975 1997 22 0.2 0.1 0.0 0.6

70675 29.85000 -92.85000 1973 2002 29 2.0 4.3 0.0 33.5

70750 29.85000 -92.85000 1975 1982 7 3.8 5.5 0.0 23.5

73472 30.25000 -93.21667 1975 2002 27 2.0 3.1 0.0 21.7

76060 30.01667 -90.01667 1957 2001 44 5.3 2.9 0.0 22.2

76303 29.38333 -90.61667 1975 2002 27 4.8 3.9 0.0 24.8

76305 29.23333 -90.65000 1987 2002 15 7.7 5.1 0.1 26.1

76320 29.58333 -90.70000 1975 1993 18 0.4 1.2 0.0 18.4

76323 29.45000 -90.70000 1948 2001 53 2.1 3.9 0.0 41.8

76343 29.55000 -90.70000 1961 1993 32 0.5 1.7 0.0 25.3

76403 29.58333 -90.50000 1975 1993 18 0.6 1.8 0.0 36.1

76480 29.63908 -91.31845 1991 1993 2 0.6 0.1 0.3 0.8

76592 29.55000 -92.30000 1975 2002 27 8.6 4.9 0.0 37.9

76600 29.75000 -92.25000 1975 1982 7 2.0 2.5 0.0 15.2

76680 29.75000 -92.25000 1975 2001 26 0.9 1.2 0.0 11.4

76720 29.78333 -92.18333 1975 1982 7 2.1 2.5 0.0 13.9

76800 29.78333 -92.18333 1975 2002 27 1.3 1.9 0.0 14.8

76873 29.93333 -93.08333 1951 1981 30 0.4 0.8 0.0 14.9

76880 30.08333 -93.28333 1987 2000 13 5.7 5.9 0.0 30.9

81050 29.51667 -90.40000 1987 1999 12 0.2 0.4 0.0 5.5

82203 29.56667 -90.38333 1949 1981 32 0.6 1.2 0.0 19.9

82250 29.34165 -90.24561 1991 1996 5 4.2 3.1 0.5 17.5

82300 29.36667 -90.25000 1961 1984 23 1.9 3.5 0.0 25.4

82750 29.73333 -90.11667 1967 2002 35 5.7 3.1 0.1 18.5

82875 29.66667 -90.10000 1955 1988 33 2.2 2.4 0.0 24.6

85600 30.18333 -90.11667 1973 1996 23 3.0 1.2 0.0 6.6

85800 29.85000 -89.68333 1988 1996 8 9.8 4.0 0.0 21.2

85850 29.68834 -89.40338 1987 1993 6 15.7 5.9 0.0 28.3

88410 29.24764 -89.98630 1992 1997 5 17.3 5.8 0.7 30.1

88550 29.38609 -91.39783 1990 1997 7 0.7 1.1 0.0 10.2

88800 29.59786 -91.54145 1992 1999 7 0.0 1.2 0.0 19.5

88850 29.68333 -91.88333 1973 2001 28 4.1 3.5 0.0 21.3

38
Figure 22. Average salinity for 44 stations used to analyze salinity in coastal Louisiana, 

1951–2005. 

39
The results of the trend analysis (linear regression), which used daily salinity as the 

dependent variable and time as the independent variable, are presented in table 7. This table 

presents, for each station, the data start year, the data end year, the total number of years, the 

intercept of the linear fit, the slope (parts per thousand per year) of the linear fit, the model rsquare, the F value for the overall linear model, the probability, and the salinity change (parts per 

thousand) over the period of record. The spatial distribution of the trends is presented in figure 23. 

The linear fit was significant at 42 of the 44 stations. Salinity increases occurred at 27 (64 percent) 

of the stations, and salinity decreases occurred at 15 (36 percent) of the stations. The changes in 

salinity were small, with 74 percent being increases or decreases of less than 2 ppt over the period 

of record. The long-term trends explained less than 10 percent (r-square<0.1) of the variance at 

most (72 percent) of the stations. The lower r-square was also noted by Wiseman and others 

(1990a), who stated that the statistically significant but weak trends are hidden by the high natural 

variability in the Mississippi River discharge and local climatology. 

The coastal salinities in Louisiana exhibit interannual variability, which is inversely related 

to Mississippi River discharge (Wiseman and others, 1990b). The trends presented in figure 23 

contain data from many stations with short records, as well as data from different time periods. 

Wiseman and others (1990a) pointed out that there are longer term (years to decades) climatic 

variations that affect the estuary. To address this point, a subset of stations which had data over 

the same 23-year period (1975–98) was used. There were a total of 15 stations with data over this 

time period; 12 (80 percent) of these had statistically significant trends. Salinity increases occurred 

at 6 (50 percent) of the 12 stations, and salinity decreases occurred at 6 (50 percent) of these 

stations. The spatial distribution of the 1975–88 trends is presented in figure 24, and the trend 

analysis results are presented in table 8. In general, most of the changes over the time period were 

fairly small; 67 percent of the increases and 50 percent of the decreases were 2 ppt or less. As was 

the case when using all data (table 7), the trend explained a small amount of the observed salinity 

signal (5 ppt or less at 75 percent of the stations). Although there is no coherent coastwide trend of 

increasing or decreasing salinity, there is a consistent salinity increase for the internal basins of the 

Chenier Plain. There is also an indication of salinity increase in the Houma Navigation Canal as 

opposed to the stations on either side, which both show decreases. 

40
Table 7. Results of a trend analysis (linear regression) in coastal Louisiana that 

uses time as the independent variable and salinity as the dependent variable. 

[ppt, parts per thousand; ppt/yr, parts per thousand per year; >, greater than; POR, period of record; <, less than]

Station 

number

Start 

year

End 

year

Elapsed 

years

Intercept 

(ppt)

Slope 

(ppt/yr) R-square

Overall 

model F

Overall 

model Pr>F

POR salinity 

change (ppt)

315 1961 2005 44 21.3 -0.0178 0.002 24.21 <0.0001 -0.78

317 1973 2005 32 11.7 0.0690 0.010 93.70 <0.0001 2.21

326 1981 2005 24 5.4 -0.0511 0.010 72.70 0.0871 -1.23

518 1967 2005 38 10.7 0.0088 0.000 2.43 0.1188 0.33

619 1974 2005 31 3.5 0.0146 0.003 12.75 0.0004 0.45

719 1967 1994 27 18.2 -0.1376 0.037 178.90 0.0001 -3.72

1386 1988 1996 8 0.5 -0.0107 0.130 148.63 <0.0001 -0.09

1420 1975 1997 22 -0.1 0.0114 0.255 1762.02 <0.0001 0.25

1480 1991 1998 7 4.2 -0.1095 0.154 218.21 <0.0001 -0.77

1575 1988 1995 7 5.3 -0.1502 0.288 271.03 <0.0001 -1.05

1670 1990 1999 9 0.1 0.1139 0.006 11.57 0.0007 1.03

67890 1987 1999 12 -0.8 0.1278 0.005 16.13 <0.0001 1.53

70600 1975 1997 22 -0.1 0.0099 0.313 957.62 <0.0001 0.22

70675 1973 2002 29 0.3 0.0588 0.012 77.08 <0.0001 1.71

70750 1975 1982 7 -0.3 0.2232 0.008 16.79 <0.0001 1.56

73472 1975 2002 27 0.6 0.0502 0.017 112.06 <0.0001 1.35

76060 1957 2001 44 6.5 -0.0417 0.027 121.63 <0.0001 -1.83

76303 1975 2002 27 2.1 0.0949 0.052 328.62 <0.0001 2.66

76305 1987 2002 15 -4.6 0.3642 0.090 384.70 <0.0001 5.46

76320 1975 1993 18 0.0 0.0185 0.005 19.26 <0.0001 0.33

76323 1948 2001 53 1.0 0.0949 0.136 2321.96 <0.0001 5.03

76343 1961 1993 32 0.6 -0.0086 0.002 14.13 0.0002 -0.28

76403 1975 1993 18 0.1 0.0195 0.002 9.46 0.0021 0.35

76480 1991 1993 2 -3.6 0.1277 0.591 862.22 <0.0001 0.26

76592 1975 2002 27 16.6 -0.2802 0.205 1720.01 <0.0001 -7.57

76600 1975 1982 7 -1.4 0.1797 0.031 75.07 <0.0001 1.26

76680 1975 2001 26 1.5 -0.0231 0.020 114.80 <0.0001 -0.60

76720 1975 1982 7 -2.2 0.2295 0.047 113.47 <0.0001 1.61

76800 1975 2002 27 3.2 -0.0670 0.078 1097.52 <0.0001 -1.81

76873 1951 1981 30 0.4 0.0059 0.002 18.85 <0.0001 0.18

76880 1987 2000 13 -17.4 0.7191 0.169 512.72 <0.0001 9.35

81050 1987 1999 12 0.8 -0.0167 0.018 46.18 <0.0001 -0.20

82203 1949 1981 32 0.5 0.0071 0.003 22.90 <0.0001 0.23

82250 1991 1996 5 26.3 -0.6502 0.105 99.71 <0.0001 -3.25

82300 1961 1984 23 3.6 0.0772 0.584 329.65 <0.0001 1.77

82750 1967 2002 35 1.2 0.0590 0.013 100.53 <0.0001 2.07

82875 1955 1988 33 1.6 0.0754 0.097 1623.23 <0.0001 2.49

85600 1973 1996 23 1.2 0.0206 0.003 8.45 0.0039 0.47

85800 1988 1996 8 3.9 -0.1504 0.010 26.91 <0.0001 -1.20

85850 1987 1993 6 29.8 -0.4611 0.020 26.91 <0.0001 -2.77

88410 1992 1997 5 6.2 0.3170 0.007 7.92 0.0050 1.59

88550 1990 1997 7 -6.1 0.1961 0.176 416.77 <0.0001 1.37

88800 1992 1999 7 -6.1 0.1895 0.009 15.58 <0.0001 1.33

88850 1973 2001 28 3.5 -0.0135 0.001 4.80 0.0285 -0.38

41
Figure 23. Summary of salinity trend analysis in coastal Louisiana. The salinity change in 

parts per thousand (ppt) over the period of record is shown. 

42
Figure 24. Summary of salinity change in parts per thousand (ppt) over the period from 

1975 through 1998 in coastal Louisiana. 

43
Table 8. Results of a trend analysis (linear regression) using time as the 

independent variable and salinity as the dependent variable. Only stations in 

Louisiana with data over the time period from 1975 through 1998 were used in this 

analysis. 

[ppt, parts per thousand; ppt/day, parts per thousand per day; ppt/yr, parts per thousand per year; >, greater than; POR, period of record; <, 

less than] 

Station 

number

Decimal 

latitude

Decimal 

longitude

Mean 

salinity 

Intercept 

(ppt)

Slope 

(ppt/day)

Slope 

(ppt/yr) R-square

Overall 

model F

Overall 

model Pr>F

315 29.27444 -89.94222 19.8 19.1 0.00007 0.026 0.001 7.96 0.0048

518 29.25306 -90.92167 10.5 11.8 -0.00015 -0.054 0.009 26.46 <0.0001

619 29.71361 -91.87806 3.8 3.7 0.00001 0.003 0.000 0.34 0.5598

719 29.82722 -93.33361 14.8 18.6 -0.00041 -0.150 0.030 116.81 <0.0001

70675 29.85000 -92.85000 2.0 0.1 0.00019 0.068 0.014 80.87 <0.0001

73472 30.25000 -93.21667 1.7 1.6 0.00001 0.004 0.000 0.54 0.4625

76060 30.01667 -90.01667 4.6 5.5 0.00007 0.025 0.001 3.49 0.0617

76303 29.38333 -90.61667 4.1 4.6 -0.00005 -0.019 0.002 11.13 0.0009

76323 29.45000 -90.70000 3.4 0.0 0.00033 0.122 0.052 254.40 <0.0001

76403 29.58333 -90.50000 0.6 0.1 0.00010 0.035 0.002 9.46 0.0021

76592 29.55000 -92.30000 8.7 17.5 -0.00087 -0.319 0.220 1713.70 <0.0001

76680 29.75000 -92.25000 0.9 1.8 -0.00010 -0.036 0.047 265.73 <0.0001

76873 29.93333 -93.08333 0.6 -2.8 0.00050 0.182 0.162 346.05 <0.0001

82750 29.73333 -90.11667 5.2 4.0 0.00012 0.044 0.021 95.86 <0.0001

88850 29.68333 -91.88333 3.7 6.3 -0.00025 -0.090 0.055 242.36 <0.0001

Salinity Pulse Analysis

Long-Term Data Records 

The daily datasets were summarized to calculate the percent of time, on an annual basis, 

that the salinity at each station exceeded values of 2.5, 5.0, 10.0, 15.0, 20.0, and 25.0 ppt. The 

results for all stations are presented in appendix 1. Although there appears to be no coherent 

coastwide pattern, the effects of the drought of 1999–2000 is evident on many of the plots 

(appendix 1). Of primary interest to this study is salinity behavior across the stations in and near 

the Houma Navigation Canal (HNC). The stations used for the pulse analysis are shown in figure 8 

along with canals that existed during the pre- and post-construction periods of the HNC. The 

annual salinity level data for six long-term stations near the HNC are presented in figures 25 

through 30. These figures indicate the percent of time during the year that the salinity exceeded a 

given level. The completion date for the HNC is indicated on each figure. 

In general, salinity levels exceeded 10–15 ppt less than 10 percent of the time at all of the 

stations, although there were time periods when these levels occurred at greater frequency (up to 60 

percent of the time). The effect of the 1999–2000 droughts (higher percentage of values greater 

than 10.0 ppt) is an example of this and can be seen in the data from Bayou Petite Caillou at 

Cocodrie (fig. 25), Bayou Petite Caillou near Boudreaux Canal (fig. 26), and Bayou Grand Caillou 

at Dulac (fig. 27). With the exception of the 1999–2000 drought, there does not appear to be any 

obvious pattern in increasing salinity levels closely following the completion of the HNC, except 

for the immediate increase in salinity spikes during completion of construction in 1961 (fig. 29). 

44
The data from Bayou Grand Caillou at Dulac (fig. 27) do show a longer term trend of increasing 

salinity levels. Salinity values were greater than 5 ppt about 10 percent of the time in 1950; this 

frequency of occurrence increased substantially in 1978 and rose to a level of 50–60 percent by 

2001. This pattern is similar to the pattern observed at the HWTP (fig. 9). A potential explanation 

for these patterns is based on the dredging history of the HNC. The HNC was completed in 1962 

but required maintenance dredging on a large extent of it in 1964–65 (2.73 million m3). Spot 

dredging (< 0.1 million m3

) was done in the main channel between each full dredging event ( 6.30 

million m3

 between 1972 and 1974 and 6.38 million m3 between 1980 and 1983; U.S. Army Corps 

of Engineers, 1975, 1984). These events opened a deeper route from the HNC to Crozier and into 

Grand Bayou Caillou. Although the available salinity data are insufficient to conduct statistical 

correlations, the salinity exceedance values shown in figures 27 and 29 correspond fairly closely to 

the timing of the dredging events. The increase also may be a reflection of the general breakup of 

the marsh in the area, which led to increased exchange with the HNC as spoil banks deteriorated. 

A comparison of data from Bayou Terrebonne at Bourg (fig. 28) and the HNC at Crozier 

(fig. 29) indicates that the magnitude and frequency of the salinity spikes (especially those greater 

than 10.0 ppt) are greater at the HNC station, even though it is almost 6 km further inland. This 

observation agrees with the Wang (1988) study discussed previously. 

Short-Term Data Records 

To investigate the salinity characteristics of the Houma Navigation Canal and adjacent 

natural bayous in greater detail, hourly salinity, water level, and discharge data from 2003 were 

obtained from stations in the area. The purpose of the study was to characterize the nature of the 

salinity spikes discussed previously. Data came from the LDNR, the USGS, and the Louisiana 

Universities Marine Consortium (LUMCON). The data availability is shown in table 9, and the 

station distribution is shown in figure 31. The stations used for this analysis are also shown in 

figure 8 along with canals that existed during the pre- and postconstruction phases of the HNC. 

Data from these stations defined three coastal-inland transects (fig. 32): 

1. Bayou Terrebonne transect: from Terrebonne Bay northward to Montegut (stations 102, 

TE03-20R, TE01-16R) 

2. Bayou Grand Caillou transect: from the Houma Navigation Canal northward to Ashland 

(stations 07381324, TE32-36R, TE32-35R, TE32-33R). 

3. The Houma Navigation Canal transect: from Terrebonne Bay northward to Theriot 

(stations 102, TE03-20R, TE01-16R) 

Datasets were obtained either from Web servers, in the case of the LDNR and LUMCON 

data, or from files supplied by the agency (USGS). There were few corrections that needed to be 

made to the datasets since the agencies collecting the data had already performed QA/QC checks 

before making the data available. The data obtained were in various formats and had to be 

converted to a standard file format for each station. The data from all stations were then merged, 

by hour, into a final dataset for analysis. The data were filtered by using a 25-hour running mean to 

remove the tidal fluctuations. The salinity and water level patterns were analyzed further by using 

signal analysis techniques, specifically spectral density analysis to compute the variance spectrum. 

The variance spectrum partitions the variance in the observed data signal into a number of 

intervals, or frequency bands, with the quantity shown being the spectral density (Haan, 1977). 

45
The spectral density is the amount of variance per interval of frequency (Haan, 1977). The results 

are presented with frequency (cycles per hour) or period (hours) on the horizontal axis, and the 

spectral density is presented on the vertical axis. 

Available information on the historical dredging activities of navigation canals within the 

selected study site was obtained from the USACE. These datasets and supporting attribute 

information (for example, river mile locations and center lines) were processed and digitized into a 

georectified vector layer. This information was combined with salinity data to perform an 

assessment to analyze the relationship between salinity changes and specific dredging events. 

Figure 25. Plot of salinity level data for Bayou Petite Caillou at Cocodrie in coastal 

Louisiana (U.S. Army Corps of Engineers station 76305). The percent of time per year 

that salinity values exceeded the given threshold values is indicated. The distance inland 

(from Terrebonne Bay) is also indicated on the plot. The top panel indicates the percent of 

time that the salinity exceeded 2.5 parts per thousand (ppt) and 5.0 ppt, and the bottom 

panel indicates the percent of time that the salinity exceeded 10.0 ppt and 15.0 ppt. The 

colored horizontal bar between the two panels indicates the marsh type in the vicinity of 

the station for a given time period. 

46
Figure 26. Plot of salinity level data for Bayou Petite Caillou near Boudreaux Canal in 

coastal Louisiana (U.S. Army Corps of Engineers station 76303). The percent of time per 

year that salinity values exceeded the given threshold values is indicated. The distance 

inland (from Terrebonne Bay) is also indicated on the plot. The top panel indicates the 

percent of time that the salinity exceeded 2.5 parts per thousand (ppt) and 5.0 ppt, and 

the bottom panel indicates the percent of time that the salinity exceeded 10.0 ppt and 15.0 

ppt. The colored horizontal bar between the two panels indicates the marsh type in the 

vicinity of the station for a given time period. 

47
Figure 27. Plot of salinity level data for Bayou Grand Caillou near Dulac in coastal 

Louisiana (U.S. Army Corps of Engineers station 76323). The percent of time per year 

that salinity values exceeded the given threshold values is indicated. The distance inland 

(from Terrebonne Bay) is also indicated on the plot. The top panel indicates the percent of 

time that the salinity exceeded 2.5 parts per thousand (ppt) and 5.0 ppt, and the bottom 

panel indicates the percent of time that the salinity exceeded 10.0 ppt and 15.0 ppt. The 

colored horizontal bar between the two panels indicates the marsh type in the vicinity of 

the station for a given time period. 

48
Figure 28. Plot of salinity level data for Bayou Terrebonne near Bourg in coastal 

Louisiana (U.S. Army Corps of Engineers station 76403). The percent of time per year 

that salinity values exceeded the given threshold values is indicated. The distance inland 

(from Terrebonne Bay) is also indicated on the plot. The top panel indicates the percent of 

time that the salinity exceeded 2.5 parts per thousand (ppt) and 5.0 ppt, and the bottom 

panel indicates the percent of time that the salinity exceeded 10.0 ppt and 15.0 ppt. The 

colored horizontal bar between the two panels indicates the marsh type in the vicinity of 

the station for a given time period. 

49
Figure 29. Plot of salinity level data for the Houma Navigation Canal near Crozier in 

coastal Louisiana (U.S. Army Corps of Engineers station 76343). The percent of time per 

year that salinity values exceeded the given threshold values is indicated. The distance 

inland (from Terrebonne Bay) is also indicated on the plot. The top panel indicates the 

percent of time that the salinity exceeded 2.5 parts per thousand (ppt) and 5.0 ppt, and 

the bottom panel indicates the percent of time that the salinity exceeded 10.0 ppt and 15.0 

ppt. The colored horizontal bar between the two panels indicates the marsh type in the 

vicinity of the station for a given time period. 

50
Figure 30. Plot of salinity level data for the Gulf Intracoastal Waterway at Houma in 

coastal Louisiana (U.S. Army Corps of Engineers station 76320). The percent of time per 

year that salinity values exceeded the given threshold values is indicated. The completion 

date for the Houma Navigation Canal is indicated by the vertical yellow line. The distance 

inland (from Terrebonne Bay) is also indicated on the plot. The top panel indicates the 

percent of time that the salinity exceeded 2.5 ppt and 5.0 ppt, and the bottom panel 

indicates the percent of time that the salinity exceeded 10.0 ppt and 15.0 ppt. The colored 

horizontal bar between the two panels indicates the marsh type in the vicinity of the 

station for a given time period. 

