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MISSISSIPPI

LOUISIANA

TEXAS

LOUISIANA

Cameron

Lake Pontchartrain

Big

 Mar

Lake Maurepas

Lake Salvador

Barataria Bay

Timbalier Bay

Terrebonne Bay

Caillou Lake

Lake

 Mechant

Lake

 Boudreaux

Little Lake

Atchafalaya Bay

GULF OF MEXICO

Lake Cataouatche

Atchafalaya

River

Lake Borgne

East Cote 

Blanche Bay

West Cote 

Blanche Bay

Vermilion Bay

White Lake

Grand Lake

Calcasieu

Lake

Black

 Lake

Freshwater Bayou

Wax Lake Outlet

Marsh

Island

 Lake Pontchartrain Causeway

Gulf Intracoastal Waterway

Gulf Intracoastal Waterway

Gulf Intracoastal Waterway

Mississippi River-Gulf Outlet Canal

Mississ pi pi Rive

r

Mississippi River

Southwest Pass

South Pass

Pass a Loutre

East Bay

West Bay

Garden

Island 

Bay

Bayou Lafourche

Labranche wetlands

Calcasieu Ship Channel

Sweet

 Lake

Breton Sound

Lake

Felicity

GULF OF MEXICO

GULF OF MEXICO

Chandeleur Sound

C

h

a

n

d

ele

ur Is

al n

d

s

Lac des 

Allemands

GULF OF MEXICO

Hackberry

Johnsons Bayou Grand Chenier

Wax Lake Delta

Atchafalaya Delta

Intracoastal

City

Pecan

Island

Houma

Golden

Meadow

Port

Fourchon

Montegut

Dulac

Theriot

Amelia

Lake 

Verret

Lake 

Palourde

The 

Pen

Redfish 

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Blind

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Quarantine

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Adams

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Bastian

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Lost Lake Four League

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Bayou Perot

Bayou Rigolettes

Grand

Lake

Lake De Cade

Lake

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Catherine

Sabine

Lake

Mermentau Basin

Lake Lery

Calcasieu-Sabine Basin

Teche-Vermilion 

Basin

Atchafalaya 

Delta Basin

Terrebonne Basin

Barataria Basin

Mississippi River 

Delta Basin

Breton Sound

Basin

Pontchartrain

Basin

0 10 20 40 KILOMETERS

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Scale 1:265,000

N

EXPLANATION

1932–56 Persistent land loss2

1956–73 Persistent land loss2,4

1973–75 Persistent land loss2,4

1975–77 Persistent land loss2,4

1977–85 Persistent land loss2,4

1985–88 Persistent land loss2

1988–90 Persistent land loss2

1990–95 Persistent land loss2

1995–98 Persistent land loss2

1998–99 Persistent land loss2

1999–2002 Persistent land loss2

2002–04 Persistent land loss2

2004–06 Persistent land loss2

2006–08 Persistent land loss2

2008–09 Persistent land loss2

2009–10 Persistent land loss2

2010–13 Persistent land loss2

2013–14 Persistent land loss2

2014–15 New water area3

1932–56 Persistent land gain1

1956–73 Persistent land gain1,4

1973–75 Persistent land gain1,4

1975–77 Persistent land gain1,4

1977–85 Persistent land gain1,4

1985–88 Persistent land gain1

1988–90 Persistent land gain1

1990–95 Persistent land gain1

1995–98 Persistent land gain1

1998–99 Persistent land gain1

1999–2002 Persistent land gain1

2002–04 Persistent land gain1

2004–06 Persistent land gain1

2006–08 Persistent land gain1

2008–09 Persistent land gain1

2009–10 Persistent land gain1

2010–13 Persistent land gain1

2013–14 Persistent land gain1

2014–15 New land area3

Basin boundary 

1

Gain is determined by the last date a particular pixel transitioned from water 

to land and remained land throughout the period of observation.

2

Loss is determined by the last date a particular pixel transitioned from land 

to water and remained water throughout the period of observation.

3

Because this date range has only one ending dataset, some of these effects 

may be temporary phenomena.

4

This date range contains at least one date in which the land/water data were 

created from Landsat Multispectral Scanner System (MSS). 

Introduction

The U.S. Geological Survey (USGS) analyzed landscape 

changes in coastal Louisiana by determining land and water 

classifications for 21 datasets. Coastal Louisiana has been losing 

wetlands because of multiple compounding and interacting 

stressors including sea-level rise (SLR), subsidence, storms, 

sediment deprivation, oil and gas extraction and infrastructure, 

navigation infrastructure, saltwater intrusion, altered hydrology, 

and others (Penland and others, 2001). The purpose of this study 

is to provide updated estimates of persistent land change and 

historical land change trends for coastal Louisiana and for each 

hydrologic basin from 1932 to 2016. The use of 21 datasets 

plus the application of consistent change criteria in this study 

provide opportunities to better understand the timing and causal 

mechanisms of wetland loss that are critical for forecasting 

landscape changes in the future.