51
Figure 31. Stations used in salinity pulse analysis in coastal Louisiana. The green stations denote the 

were used in the final analysis. 

52
Table 9. Data availability for salinity “spike” analysis in the Houma Navigation Canal area 

in coastal Louisiana, by month, for the year 2003. 

[An X indicates that data were available at a station for the indicated month. Data types: L, water level; S, salinity; Q, discharge. LDNR, Louisiana 

Department of Natural Resources; LUMCON, Louisiana Universities Marine Consortium; USGS, U.S. Geological Survey] 

Agency Station Types Jan. Feb. Mar. Apr. May June July Aug. Sept. Oct. Nov. Dec.

LDNR TE01-16R L, S X X X X X X X X X X X 

LDNR TE03-02 L, S X X X X X X X X X X X X 

LDNR TE03-09 L, S X X X X X X X X X X X X 

LDNR TE03-20R L, S X X X X X X X X X X X X 

LDNR TE32-33R L, S X X X 

LDNR TE32-34R L, S X X X 

LDNR TE32-35R L, S X X X 

LDNR TE32-36R L, S X X X 

LDNR DCP-TE03 L, S X X X X X X X X X X X X 

LDNR DCP-TE04 L, S 

LUMCON 101 L, S X X X X X X X X X X X X 

LUMCON 102 L, S X X X X X X X X X X X X 

USGS 07381324 L, S, Q X X X X X X X X X X X X 

USGS 07381328 L, S, Q X X X X X X X X X X X X 

USGS 07381343 L, S X X X X X X X X X X X X 

USGS 07381349 L, S X X X X X X X X X X X X 

USGS 073813375 L, S X X X X X X X X X X X X 

USGS 0738165057 L, S X X X X X X X X X X X X 

USGS 424200 L, S, Q X X X X X X X X X X X X 

USGS 465600 L, S, Q X X X X X X X X X X X X 

Time series plots of data from the Terrebonne transect, the Bayou Grand Caillou transect, 

and the Houma Navigation Canal (HNC) transect are shown in figures 33, 34, and 35, respectively. 

Water level data from Bayou Terrebonne transect, the Bayou Grand Caillou transect, and the HNC 

transect are shown in figures 36, 37, and 38, respectively. The plots present the hourly and the 25-

hour filtered data. The water level patterns are typical for the Louisiana coastal marshes, with a 

tidal signal superimposed on other larger scale events (Byrne and others, 1976; Adams and 

Baumann, 1980; Chuang and Swenson, 1981; Swenson and Turner 1987; Sasser and others, 1994). 

The salinity pattern is very similar to the water level patterns, although the tidal fluctuations are not 

as strong. Several salinity events (spikes) can be seen in the data plots. 

Spectral density estimates for salinity and water level for Terrebonne Bay and the 

northernmost stations from each transect are shown in figures 39 and 40, respectively. The diurnal 

tidal signal (approximately 25 hours) is quite strong for the water levels at all stations but is only 

noticeable in the salinity at the Terrebonne Bay station. 

Correlations between filtered salinity and water level in Terrebonne Bay and filtered 

salinity and water level at each of the transect stations are presented in table 10. Water levels have 

a fairly high correlation (>0.7 at 7 of 10 stations), indicating that the water level patterns in this 

area are mostly driven by the coastal water levels. The salinity levels show much lower 

correlations (<0.5 at 7 of 10 stations), indicating that the salinity of the Terrebonne Bay waters is 

not the entire driving force for the more interior locations. Although high coastal water levels result 

53
in high water levels throughout the system, high salinity at the coast does not always result in high 

salinity at the inland stations. Figure 41 presents the filtered discharge data from Bayou Grand 

Caillou and the HNC. It can be seen that time periods characterized by flow reversals, or inflow 

events, tend to correspond with higher salinity events. Swarzenski (2003) pointed out that, during 

tropical storm events when normal hydraulic gradients are reversed, saline waters move inland into 

the Gulf Intracoastal Waterway (GIWW) along north-south trending ship channels, such as the 

HNC. Although the GIWW normally moves fresh water into and towards the coast, under these 

conditions it also can serve as a conduit for saltwater movement, resulting in pulses of saline water 

moving laterally via the GIWW into areas which contain marsh plants with a low tolerance for salt 

(Swarzenski, 2003). 

The characteristics of the salinity pulses that occurred during the time period of August 

2003 through September 2003 were summarized for each transect (figs. 8 and 32), and the means 

were compared using Duncan’s Multiple Range test (SAS, 1990c). The pulse magnitude (fig. 42) 

ranged from values of about 2.0 ppt to about 14 ppt depending on station location. The salinity at 

the southern station in the HNC (26.2 km from Terrebonne Bay) was 13.3 ppt, and the salinity at 

the southern station in Bayou Grand Caillou (24.7 km from Terrebonne Bay) was 10.3 ppt; these 

values are not statistically different. The mean salinity at the middle station in Bayou Terrebonne 

(21.2 km from Terrebonne Bay) was 6.1 ppt, which is significantly different. The mean salinity at 

the middle station in the HNC (41.5 km from Terrebonne Bay) was 5.5 ppt, which is significantly 

higher than the salinity of 1.8 ppt at the middle station in Bayou Grand Caillou (42.3 km from 

Terrebonne Bay). The mean salinity at Bayou Terrebonne north (31.8 km from Terrebonne Bay) 

station was 3.3 ppt, which is not significantly different from either the middle HNC station or the 

middle Bayou Grand Caillou station. This similarity in mean salinity levels at Bayou Terrebonne 

north and the middle HNC station or the middle Bayou Grand Caillou station is most likely because 

of the closeness of the Bayou Terrebonne north station (about 10 km closer) to Terrebonne Bay. 

The coast-inland decrease in salinity, calculated as parts per thousand per kilometer, is 

presented in figure 43. The decreases at all stations in Bayou Grand Caillou and the north station 

in the Bayou Terrebonne transect were about 0.6 ppt km-1 and are not statistically different from 

each other or from the Houma Navigation Canal transect. The middle station on the Bayou 

Terrebonne transect exhibited the largest decrease, about 0.8 ppt km-1, which is statistically 

significant compared to all the other stations. The HNC stations exhibited the smallest decreases, 

about 0.4 ppt km-1, and are significantly different from all of the other stations. 

The duration, in days, of each pulse was also calculated. The results are presented in figure 

44. The data indicate that the shortest salinity pulse duration, about 1.5 days, occurred in the 

middle and north stations in Bayou Grand Caillou. These two stations are statistically different 

from all of the other stations. The longest salinity pulse duration, about 5 days, which is 

statistically different from that of all the other stations, occurred at the middle station in Bayou 

Terrebonne. The shortest pulse durations, about 1.5 days, which are statistically different from all 

of the other stations, occurred in the Houma Navigation Canal. The remainder of the stations are 

statistically all the same, with a duration of 3–4 days. 

The southern stations (Houma Navigation Canal at Falgout Canal [station 07381328] and 

Bayou Grand Caillou at Dulac [station 07381324]) on the HNC and Bayou Grand Caillou salinity 

transects (figs. 8 and 32) had data for the entire year of 2003. Their salinity pulse characteristics 

were compared by using a paired t-test (SAS, 1990c). The results indicated salinity pulse 

magnitudes of 10.5 ppt for the Houma Navigation Canal at Falgout Canal station and 11.0 ppt for 

the Bayou Grand Caillou at Dulac station. These values are not statistically different. Analysis of 

the salinity pulse duration indicated a duration of 4.8 days at the HNC station, which is statistically 

different from the value of 7.3 days at the Bayou Grand Caillou station. This difference indicates 

54
that the higher salinity waters associated with the pulses tend to remain longer in the Bayou Grand 

Caillou system. The longer duration of higher salinity in the Bayou Grand Caillou system may be a 

reflection of the flushing that also can occur in the HNC, which also is directly connected to the 

Gulf Intracoastal Waterway, which can serve as a conduit of fresh water (Swarzenski, 2003). 

The salinity pulse data were compared with discharge data for the HNC to investigate the 

influence of freshwater head on the observed pulses. The relationship between discharge and the 

magnitude of the salinity pulses is presented in figure 45. Regression analysis indicates that 

discharge explains almost 50 percent of the observed salinity pulses in the HNC. This result 

supports the study of saltwater intrusion in the Houma Navigation Canal area by Wang (1988), who 

stated that the freshwater head was one of the major driving forces influencing the intrusion of salt 

water in the channels. 

55
Figure 32. Schematic of the three coast-inland transects in the Houma Navigation Canal 

area in Louisiana. The station numbers are indicated next to each station. Eight-digit 

numbers indicate U.S. Geological Survey (USGS) stations, three-digit numbers indicate 

Louisiana Universities Marine Consortium (LUMCON) stations, and the numbers starting 

with TE (Terrebonne) indicate Louisiana Department of Natural Resources stations. 

56
Figure 33. Time series plot of hourly salinity and 25-hour filtered salinity from the 

Bayou Terrebonne transect in coastal Louisiana. Station locations are shown in figure 

31. 

57
Figure 34. Time series plot of hourly salinity and 25-hour filtered salinity from the 

Bayou Grand Caillou transect in coastal Louisiana. Station locations are shown in 

figure 31. 

58
Figure 35. Time series plot of hourly salinity and 25-hour filtered salinity from the 

Houma Navigation Canal transect in coastal Louisiana. Station locations are shown in 

figure 31. 

59
Figure 36. Time series plot of hourly water level and 25-hour filtered water level from 

the Bayou Terrebonne transect in coastal Louisiana. Station locations are shown in 

figure 31. 

60
Figure 37. Time series plot of hourly water level and 25-hour filtered water level from 

the Bayou Grand Caillou transect in coastal Louisiana. Station locations are shown in 

figure 31. 

61
Figure 38. Time series plot of hourly water level and 25-hour filtered water level from 

the Houma Navigation Canal transect in coastal Louisiana. Station locations are 

shown in figure 31. 

62
Figure 39. Examples of spectral density estimates for water level in Terrebonne Bay 

(102), Montegut (TE01-16R), the northern portion of Bayou Grand Caillou (TE32-33R), 

and the northern portion of the Houma Navigation Canal (TE32-34R). Station 

locations are indicated in figure 31. 

63
Figure 40. Examples of spectral density estimates for salinity in Terrebonne Bay 

(102), Montegut (TE01-16R), the northern portion of Bayou Grand Caillou (TE32-33R), 

and the northern portion of the Houma Navigation Canal (TE32-34R). Station 

locations are indicated in figure 31. 

64
Table 10. Correlation between filtered (25-hour mean) salinity and water level 

in Terrebonne Bay (station 102) and filtered (25-hour mean) water level and 

salinity in the Bayou Terrebonne, Bayou Grand Caillou, and Houma Navigation 

Canal coastal-inland transects in coastal Louisiana. Indicated are the Pearson 

correlation coefficients. Station locations are indicated in figure 31. 

[<, less than] 

Salinity Water level 

Transect

Station

Correlation 

coefficient Probability 

Correlation 

coefficient Probability

Bayou Terrebonne TE03-20R 0.388 <0.0001 0.704 <0.0001 

Bayou Terrebonne TE01-16T 0.403 <0.0001 0.592 <0.0001 

Bayou Grand 

Caillou 101 0.562 <0.0001 0.75 <0.0001 

Bayou Grand 

Caillou 7381324 0.439 <0.0001 0.738 <0.0001 

Bayou Grand 

Caillou TE32-36R 0.227 <0.0001 0.685 <0.0000 

Bayou Grand 

Caillou TE32-35R -0.141 <0.0001 0.891 <0.0001 

Bayou Grand 

Caillou TE32-33R 0.523 <0.0001 0.705 <0.0001 

Houma Navigation 

Canal 7381328 0.238 <0.0001 0.72 <0.0001 

Houma Navigation 

Canal TE32-34R 0.124 <0.0001 0.822 <0.0001 

65
Figure 41. Time series plot of filtered (25-hour running mean) discharge in Bayou 

Grand Caillou and the Houma Navigation Canal in coastal Louisiana. Positive values 

indicate downstream flow. Station locations are indicated in figure 31. 

66
Figure 42. Mean salinity pulse magnitude, in parts per thousand (ppt), for stations in 

the Houma Navigation Canal area in coastal Louisiana for August through September 

2003. The station name and distance from Terrebonne Bay (in kilometers) are 

indicated. Bars with the same letter above them are not significantly different, as 

determined by using Duncan’s multiple range test. Station locations are indicated in 

figure 31.

67
Figure 43. Mean salinity pulse coast-inland decrease, in parts per thousand per 

kilometer (ppt/km), for stations in the Houma Navigation Canal area in coastal 

Louisiana for August through September 2003. The station name and distance from 

Terrebonne Bay (in kilometers) are indicated. Bars with the same letter above them 

are not significantly different, as determined by using Duncan’s multiple range test. 

Station locations are indicated in figure 31. 

68
Figure 44. Mean salinity pulse duration, in days, for stations in the Houma Navigation 

Canal area in coastal Louisiana for August through September 2003. The station 

name and distance from Terrebonne Bay (in kilometers) are indicated. Bars with the 

same letter above them are not significantly different, as determined by using 

Duncan’s multiple range test. Station locations are indicated in figure 31. 

69
Figure 45. Plot of salinity event (or pulse) amplitude, in parts per thousand (ppt), in 

the Houma Navigation Canal at Falgout Canal in coastal Louisiana against discharge 

through the Houma Navigation Canal, in cubic meters per second. Positive discharge 

values indicate outflow, and negative discharge values indicate inflow. The result of a 

regression analysis is indicated on the plot. The data cover the time period from 

January through December 2003. Station locations are indicated in figure 31. 

Salinity Summary 

Figure 46 summarizes the salinity and marsh types for the long-term stations in the Houma 

Navigation Canal (HNC) vicinity by time periods based on dates of vegetation data as well as on 

photography from before and after construction of the canal. Salinity data from the Bayou Grand 

Caillou station at Dulac show that a major shift to higher salinity levels occurred by 1978; this shift 

actually occurred around 1975 (there was a slight increase post-construction). This time period 

coincides with the first full dredging of the full length of the HNC (6.30 million m3

) that occurred 

between April 1972 and August 1974 (U.S. Army Corps of Engineers, 1975). The upper portion of 

the canal never had been fully dredged until 1964. The marshes in this area (east of the HNC) also 

show a shift from fresh marsh prior to construction of the HNC (completed in approximately 1962) 

to intermediate marsh by 1978. This vegetation shift may be a result of the general breakup of the 

marshes east of the HNC, which was quite extensive by 1978, as opposed to a direct impact of the 

channel. 

70
71

The salinities at Bayou Grand Caillou were high enough prior to construction of the HNC to 

impact the fresh floating marshes in the areas west of the canal; however, the connections between 

the marshes west of the canal and the Bayou Grand Caillou station at Dulac were mainly smaller, 

sinuous channels, which limited water exchange. Once the HNC was constructed and after 

maintenance dredging, the connections between the canal and Bayou Grand Caillou were found to 

be efficient (U.S. Army Corps of Engineers, 1975). The construction of the canal resulted in 

increases to the connections between the marshes west of the canal and the lower portion of Bayou 

Grand Caillou (south of Dulac), which was essentially replaced by the lower portion of the canal. 

These connections may have allowed higher salinity waters to reach the marshes west of the canal, 

particularly those areas just north and south of Falgout Canal. These areas also became impounded 

by numerous canal spoil banks, which may have exacerbated any salinity intrusion events since 

water would be held in these impounded areas for greater lengths of time (Swenson and Turner, 

1987). 
Figure 46. Summary of salinity records in the Houma Navigation Canal area in coastal Louisiana. The 

kilometers), the average and maximum salinity in parts per thousand (ppt) over the listed time interval, for the marshes in the vicinity of the station are shown for each station. 

72
Landscape Metrics 

Fragmentation Introduction 

With their ability to sustain wildlife, reduce erosion, filter out pollutants, and act as a buffer 

against storms and flooding, Louisiana’s coastal marshes are among the Nation’s most fragile and 

valuable wetlands (Williams, 1995). Therefore, understanding how those marshes respond to 

natural and anthropogenic forcing events (for example, increases in salinity and canal construction) 

has become vital to both the maintenance and the restoration of Louisiana’s coastline. Methods for 

establishing percentage of water, along with configuration and connectivity of that water within the 

marsh landscape, have been developed for evaluating the effects of navigation canals related to the 

Outer Continental Shelf (OCS) and of salinity on wetland landscape patterns. A water body 

configuration and fragmentation index was used to classify study area marshes into multiple 

landscape categories. The initial step in the development of this methodology was a search of 

habitat fragmentation and patch connectivity literature. The review helped to uncover numerous 

studies that successfully utilized fragmentation and patch analysis software to describe and predict 

habitat patterns and change. Though this review provided project feasibility, as well as standard 

procedures and software for a “typical” fragmentation study, it did not produce any literature 

regarding fragmentation analysis methods that used a marsh landscape. Therefore, a purpose of 

this project was to establish, evaluate, and improve methods of analyzing and classifying marsh 

fragmentation and configuration. Three test methods were proposed for this study: (1) a holistic 

manual interpretation method, (2) a holistic computer interpretation method, envisioned as a 

“moving window”-class boundary delineation, and (3) a FRAGSTATS landscape statistical gridbased method. 

The preferred method for determining marsh configurations is the holistic approach. 

Previous studies have used a manual method to analyze areas of interest to determine wetland loss 

rates with a classification system based upon percentages of water and their configurations within 

the marsh (Dozier, 1983; Sasser and others, 1986; Evers and others, 1992), but this method can be 

subjective, lacks accurate reproducibility, and is not adequate for potential development into a 

packaged management tool. A holistic computer interpretation method was investigated, but the 

limitations and restrictions (for example, there are infinitely many polygons corresponding to each 

water class) that are associated with this method proved to be insurmountable. 

Because of the shortcomings of both holistic approaches, a grid-based method of 

determining water percentage and configurations that used the FRAGSTATS computer software 

was developed. Though this grid-based approach is not suitable for class-scale determinations on 

the landscape as a whole, it can provide class-level metrics and classification through individual 

nonrelated grid tiles and separate landscape-level metrics and analyses. Ultimately, this method 

provides the greatest likelihood of product reproducibility and the highest potential as a packaged 

management tool. 

 FRAGSTATS was designed to compute a wide variety of landscape metrics for categorical 

map patterns (McGarigal and others, 2002). Historically, FRAGSTATS has been used for habitat 

suitability, change, and connectivity dynamics for forested ecosystems. Though no literature on the 

study of marsh fragmentation and classification that used FRAGSTATS was found, personal 

communications with coastal GIS specialists suggest that FRAGSTATS is well suited for research 

of this nature. 

73
FRAGSTATS Method Development 

The FRAGSTATS grid method developed as part of this project utilizes a land-water 

classified image and a two-part classification system. The two levels used in this system are (1) 

category: ratio of water to land and (2) configuration: marsh water density, shape, and 

connectivity. This classification system (modified from Dozier, 1983) assigns values 1–7 to 

represent percentages of water as follows: category 1, 0–<5 percent water within marsh; category 

2, 5–<10 percent water; category 3, 10–<25 percent water; category 4, 25–<40 percent water; 

category 5, 40–<60 percent water; category 6, 60–<80 percent water; and category 7, ≥80 percent 

water. The system subclasses are designated by the configuration of water bodies in the marsh. 

Subclass A includes configurations that are typically large water (in relation to percent water class) 

and have connected water patches with linear edge. Subclass B includes configurations that are 

typically small (as related to associated percent water class) disconnected patches with a more 

random distribution and fewer instances of connection. Subclass C includes configurations that are 

a combination of both subclass A and subclass B (with discernible regions of both). Figure 47 

illustrates the classification system by using example grid tiles from the Dulac digital orthophoto 

quarter quadrangle (DOQQ) (Louisiana Oil Spill Coordinator’s Office, 1999) (see test site 

information below) to represent category and configuration combinations. 

The Lake Boudreaux area was chosen as the test site for establishing and evaluating the 

FRAGSTATS grid-based method. Selection of this test site was multipurposed: (1) a previous 

manual marsh classification project was performed using the 1998 Dulac DOQQ (allowing for 

comparison of results), and (2) it was located within the proposed Houma Navigation Canal study 

area. Prior to FRAGSTATS processing and evaluation, Erdas Imagine was used to perform a landto-water unsupervised classification analysis on the Dulac DOQQ. This land-water classification 

process was used to designate image pixels as either “water” or “land” or “other” classes based on 

individual pixel signature (fig. 48). All nonwater and nonmarsh features were initially recoded into 

the “other” category. This category—which consisted of fastlands and forested, agricultural, and 

developed lands—was ultimately appended with large bayous and channelized canals to ensure 

their exclusion from all statistical analyses and classification. 

The initial phase of the FRAGSTATS system development was to determine the 

appropriate grid scale required for maximizing accuracy of classification. To compensate for 

extensive computer processing and to determine appropriate grid scale, the entire classified Dulac 

image was subset down to 62,500-m2

 (1/16-km2), 15,625-m2 (1/64-km2), and 3,906-m2 (1/256-km2) 

raster grids. Each extracted raster grid, or “tile,” was then processed using FRAGSTAT and 

analyzed at the class metric level (statistics computed for every patch type or class in the 

landscape) and at each designated grid scale. Tiles were sorted by adjusted water percentages 

(recalculated category class, excluding “other” class) and by preliminary configuration thresholds 

(established to assess suitable metrics and metric combinations). Countless arrangements of 

metrics and metric combinations were selected and tested against configuration definitions. 

Ultimately, category and configuration class outputs were assessed to evaluate results of various 

metric, threshold, and scale combinations. 

74
75

Figure 47. Examples of the classification scheme from the Dulac, La., quadrangle, 

with the percentages of water (modified from Dozier, 1983) used for interpretation of 

aerial photographs. The numbers 1–7 represent percentages of water as category 1, 

0–<5 percent water within marsh; category 2, 5–<10 percent water; category 3, 10–

<25 percent water; category 4, 25–<40 percent water; category 5, 40–<60 percent 

water; category 6, 60–<80 percent water; and category 7, ≥80 percent water. Letters 

A, B, and C are subclasses determined by configuration of water bodies in the 

marsh.
Figure 48. Dulac, La., digital orthophoto quarter quadrangle (DOQQ) (Louisiana Oil Spill Coordinato

classified via the FRAGSTATS system.

76
Scale emerged as a major factor in metric output and classification accuracy. Ideally, 

each individual grid tile would contain a landscape with only one category/configuration class. 

Visual assessments determined that, at the 1/16-km2 scale, tiles regularly contained three or more 

configuration classes. Conversely, the 1/256-km2

 scale tiles resulted in areas that were too small 

to accurately classify the landscape and, therefore, repeatedly over classified water features. The 

1/64-km2

 scale tiles provided the most accurate results and seemed to best satisfy the one 

category/configuration class per grid-tile criteria. 

A shift error was observed in which a change in classification resulted from a one-half 

pixel (0.5 m east and north) shift in the vector grid boundary origin (northing and easting). A 

shift analysis was performed on the Dulac land-water image for all three scales (table 11). The 

total percent change for the 1/16-km2, 1/64-km2, and 1/256-km2 tiles were 70.33, 58.02, and 

54.74 percent, respectively. Though the shift analysis of the 1/64-km2

 grid produced more 

change in overall classification than did the 1/256-km2 grid, the difference was not significant. 

Through this shift analysis and the aforementioned scale classification assessment, it was 

concluded that the 1/64-km2 grid provides the most accurate and suitable scale for this study. To 

standardize the tiling origin and alleviate potential shift error, the project vector grid origin was 

based on an established grid system developed by Twilley and Barras (2004) for the Louisiana 

Coastal Area Ecosystem Restoration Study. 

Table 11. Dulac, La., sample area shift and scale analyses (1/16 km2, 1/64 km2, 1/256 km2). 

[km2, square kilometer; ha, hectare; %, percent]

1/16-km2

 scale 1/64-km2 scale 1/256-km2 scale 

Class Change (ha) Change (%) Class Change (ha) Change (%) Class Change (ha) Change (%) 

1 729.9 74.0 1 252.3 44.9 1 386.7 52.0 

2A 59.4 90.3 2A 86.1 92.9 2A 74.0 90.6 

2B 97.3 81.7 2B 105.6 82.6 2B 137.9 82.0 

2C 60.8 88.4 2C 106.5 88.9 2C 95.5 90.6 

3A 194.7 84.5 3A 367.5 79.1 3A 160.6 88.3 

3B 199.3 63.0 3B 182.0 81.5 3B 268.8 76.5 

3C 255.3 86.9 3C 208.2 82.3 3C 198.4 83.8 

4A 219.5 90.9 4A 180.9 90.2 4A 223.9 88.0 

4B 263.1 69.5 4B 295.3 74.9 4B 170.0 81.9 

4C 267.2 89.1 4C 129.1 84.8 4C 134.0 88.0 

5A 289.3 88.1 5A 401.9 78.8 5A 209.2 87.8 

5B 299.9 84.6 5B 97.1 92.6 5B 269.0 84.6 

5C 280.1 84.5 5C 151.5 82.7 5C 90.5 86.0 

6 492.9 66.7 6 443.0 61.7 6 388.3 66.8 

7 239.9 40.4 7 250.6 30.8 7 266.0 25.0 

Total 3948.8 70.3 Total 3257.6 58.0 Total 3072.7 54.7 

Of the approximately 100 FRAGSTATS class and landscape metrics that were assessed, 

eight emerged as responsive control metrics within the fragmentation classification process 

(table 12) (McGarigal and Marks, 1995). Of those eight, three primary metrics demonstrated the 

greatest and most accurate control over class designation. These metrics were largest patch 

index, patch cohesion index, and clumpy index. As the name implies, largest patch index 

77
quantifies the percentage of total landscape area composing the largest patch. This index is a 

simple measure of dominance, with percentage calculated by the equation: 

)100(

)(max1

A

a

LPI

ij

b

j= = , 

where aij is the area (in square meters) of patch ij, and A is the total landscape area (in square 

meters). 

Table 12. Description of metrics selected as part of FRAGSTATS fragmentation and 

classification system. 

Metric Description 

Largest patch index (LPI) Percentage of total landscape area composing the largest patch 

Clumpiness index (clumpy) Frequency with which different classes appear side by side in the landscape 

Patch cohesion index (cohesion) Physical connectedness of the corresponding patches 

Total class area (TCA) Sum of the areas of all patches of the corresponding class 

Percentage of landscape (PLAND) Percentage that the landscape composed of the corresponding class 

Number of patches (NP) Number of patches of the corresponding class 

Patch density (PD) Number of patches of the corresponding class divided by total landscape area 

Landscape shape index (LSI) Class perimeter length divided by min perimeter needed for max aggregation 

Patch cohesion index measures the physical connectedness of the corresponding patch 

type. Patch cohesion increases as the patch type becomes more clumped or aggregated in its 

distribution and therefore becomes more physically connected. This percentage is calculated by 

the equation: )100( 1 1 1

1

1

1

−

=

=

⎥

⎦

⎤ ⎢

⎣

⎡ −

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

−=

∑

∑

A aP

P

COHESION n

j

ijij

n

j

ij

, 

where Pij is the perimeter of patch ij in terms of number of cell surfaces, aij is the area of patch ij

in terms of number of cells, and A is the total number of cells in the landscape. Patch cohesion 

index ranges from 0 to 100 and equals 0 if the landscape consists of a single nonbackground cell. 

The clumpiness index shows the frequency with which different pairs of patch types 

(including like adjacencies between the same patch types) appear side by side on the map, 

varying from -1 (totally disaggregated class) to 1 (maximally clumped). This unitless value is 

calculated by the equation: 

⎥

⎦

⎤ ⎢

⎣

⎡

−

− ⎥ <<

⎦

⎤ ⎢

⎣

⎡ − =

i

ii

iii

i

ii

P

PG elsePPforG PPG CLUMPY

1 ,5.& , 

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

−−

=

∑=

)(min

1

i

m

k

ik

ii

i

g e

g GivenG , 

78
where gii is the number of like adjacencies (joins) between pixels of patch type (class) i based on 

the double-count method, gik is the number of adjacencies (joins) between pixels of patch types 

(classes) i and k based on the double-count method, min-ei is the minimum perimeter (in number 

of cell surfaces) of patch type (class) i for a maximally clumped class, and Pi

 is the proportion of 

the landscape occupied by patch type (class) i. 

In addition to these primary FRAGSTATS metrics, secondary metrics (and their 

combinations) were evaluated to determine sensitivity to change in configuration class. Few 

combinations significantly increased the precision of the classification, while most decreased the 

accuracy and were therefore discarded. The set of secondary metrics that added marked 

precision to the classification scheme included adjusted patch density, ratio of number of patches 

to clumpiness index, and landscape shape index. Adjusted patch density calculates the number 

of patches of the corresponding patch type, divided by total grid-tile landscape area, excluding 

all “other” patches. This value—number per 100 ha —is calculated with the equation: 

A )100)(000,10( A

n PD i = , 

where ni is the number of patches in the landscape of patch type (class) i, and A is the total 

landscape area (in square meter). 

The ratio of number of patches to clumpiness index is a metric combination that provides 

a quasi density-by-shape value that separates configurations A and C that are approaching the 

class threshold. This index is calculated by using the equation: 

⎥

⎦

⎤ ⎢

⎣

⎡

−

− ⎥ <<

⎦

⎤ ⎢

⎣

⎡ − =

i

ii

iii

i

ii

i

P

PG elsePPforG PPG

n

CLUMPY

NP

1 ,5.&

, 

where ni is the number of patches in the landscape of patch type (class) i. 

Finally, the landscape shape index is the total length of edge involving the corresponding 

class, given in number of cell surfaces, divided by the minimum length of class edge possible for 

a maximally aggregated class. Simply, this unitless statistic is the number of cell surfaces 

divided by the minimum length of class edge as calculated by the equation: 

∑=

=

m

k

LSI e

1

ik "25.0

, 

where ei

 is the total length of edge of class i in terms of number of cell surfaces, including all 

landscape boundary and background edge segments involving class i, and min ei

 is the minimum 

total length of edge of class i in terms of number of cell surfaces. The minimum value for LSI is 

1, with the value increasing as the patch type becomes more disaggregated. 

Visual classification of sample site tiles was used to assess accuracy of the computergenerated classification. This method was used to evaluate all potential metric and metric 

combinations, as well as to fit value thresholds to visually derived classes (see classification 

criteria/thresholds, appendix 2). Final arrangement of metric and threshold combinations was 

established by assessing approximately 25 percent of all category 2 and category 3 sample tiles 

and 12.5 percent of category 4 and category 5 sample tiles. Of those randomly selected tiles, 

approximately one-third of categories 2 and 3 and one-half of categories 4 and 5 contained a 

79
80

metric that was in close proximity to the threshold value. Sampled tiles were used to perform 

final threshold value adjustments, producing a metric and threshold design with >90 percent 

classification accuracy. Figure 49 illustrates the preliminary grid-based classification image for 

the Dulac DOQQ. 
Figure 49. Preliminary FRAGSTATS marsh fragmentation and water configuration classification pe

1998 Dulac, La., digital orthophoto quarter quadrangle (DOQQ) (Louisiana Oil Spill Coordinator’s Oand water classified image.

81
Marsh Fragmentation Classification System 

Study Site Spatial Data 

Geospatial Datasets 

Multiple unique geospatial datasets were acquired or constructed for this study. These 

datasets consist of the project area boundary, developed areas, location canals (canals that are 

primarily dredged within marsh landscapes to provide greater accessibility for oil and gas 

extraction [personal communication, Richard M. Jones]), waterway centerlines and mile 

markers, 3-km buffer array, vegetation zone and buffer intersection, tiling vector grid, canal 

dredge locations, salinity station locations, vegetation zones, forested areas, and relevant 

attribute data. The study area boundary was digitized by using Landsat Thematic Mapper (TM) 

imagery as a base map, taking into account the estimated distance of influence from the Houma 

Navigation Canal (HNC) and utilizing neighboring waterways as boundaries. The boundary—

which consists of Minors Canal, Lake de Cade, and Bayou du Large to the west and Bayou 

Terrebonne to the east and extends south from the Gulf Intracoastal Waterway (GIWW) towards 

the southern portions of the Bayou Sauveur and Lake Quitman quadrangles—encompasses 

approximately 805 km2

 of various landscapes. There are two primary water features of interest 

within the study area: the channelized (HNC) and Bayou Terrebonne (reference bayou). The 

USACE survey and dredge reference digital plates for the HNC were acquired. These plates 

were used to extract and merge waterway centerlines and mile marker data into unique layers. 

These extracted data, in conjunction with the USACE dredge history data, were used to create a 

dredge location data layer with supporting attribute data (fig. 50). Additionally, a point theme 

was constructed by using salinity monitoring station data from the USACE, LDNR, and USGS, 

and the coastal marsh vegetation type data (USGS and LDWF) and a subset using the project 

boundary were acquired. Forested and shrub/scrub areas from the raster Gap Analysis Program 

(U.S. Geological Survey, 1998) dataset were converted to vector format and intersected with the 

study area boundary. In addition, multiple vector grids (differing scale) were constructed for 

batch grid tiling and class statistics processing. 

Photography Acquisition and Processing 

Photography was used to evaluate land change and wetland landscape patterns as 

influenced by Outer Continental Shelf waterways, specifically the Houma Navigation Canal. 

Aerial photography selected for analysis consisted of dates that (1) encompassed canal 

construction and major dredging activity (widening or deepening), (2) closely coincided with 

dates of supporting data (e.g., vegetation zones), and (3) exhibited best available quality, 

coverage, and scale and offered greatest ease of rectification. 

Table 13 lists all available photography and status by canal construction phase. Three 

temporal data points—preconstruction, postconstruction, and end-date phase—were proposed for 

this study. Datasets considered for this project included three preconstruction phase (1953, 

1955/56, and 1958), two postconstruction (1965 and 1968/69), and three end-date (1998, 2001, 

and 2004) datasets. 

82
83

 Assessments of all available photography were performed for each canal construction 

phase. Each date of photography was evaluated to determine format (hardcopy or digital), 

rectification status, and completeness of coverage and to ensure appropriate scale and quality for 

image georectification (if needed) and classification. Preliminary assessments showed that the 

majority of the historical panchromatic photography (and some color photography frames) were 

inconsistent and problematic. These frames were either dark or contained significant cloud 

cover, haze, sun glint, or burned marsh. Possessing one, or a combination, of these features can 

greatly compromise land-water interpretation and classification. 

Due to the status of coverage, scale, format, rectification, and proximity to canal 

construction, the 1958 panchromatic photography (Tobin, obtained from T. Baker Smith & Son, 

Inc., and LDNR) was the preconstruction photography selected for this study. Though the 

1955/56 and 1958 photographs were similar in most characteristics, the 1958 photography 

contained fewer instances of atmospheric contamination. The 1968/69 panchromatic 

photography (U.S. Army Corps of Engineers, 1:20000 scale) was selected as the 

postconstruction dataset. This photography satisfied all selection criteria, while the comparable 

1965 imagery lacked adequate coverage. The 1998 DOQQs (LOSCO) were selected as the endpoint dataset; this dataset satisfied all selection criteria (the 2001 photography exhibited poor 

quality, and the 2004 photographs lacked complete project coverage [at the time of assessment]). 

Table 13. Status of available photography for the preconstruction, postconstruction, 

and end-point dates, related to the salinity study in coastal Louisiana. 

[LSU, Louisiana State University; USGS, U.S. Geological Survey; USACE, U.S. Army Corps of Engineers; H, hardcopy prints; D, imagery is 

digital; G, imagery is rectified; Q, old type orthophoto quadrangle on paper; X, problems with the imagery, not useful for this project; N/A, not 

applicable.] 

Phase Year Source Coverage Scale Status 

Preconstruction: 

1953 LSU Partial N/A H 

 1955/56 Amman1 Partial 1:24000 Q 

1958 Tobin2 Full 1:24000 D,G 

Postconstruction: 

1965 Tobin Partial 1:20000 D,X 

1968/69 USACE Full 1:20000 H,D,G 

Endpoint: 

1998 USGS Full 1:24000 D,G 

2001 USGS Full 1:24000 D,G,X 

 2004 USGS Partial 1:24000 D,G 

1

 Amman = National Aerial Resources. 12 Arrowhead Lane, Cohoes, NY 12047. 2

 Tobin = Tobin International, Ltd. 1626 Broadway, Suite 500, Denver, CO 80202. 
84

Figure 50. Salinity monitoring station, dredge history, and river mile locations within the Houma Nstudy area in coastal Louisiana. 
Data Processing 

Photography obtained for this project was received in either hardcopy or digital form. 

Hardcopy photography was scanned at 300 dots per inch (dpi) and converted to Tagged Image File 

Format (TIFF). All photography was then converted into an Erdas Imagine format, georectified by 

using the 1998 DOQQs as a control, indexed, and mosaicked. Georectification of the acquired 

photography was performed by the USGS (1958 and 1998 photography) and Science Applications 

International Corporation (SAIC, 1968 photography). The photography was then subset by project 

boundary and divided into smaller overlapping regions for ease of processing. 

A land-water analysis was performed on each of the preconstruction, postconstruction, and enddate photography sets. An unsupervised classification within ERDAS Imagine 8.7©

 software was 

performed with a 50-class assignment, 0.990 convergence threshold, and a maximum of 30 iterations 

on all subset photography. The intermediate cluster image was then analyzed by using the original 

photography, interpreted by using ancillary photography, and manually recoded to improve accuracy 

of classification. The original 50 classes were collapsed into three final classes: “land,” “water,” and 

an “other” class. The “other” class—which consists of all major canals, drilling canals, and fastlands 

as well as agriculture and developed land—was used to exclude all features that were not either marsh 

or marsh water from class and landscape statistics. Though the canals were excluded from the initial 

landscape fragmentation analysis and classification, they were of primary interest to the overall 

landscape evaluation and were later utilized in the salinity pathway assessments. All vegetation, such 

as marsh, burned marsh, scrub/shrub, forested wetlands, emergent vegetation, and circular vegetation 

within water bodies received a “land” classification. Open water, floating aquatics, nonvegetated 

mudflats, and structures positioned over water were classified as “water.” In order to minimize 

incorrectly classified habitat types, a photointerpreter was consulted as needed. After classification 

was complete, an accuracy assessment was performed by other project members to ensure proper 

classification. This QA/QC provided a peer review process by which all edits and suggestions were 

considered and amended where appropriate. 

The final 1958, 1968/69, and 1998 land-water images were remosaicked, resampled (1-m 

horizontal resolution), and shifted to ensure alignment of raster pixel edge to origin of the project 

vector grid. This vector grid was then used to subset each image into individual 1/64-km2

 tiles. Given 

the substantial number of project tiles (approximately 100,000) and the required processing time (on 

the order of weeks), extraction of individual tiles proved laborious and problematic. A geoprocessing 

routine was developed to expedite the preparation and extraction of raster datasets by using ArcGIS 9.1 

software and Visual Basic for Applications (VBA). This routine automated the process of subsetting 

the project-wide raster dataset into 1/64-km2

 ASCII rasters, thereby reducing the processing time to 

approximately 24 hours. The ASCII format was utilized in order to circumnavigate multiple Microsoft 

Windows and FRAGSTATS limitations (for example, a limit of 10,000 ArcGIS Grid INFO files per 

Microsoft Explorer window). Since FRAGSTATS processing requires headerless input data, a second 

automated routine was developed to efficiently remove header information from large volume of 

ASCII files. 

The FRAGSTATS software package has an integrated batch function for rapid processing of 

multiple raster files. This feature, which utilizes a batch file, specifies raster name and location, cell 

size, background value, file type, and number of columns and rows per ASCII tile. This batch file was 

used in conjunction with user-defined settings—patch neighbors, output statistics, and analysis type—

to compute statistics of desired metrics for the 1958, 1968/69, and 1998 ASCII tiles. For the 

FRAGSTATS grid-based class statistics, the following metrics were required for calculating and 

85
assigning category and configuration classes: class area, percentage of landscape, number of patches, 

patch density, largest patch index, landscape shape index, connectivity index, and clumpiness index. 

These statistics were calculated at the class level for each unique ASCII tile, and the output was 

compiled and exported as a single output file. 

Transforming the raw FRAGSTATS data into final category and configuration classes requires 

a two-part system. The first part comprises the definition and equation components, which are the 

criteria and thresholds by which the raw statistical data are classified. These rules are used to parse 

unneeded data, calculate secondary values, assign category classes, and apply conditional 

configuration criteria (appendix 2). The second part, which is a transforming routine that applies 

criteria equations and performs sorting routines, is known as the FRAGSTATS transformer. 

Specifically, the transformer uses a multirelated table database as the target for importing and parsing 

raw FRAGSTATS output. The application uses a data transformation workflow to convert data types, 

perform configuration equation calculations, and channel data through approximately 25 conditional 

assessments (many of which contain multiple nested conditions). The application provides both 

data preview and export interface which allow data to be exported to numerous file types. Class 

statistics from the 1958, 1968/69, and 1998 FRAGSTATS processes were transformed and exported as 

unique comma-separated value (csv) files. 

A project region layer was created to aid in the determination of areas that exhibit significant 

landscape change and influence. This “regions” dataset was developed by using the LDEQ Office of 

Water Resources’ watershed basin subsegment management units (Louisiana Department of 

Environmental Quality, 1998). These units are subwatersheds that were delineated on the basis of 

regional topography, drainage boundaries, and principal waterways. There are 13 project regions that 

fall within the project boundary. They are Duplantis, Hache, West/Central/East Falgout, Rambio, Sale, 

Petite Caillou, La Cache, Pelton, North and South Boudreaux, and Klondyke (fig. 51). 

In addition to the standard grid-based classification analysis, landscape statistics and zonal 

analyses were computed for all three temporal data points. The landscape statistics incorporate 

metrics that measure the aggregate properties of an entire patch mosaic. They were used to describe 

the change in marsh density, shape, and connectivity at the project scale. 

Land-Water Analysis 

The principal component of a landscape fragmentation and configuration study is the landwater classification analysis. Three project-scale land-water analyses were performed (1958, 1968/69, 

and 1998; fig. 52), producing 805-km2 landscapes consisting of “land,” “water,” and “other” feature 

classes. Table 14 shows the area of each landscape feature, categorized by image date. Changes in 

feature class area were measured as decreases in “land” of 26.5, 150.1, and 176.6 km2

 and increases in 

“water” of 23.5, 143.4, and 166.8 km2

 for time period A (TA, 1958–68/69), time period B (TB, 

1968/69–98), and the overall time period (OA, 1958–98), respectively. The “other” landscape was 

classified by using the fixed area developed mask (generated by using the 1998 photography), allowing 

for the area of location canals to be derived for each date. An increase in the amount of project area 

consisting of location canals was observed. These areas increased by 3.0, 6.8, and 9.8 km2 over the 

TA, TB, and OA time periods, respectively. 

 Table 15 shows land lost, rate of loss, and percentages of loss at various scales (temporal and 

spatial) within the Louisiana coastal zone. This table compares the land loss trends calculated for the 

TA, TB, and OA periods of the Houma Navigation Canal (HNC) project area, with estimates at the 

basin and coastwide scale for 48-year (Barras, 2006) and 68-year (Barras and others, 2003) trends, 

86
respectively. The land loss rate estimates are 72.4 and 62.0 km2

/year for the 1932–2000 and 1956–

2004 periods for the coastal areas, respectively; 17.3 km2/year for the Terrebonne basin (1956-2004); 

and 4.4, 5.0, and 2.7 km2/year for the project area OA, TB, and TA time periods, respectively. To 

normalize these rates, the percent land loss per year for land class area and total area were calculated. 

The 1932–2000 and 1956–2004 period studies, which produced coastal loss rates of 0.2 percent 

(percent loss per year of total area), were approximately 1.6 times slower than the rates of the TA 

period for the HNC area and the 48-year period for the Terrebonne basin. These increases in rate 

indicate that, compared to the coastal zone, the Terrebonne basin was a region of more extreme 

degradation and loss. Furthermore, the TB and OA periods within the HNC area experienced 

percentages of loss at twice the rate of both the TA and Terrebonne basin (1956-2004) periods. These 

results suggest that the HNC study area was losing land at a significantly faster rate than both the 

marshes of coastal Louisiana and the other highly degraded neighboring marshes within the 

Terrebonne basin. 

Table 14. Area of total project landscape and feature classes for the 1958, 1968/69, and 

1998 land-water images in coastal Louisiana. 

 

Area (km2

) 

Date 

Total 

area Land Water

Location 

canals Other 

1958 805.24 474.86 131.98 7.46 190.94 

1968/69 805.24 448.4 155.44 10.46 190.94 

1998 805.24 298.3 298.79 17.21 190.94 

Table 15. Total area lost and land loss rates within coastal Louisiana at various spatial and 

temporal scales. Land class areas are all landscapes within the coastal zone that have 

been classified as land—here the land loss rates are provided both as a percentage based 

on total area within the coastal zone and as a percentage based on the area of the land 

class at the start of each time period. 

[“Canal” refers to the Houma Navigation Canal] 

Land loss 

Boundary Period 

Square 

kilometer 

Percent 

of total 

land 

class 

area 

Square 

kilometer 

per year 

Percent 

loss per 

year 

(land 

class 

area) 

Percent 

loss per 

year 

(total 

area) 

Canal 1958–68 26.5 5.6 2.7 0.6 0.3 

Canal 1968–98 150.1 33.5 5.0 1.1 0.6 

Canal 1958–98 176.6 37.2 4.4 0.9 0.6 

Terrebonne1 1956–2004 831.4 27.6 17.3 0.6 0.4 

Coastal1 1956–2004 2975.9 19.6 62.0 0.4 0.2 

Coastal2 1932–2000 4921.0 72.4 0.2 

1. Data derived from Barras (2006). 

2. Data derived from Barras and others (2003). 

87
Figure 51. Houma Navigation Canal project regions in coastal Louisiana (based on 

Louisiana Department of Environmental Quality watershed subsegments).

88
Figure 52. Three-panel map of the 1958, 1968/69, and 1998 land and water classified images in c

89
The landscape-level marsh density, shape, and connectivity metrics also were computed for 

all three dates (table 16). This table shows that values for landscape shape index (LSI) increased 

and that values for largest patch index (LPI) decreased across all TA, TB, and OA time periods. 

The changes in patch density (PD) and patch cohesion index (cohesion) values were less typical. 

The PD values showed decrease at the TA period and increases for both TB and OA periods. The 

cohesion value changes where similar to PD, except there was a decrease in value over the OA 

period. In general, these shifts in landscape configuration and connectivity metric values are 

indicative of a marsh that transitioned from a solid and less complex landscape with high levels of 

marsh connectivity to a highly complex and degraded landscape with increased water connectivity. 

Table 16. Project-scale landscape metrics for the 1958, 1968/69, and 1998 land-water 

images in coastal Louisiana. 

Metric (weighted average) 1958 1968/69 1998

Patch density 433.1 301.1 637.2

Largest patch index 40.8 34.3 31.8

Landscape shape index 19.3 20.6 34.2

Cohesion 99.9 99.4 99.5

Marsh Classification 

A total of 52,146 individual project tiles (1 tile = 15,625m2) were used to generate classlevel metric statistics. To determine changes in the marsh, the developed and forested areas were 

removed, lowering the number of tiles used in the rest of the calculations to 35,344. Class-level 

statistics were used independently to assess change of landscape characteristics over time and in 

combination to classify marsh category and configuration. For independent metric evaluations of 

marsh condition over time, the derived metric values of a former year were deducted from a latter 

year to develop a scale of comparison. These comparisons were used in conjunction with the 

category and configuration classification to determine areas of significant degradation. 

Table 17 shows summary statistics for each class-level metric by date. Raw metric values 

were used in all cases, except where tile landscapes contained no water and were then assigned 

values of 0.0 for all class-level metric. Consistent with the project-scale water change, the tilebased water metric statistics show an increase in mean percentage of water to land (PLAND) of 4.5, 

25.2, and 29.7 percent and LPI of 4.5, 22.1, and 26.9 percent for the TA, TB, and OA time periods, 

respectively. These increases indicate an average water gain (land loss), and an increase in the 

average size of largest water patch per tile, between the three project dates. Specifically, the 

percentage of total project area that was composed of tiles containing less than 5 percent water was 

49 and 42 percent for pre- and postconstruction dates, respectively. Of those tiles, 12 and 13 

percent, respectively, were located in areas classified as forested/shrub/scrub (Gap Analysis 

Program, U.S. Geological Survey, 1998). By 1998, 18 percent of the total project area contained 

tiles with <5 percent water, while 25 percent of those tiles were classified as forest/shrub/scrub. 

Conversely, the tiles containing >60 percent water covered 14 and 17 percent of the study area in 

90
1958 and 1968/69, respectively. The number of “water” tiles more than doubled by 1998, 

encompassing 39 percent of the study area. 

Core PLAND metric statistics, LPI metric statistics, PLAND difference analysis, and LPI 

difference analysis are shown in figures 53, 54, 55, and 56, respectively. These figures illustrate 

the location and magnitude of water changes within the project area. The largest increases in 

percentage of water occurred primarily in the Rambio, Hache, West Falgout, and eastern portions 

of South Boudreaux regions for the TA time period. The increases in PLAND and LPI values 

observed for this time period and within these regions appear to be related to shoreline and bank 

erosion in the southern portion of the study area, or they occurred in northern regions near major 

canals or pathways with direct connectivity to the Houma Navigation Canal (HNC). While all 

regions—except Petite Caillou—underwent significant increase in PLAND and LPI values for the 

TB period, the more extreme increases were observed in the Central and East Falgout, North and 

South Boudreaux, Pelton, Klondyke, and various portions of the Rambio, West Falgout, and La 

Cache regions (figs. 55 and 56). Similar to increases during the TA period, these increases appear 

to be related to shoreline erosion in the southern regions—which were more prominent for the TB 

time period—or were near the HNC, Bayou Terrebonne, or one of their connecting pathway 

canals. These increases, or amount of land loss, are indicative of the natural shoreline erosion that 

is typical in the southern brackish and saline marshes and of the interior marsh degradation caused 

by hydrology and salinity shifts in the northern fresh and intermediate zones. Figures 55 and 56 

illustrate that the majority of the interior marsh loss occurred near the fresh-intermediate-brackish 

vegetation zone boundaries (see fig. 3). 

Summary statistics for water patch density, shape, and connectivity metrics are shown in 

table 17. The connectivity metric shows a steady increase in mean cohesion value from the pre- to 

postconstruction periods and from the postconstruction to end-point dates. These increases indicate 

a progression from lesser connectivity of water in the preconstruction tiles to significantly greater 

connectivity within the end-point tiles. The PLAND (water/land ratio) and category class values 

also indicated a noticeable increase in water over time. The category class values increased from a 

preconstruction average of 2.6 (5–10 percent water) to a postconstruction average of 3.0 (10–25 

percent water) and an end-point value of 4.8 (25–40 percent water). Relative increases were 

observed between the preconstruction and end-point tiles for all density and shape metrics. The 

overall (1958–98) mean metric values increased by 3.8, 0.3, and 0.02 for PD, LSI, and clumpy, 

respectively. These decreases indicate a landscape that progressed from stable marsh (less 

aggregated water) through the degraded marsh stages (highly aggregated) and into the water stage 

(more connected, less aggregated water). Figures 57, 58, 59, and 60 show the difference values for 

cohesion, PD, LSI, and clumpy metrics, respectively, while the distribution of those core statistical 

values are shown in appendix 2. The project regions that exhibited the highest increase in cohesion 

value for the OA period were Duplantis; Hache; North Boudreaux; West, Central, and East Falgout; 

and the northern portion of Rambio. The shape and density metrics showed the greatest increases 

in North Boudreaux, Hache, West and East Falgout, and eastern Rambio regions for PD and LSI, 

while Duplantis, Hache, North Boudreaux, West/East Falgout, and northern Rambio regions show 

marked increases in clumpy values. In this context, shape and density value increases are 

indicative of marsh that experienced relatively rapid degradation and/or conversion to water. 

Decreases in the patch density metric value were also observed between 1958 and 1968 in the 

project area. Both increases (change in marsh configuration) and decreases (spoil bank creation) 

would be expected because of the direct and indirect effects of oil and gas canal construction and 

subsequent shifts in hydrology (Penland and others, 2000). 

Comparisons of the 1958 statistics to the 1968/69 statistics show that, while the landscape 

remained solid, there were large positive changes in clumpy values. In 1958 most regions were 

91
solid marsh, with little to no clumpiness, yet by 1968/69 the North Boudreaux and Falgout regions 

began experiencing ponding, with a concurrent increase in clumpiness. Similar to clumpy values, 

the North Boudreaux and Falgout regions experienced increasing cohesion values. The majority of 

the change in cohesion occurred in the transition from the 1958 solid marsh to 5–25 percent water 

by 1968/69. Some decreases in cohesion values for the TA period were observed. These changes 

appear to be the effects of altered hydrology on marsh and spoil bank creation within the North 

Boudreaux, Rambio, and Falgout regions and of interior marsh degradation caused by hydrology 

and salinity shifts in the northern fresh and intermediate vegetation zones. 

Table 17. Summary statistics of core class metrics for 1958, 1968/69, and 1998 dates 

for coastal Louisiana study. 

[StdDev, standard deviation; PLAND, percentage of landscape; LPI, largest patch index; PD, patch density; LSI, landscape shape index; 

Cohesion, cohesion of patch index; Clumpy, clumpiness index; Category, water category class] 

Min Max Sum Mean StdDev

1958

PLAND 0 100 850,539 24.1 32.0

LPI 0 100 777,400 22.0 31.7

PD 0 434.783 154,929 4.4 8.4

LSI 0 16.942 82,062 2.3 2.1

Cohesion 0 100 2,585,713 73.2 40.5

Clumpy 0* 1 25,092 0.7 0.4

Category 0 7 93,447 2.6 2.3

1968

PLAND 0 100 1,009,538 28.6 33.4

LPI 0 100 937,283 26.5 33.4

PD 0 119.04 117,702 3.3 7.1

LSI 0 16.671 86,705 2.5 2.0

Cohesion 0 100 2,856,831 80.8 35.4

Clumpy 0* 1 27,555 0.8 0.3

Category 0 7 107,299 3.0 2.3

1998

PLAND 0 100 1,901,014 53.8 35.7

LPI 0 100 1,728,883 48.9 37.5

PD 0 5000 278,884 7.9 60.2

LSI 0 17.072 110,153 3.1 2.4

Cohesion 0 100 3,296,549 93.3 20.8

Clumpy 0* 1 31,197 0.9 0.2

Category 0 7 168,796 4.8 2.1

* Because very few values were below 0, values less than 0 were rounded to 0. 

In general, the northernmost solid areas that showed no change in shape or density metric 

values during the TA period underwent significant changes by 1998. By this end date, the majority 

of the North Boudreaux, Hache, Duplantis, and all Falgout regions experienced significant 

increases in clumpy values. Similarly, these same regions showed a strong trend towards cohesion 

of water patches between the TB and OA periods. Figures 55 and 56 show that by 1998 the 

northern two-thirds of the study area experienced significant increases in PLAND and LPI. Table 

17 and figure 56 show that most of the areas containing solid marsh had degraded to 25–60 percent 

water, increased in water patch connectivity, and underwent discernable shoreline degradation by 

92
93

1998. Although some changes in percentage of water per tile occurred in the southernmost portion 

of the study area, the majority of change in these regions was less severe. One noticeable area of 

decreasing percentage of water during the TB and OA periods was along the southern section of the 

Houma Navigation Canal. This observed decrease was due to the building of spoil banks along that 

portion of the canal. 
 Figure 53. Map of percentage of landscape (PLAND) for 1958, 1968/69, and 1998. The PLAND (perc

that is composed of the corresponding water class), quantified by using the FRAGSTATS analysis toowith project regions in coastal Louisiana.

94
Figure 54. Map of largest patch index (LPI) for 1958, 1968/69, and 1998. The LPI (percentage of tota

composed of the largest water patch), quantified by using the FRAGSTATS analysis tool, is shown in cregions in coastal Louisiana. 

95
Figure 55. Map of percentage of landscape (PLAND) difference per tile among 1958, 1968/69, and 19

(percentage of tile landscape that is composed of the corresponding water class) values were used to 

per tile by subtracting values of a previous date from a latter date. Those difference values are shown regions in coastal Louisiana.

96
Figure 56. Map of largest patch index (LPI) difference per tile among 1958, 1968/69, and 1998. The L

tile landscape area that is composed of the largest water patch) values were used to calculate the diffesubtracting values of a previous date from a latter date. Those difference values are shown in conjunc

in coastal Louisiana. 

97
Figure 57. Map of cohesion difference per tile among 1958, 1968/69, and 1998. The cohesion (an ind

physical connectedness of the corresponding water patches) values were used to calculate the differevalues of a previous date from a latter date. Those difference values are shown in conjunction with pro

Louisiana.

98
Figure 58. Map of patch density (PD) difference per tile among 1958, 1968/69, and 1998. The PD (nu

corresponding water class divided by total landscape area) values were used to calculate the differenc

values of a previous date from a latter date. Those difference values are shown in conjunction with pro

Louisiana.

99
Figure 59. Map of landscape shape index (LSI) difference per tile among 1958, 1968/69, and 1998. T

perimeter length divided by minimum perimeter length required for maximum aggregation) values were 

difference per tile by subtracting values of a previous date from a latter date. Those difference values 

with project regions in coastal Louisiana.

100
Figure 60. Map of clumpy difference per tile among 1958, 1968/69, and 1998. The clumpy (frequency 

classes appear side by side in the landscape) values were used to calculate the difference per tile by sprevious date from a latter date. Those difference values are shown in conjunction with project regions 

101
The category classification process utilizes the percentage of water to land to assign class 

value to each tile. Figure 61 shows the distribution of classified tiles throughout the project area 

for all three dates. The project area went from a predominantly solid marsh landscape in the north, 

with water classes extending south from Lake Boudreaux in 1958, to a similar landscape with 

minor increases in water in the northern portion of the Rambio region in 1968, to an overall water 

dominated landscape by 1998. Table 18 shows the class-level category results for each of the three 

study dates. The number of tiles making up category classes 2 and 3 remained relatively constant 

over the TA time period (slight increase for class 3) but underwent significant decreases during the 

TB time period. Category classes 4 and 5 showed greater increases during the TA time period than 

did category classes 2 and 3, but those increases were inversely proportional to the decrease in 

number of category class 2 and 3 tiles during the TB period. The greatest change between all dates 

occurred in category classes 1 (stable marsh) and 7 (open water). Changes in these “indicator” 

category classes are significant since they represent extremes within the marsh degradation 

spectrum. The number of category class 1 tiles went from 15,164, to 12,675, to 3,692, while the 

number of category class 7 tiles went from 4,065, to 4,863, to 11,574 for the 1958, 1968/69, and 

1998 dates, respectively. Figure 62 shows the change in category class values between all project 

dates. The largest changes in category values are observed in the Duplantis, Hache, all three 

Falgout, North and South Boudreaux, Klondyke, and the northern portions of both the Rambio and 

La Cache regions. 

In general, the mean category class value went from 2.6 in 1958, to 3.0 in 1968/69, to 4.8 in 

1998. Table 18 and figure 62 show that since the number of category class 6 and 7 tiles remained 

relatively constant in the TA period, and since the areas of significant loss were isolated in the fresh 

and intermediate zones, the majority of change occurred as stable category class 1 tiles shifted to 

degraded category classes 4 and 5. The change in average category class value increased by 1998 

and was due to the collapse of solid fresher marshes in the northern regions to highly degraded 

marsh or a conversion to open water. Extensive changes in category class values were not 

observed in the southern regions because the majority of tiles in these areas were classified as 

degraded or water in the preconstruction image and because the degradation patterns are indicative 

of the less severe shoreline and bank erosion. 

The configuration classification was performed for all three project dates by using metric 

statistic thresholds to classify marsh pattern. Figures 63 and 64 show the distributions and changes 

in configuration classes for all three dates. Figure 63 shows that the majority of the landscape in 

1958 was classified as configuration class A, which is indicative of a stable marsh with prominent 

and linear water patches. The majority of the configuration class C tiles that make up the 1958 

landscape are in the East Falgout and northern Rambio regions and are scattered throughout the 

North/South Boudreaux and La Cache regions. The number of configuration class A tiles remained 

relatively constant, showing an increase in 1968/69 and then a decrease in 1998. By 1998, there 

was approximately a fourfold increase in the number of configuration class B tiles and threefold 

increase in the number of configuration class C tiles within the project area. These tiles appear in 

the West and East Falgout, North Boudreaux, Pelton, and Rambio regions and are scattered 

throughout the South Boudreaux region. Figure 64 (Marsh Configuration Difference [MCD]) uses 

the configuration classes A, B, and C, along with category classes 1, 6, and 7, to assign 

configuration value to each tile. Values are assigned to each class as follows: category class 1 = 0, 

configuration class A = 1, configuration class B = 2, configuration class C = 3, and category classes 

6 and 7 = 4. To calculate the difference per tile, configuration values for a previous date were 

subtracted from a later date. For example, marsh configuration difference values of 4 represent 

areas that went from solid marsh (value = 0) in 1958 to category 7, or open water (value = 4), in 

1968 or 1998. The marsh configuration difference figure shows extreme change in class values in 

102
103

Category 1958 1968/69 1998

1 15,164 12,675 3,692

2 3,399 3,285 1,776

3 5,726 5,859 4,801

4 2,939 3,548 4,211

5 2,436 3,086 4,870

6 1,615 2,028 4,420

7 4,065 4,863 11,574

Total 35,344 35,344 35,344

Configuration 1958 1968/69 1998

Land 15,164 12,675 3,692

A 13,711 15,120 13,360

B 100 84 417

 C 689 574 1,881

Water 5,680 6,891 15,994

Total 35,344 35,344 35,344

The fragmentation classes were also used to evaluate general marsh condition by grouping 

tiles based on the degree of marsh degradation (table 19). There are three levels of marsh 

condition: solid marsh, degraded marsh, and water. These marsh conditions are classified by using 

several marsh water-to-shape criteria. Solid marshes are landscapes that contain less than 10 

percent water and contain the relatively less degraded configuration subclasses 2A and 2B. 

Degraded marshes are landscapes that are classified as degraded configuration subclass 2C or 

contain between 10 and 60 percent water. And finally, the “water” class is any tile that contains 

more than 60 percent water. 

the Duplantis; Hache; West, Central and East Falgout; Pelton; North and South Boudreaux; and 

northern portions of Rambio and La Cache regions for the OA period. The configuration section of 

table 18 shows that, though there were sizable changes in the number of configuration B and C 

classified tiles, the majority of the OA change was in the land (category class 1) and water 

(category classes 6 and 7) classes. 

Table 18. Summary count of category and configuration classes for the 1958, 1968/69, 

and 1998 dates. 

The category and configuration classifications were combined to create the two-tier marsh 

fragmentation classification system. These classes combine the percentage of water (category) 

with the landscape pattern (configuration) to give a more complete representation of marsh 

fragmentation. Figure 65 shows the distribution of fragmentation classes for each date. In 1958 

the majority of the landscape was composed of classes 1 and 2A in the north, classes 3A, 4A, and 

5A in the southern marshes, and classes 2B and 2C scattered throughout the North Boudreaux; 

West, Central, and East Falgout; La Cache; and northern portions of Rambio regions. The 1968/69 

landscape was similar to the 1958 landscape, with the addition of the 3B, 3C, 4B, 4C, 5B, and 5C 

classes in the North and South Boudreaux, southern La Cache, and Rambio regions. By 1998 there 

were fewer of the solid marsh tiles, and those remaining were isolated to the southwest portion of 

Rambio, areas north of Lake Boudreaux, and along the northern edge of the West Falgout region. 
Figure 61. Map of category classification (per tile) for 1958, 1968/69, and 1998. Category classes (w

percentages of water as category 1, 0–<5 percent water within marsh; category 2, 5–<10 percent wa

percent water; category 4, 25–<40 percent water; category 5, 40–<60 percent water; category 6, 60–category 7, ≥80 percent water) are shown in conjunction with project regions in coastal Louisiana.

104
Figure 62. Map of category difference per tile among 1958, 1968/69, and 1998. Category classes wer

difference per tile by subtracting classes of a previous date from a latter date. Those difference values 

with project regions in coastal Louisiana. 

105
 Figure 63. Map of configuration classification (per tile) for 1958, 1968/69, and 1998. Configuration clas

connected water patches with linear edge; B, typically small disconnected patches with a more random 

instances of connection; and C, a combination of subclass A and subclass B) are shown in conjunction coastal Louisiana.

106
107

 Figure 64. Map of configuration difference per tile among 1958, 1968/69, and 1998. Values were ass

class A = 1, class B = 2, class C = 3, and classes 6 and 7 = 4), and the difference was calculated by su

from a latter date. Those difference values are shown in conjunction with project regions in coastal Lo
Table 19. Marsh classes in coastal Louisiana as defined by using the Dozier 

classification system, which is used for interpretation of aerial photography. 

Class criteria

1 Marsh classes Percentage of water Configuration classes Dozier categories2

Solid marsh 0─10 percent and A, B 1, 2A, 2B

2C, 3A, 3B, 3C, 4A, 4B, 

Degraded marsh 10─60 percent and/or C 4C,5A, 5B, 5C

Water >60 percent 6, 7

1

 From Sasser and others (1986). 2 From Dozier (1983). 

Table 20 shows the number of tiles and total area for each class by date, where each 

fragmentation class is grouped according to its general marsh condition (degree of degradation). 

Table groupings show that, between 1958 and 1968/69, changes were already occurring in the 

distribution of marsh classes; however, by 1998, a large shift from solid marsh (<10 percent water) 

to open water (>60 percent water) occurred. Between 1958 and 1968/69, solid marsh declined by 

Table 20. Summary of fragmentation class groupings for solid marsh (less than 10 

percent water), degraded marsh (10–60 percent water), and water (greater than 60 

percent water) in coastal Louisiana for three time periods. 

1958 1968 1998

Class No. of tiles 2 Area (km ) No. of tiles Area (km2

) No. of tiles Area (km2)

1 15,164 236.9 12,675 198.0 3,692 57.7

2A

2B

3,137

46

49.0

0.7

3,041

56

47.5

0.9

1,419

173

22.2

2.7

Total solid 18,347 286.6 15,772 246.4 5,284 82.6

2C 216 3.4 188 2.9 184 2.9

3A 5,416 84.6 5,620 87.8 4,029 63.0

3B 26 0.4 23 0.4 181 2.8

3C 284 4.4 216 3.4 591 9.2

4A 2,850 44.5 3,483 54.4 3,751 58.6

4B 14 0.2 2 0.0 33 0.5

4C 75 1.2 63 1.0 427 6.7

5A 2,308 36.1 2,976 46.5 4,161 65.0

5B 14 0.2 3 0.0 30 0.5

5C 114 1.8 107 1.7 679 10.6

Total degraded 11,317 176.8 12,681 198.1 14,066 219.8

6

7

1,615

4,065

25.2

63.5

2,028

4,863

31.7

76.0

4,420

11,574

69.1

180.8

Total water 5,680 88.7 6,891 107.7 15,994 249.9

Grand total 35,344 552.1 35,344 552.2 35,344 552.3

 

108
more than 7 percent in the study area. An accelerated decline continued, and by 1998 only 15 

percent of the entire landscape was composed of solid marsh. The degraded marsh class (10–60 

percent water) steadily increased from 32, to 35, to 40 percent of the study area, while the water 

class increased from 16, to 20, to 45 percent of the study area for the 1958, 1968/69, and 1998 

dates, respectively. Not only do these changes, shown in figure 66, illustrate the extreme projectwide shifts from solid marsh to water classes between the 1958 and 1998 dates, but they also 

illustrate the initial shift to degraded marsh in the western regions (Hache, Falgout, and Rambio) by 

1968/69. 

Table 21 further illustrates the change in marsh condition between dates for individual 

project regions. The regions are grouped according to an east-west designation with respect to the 

Houma Navigation Canal (HNC). The project area consists of six western regions (Central 

Falgout, Duplantis, East Falgout, Hache, Rambio, and West Falgout), six eastern regions 

(Klondyke, La Cache, North Boudreaux, Pelton, Petite Caillou, and South Boudreaux), and one 

region (Sale) that falls both east and west of the HNC. Overall, the frequency (and percentage 

within regions) of marsh condition classified tiles that fall within the western regions are 

considerably different from those observed in the eastern regions. The percentage of solid marsh 

tiles decreased over time from 59, to 46.9, to 18.1 percent in the western regions and from 49.1, to 

46.4, to 10.4 percent in the eastern regions for the 1958, 1968/69, and 1998 dates, respectively. 

The inverse was true for percentage of water tiles in both the western and eastern regions. Minimal 

increases were observed in the percentage of water tiles in the western regions, 9.9 to 13.7 percent, 

and eastern regions, 20.5 to 23.5 percent, for 1958 and 1968/69, respectively, but the percentage of 

those tiles increased significantly to 37.2 and 54.9 percent by 1998 in the western and eastern 

regions, respectively. Most notable are the different rates at which the solid and degraded tiles 

change over the TA and OA time periods. For the pre- to postconstruction period, there was an 

average decrease in solid tiles of 12.3 and 2.5 percent for the western and eastern regions, 

respectively, while the degraded tile increased by 9.5 percent in the west and decreased 0.06 

percent in the east by during the same period. The preconstruction to end-point period saw similar 

change for the degraded tile in the west and a shift from slightly decreasing during the TA period to 

slightly increasing for the OA period. With a range of 10–13 percent, the separation in average 

percent difference for solid tiles between the western and eastern regions during the OA period 

remained relatively constant; however, these regions experienced significant decreases over time. 

These trends, and their variations across classes and regions, are indicative of marshes that were at 

different phases along the degradation scale. By 1958, a difference existed between the areas east 

and west of where the HNC would be dredged. The preconstruction western region marshes were 

more stable marshes, affected by fewer and more naturally occurring waterways, and overall were 

farther from the bay and gulf waters. The eastern regions consisted of more degraded and waterdominated marshes, with the marshes in closer proximity to the bay. It is assumed that the 

advanced degree of degradation of these marshes (in relation to the western region marshes) was 

influenced by salinity pathways created by closer proximity to the bay, connections between larger 

natural waterways (for example, Bayou Terrebonne), and channelized pipelines and canals. 

In addition to differences by region, the average class-level values for all metrics were 

assessed to quantify differences in marsh condition with respect to locations east and west of the 

HNC. Table 22 shows the four primary landscape metrics, their average values by year, and eastwest bearing with respect to the HNC. The PLAND, which measures the amount of water within 

each tile, shows that the average percentages of water per tile that fell west of the HNC were 

significantly less than the averages in the east and therefore were less degraded at each of the three 

time series. Similarly, the LPIs—which were used in part to classify landscape configuration 

within each tile—have trends similar to those observed in the PLAND averages. 

109
Table 21. Frequency (and percentage) within year of solid marsh, degraded marsh, and 

water class tiles within each region in coastal Louisiana for three project dates. 

[Dark gray rows indicate regions that are west of the Houma Navigation Canal, light gray rows indicate those that are east of the Houma Navigation 

Canal (Sale, in the white row, is both east and west of the canal] 

Regional marsh condition during the three time periods 

Region

1958 1968/69 1998

Solid Degraded Water Solid Degraded Water Solid Degraded Water

344 35 312 163 36 12 435 

Central Falgout (67.32) 132 (25.83) (6.85) (61.06) (31.90) (7.05) (2.35) 64 (12.52) (85.13)

772 672 258 340 328 318 

Duplantis (78.30) 155 (15.72) 59 (5.98) (68.15) (26.17) 56 (5.68) (34.48) (33.27) (32.25)

2,292 49 2,085 902 148 573 1,204 1,358 

East Falgout (73.11) 794 (25.33) (1.56) (66.51) (28.77) (4.72) (18.28) (38.41) (43.32)

564 10 394 444 34 352 369 151 

Hache (64.68) 298 (34.17) (1.15) (45.18) (50.92) (3.90) (40.37) (42.32) (17.32)

252 208 87 110 51 160 

Klondyke (78.50) 50 (15.58) 19 (5.92) (64.80) (27.10) 26 (8.10) (34.27) (15.89) (49.84)

1,190 1,400 420 1,244 1,292 474 404 1,117 1,489 

La Cache (39.53) (46.51) (13.95) (41.33) (42.92) (15.75) (13.42) (37.11) (49.47)

3,642 3,484 833 599 2,206 1,584 

North Boudreaux (82.98) 704 (16.04) 43 (0.98) (79.38) (18.98) 72 (1.64) (13.65) (50.26) (36.09)

700 91 692 539 128 120 655 584 

Pelton (51.51) 568 (41.80) (6.70) (50.92) (39.66) (9.42) (8.83) (48.20) (42.97)

100 56 116 102 62 137 88 55 

Petite Caillou (35.71) 124 (44.29) (20.00) (41.43) (36.43) (22.14) (48.93) (31.43) (19.64)

4,529 3,265 1,477 3,413 4,028 1,830 1,332 4,569 3,370 

Rambio (48.85) (35.22) (15.93) (36.81) (43.45) (19.74) (14.37) (49.28) (36.35)

1,031 1,614 1,054 974 1,550 1,175 509 1,423 1,767 

Sale (27.87) (43.63) (28.49) (26.33) (41.90) (31.77) (13.76) (38.47) (47.77)

1,126 1,504 2,299 887 1,444 2,598 121 830 3,978 

South Boudreaux (22.84) (30.51) (46.64) (18.00) (29.30) (52.71) (2.45) (16.84) (80.71)

1,583 1,140 943 230 476 1,116 721 

West Falgout (68.44) 669 (28.92) 61 (2.64) (49.29) (40.77) (9.94) (20.58) (48.25) (31.17)

Other 269 269 269

Total 18,125 11,277 5,673 15,621 12,585 6,869 5,085 14,020 15,970

7,010 4,350 2,928 6,631 4,297 3,360 1,491 4,947 7,850 

East total (49.06) (30.45) (20.49) (46.41) (30.07) (23.52) (10.44) (34.62) (54.94)

10,084 5,313 1,691 8,016 6,738 2,334 3,085 7,650 6,353 

West total (59.01) (31.09) (9.9) (46.91) (39.43) (13.66) (18.05) (44.77) (37.18)

The cohesion and clumpy values were significantly less in the western tiles than in the 

eastern tiles for both the preconstruction and end-point dates; however, the reverse was observed in 

the postconstruction data. This variation in trend was due to the initial deterioration of stable 

marsh in the west, resulting in sharper increases in water patch formation, aggregation, and 

connectivity. Though the eastern marshes were less affected by the initial influence of the 

construction and maintenance of the Houma Navigation Canal, the long-term exposure of higher 

salinities in the fresher marshes, and the shoreline erosion in the more saline marshes, propelled the 

eastern marshes to landscapes classified as highly degraded marsh and water by the end-point date. 

110
111

1958 1968/69 1998

Metric West East West East West East

PLAND 19.17 29.10 25.05 32.18 47.89 59.85

LPI 17.10 27.03 22.96 30.18 42.60 55.40

Cohesion 71.77 74.58 81.43 80.21 92.90 93.66

Clumpy 0.69 0.72 0.79 0.77 0.88 0.89

Table 23 utilizes the 1958 category 1 tiles as an indicator class and illustrates how those 

tiles changed over the TA and OA time periods. In general, the landscape was relatively solid in 

1958, with 44 percent of all project tiles being classified as category 1. Sixty-four percent of those 

tiles remained class 1 through 1968/69, while only 17 percent of the original category 1 remained 

by 1998. Additionally, approximately 47 and 23 percent of the tiles that were class 1 in 1958 

changed to degraded marsh by 1968/69 and 1998, respectively. Table 23 also shows that, of the 

tiles that were class 1 in 1958, only 1 percent of them changed to water class by 1968/69 and that 

29 percent were classified as water in 1998. Ultimately this indicator class further illustrates the 

shift from stable marsh in 1958 to degraded marsh and water-dominated landscapes by 1968/69 and 

1998. 

[PLAND, percentage of landscape; LPI, largest patch index; Cohesion, cohesion of water patch; Clumpy, clumpiness of water patch] 

Table 22. Average primary metric values by year and by orientation to the Houma 

Navigation Canal in coastal Louisiana. 
Figure 65. Map of fragmentation classification (per grid tile) for 1958, 1968/69, and 1998. The two tier 

configuration) are shown in conjunction with project regions in coastal Louisiana.

112
Figure 66. Map of marsh degradation classification (per grid tile) for 1958, 1968/69, and 1998. The ma

three levels of marsh condition: solid marsh (1, 2A, 2B), degraded marsh (2C, 3A, 3B, 3C, 4A, 4B, 4C, 

7)—are shown in conjunction with project regions in coastal Louisiana. 

113
Table 23. Change in classification from 1968 to 1998 for all 1958 class 1 tiles for coastal Louisian

[Counts in the columns represent the values in 1968, and the 1998 classification is represented across the rows. Bold values represent tiles that remained the sam(1 tile= 15,625 square meters). Dashes represent no tiles]

1998 Change matrix

1968

1 2A 2B 2C 3A 3B 3C 4A 4B 4C 5A 5B 5C 

1 2,040 171 4 12 189 - 7 87 1 0 42 - 

2A 744 125 2 7 73 - 1 14 - 1 7 - 2B 89 10 1 1 8 - - 2 - - - - 

2C 105 9 - - 14 - - 1 - 1 1 - 

3A 1,572 361 4 16 337 - 19 46 - 1 21 - 3B 98 7 - 1 11 - 3 1 - 1 - 

3C 341 53 - 4 26 - 2 - - 1 1 - 

4A 1,046 242 4 20 315 2 12 75 - 3 17 - 2 4B 11 1 - - 4 - - - - - - - 

4C 171 41 3 4 35 - - 2 - - 1 - 

5A 924 186 5 16 303 2 20 165 - 3 56 - 2 5B 7 2 - - - - - 1 - - 1 - 

5C 172 42 - 7 61 - 5 18 - 2 7 - 1 

6 929 181 12 19 260 3 20 132 - 8 94 - 8 7 1,426 276 4 26 396 7 31 159 - 10 161 - 11 

1968

total 9,675 1,707 39 133 2,032 14 120 703 1 30 410 - 24 

114
Connectivity Assessment 

The degree of connectivity and distance from primary waterways may influence movement 

of water both into and out of interior estuarine marshes and can subsequently affect salinity 

patterns and vegetation distributions. As a means of quantifying the distance and degree of 

influence that the Houma Navigation Canal (HNC) has on marsh degradation, a 3-km interval 

buffer array (fig. 67) was used to describe the change in marsh density, shape, and connectivity 

within each buffer. In addition, the 1968 vegetation zones (Chabreck and others, 1968) were used 

as the standard for subsetting grid tiles for all years and in conjunction with landscape statistical 

analyses and classifications to determine the change in marsh condition by vegetation salinity 

zones. All tiles that were located within areas that were classified as forested or developed were 

excluded from both the buffer and vegetation zone analyses. 

Buffer Zone 

Changes in marsh condition and landscape pattern were analyzed for each project buffer 

and coastal marsh vegetation zone. Buffers that represented an array of consecutive zones (3-km 

intervals from the HNC, table 24) were used to examine the effects of the HNC and subsequent 

salinity intrusion on marsh condition and changes. Though the project area extends to 

approximately 18 km from the HNC, the 15-km and 18-km buffers contained an inadequate 

number of tiles and were excluded from all buffer zone analyses. Figure 68 shows the number of 

grid tiles within each buffer and the percentage of those tiles that were classified as solid marsh, 

degraded marsh, or open water. Respectively, buffers 3, 6, 9, and 12 accounted for 22, 28, 28, and 

15 percent of the 35,344 tiles that were classified within the project area. The buffers contained 

higher percentages of solid marsh tiles in 1958 (between 49 and 57 percent) than in 1998, which 

contained between 11 and 20 percent. There was less solid marsh in buffer 6 than in all other 

buffers in 1958 (only slightly less than buffer 3), but there was a higher percentage of solid marsh 

in buffer 6 than in all 1968/69 and 1998 buffers. Overall, there was a slight increase in the number 

of degraded marsh tiles between 1958 and 1968/69 and a more observable increase by 1998. 

Buffers 3 and 12 had higher percentages of degraded marsh in 1958 and 1968/69 than did buffers 6 

and 9 but only slightly greater percentages than buffer 9 by 1998. Conversely, buffers 6 and 9 

contained higher percentages of water classified tiles in 1958 and 1968/69, but rapid conversion to 

water during the TB time period drew buffer 12 near the end-point percentage for buffer 9. 

Table 24. Explanation of buffer intervals in Houma Navigation Canal project area in 

coastal Louisiana.

Buffer Distance from canal

3 km 0–3 km

6 km 3–6 km

9 km 6–9 km

12 km 9–12 km

15 km 12–15 km

18 km 15–18 km

115
Figure 67. Map of buffers (3-km intervals from the Houma Navigation Canal) and 

vegetation zones (Chabreck and others, 1968) within the canal project area in coastal 

Louisiana. The base map is the 1968 land and water analysis image.

116
In general, though buffers 6 and 12 were the most and least degraded buffers in 1958, 

respectively, by 1998 they were the most degraded (highest percentage of water and lowest 

percentage of solid marsh) buffers, followed closely by buffer 9 and less closely by buffer 3. 

Figure 68. Tile summary for each buffer, by year, along with totals for each buffer 

zone in coastal Louisiana. 

Changes in primary metric values were assessed for all four buffers across the three project 

dates. A Duncan’s multiple range test (SAS, 1990c) was performed to compare LPI, cohesion, and 

clumpy value means to determine how the marsh landscape changed with respect to distance or 

influence from the Houma Navigation Canal. The cohesion and clumpy analyses produced nearly 

identical output; therefore, only the results from LPI and cohesion results are illustrated in figures 

69 and 70, respectively. Figure 69 shows that within 1958 only buffer 12 had average LPI value 

that was significantly different (p-value <0.05) from all other buffers. By 1968/69 there were 

significant increases in average LPI values in all buffers except buffer 3. By this postconstruction 

date, buffer 6 had a significantly higher average LPI value, and buffer 12 was significantly lower 

than all other buffers. The largest patch averages for 1998 exhibited significant increase over all 

1958 and 1968/69 buffers. Within this end-point date, buffers 6 and 12 showed the largest increase 

in average LPI, while buffer 9 underwent the smallest increase. Among all the 1998 buffers, only 

buffer 6 was significantly different from the others. In general, the average LPI was highest in 

buffer 6 for all three dates; however, the rate of change between dates was greatest in buffer 12. 

117
Figure 69. Average largest patch per tile, in square meters, for buffers within the 

Houma Navigation Canal project area in coastal Louisiana. Data are shown for three 

dates: preconstruction (1958), postconstruction (1968/69), and end date (1998). Bars 

with the same letter above them are not significantly different (p-value >0.05) as 

determined by Duncan’s multiple range test. 

The cohesion analysis (as illustrated in fig. 70) showed that buffers 9 and 12 had average 

cohesion values that were significantly different (p-value <0.05) from buffers 3 and 6. The average 

cohesion for buffer 9 was the lowest in 1958, followed by buffer 12, and the maximum averages 

were observed in buffers 3 and 6. By 1968/69 there were significant increases in average cohesion 

values for buffers 6, 9, and 12. The within-year comparison for all 1968/69 data showed that all 

buffers were significantly different from each other. Finally, the 1998 data showed significant 

increases in cohesion values compared to both 1958 and 1968/69, for all buffers. These increases 

resulted average cohesion values ranging from 88 to 96. Buffers 6 and 12 were not significantly 

different from each other and buffer 9 was not significantly different from buffer 12. However, 

buffer 3 was significantly different from all other buffers. 

These analyses show that prior to the construction of the Houma Navigation Canal (HNC) 

there was less variation in average largest patch size across all buffers. In addition, the minimum 

and maximum LPI values of the preconstruction buffers were significantly smaller for nearly all 

buffers and dates. The mid-distance buffers (9 and 12) showed the greatest increase in largest patch 

size from preconstruction to end-point dates. Similarly, these buffers exhibited significantly lower 

cohesion values than did the interior buffers (3 and 6) for the preconstruction date. The 1968/69 

and 1998 buffers were significantly different for nearly all buffers between dates but not 

significantly different for nearly all buffers within specific dates. Figure 69 shows that a parabolic 

trend in largest patch values was present for all dates, with minimum values located in buffers 3 

and 12 and the maximum values located in buffers 6 and 9. Figure 70 shows varying trends by 

date, where the minimum preconstruction cohesion value was found in buffer 9 and in buffers 3 

and 12 for postconstruction; by 1998—which had the majority of its buffers approaching the upper 

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limits—the minimum average cohesion value was found in buffer 3. These analyses show that, 

though the HNC may have an influence on marsh degradation, the degree and distance of that 

influence are not discernable through change in largest patch indices and cohesion values. 

Figure 70. Average cohesion value per tile for buffers within the Houma Navigation 

Canal project area in coastal Louisiana. Data are shown for three dates: 

preconstruction (1958), postconstruction (1968/69), and end date (1998). Bars with 

the same letter above them are not significantly different as determined by Duncan’s 

multiple range test. 

Vegetation Zone 

Vegetation surveys were taken in 1949, 1968, 1978, 1988, and 1997 across the Louisiana 

coast. O’Neil (1949) classified the northern portion of the study area from Lake de Cade east to 

Bayou Grand Caillou and then arcing north of Lake Boudreaux as floating freshwater marsh (fig. 

3). The area directly south of this was classified as floating three-cornered grass marsh, dominated 

by Schenoplectus olneyi, that graded into fresh maidencane marsh, dominated by Panicum 

hemitomon, with a thin band of excessively drained salt marshes, dominated by wiregrass (Spartina 

patens), oystergrass (Spartina alterniflora), or black rush (Juncus roemerianus) on the east and 

west corners of the study area. By 1968, Chabreck and others (1968) identified the marshes as 

mostly fresh and brackish, with a thin band of intermediate in between. The salt marsh line 

encroached into the area formerly identified as brackish. Chabreck and Linscombe (1978) 

designated only the most northern regions of the study area flanking the Houma area as fresh 

marshes, with intermediate claiming the rest of the marsh previously identified as fresh. The 

brackish and saline boundaries were similar to those from 1968. The boundaries for each marsh 

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type moved slightly north by 1988; however, by 1997 the intermediate marsh had increased in area, 

encroaching southward into the 1988 brackish marsh. Some of the vegetation changes that 

occurred over time are worth noting. For example, the intermediate vegetation zone in 1968 

included areas that were classified as brackish in 1978 and then as intermediate again in 1997 (fig. 

71). Another area designated as fresh in 1968 was classified as intermediate in both 1978 and 

1997. The interesting point about these changes is that these areas are within what is now classified 

as a hotspot located south of Falgout Canal. 

Figure 71. A section of the 1998 land change image with a vegetation change layer 

derived from a U.S. Geological Survey classification of coastal Louisiana marshvegetation types. Solid areas beneath the vegetation layer represent open water. 

This figure illustrates higher loss in areas that were fresh in 1968 and became 

intermediate by 1978 and those that were intermediate in 1968, were classified as 

brackish in 1978, and were reclassified as intermediate in 1997. 

Figure 72 shows both the total number of tiles per vegetation zone and the proportion of 

those tiles by marsh condition and year for each zone. The project area was divided into four 

vegetation zones: brackish (13,592 tiles), fresh (9,683 tiles), intermediate (3,481 tiles), and saline 

(5,512 tiles). Overall, the fresh vegetation zone had the largest percentage of solid marsh and 

lowest percentage of water for all three dates. Though the more saline zones (brackish and saline) 

exhibited the largest percentages of water and lowest percentages of solid marsh (significantly 

different from fresh and intermediate zones) for 1958 and 1968/69, the percentages were not 

significantly different for the intermediate, brackish, and salt zones by 1998. Figure 72 shows that, 

at a minimum, the vegetation zones exhibited a slight decrease in percentage of solid marshes 

between 1958 and 1968/69 and significant decreases by 1998. The fresher marshes had the largest 

overall decreases in percentage of solid marsh, with 60 and 58 percent reductions in solid marsh for 

the intermediate and fresh zones, respectively. The reverse was true for water percentages, where a 

slight increase occurred between pre- and postconstruction and significant increases occurred by 

the end date. In the fresh and intermediate zones, increases in degraded marsh for the OA period 

also occurred. The percentages of degraded marsh in the fresh and intermediate zones increased 

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from approximately 23 and 27 percent of these marsh areas to approximately 43 and 47 percent, 

respectively. These fresh and intermediate zones, which also account for the largest increases in 

water over time, contain the degradation hotspots that are identified in the land change analysis. 

Both the brackish and salt marshes incurred land loss over the OA time interval, but the 

marsh loss was not as drastic. In 1958 much of these marshes were already degrading by means of 

bank and shoreline erosion, as opposed to the fresh and intermediate vegetation types which were 

composed mostly of solid marsh (fig. 72). In the brackish and salt vegetation types, the degraded 

marsh area remained relatively constant, accounting for approximately 40–44 percent of those 

vegetation types each year. The brackish and saline zones included more water area in 1958 than 

did the fresh and intermediate types and continued each year to have a higher frequency of water 

tiles until 1998, when the intermediate vegetation type had similar percentages of water. In 

addition to the interior marsh degradation, shoreline and bank erosion (near streams and ponds) 

occurred throughout the brackish and salt marshes. 

Figure 72. Percentage of tiles in each marsh condition class by vegetation type (F, 

fresh; I, intermediate; B, brackish; and S, saline) in 1968 (Chabreck, 1972), along with 

the total number of tiles per vegetation type in coastal Louisiana. 

Analogous to the buffer analysis, changes in primary metric values also were assessed for 

four vegetation zones across the three project dates. The same Duncan’s multiple range test (SAS, 

1990c) was performed on all LPI, cohesion, and clumpy values for comparing vegetation zones and 

project dates within the Houma Navigation Canal project area. The cohesion and clumpiness 

analyses produced identical output; therefore, to avoid the redundancy, only the results of LPI and 

cohesion are provided in figures 73 and 74, respectively. Figure 73 shows increases in average 

largest patch size for the preconstruction (1958) date when progressing from the fresh to more 

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saline marsh zones. All zones had significantly different (p-value < 0.05) average LPI values in 

1958, with mean largest patch sizes of 960 m2 (fresh), 1,265 m2 (intermediate), 3,844 m2 (brackish), 

and 5,900 m2 (salt). Similar trends were observed in the 1968/69 postconstruction data, where 

there were significant increases in LPI for all zones; however, the within-year comparison resulted 

in fresh and intermediate zones that were not significantly different from each other. The same 

general trend continued in the 1998 data, where all within- and between-year comparisons were 

significantly different for all zones. Overall, the general rate of change increased with movement 

from more saline to fresh zones. This movement is evident in the change in LPI values for the OA 

period (1958–98), where those changes were 2,215 m2 (salt), 3,900 m2 (brackish), 5,870 m2

(intermediate), and 5,843 m2

 (fresh) for the indicated zones. 

Figure 73. Average largest patch per tile, in square meters, for vegetation zones 

within the Houma Navigation Canal project area. Data are shown for three dates: 

preconstruction (1958), postconstruction (1968/69), and end date (1998). Bars with 

the same letter above them are not significantly different (p-value > 0.05) as 

determined by Duncan’s multiple range test. 

Figure 74 shows the average cohesion index values for the four vegetation zones and across 

the three project dates. The cohesion metric value is a measurement that indicates the 

connectedness of water patches within a tile. These results are similar to those observed with the 

average largest patch assessments. There was a significant increase in average cohesion value with 

movement from the fresh to saline zones for both 1958 and 1968/69, with the largest within-year 

difference of neighboring zones occurring between the intermediate and brackish marsh zones. 

The end-date (1998) data show that, though the fresh zone is significantly different from both the 

brackish and saline zones and though the intermediate zone is significantly different from the 

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brackish zone, the range of values between all 1998 marsh zones is considerably less than that 

observed in the two previous dates. In all, the maximum changes in average cohesion value 

occurred in the fresher vegetation zones across all three project dates. These values, which 

measure the connectedness of water within the marsh, show that in 1958 the water patches had a 

more natural random distribution and less connectivity in the fresher marshes and highly 

aggregated and connected patches in the brackish and saline marshes. By 1968, those random 

distributions in the fresh and intermediate marshes began to degrade, and the water within those 

marshes began to connect and aggregate. Ultimately, the 1998 values for the fresh and 

intermediate marshes were comparable to those for the 1958 brackish and saline zones. 

Figure 74. Average cohesion index per tile for four vegetation zones within the 

Houma Navigation Canal project area in coastal Louisiana. Data are shown for three 

project dates: preconstruction (1958), postconstruction (1968/69), and end date 

(1998). Bars with the same letter above them are not significantly different (p-value > 

0.05) as determined by Duncan’s multiple range test. 

Buffer-by-Vegetation Analysis 

Fresh Vegetation Type 

Figure 75 shows the marsh classification in the fresh vegetation type by buffer zones and 

years. Preconstruction data showed that the majority of project landscape that was classified as 

fresh vegetation was located in buffers 6 and 9. For both the pre- and postconstruction dates, 

respectively, the fresh vegetation zones within buffers 3, 6, 9, and 12 contained between 74 and 66 

percent solid marsh and between 24 and 28 percent degraded marsh. By 1998 buffers 3, 6, and 12 

had significant increases in water tiles (40 percent or more), while the conversion to water in buffer 

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9 was less intense over the same period. Degraded marsh represented between 20 and 29 percent 

of fresh zone for all buffers in 1958. By 1968, 28–33 percent of the buffer zones were 

characterized by degraded marshes, with a concomitant reduction in solid marsh. Though these 

increases in degraded marsh tiles were relatively slight for the TA period, significantly larger 

increases were observed by 1998. The largest changes within the fresh zone buffers in the TA 

period occurred between the solid and degraded marshes, with slight increases in the percentages of 

water. By 1998, large increases were apparent in the number of degraded marsh and water 

classified tiles. The changes in this vegetation zone are indicative of marsh that was primarily solid 

for the pre- and postconstruction dates but saw rapid disintegration of marsh and conversion to 

open water by 1998. 

Figure 75. Marsh classification in the fresh vegetation type by buffer zones and years 

in coastal Louisiana. 

Intermediate Vegetation Type 

Figure 3 shows that in 1968 the intermediate vegetation zone encompassed only a narrow 

swath of the landscape; therefore, the numbers of tiles within this zone are relatively small. As 

shown in figure 76, all of the buffer zones except zone 12 consisted of at least 95 percent marsh 

(solid and degraded) in both 1958 and 1968/69, while buffer zone 12 consisted of slightly less solid 

and degraded marsh in the same two years. In all buffers, solid marsh was the largest component, 

approximately 63 percent, of the intermediate zone in 1958 and 1968/69. By 1998, the water 

component increased in every buffer zone, as did the degraded marsh. The greatest increases in 

water classified tiles were seen in buffers 6 and 12, where water accounted for 53 and 46 percent, 

respectively. While the extent of the increases found in buffers 6 and 12 are important, the location 

of these buffers-by-vegetation zones within the project landscape may be most significant. The 

locations of these segments are located within hotspots, which are identified in the land change 

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analysis. Buffer 6 is located in hotspot 2 near the Central/East Falgout and Rambio region 

boundary, while buffer 12 is located in hotspot 4 (eastern side of the Houma Navigation Canal) in 

the interdistributary basin between Bayou Petit Caillou and Bayou Terrebonne. 

Figure 76. Marsh classification in the intermediate vegetation type by buffer zones 

and years in coastal Louisiana. 

Brackish Vegetation Type 

As stated previously, the brackish zone as a whole contained less than 50 percent solid 

marsh in 1958. The percentage of solid marsh ranged from less than 40 percent for buffers 3 and 6 

to 51 percent (the largest percentage) for buffer 9 (fig. 77). The percentages for buffers 6 and 9 

may be skewed slightly since this portion of the brackish zone contains Lake Boudreaux and 

surrounding water bodies. These water areas did not contribute to the buffer-by-zone classification 

because they were classified as water in the vegetation type layer; however, the area surrounding 

these water bodies did fragment into degraded marsh or open water by 1998, leaving 12 percent or 

less solid marsh in the eastern portion of both buffers 6 and 9. Also contributing to the decrease in 

solid marsh were the northeast portions of buffers 6 and 9, which were designated as solid marsh in 

1958, became heavily degraded by 1968, and changed to water class by 1998. In 1958 buffers 3 

through 12 had significantly higher percentages of water within the brackish zone than in the fresh 

and intermediate zones combined. Therefore, the changes in percentages of water over time were 

not as great in the brackish vegetation zone as in those fresher zones. 

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Figure 77. Marsh classification in the brackish vegetation type by buffer zones and 

years in coastal Louisiana.

Saline Vegetation Type 

The three largest buffer-by-saline zones (buffers 3, 6, and 9) each had less than 27 percent 

solid marsh in 1958, experienced only slight change by 1968, and had less than 18 percent solid 

marsh by 1998 (fig. 78). Each buffer had at least 32 percent degraded marsh in every year. Of all 

of the saline buffers, buffer 12 was the only one to have less than 25 percent water in 1958 and less 

than 42 percent water in 1998. Across all years, buffers 6 and 9 contained the lowest percentages 

of solid marsh and the highest percentages of water tiles. The patterns in these regions were 

strongly affected by shoreline and bank erosion. Though buffers 3, 6, and 9 decreased in 

percentage of degraded tiles over the OA period, buffer 12 experienced a significant increase over 

that period. 

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Figure 78. Marsh classification in the salt vegetation class by buffer zones and years 

in coastal Louisiana.

Summary of Buffer-by-Vegetation Interactions 

Fresh and intermediate vegetation zones were over 65 percent solid marshes in 1958 and 

1968. By 1998 these landscapes contained less than 18 percent solid marsh, having deteriorated to 

degraded marsh and water. Buffer zones 3 and 6 in the fresh and intermediate vegetation types 

included portions of hotspot 2 (south of Falgout Canal). The fresh vegetation type in buffer zones 

3 and 6 were classified as fresh in 1958 and intermediate in 1968 and 1998. The intermediate 

vegetation portion of buffer 6 was classified as intermediate in 1958, brackish in 1978, and then 

intermediate again in 1998. Buffer zone 3 in the intermediate zone, which showed less conversion 

from solid marsh to open water, was classified as intermediate in all three dates. The majority of 

the salt and brackish vegetation zones were already degraded by 1958. Most of the degradation 

noted in these two vegetation types occurred in areas that were already open water. Buffer zone 6 

within the brackish marsh was solid in 1958, degraded in 1968, and open water in 1998. Buffer 

zones 3 through 12 in the brackish marsh, east of the Houma Navigation Canal and south of Lake 

Boudreaux, became open water by 1998. 

Hotspots of Landscape Change 

Though the patterns of land loss differ spatially within the study area, there were four areas 

of extensively degraded marsh within the project landscape. These areas—or “hotspots”—are 

defined as relatively large areas that displayed significant change of one or more landscape metric 

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128

values. It is interesting to note that all four hotspots were located near the intermediate-brackish 

confluence. Figure 79 shows the four hotspots in relation to the FRAGSTATS category and 

configuration classification differences. These are the differences between the 1958 and 1998 

classes (values were assigned to both the category and configuration classes [configuration class 

assigned values: class 1 = 1, class A = 2, class B = 3.5, class C = 5, and classes 6 and 7 = 4], all 

category and assigned configuration values were multiplied by 2 and summed by tile, and the 

difference was calculated by subtracting a previous date from a latter date, and those within-year 

values are summed prior to calculating the change over time). This figure gives an overall 

representation of marsh condition and degradation from start to end-point dates. 

Hotspot 1, which is located west of Bayou du Large on the north side of Falgout Canal, was 

characterized as floating fresh marsh dominated by canouche (Panicum hemitomon) in 1949, 

characterized as fresh marsh comprising fresh maidencane marsh (dominated by Panicum 

hemitomon), fresh bulltongue marsh (dominated by Sagittaria lancifolia), and oligohaline mix 

marsh (dominated by Sagittaria lancifolia) (Visser and others, 1996) in 1968, and shifted to 

predominantly intermediate marsh by 1978 and 1997. This hotspot was a solid fresh floating marsh 

in 1958, became degraded marsh by 1968, and exhibited a significant negative change in PLAND, 

LSI, LPI, and PD values between the preconstruction and end-point dates. The clumpy and 

cohesion values increased between 1958 and 1968/69 but then underwent a significant negative 

change by 1998, with the areas designated as fresh maidencane marsh in 1968 becoming open 

water. On its east side, the initial marsh degradation and loss in hotspot 1 may be related to natural 

impoundment on the east by Bayou du Large and by the manmade Falgout Canal in the south and 

Minors Canal in the west. Both Lake de Cade and Falgout Canal may also be conduits of higher 

salinity water. On the west side of this hotspot, initial loss occurred in areas of levee flank 

depressions. Ultimately this area, which was solid marsh in 1958, became predominantly open 

water by 1998, including larger areas of open water bodies. In 1978, the area was classified as 

intermediate (Chabreck and others, 1978), indicating increased salinity within the area. With 

intermediate vegetation persisting in 1988, salinity stress is probably a factor that contributed to the 

marsh degradation and loss in this area. Work by Morton and others (2005) and Gagliano (1999) 

indicated a fault trace east/west along Falgout Canal in our study area, cutting across the southern 

edge of Lake Boudreaux. Another trace splits from this trace west of the Houma Navigation Canal, 

veering to the northeast along the area north of Lake Boudreaux. Morton and others (2006) also 

depicted oil and gas fields in the areas which correspond to hotspots of land loss. 
Figure 79. Fragmentation difference per tile between 1958 and 1998. Values were assigned to each 

and assigned configuration values multiplied by 2), assigned to each configuration class (class 1 = 1, c

class C = 5, and classes 6 and 7 = 4), and summed by tile, and the difference was calculated by subtraa latter date. Values are shown in conjunction with project regions in coastal Louisiana. 

129
Morton and others (2005) collected vibracores from near the center of each of five 

wetland-loss hotspots in the vicinity of our study area. One of the sites (DeLarge) is in 

the area of our hotspots 1 and 2, which are located south of Falgout Canal, and another 

one is in the area of our hotspot 4. From these cores, facies and stratigraphic layers were 

matched to determine a subsidence estimate of approximately 70 cm between marsh and 

the sunken marsh layer. Morton and others (2006) theorize that the areas of greatest land 

loss and subsidence were controlled by the coupling between reservoir compaction and 

slip along growth faults when sufficiently large volumes of fluid were removed from the 

subsurface, with loss occurring next to the fault zone and not necessarily directly over the 

top of the reservoir, explaining why there may not be a direct correlation between 

identified fields and hotspots of land loss. 

There are other possible mechanisms for marsh substrate submergence. In studies 

of more saline marshes, Nyman and others (1993) found that the marsh surface sinks 

approximately 10 cm when marsh plants die. Gosselink and Sasser (1995) postulated that 

a sudden decrease in marsh elevation might be caused by either a collapse or 

decomposition of the structure of the marsh’s root system when stressed enough. 

Historical vegetation data available for some of the area within hotspot 1 

indicated a fresh maidencane marsh within the area of loss (Chabreck, 1972; Visser and 

others, 1996). When stressed, fresh maidencane marsh responds differently than do 

attached marshes. First, the thickness of a floating marsh mat does not have to keep up 

with sea-level rise because the mat floats at or near the water surface. Mats 

approximately 50 cm thick float above the underlying substrate at a depth that varies with 

the ambient water level. Fresh maidencane marsh has been shown to be adversely 

affected by salinities as low as 1.5–3 ppt (Willis and Hester, 2004). Stress on these 

marshes resulting in lowered production or death of the highly organic mat could also 

result in the collapse and submergence of the marsh mat to the underlying firm substrate. 

The loss or decrease in mat buoyancy of a floating marsh could cause a greater decrease 

in elevation than in an attached marsh, depending upon the depth to the underlying firm 

substrate. It seems unlikely that fault reactivation or fluid withdrawal/faulting coupling 

caused most of the loss in hotspot 1; the more likely explanation for the loss was stress by 

impoundment exacerbated later by salinity stress. 

Hotspot 2, which is east of Bayou du Large and just slightly southeast of hotspot 

1, was designated as floating three-cornered grass marsh in 1949 (O’Neil, 1949) and by 

1968 was classified as fresh and intermediate marsh in the north and brackish marsh in 

the south (Chabreck and others, 1968). Visser and others (1996) classified the stations on 

the transect in the northernmost section as oligohaline mix marsh and fresh bulltongue 

marsh vegetation types. The floating three-cornered grass marsh described by O’Neil in 

1949 was likely a mat formed originally by the high productivity of Panicum hemitomon, 

then undergoing a transition to other species such as three-cornered grass in response to 

stresses to the marsh. O’Neil (1949) described floating three-cornered grass marshes as 

having a weak mat that would most probably collapse under stress. Between 1958 and 

1968/69, this hotspot was mainly composed of classes 1 through 4A, with a small 

distribution of classes 5A through 7. In 1965, landscape features described as “rips” in 

the marsh surface, which compare to those found in floating marshes that are affected by 

hurricanes (Evers and others, 2007), were evident within hotspot 2. These features 

provide further evidence that this hotspot included floating marsh. In aerial imagery 

taken during the flooding of 1973, some of this marsh was under water or had already 

converted to open water. Vegetation survey shows that hotspot 2 was intermediate (north) 

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and brackish marsh (south) in 1978, but by 1997 this region was classified as 

predominantly intermediate marsh. At the same time the brackish marsh was 

encroaching into the area where the vegetation bordering the Houma Navigation Canal 

(HNC) at the same latitude was classified as intermediate. However, it seems unlikely 

that higher salinity water came directly out of the HNC into these marshes, but there may 

have been another conduit for it, such as the Falgout Canal and even possibly Lake de 

Cade. The change in shape, density, and connectivity metric values are less 

comprehensive in this area than in hotspots 1 and 3; however, the areas in which there 

was negative change are significantly greater than the average change project wide. As 

with the other hotspots, hotspot 2 shifted from relatively solid marsh in 1958 to an area 

that was >60 percent water by 1998. An area that was classified as freshwater floating 

marsh again became either open water or degraded marsh. Because of the character of 

the hotspot 2 vegetation over time and other evidence, it seems unlikely that fault 

reactivation or fluid withdrawal/faulting coupling caused most of this loss. 

Hotspot 3 is an area north of Lake Boudreaux, in the North Boudreaux region, 

that was predominantly fresh and intermediate in 1968, underwent a significant shift to 

brackish by 1988, and was classified as intermediate vegetation in 1997 (fig. 3). In 1958 

and 1968/69 this area was predominantly solid marsh but ultimately shifted to highly 

degraded and water classes. Unlike hotspot 2, these marshes were still viable after the 

Mississippi/Atchafalaya flood of 1973. Vegetation data from Chabreck (1972) indicate 

that marsh in areas surveyed within hotspot 3 in 1968 was classified as fresh maidencane 

marsh and fresh bulltongue marsh. By 1998, data from this area indicated an extreme 

negative change in PLAND, LSI, and cohesion and clumpy values; mid-level shifts of PD 

values; and significant change of the LPI values towards the north. The portion of this 

hotspot that became open water by 1998 was classified as freshwater marsh, either fresh 

maidencane marsh or fresh bulltongue marsh and likely floating marsh, while the 

southern part of the hotspot denoted degraded marsh composed of oligohaline wiregrass 

(dominated by Spartina patens) (Visser and others, 1996). Gagliano (1999) has 

described the degradation and loss in this region as severe and attributed that loss to fault 

reactivation by phenomena such as the Alaskan earthquake in 1964. Like hotspot 1, 

hotspot 3 appears to be mostly a freshwater marsh, probably flotant, which may have 

been stressed by other factors, causing the deterioration of the marsh mat. Evidence of 

the change to more saline-tolerant vegetation classification points to an influence by 

salinity and contributed to land-loss in hotspot 3. 

Hotspot 4 is located on either side of Bayou Petite Caillou and between Lake 

Boudreaux and Bayou Terrebonne. This area consisted of floating three-cornered grass 

marsh and brackish three-cornered grass marsh (dominated by Schoenoplectus

americanus) in 1949; intermediate and brackish marsh in 1968, 1978, and 1997; but was 

predominantly brackish marsh in both 1988 and 2001. Hotspot 4 was the only hotspot 

that was not classified as solid marsh in 1958. This hotspot, which was stippled with 

small water patches in 1958, experienced both enlargement of existing patches and the 

creation of new ones by 1968 and shifted to predominantly solid water by 1998. Between 

1958 and 1968, this hotspot experienced only moderate increases in the south, but by 

1998 the northwest regions underwent intense increases in PD. Only the northern portion 

of hotspot 4 experienced measurable change in LSI, with a shift to less complex shape in 

the northeast across all dates and a shift to more complex in the northwest by 1998. 

Small changes were observed in clumpy value across all dates, with few select sections 

experiencing significant increases. The clumpy values underwent both decreases (along 

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Bayou Petite Caillou) and increases (between Petite Caillou and Lake Boudreaux) for the 

pre- to postconstruction time period but experienced more extreme increases by the endpoint date. Ultimately, this area, which was already experiencing marsh fragmentation in 

1958, shifted extensively to highly degraded marsh or open water by 1998. This hotspot 

is near/within two oil/gas fields, one of which also includes a marsh studied previously 

(Madison Bay, Sasser and Evers, 1995). In a study funded by Barataria Terrebonne 

National Estuary Program (BTNEP), Sasser and Evers (1995) identified a submerged 

marsh (an area of marsh visible beneath the water surface and surrounded by nonflooded 

marsh) in an area east of Bayou Terrebonne in the Madison Bay area between 1969 and 

1972 that became open water between 1978 and 1985. Madison Bay is also an area 

where Morton and others (2006) collected vibracores and found like facies offset by 

approximately 70 cm. It seems likely that fluid withdrawal/faulting coupling was a major 

factor in the loss in hotspot 4. 

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Synthesis 

Salinity—Coastwide 

Based upon analysis of daily salinity data within the Louisiana coastal zone, the 

overall coastal salinity pattern exhibited seasonal fluctuations, as well as large-scale 

fluctuations (usually from frontal passage influence), but typically remained above 15–20 

ppt. Generally, the overall inland salinity pattern is characterized by a more pronounced 

seasonal change from low salinities (0–2 ppt) to moderate salinities (5–10 ppt). The 

presence of short duration pulses or “spikes” up to 15–20 ppt also are apparent. These 

spikes are the result of meteorological forcing (strong frontal passages). A typical inland 

station on a large channel in coastal Louisiana is characterized by low salinities (0–1 ppt) 

most of the time with short duration, high salinity (approximately 15 ppt) spikes from 

meteorological forcing (strong frontal passages). Additionally, on the Houma Navigation 

Canal (HNC), spikes were evident after major maintenance dredging events. 

Overall, the coastal waters have a mean salinity of 15–20 ppt, which decreases 

fairly rapidly moving inland away from the gulf. The inland water bodies in the eastern 

part of the State tend to have higher salinity than do the inland water bodies in the 

western part of the State. The coastal salinities in Louisiana exhibit interannual 

variability, which is inversely related to Mississippi River discharge (Wiseman and 

others, 1990b). 

At those stations within the Louisiana coastal zone that had statistically 

significant trends (42 of 44, or 95 percent), salinity increases occurred at 27 of the 

stations (64 percent), and salinity decreases occurred at 15 (36 percent) of the stations. 

Changes in salinity over time were small, with 74 percent being increases or decreases of 

less than 2 ppt, over the period of record. The long-term salinity trends explain less than 

10 percent of the variance at most (72 percent) of the stations. It is interesting and 

probably indicative of the physiographic differences between Louisiana’s eastern Deltaic 

Plain region and the western Chenier Plain region that, although there is no coherent 

coastwide trend of increasing or decreasing salinity, there is a consistent salinity increase 

for the internal basins of the Chenier Plain. 

Salinity—Houma Navigation Canal Area 

Analysis of daily and hourly salinity data within the Houma Navigation Canal 

(HNC) area indicated the following regarding salinity duration and salinity events or 

pulses. With the exception of the 1999–2000 drought, there does not appear to be any 

obvious pattern in increasing salinity levels closely following the completion of the canal, 

except for the immediate increase in salinity spikes during completion of construction in 

1961. The data from Bayou Grand Caillou at Dulac does show a longer term trend of 

increasing salinity levels. This pattern is similar to the pattern observed at the Houma 

Water Treatment Plant. A possible explanation for these patterns is based on the 

dredging history of the HNC, where dates of maintenance dredging correspond fairly 

closely to the salinity peaks in Bayou Grand Caillou and HNC. It appears that the 

dredging events opened up a deeper route from the HNC to Crozier and into Grand 

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Bayou Caillou, but it also may be a result of the general breakup of the marsh in the area, 

resulting in greater exchange of bay water. 

Water levels have a fairly high correlation (>0.7 at 7 of 10 stations), indicating 

that the water level patterns in this area are driven mostly by the coastal water levels. 

The salinity levels show much lower correlations (<0.5 at 7 of 10 stations), indicating 

that the salinity of the Terrebonne Bay waters is not the entire driving force for the more 

interior locations. Although high coastal water levels result in high water levels 

throughout the system, high salinity at the coast does not always result in high salinity at 

the inland stations. 

There is an indication of salinity increase in the HNC as opposed to the stations 

on either side, both of which show decreases. Salinity decrease along a coast-inland 

gradient exhibited the lowest values in the HNC, indicating that salt water can intrude 

further inland in this straight canal. Based upon an analysis of salinity pulse 

characteristics during the fall of 2003 at a distance inland of about 40 km, the station in 

the HNC has a significantly higher salinity when compared to a station at a similar 

distance inland in Bayou Grand Caillou (5.5 ppt versus 1.8 ppt). Closer to the coast 

(about 20 km), the salinity in the HNC (13.3 ppt) is significantly higher than the salinity 

in Bayou Terrebonne (6.1 ppt) but not significantly higher than the salinity in Bayou 

Grand Caillou (10.3 ppt). 

Salinity pulse analysis for the entire year of 2003 indicated an average duration of 

4.8 days at the HNC station, which was significantly lower than the value of 7.3 days at 

the Bayou Grand Caillou station. However, the mean pulse magnitudes 10.5 ppt (at the 

HNC station) and 11.0 (at the Bayou Grand Caillou station) were not significantly 

different. These data indicate that the higher salinity waters associated with the pulses 

tend to remain longer in the Bayou Grand Caillou system. The duration difference may 

be due to the influence of fresh water entering the HNC through its connection to the 

Gulf Intracoastal Waterway. Regression analysis indicates that discharge explains almost 

50 percent of the observed salinity pulses in the HNC. 

The construction of the HNC may have provided a salinity pathway to the 

marshes west of the HNC, particularly the marshes in “hotspots” 1 and 2 in the Falgout 

Canal area. These areas also were impounded by numerous spoil banks, which may have 

exacerbated any saltwater intrusion problems. Although the available salinity data are 

insufficient to conduct statistical correlations, there was close agreement between salinity 

changes and specific dredging events of the HNC. 

Salinity and Connectivity 

Salinity data from the Bayou Grand Caillou station at Dulac indicated a major 

shift to higher salinity levels around 1975 (there was a slight increase postconstruction). 

This time period coincides with the first full maintenance dredging of the full length of 

the Houma Navigation Canal (HNC) (6.30 million m3

) that occurred between 1972 and 

1974. The marshes in this area (east of the canal) also show a shift from fresh marsh 

prior to construction of the canal (completed approximately 1962) to intermediate marsh 

by 1978. This vegetation shift may be a result of the general breakup of the marshes east 

of the canal, which was quite extensive by 1978, as opposed to a direct impact of the 

channel. 

134
The salinities at Bayou Grand Caillou were high enough prior to construction of 

the HNC to impact the fresh floating marshes in the areas west of the canal; however, the 

connections between the marshes west of the canal and the Bayou Grand Caillou station 

at Dulac were mainly smaller, sinuous channels with limited water exchange. Once the 

HNC was constructed and after maintenance dredging, the connections between the HNC 

and Bayou Grand Caillou were found to be efficient (U.S. Army Corps of Engineers, 

1975). The construction of the HNC resulted in increases in connectivity between the 

marshes west of the canal and the lower portion of Bayou Grand Caillou (south of 

Dulac), which was essentially replaced by the lower portion of the HNC. This increase in 

connectivity between marshes may have allowed higher salinity waters to reach the 

marshes west of the HNC, particularly those areas just north and south of Falgout Canal 

(hotspot areas 1 and 2). These areas also became impounded by numerous canal spoil 

banks, which may have exacerbated any salinity intrusion events since water would be 

held in these impounded areas for greater lengths of time (Swenson and Turner, 1987). 

FRAGSTATS Method Assessment 

Three methods were assessed for potential development and implementation of 

landscape fragmentation and configuration analyses. The FRAGSTATS grid system was 

the method selected because of its class-level metrics, output reproducibility, and 

potential as a packaged management tool. The FRAGSTATS system was found to be a 

useful tool in determining the composition and configuration of marsh and water in the 

study area tiles. Individual landscape metrics were used to determine the percentage and 

rate of land change and the shifts in density, shape, and cohesiveness of water within the 

marsh. Individual metrics also were combined into a classification system on the basis of 

the Dozier (1983) and Evers and others (1991) manual classification schemes, which 

allowed for the categorization of marsh based on the percentage of water present and the 

type and degree of degradation within the tile landscape. 

Though the FRAGSTATS method provided many advantages over other systems, 

it is not without limitations. These limitations include the inability to account for patch 

type adjacency, the time intensive nature of the required land-water analysis, and the 

overall scale specificity of the technique. Through modification and further evaluation of 

the FRAGSTATS method, however, solutions to many of these limitations are possible. 

Long term, the FRAGSTATS grid system provides reliable landscape evaluation 

capabilities and, with the use of higher quality imagery, should evolve into a historic and 

predictive tool for assessing ecological processes. 

Landscape Assessments 

Wetland loss rates for coastal Louisiana and the Terrebonne basin (Barras, 2006; 

Barras and others, 2003) were compared to the long- and short-term rates of the Houma 

Navigation Canal (HNC) study area that were quantified by using FRAGSTATS. These 

results suggest that the HNC. study area was losing land at a significantly faster rate than 

both the marshes of coastal Louisiana (all periods) and the other highly degraded 

135
neighboring marshes within the Terrebonne basin (TB and OA periods). Overall, 37 

percent (176.56 km2) of the project area marsh was lost by 1998.

Project-scale marsh density, shape, and connectivity metrics were computed for 

three project dates (1958, 1968/69, and 1998). In general, the increases in the projectwide average configuration (PLAND, LPI, LSI) and connectivity metric (cohesion and 

clumpy) values are indicative of a marsh that transitioned from a solid and less complex 

landscape, with high levels of marsh connectivity, to a highly complex and degraded 

landscape with increased water connectivity. 

By 1958, significant differences were observed in quantity and configuration of 

marsh located in areas east and west of the proposed HNC. The preconstruction western 

region marshes were more stable marshes, affected by fewer and more naturally 

occurring waterways, and were therefore less hydrologically connected to the higher 

salinity waters of Terrebonne Bay and the Gulf of Mexico. The eastern regions contained 

marshes that were either in closer proximity, or had direct connectivity, to the Gulf of 

Mexico and therefore consisted of more degraded and water-dominated landscapes. It is 

assumed that the advanced degree of degradation of these marshes (in relation to the 

western region marshes) was influenced by the closer proximity to the Gulf of Mexico in 

the south and by connections between larger natural waterways (for instance, Bayou 

Terrebonne) and channelized pipelines and canals in the north. 

As a means of quantifying the distance and degree of influence that the HNC had 

on marsh degradation, a 3-km interval buffer array was used to describe the changes in 

primary metric values across the three project dates. The patterns across landscape 

metrics varied, and it was difficult to discern direct relationships on the basis of 

proximity to the HNC. Even though the HNC may have an influence on marsh 

degradation, these analyses show that the degree and distance of that influence is not 

discernible through change in class-level landscape metric values. 

The project area was assessed by using both existing vegetation surveys (O’Neil, 

1949; Chabreck and others, 1968; Chabreck and Linscombe, 1978, 1988, 1997; 

Linscombe and others, 2001) and degradation class (via average landscape metric value) 

for each vegetation zone (zone extent standardized by using the 1968 survey). The 

surveys revealed a general northerly shift of higher salinity marshes, where the southern 

portions of the fresh and intermediate zones transitioned to intermediate and brackish 

vegetation respectively. These fresher vegetation zones experienced the most significant 

increases in average primary metric values (PLAND, LPI, cohesion and clumpy)—and 

therefore suffered the greatest decrease and increase in solid and degraded marsh, 

respectively—for all three time periods. Ultimately, the 1998 values for the fresh and 

intermediate marshes were comparable to those for the 1958 brackish and saline zones. 

The vegetation zones within buffers show a lot of variability. The results appear 

to be dominated by both the north-south vegetation delineation and by year, but influence 

based on distance from the HNC is not evident. Similar to the vegetation zone analyses, 

these results show that fresher marshes were more susceptible to degradation. This 

degradation was observed in fresh and intermediate marshes where the primarily solid 

landscapes in the pre- and postconstruction dates converted to degraded marsh or open 

water by the end date; however, as a result of higher connectivity to the Gulf of Mexico 

and subsequent shoreline and bank erosion, the brackish and saline marshes had higher 

percentages of water in 1958 and therefore experienced lower levels of degradation than 

the fresh and intermediate zones by 1998. The inability to differentiate effects based on 

distance from the HNC could be a result of influence from Bayou Terrebonne in the east. 

136
Hotspots were defined as areas that experienced extensively degraded marsh and 

were distinguished by using significant change in landscape metric values. There were 

four hotspots within the project area, all of which were located near the intermediate to 

fresh and brackish boundaries. Hotspots 1 and 2 show some of the larger changes in 

metric value between 1958 and 1968/69. These changes are evident in PLAND values 

located in hotspot 1, west of Bayou du Large and within a linear band east of Bayou du 

Large, where there is a trenasse (A small manmade trench through a swamp or marsh an 

area ) into floating fresh marsh, and in hotspot 2, in the northern section of the Rambio 

region. The cohesion, PD, and clumpy values represent more of the relative disposition 

of water patches within the marsh. The changes in the metrics between 1958 and 1968/69 

show the early sign of deterioration and shifts in configuration within the marsh. These 

areas of intense configuration change—which were more prevalent than the PLAND 

changes observed in hotspots 1, 2, and 3—were most intense to the west of Bayou du 

Large in hotspot 1, throughout all of hotspot 3, and near the most northern and southern 

sections of hotspot 2. Hotspot 2 also experienced decreases in configuration along the 

spoil bank and levee of the channelized Falgout Canal. These decreases in configuration 

are landscapes that transitioned to aggregated land, disconnected water, and reduced 

patch density in relatively solid marsh. The larger change in shape metrics as related to 

change in percentage of water alludes to the floating nature of the vegetation in these 

hotspots. 

The changes in hotspots 1 and 2 during the TA period coincide with the 

channelization of the HNC and Falgout Canal. Construction of the HNC was completed 

in 1962, and the widening and straightening of the attached Falgout Canal can be 

observed in as early as 1963 photography. Records show that the original Falgout Canal, 

which was a modest trenasse created and used for local recreational navigation, was 

dredged from a depth of approximately 0.76 m to 2.74 m and widened to approximately 

27.43 m. Though these canals were constructed and enhanced to provide navigable 

routes for shipping, recreation, and oil and gas usage, they produced unexpected negative 

effects to the surrounding marsh. These canals provided a deep water channel by which 

high salinity waters were transported north into fresher marsh and water systems. The 

overall tile and regional statistics show the significantly different conditions of the marsh 

located east and west of the HNC. The accelerated deterioration of the marshes west of 

the HNCcoincides with the construction of that canal and the widening and deepening of 

the Falgout Canal. This HNC-to-Falgout Canal pathway was able to efficiently deliver 

higher salinity waters to the fresh and intermediate marshes located west of theHNC. 

Within hotspots 1 and 2, the effect on the landscape was twofold; an increase in salinity 

and alteration of hydrology led to impoundment and the eventual shift and deterioration 

of the marsh. 

The hotspots located east of the HNC underwent very different land loss and 

configuration change when compared to hotspots 1 and 2 and between hotspots 3 and 4. 

Hotspot 4, which experienced greater land loss by 1968/69 than did hotspot 3, underwent 

only minimal configuration change over that period. The opposite was true for hotspot 3 

by 1968/69, though by 1998 all hotspots underwent significant change in both 

configuration and extent of water within the landscape. The causes and/or progression of 

these changes, however, were different both east and west of the HNC and between 

hotspots 3 and 4. Hotspots 1, 2, and 3 were affected by increased salinity and altered 

hydrology via the HNC and its distributaries. Hotspot 3 experienced land loss at a slower 

rate than did hotspots 1 and 2 because of its proximity to preexisting natural channels, 

137
which were capable of delivering higher salinity waters before the construction of the 

HNC. Hotspot 4 was primarily a brackish marsh during the OA period and was therefore 

less affected by higher salinity waters. As mentioned previously, studies by Sasser and 

Evers (1995) and Morton and others (2006) show that this hotspot is located within two 

oil and gas fields, and therefore, its minimal configuration change and rapid conversion 

to water can be linked to fluid withdrawal and faulting coupling. 

Acknowledgments 

The authors would like to thank the Minerals Management Service’s Project 

Officers Susan Childs and Herb Leedy and the USGS Program Officer Gary Brewer for 

funding support and technical guidance on this project. We especially thank Craig 

Conzelmann and Bill Sleavin for supplying data management and application help and 

John Barras for his invaluable advice. Assistance in both land-water analysis and 

interpretation was provided by USGS personnel Bill Jones and IAP World Services’ 

personnel Adrienne Garber, Dayna Price, Andy Boudreaux, and Matt Keller. 

138
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Sasser, C.E., Swenson, E.M., Evers, D.E., Visser, J.M., Holm, G.O., Jr., and Gosselink, 

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Sikora, W.B., and Kjerfve, Bjorn, 1985, Factors influencing the salinity regime of Lake 

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9, in Ning, Z.H., Turner, R.E., Doyle, Thomas, and Abdollahi, K.K., eds., Integrated 

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Swenson, E.M., and Swarzenski, C.M., 1995, Water levels and salinity in the BaratariaTerrebonne estuarine system, part 6, in Reed, D.J., ed., Status and trends of hydrologic 

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Wiseman, W.J., Jr., and Swenson, E.M., 1988, Analysis of historical data records, in

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of Mexico, chap. 6, v. 2 – Technical Narrative: New Orleans, Mineral Management 

Service, [Contract no. 14-12-002-30252.ocs Study/MMS 87-0119], 400 p. 

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147
Appendix 1. Salinity Level Plots 

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Figure 1–1. Plot of salinity level data for Grand Terre (Louisiana Department of 

Wildlife and Fisheries station 315). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–2. Plot of salinity level data for St. Marys Point (Louisiana Department of 

Wildlife and Fisheries station 317). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–3. Plot of salinity level data for Little Lake (Louisiana Department of Wildlife 

and Fisheries station 326). Indicated is the percent of time per year that salinity values 

exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–4. Plot of salinity level data for Sister Lake (Louisiana Department of Wildlife 

and Fisheries station 518). Indicated is the percent of time per year that salinity values 

exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–5. Plot of salinity level data for Cypremort Point (Louisiana Department of 

Wildlife and Fisheries station 619). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–6. Plot of salinity level data for Cameron (Louisiana Department of Wildlife 

and Fisheries station 719). Indicated is the percent of time per year that salinity values 

exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–7. Plot of salinity level data for the Mississippi River at Southwest Pass (U.S. 

Army Corps of Engineers station 01670). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–8. Plot of salinity level data for the Mississippi River at Southwest Pass 

(U.S. Army Corps of Engineers station 67890). Indicated is the percent of time per 

year that salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–9. Plot of salinity level data for the Mermentau River north of the Catfish 

Point control structure (U.S. Army Corps of Engineers station 70675). Indicated is the 

percent of time per year that salinity values exceeded the given threshold values; ppt, 

parts per thousand. 

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Figure 1–10. Plot of salinity level data for the Mermentau River south of the Catfish 

Point control structure (U.S. Army Corps of Engineers station 70750). Indicated is the 

percent of time per year that salinity values exceeded the given threshold values; ppt, 

parts per thousand. 

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Figure 1–11. Plot of salinity level data for the Calcasieu River saltwater barrier 

channel at Lake Charles (U.S. Army Corps of Engineers station 73472). Indicated is 

the percent of time per year that salinity values exceeded the given threshold values; 

ppt, parts per thousand. 

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Figure 1–12. Plot of salinity level data for the Inner Harbor Navigation Canal at the 

Seabrook Bridge (U.S. Army Corps of Engineers station 76060). Indicated is the 

percent of time per year that salinity values exceeded the given threshold values; ppt, 

parts per thousand. 

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Figure 1–13. Plot of salinity level data for Bayou Petite Caillou near Boudreau Canal 

(U.S. Army Corps of Engineers station 76303). Indicated is the percent of time per 

year that salinity values exceeded the given threshold values; ppt, parts per thousand. 

The completion date for the Houma Navigation Canal is exhibited by the arrow. 

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Figure 1–14. Plot of salinity level data for Bayou Petite Caillou at Cocodrie (U.S. Army 

Corps of Engineers station 76305). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. The 

completion date for the Houma Navigation Canal is exhibited by the arrow. 

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Figure 1–15. Plot of salinity level data for the Gulf Intracoastal Waterway at Houma 

(U.S. Army Corps of Engineers station 76320). Indicated is the percent of time per 

year that salinity values exceeded the given threshold values; ppt, parts per thousand. 

The completion date for the Houma Navigation Canal is exhibited by the arrow. 

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Figure 1–16. Plot of salinity level data for Bayou Grand Caillou near Dulac (U.S. Army 

Corps of Engineers station 76323). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. The 

completion date for the Houma Navigation Canal is exhibited by the arrow. 

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Figure 1–17. Plot of salinity level data for the Houma Navigation Canal near Crozier 

(U.S. Army Corps of Engineers station 76343). Indicated is the percent of time per 

year that salinity values exceeded the given threshold values; ppt, parts per thousand. 

The completion date for the Houma Navigation Canal is exhibited by the arrow. 

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Figure 1–18. Plot of salinity level data for Bayou Terrebonne near Bourg (U.S. Army 

Corps of Engineers station 76403). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. The 

completion date for the Houma Navigation Canal is exhibited by the arrow. 

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Figure 1–19. Plot of salinity level data for Freshwater Canal north of Freshwater 

Bayou Locks (U.S. Army Corps of Engineers station 76592). Indicated is the percent 

of time per year that salinity values exceeded the given threshold values; ppt, parts per 

thousand. 

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Figure 1–20. Plot of salinity level data for Schooner Bayou east of the control 

structure (U.S. Army Corps of Engineers station 76600). Indicated is the percent of 

time per year that salinity values exceeded the given threshold values; ppt, parts per 

thousand. 

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Figure 1–21. Plot of salinity level data for Schooner Bayou west of the control 

structure (U.S. Army Corps of Engineers station 76680). Indicated is the percent of 

time per year that salinity values exceeded the given threshold values; ppt, parts per 

thousand. 

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Figure 1–22. Plot of salinity level data for the Intracoastal Waterway east of the 

Vermilion lock (U.S. Army Corps of Engineers station 76720). Indicated is the percent 

of time per year that salinity values exceeded the given threshold values; ppt, parts per 

thousand. 

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Figure 1–23. Plot of salinity level data for the Intracoastal Waterway west of the 

Leland Bowman locks (U.S. Army Corps of Engineers station 76800). Indicated is the 

percent of time per year that salinity values exceeded the given threshold values; ppt, 

parts per thousand. 

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Figure 1–24. Plot of salinity level data for Bayou Blue near Grand Bayou Canal (U.S. 

Army Corps of Engineers station 81050). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–25. Plot of salinity level data for Bayou LaFourche at Larose (U.S. Army 

Corps of Engineers station 82203). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–26. Plot of salinity level data for Bayou LaFourche north of the Golden 

Meadow floodgate (U.S. Army Corps of Engineers station 82250). Indicated is the 

percent of time per year that salinity values exceeded the given threshold values; ppt, 

parts per thousand. 

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Figure 1–27. Plot of salinity level data for Bayou LaFourche at Galliano (U.S. Army 

Corps of Engineers station 82300). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–28. Plot of salinity level data for Bayou Barataria at Barataria (U.S. Army 

Corps of Engineers station 82750). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–29. Plot of salinity level data for Bayou Barataria at Lafitte (U.S. Army Corps 

of Engineers station 82875). Indicated is the percent of time per year that salinity 

values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–30. Plot of salinity level data for Lake Pontchartrain at midlake (U.S. Army 

Corps of Engineers station 85600). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–31. Plot of salinity level data for the Mississippi River Gulf Outlet at Shell 

Beach (U.S. Army Corps of Engineers station 85800). Indicated is the percent of time 

per year that salinity values exceeded the given threshold values; ppt, parts per 

thousand. 

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Figure 1–32. Plot of salinity level data for Breton Sound near Garden Island (U.S. 

Army Corps of Engineers station 85850). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–33. Plot of salinity level data for Grand Isle at the Coast Guard Station (U.S. 

Army Corps of Engineers station 88410). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–34. Plot of salinity level data for Atchafalaya Bay near Eugene Island (U.S. 

Army Corps of Engineers station 88550). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–35. Plot of salinity level data for East Cote Blanche Bay at Luke’s Landing 

(U.S. Army Corps of Engineers station 88800). Indicated is the percent of time per 

year that salinity values exceeded the given threshold values; ppt, parts per thousand. 

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Figure 1–36. Plot of salinity level data for Vermilion Bay at Cypremort Point (U.S. 

Army Corps of Engineers station 88850). Indicated is the percent of time per year that 

salinity values exceeded the given threshold values; ppt, parts per thousand. 

Salinity was less than 2.5 ppt at the following stations, so no plots were produced: 

U.S. Army Corps of Engineers (USACE) 01386, USACE 01420, USACE 01481, USACE 

01575, USACE 70600, and USACE 76480. 

183
Appendix 2. FRAGSTATS Classification Criteria and 

Thresholds 

1. FRAGSTATS output contains three values for TYPE field (0, 1, and 2) 

a. TYPE=2 data used in secondary calculations 

b. All criteria and sorting applied to TYPE=1 records. 

2. Create a new field “NEW_PLAND” 

a. =CA1/[CA2+CA1]*100 

3. Create a new field “LARGEST_PATCH” 

a. =(LPI1*1.5625)/100*10000 

4. Create a new field “PD_NEW” 

a. =((NP1/(CA1+CA2))) 

5. Create a new field “NP/CLUMP” 

a. =NP1/CLUMPY1 

6. Sort and separate records by LID 

7. Create a new field “WATER” 

a. =IF(NEW_PLAND<5,"1",IF(NEW_PLAND <=10,"2",IF(NEW_PLAND 

<=25,"3",IF(NEW_PLAND <=40,"4",IF(NEW_PLAND 

<=60,"5",IF(NEW_PLAND <=80,"6",IF(NEW_PLAND <=100,"7","7"))))))) 

b. Sort and separate records by “WATER” field 

8. Create a new field “MARSH” 

a. For WATER=“2” 

i. =IF(AND(LARGEST_PATCH>=200,(COHESION>88.84),AND(CLUMPY

>0.755),AND(PD_NEW<30),AND(NP/CLUMP<55.25),AND(LSI<8.35)),"

A",IF(AND(LARGEST_PATCH 

>200,(COHESION>50),AND(CLUMPY>0.1)),"C",IF(AND(LARGEST_P

ATCH <=200,(COHESION >0),AND(CLUMPY <84)),"B","B"))) 

b. For WATER=“3” 

i. =IF(AND(LARGEST_PATCH>=400,(COHESION>93),AND(CLUMPY>0.

77),AND(PD_NEW<40.7),AND(NP_CLUMP<60),AND(LSI<9)),"A",IF(A

ND(LARGEST_PATCH>400,(COHESION>50),AND(CLUMPY>0.1)),"C"

,IF(AND(LARGEST_PATCH<=400,(COHESION>0),AND(CLUMPY<84)

),"B","B"))) 

c. For WATER=“4” 

i. =IF(AND(LARGEST_PATCH>=875,(COHESION>96.45),AND(CLUMPY

>0.79),AND(PD_NEW<53.5),AND(NP_CLUMP<102),AND(LSI<9.1)),"A

",IF(AND(LARGEST_PATCH>=875,(COHESION>50),AND(CLUMPY>0

.1)),"C",IF(AND(LARGEST_PATCH<875,(COHESION>0),AND(CLUMP

Y<84)),"B","B"))) 

d. For WATER=“5” 

i. =IF(AND(LARGEST_PATCH>=2000,(COHESION>98.5),AND(CLUMPY

>=0.84),AND(PD_NEW<53.5),AND(NP_CLUMP<53),AND(LSI<8.2)),"A

",IF(AND(LARGEST_PATCH>=2000,(COHESION>50),AND(CLUMPY>

0.1)),"C",IF(AND(LARGEST_PATCH<2000,(COHESION>0),AND(CLU

MPY<84)),"B","B"))) 

9. Create a new field “CLASS” 

a. This field will be used to combine the WATER class number to the MARSH 

subclass letter. 

184
Glossary 

The definitions below are taken from the following references: The Concise Oxford 

Dictionary of Ecology, edited by M. Allaby, published by Oxford University Press, Oxford, 

England, 1994; The Dictionary of Ecology and Environmental Science, edited by H.W. Art, 

published by Henry Holt and Company, 1993; A Dictionary of Ecology, Evolution, and 

Systematics, edited by R.J. Lincoln, G.A. Boxshall, and P.F. Clark, published by Cambridge 

University Press, Cambridge, England, 1982; Glossary of Oceanography and the Related 

Geosciences with References, by Steven K. Baum, Texas Center for Climate Studies, Texas A&M 

University; and Webster’s Third New International Dictionary, published by Merriam-Webster, 

Springfield, Massachusetts, 1961, 1993. Louisiana Coastal Area Ecosystem Restoration: 

Comprehensive Coastwide Ecosystem Restoration Feasibility Study, Louisiana Coastal Area 

Ecosystem Restoration Program, U.S. Army Corps of Engineers New Orleans District, Louisiana, 

2002. 

ASCII (American Standard Code for Information Interchange). A standard code, consisting of 128 

7-bit combinations, for characters stored in a computer or to be transmitted between computers.

ARMA (autoregressive moving average). A stochastic process or a model that is used to describe 

time series data. An ARMA process is a stationary one made up of sums of autoregressive and 

moving-average components. 

BTNEP. Barataria Terrebonne National Estuary Program.

category. Fragmentation class that designates the percentage of water within the defined 

landscape. 

Chenier Plain. An area of coastal Louisiana bordered on the east by Vermilion Bay, on the west by 

the Sabine River, and on the north by the Pleistocene Ridge (roughly Interstate 10). It is 

characterized by abandoned beach fronts that parallel the Gulf of Mexico shoreline. 

classification. Grouping cells (often by color characteristics) from coregistered rasters to map the 

location and type of image features (like type of crop, surface cover, and map line type). 

class-level metrics. Statistics that are integrated over all the patches of a given type (class). These 

may be integrated by simple averaging, or through some sort of weighted-averaging scheme to 

bias the estimate to reflect the greater contribution of large patches to the overall index. 

clumpy (clumpiness index). An index that shows the frequency with which different pairs of patch 

types (including like adjacencies between the same patch type) appear side by side on the map, 

varying from -1 (totally disaggregated class) to 1 (maximally clumped). 

cohesion (patch cohesion index). An index that measures the physical connectedness of the 

corresponding patch type. Patch cohesion increases as the patch type becomes more clumped or 

aggregated in its distribution and therefore, becomes more physically connected. 

configuration. Fragmentation class that designates the spatial character and arrangement, position, 

or orientation of the corresponding class type. 

185
continental shelf. The shallow, gradually sloping seabed around a continental margin, not usually 

deeper than 200 m and formed by submergence of part of a continent. 

CSV (comma-separated values). A file format used as a portable representation of a database. Each 

line is one entry or record, and the fields in a record are separated by commas. Commas may be 

followed by arbitrary space or tab characters, which are ignored. 

Deltaic Plain. An area of coastal Louisiana bordered on the west by Vermilion Bay, on the east by 

the Pearl River, and the north by a line running along the north shore of Lake Pontchartrain, then 

up the old Pleistocene ridge on the river’s present east bank until around the area of Simmsport, 

and then on a line down to Vermilion Bay. 

DOQQ (digital orthophoto quarter quadrangle). A 1:12,000-scale representation of a quarter of the 

area of a conventional USGS 7.5-minute topographical map produced by using aerial 

photographs. 

easting. A rectangular (x,y) coordinate measurement of distance east from a north-south reference 

line, usually a meridian used as the axis of origin within a map zone or projection. False easting 

is an adjustment constant added to coordinate values to eliminate negative numbers. 

ecosystem. A community of organisms and their physical environment that interact as an 

 ecological unit. 

Ekman convergence. A zone of convergence of warm surface water caused by Ekman transport, 

creating a marked depression of the ocean’s thermocline in the affected area. 

Ekman transport. The natural process by which wind causes movement of water near the ocean 

surface. 

estuary. A semi-enclosed coastal body of water which has a free connection with the open sea and 

where fresh water derived from land drainage (usually mouths of rivers) is mixed with seawater; 

often subject to tidal action and cyclic fluctuations in salinity. 

evapotranspiration. Loss of water from the soil both by evaporation and by transpiration from 

plants. 

fastlands, The zone extending from the landward limits of wetlands to at least 400 ft inland. 

faulting, A fracture in the continuity of a soil or rock formation caused by a shifting or dislodging 

of the Earth's crust, in which adjacent surfaces are displaced relative to one another and parallel 

to the plane of fracture. 

floating fresh marsh, A term used by O’Neil (1949) to describe a marsh dominated by Panicum 

hemitomon. 

floating three-cornered grass, A term used by O’Neil (1949) to describe a marsh dominated by 

Schenoplectus olneyi on a floating map. According to O’Neil, muskrat (Ondatra zibethicus )

populations rapidly denude these marshes of the S. olneyi, creating extremely rotten broken 

marsh. 

186
flotant, A floating marsh that is not anchored to the ground beneath. 

FRAGSTATS. A computer software program designed to compute a wide variety of landscape 

metrics for categorical map patterns. 

fresh marsh. Intertidal herbaceous plant community typically found in that area of the estuary 

with salinity ranging from 0 to 3 parts per thousand.

fresh bulltongue marsh. A term used by Visser and others (1998) to describe a fresh marsh always 

dominated by Sagittaria lancifolia and frequently codominated by Panicum hemitomon and 

Eleocharis spp. Ferns (including Thelypteris palustris and Osmunda regalis), Myrica cerifera, 

and Hydrocotyle spp. are often present also. Bacopa monnieri is absent from this marsh type. 

fresh maidencane marsh. A term used by Visser and others (1998) to describe a fresh marsh 

dominated by Panicum hemitomon. 

GAP analysis. The process of identifying and classifying components of biological diversity to 

determine which components already occur in protected areas and which are not present or are 

underrepresented in protected areas. 

georectification. The digital alignment of a satellite or aerial image with a map of the same area. 

In georectification, a number of corresponding control points, such as street intersections, are 

marked on both the image and the map. These locations become reference points in the 

subsequent processing of the image.

GIS (geographic information system). A combination of computer hardware and software that 

allows storage and manipulation of information suitable for mapping.

GIWW. Gulf Intracoastal Waterway.

habitat fragmentation, The breaking up of a habitat into unconnected patches of water and land. 

hectare (ha). A metric unit of measure for area, equal to 2.47 acres. 

HNC. Houma Navigation Canal.

hotspot. Relatively large areas that display significant change in one or more landscape measures:

marsh composition, configuration, or density. 

HWTP. Houma Water Treatment Plant. 

hydrology. The study of the movement of water from the sea through the air to the land and back 

to the sea; the properties, distribution, and circulation of water on or below the Earth’s surface 

and in the atmosphere. 

intermediate marsh. Intertidal herbaceous plant community typically found in that area of the 

estuary with salinity ranging from 2 to 5 parts per thousand. 

Kendall Tau coefficient. A statistic used to measure the degree of correspondence between two 

rankings and to assess the significance of this correspondence. 

187
landscape ecology. The distribution of energy, materials, and species in relation to sizes, shapes, 

numbers, kinds, and configuration of landscape elements or ecosystems. 

landscape-level metrics. Metrics that are integrated over all patch types or classes over the full 

extent of the data (that is, the entire landscape). Like class metrics, these may be integrated by a 

simple or weighted averaging or may reflect aggregate properties of the patch mosaic. 

LDHH. Louisiana Department of Health and Hospitals.

LDNR. Louisiana Department of Natural Resources.

LDWF. Louisiana Department of Wildlife and Fisheries.

location canal. Canals that are primarily dredged within marsh landscapes to provide greater 

accessibility for oil and gas extraction.

LOSCO. Louisiana Oil Spill Coordinator’s Office.

LPI (largest patch index). An index that quantifies the percentage of total landscape area 

constituting the largest patch. This index is a simple measure of dominance. 

LSI (landscape shape index). The total length of edge involving the corresponding class, given in 

number of cell surfaces, divided by the minimum length of class edge possible for a maximally 

aggregated class. 

LUMCON. Louisiana Universities Marine Consortium.

marsh. An ecosystem of more or less continuously waterlogged soil dominated by emersed 

herbaceous plants but without a surface accumulation of peat. A marsh differs from a swamp in 

that it is dominated by rushes, reeds, cattails, and sedges, with few if any woody plants, and 

differs from a bog in that it has soil rather than peat as its base.

MCD (marsh configuration difference). The change in marsh configuration metric value from one 

observed temporal data point to another. 

metric. A standard or a statistic for measuring or quantifying something else. 

MMS. Minerals Management Service.

mosaic. A large image assembled from segments. Each segment may come from a different 

source and have a different cell size and angle of orientation, but all the segments must be 

geometrically rectified and calibrated to a common coordinate framework.

MRGO. Mississippi River-Gulf Outlet.

NAVD88 (North American Vertical Datum of 1988) A geodetic reference for elevations, created by 

the National Geodetic Survey (NGS) to replace the National Geodetic Vertical Datum (NGVD) 

29. This new datum was published in 1991 and incorporates vertical datums used on the Great 

Lakes. It is adjusted on the basis of field work prior to 1929 as well as surveys as recent as 1988. 

188
northing. A rectangular (x,y) coordinate measurement of distance north from an east-west 

reference line, usually the Equator or other parallel used as the axis of origin for a map zone or 

projection. False northing is an adjustment constant added to coordinate values to eliminate 

negative numbers. 

NP (number of patches). A metric combination that provides a quasi density-by-shape value that is 

used to separate the class configurations that are approaching the metric threshold. 

OA (overall time period). A term that refers to all project data that were created, and analyses that 

were performed, in relation to the preconstruction (1958) and end-point (1998) dates of the 

Houma Navigation Canal. 

OCS. Outer Continental Shelf. 

oligohaline wiregrass. A term used by Visser and others (1998) to describe an intermediate marsh 

dominated by Spartina patens and frequently codominated by Scirpus americanus.

panchromatic. An image collected in the broad visual wavelength range but rendered in black and 

white. 

PD (patch density). An index that calculates the number of patches of the corresponding patch 

type, divided by total grid-tile landscape area and excluding all nonwater and marsh patches. 

PLAND (percentage of landscape). The percentage the landscape made up of the corresponding 

patch type. 

POR (period of record). The length of time that data were collected or were available.

ppt (parts per thousand). The ratio of the amount of the substance of interest to the amount of that 

substance plus the amount of the substance it is in. 

QA/QC. quality assurance/quality control.

raster. A single, related, two-dimensionally grouped set of numbers of a single data type. Each 

number represents the value of some parameter. Its position in the group represents its relative 

position to the other values. A raster object is a raster that is stored in a raster vector converted 

(RVC) project file.

resample. To interpolate cell values in a raster object and create a raster with larger or smaller 

cells. 

SAIC. Science Applications International Corporation.

salt marsh. Intertidal herbaceous plant community typically found in that area of the estuary with 

salinity ranging from 12 to 32 parts per thousand. 

SAS. Statistical analysis system. 

189
salinity. A measure of the total concentration of dissolved salts in water. The salinity of ocean 

water is in the range of 33–38 parts per thousand.

scale. The distance ratio measured on a map to that distance measured on the ground between the 

same two points.

subsidence. The process of the sinking or settling of a land surface or a crustal elevation because 

of natural or artificial causes. 

TA (time period A). A term that refers to all project data that were created, and analyses that were 

performed, in relation to the preconstruction (1958) and postconstruction (1968/69) dates of the 

Houma Navigation Canal. 

TB (time period B). A term that refers to all project data that were created, and analyses that were 

performed, in relation to the postconstruction (1958) and end-point (1998) dates of the Houma 

Navigation Canal. 

TCA (total class area). A measure of landscape composition; specifically, how much of the 

landscape is composed of a particular patch type. 

TIFF (Tagged Image File Format). A container format for storing images, including photographs

and line art. 

TM (Landsat Thematic Mapper). One of the Earth-observing sensors introduced in the Landsat 

program. TM sensors feature seven bands of image data (three in visible wavelengths, four in 

infrared), most of which have 30-m resolution. 

trenasse. A small manmade trench through a swamp or marsh an area. 

topography. The natural and constructed relief of an area. 

USACE. U.S. Army Corps of Engineers. 

USEPA STORET. The U.S. Environmental Protection Agency (EPA) data management system that 

contains water quality information for the Nation’s waters. 

USGS. U.S. Geological Survey.

vector. An object made up of three different types of elements that can have associated attributes: 

(1) points, which are single sets of coordinates that define a point feature (such as a well); (2) 

lines, which are curvilinear strings of coordinates that define a curved line (such as a stream); and 

(3) polygons, which are collections of lines that inscribe an area (such as a lake), as well as a 

fourth type of element, nodes, which are necessary to maintain vector topology. 

vector grid. A network of parallel and perpendicular lines superimposed on a map and often used 

for referencing or processing of additional data. 

wetland. A general term applied to land areas which are seasonally or permanently waterlogged, 

including lakes, rivers, estuaries, and freshwater marshes; an area of low-lying land submerged or 

inundated periodically by fresh or saline water. 

190