Study Area

The Louisiana coastal zone encompasses approximately 37,780 

square kilometers (km2) of lowland plains, deltaic lobes, and open 

water (fig. 1). Coastal Louisiana wetlands are generally considered 

one of the most important environments in the United States 

because they support more than 30 percent of the commercial 

fisheries in the United States, 5 of the Nation’s top 20 ports are 

located in coastal Louisiana, and 20 percent of the Nation’s oil and 

gas comes from or is transported through these wetlands (Costanza 

and others, 2008; Feagin and others, 2010; Gedan and others, 

2011; NOAA, 2010; Twilley, 2007). This coast is also considered 

to be one of the most “at-risk” environments in the Nation because 

approximately a quarter of these wetlands have been lost in the 

past 84 years (Couvillion and others, 2011).

Coastal Louisiana was built by sedimentary material sourced 

from fluvial and tidally redistributed deposits. The bulk of these 

deposits were delivered via the Mississippi River as a result of 

upper catchment glaciation and melting associated with sea-level 

changes. As glaciers retreated (from about 12,000 until 6,000 

years ago), meltwater inundated valleys and transported much of 

the upper catchment sediments to the lower catchment floodplains 

that compose coastal Louisiana today. Periodic floodwaters 

would carry these sediments, and as the floodwaters retreated, 

rich sediment left behind formed a vast expanse of fertile deltaic 

wetlands (Fisk and McFarlan, 1955). Hydrology continues to 

dictate the geomorphic structure and vegetation distribution of 

this landscape but in a drastically altered fashion. Flood-control 

structures including dams and levees were built, not only in 

Louisiana but throughout the Mississippi River catchment, 

particularly after the flood of 1927. The construction of these 

features has led to an overall decrease in the amount of sediment 

carried by the Mississippi River as a result of dam and levee 

construction throughout the drainage basin (Meade and others, 

1990; Kesel and others, 1992). Anthropogenic controls also vary 

the distribution of sediment within the river by redirecting or 

altering the amount of sediment distribution at channel mouths, 

along channel banks, and across floodplains. These flood-control 

structures have contributed to a reduced capacity for sediment 

accretion and thereby reduced the ability of wetlands to maintain 

elevation in response to SLR and subsidence. 

Methodology

This study analyzed changes in the extent of land in coastal 

Louisiana by using 21 datasets summarizing land and water 

areas from 1932 to 2016 (table 1; figs. 2 and 3). The datasets 

were derived from multiple sources including (1) historical U.S. 

Army Corps of Engineers land loss data (1932) (Dunbar and 

others, 1990, 1992); (2) National Wetlands Inventory (NWI) data 

based on aerial photography (1956); (3) Landsat Multispectral 

Scanner (MSS) data (1973–79); (4) Landsat Thematic Mapper 

(TM) (1985–2010); and (5) Landsat Operational Land Imager 

(OLI) (2013–2016) satellite imagery classified into land and 

water categories. Variable data coverage among the 1932 and 

1956 datasets and all subsequent data required an assumption of 

no change in the area of no data coverage in those datasets. No 

change is assumed in land area from 1932 to 1956 and from 1956 

to 1973 in portions of the dataset that occur outside the boundaries 

of the 1932 and 1956 data coverage. (Refer to fig. 1 for more 

information regarding the areas within the coastal basins to which 

this assumption was applied.) The areas missing from the 1932 and 

1956 datasets mostly consist of forested wetlands. These areas are 

historically stable, so the assumption of “no change” is correct in 

most instances; however, the area and change summaries should 

be used with the implicit understanding of this assumption in areas 

lacking data in 1932 and 1956.

Preprocessing of all datasets included use of ERDAS 

IMAGINE AutoSync software (Hexagon Geospatial, 2016) that 

uses an automated process to align common landform features 

between images. To assess change, the geometric registration 

between datasets must be highly accurate. Misalignment of 

features at the same location could produce invalid land change 

results. This processing step was critical in ensuring that the 1932 

and 1956 data aligned with the later satellite-based data. The 

Landsat MSS data (1973, 1977, and 1979), which have a native 

68- by 83-meter (m) resolution, were first resampled to a 30-m 

resolution by using a cubic convolution resampling method to 

facilitate comparability with later Landsat TM- and OLI-based 

data (native 30-m resolution). All satellite imagery data were then 

classified into land/water categories by using a methodology which 

makes use of a modified Normalized Difference Water Index 

(mNDWI) (Xu, 2005, 2006). The mNDWI enhances water features 

while cutting down on noise from land, vegetation, and soil (Xu, 

2006). The mNDWI is seen in equation 1 below:

mNDWI = (Green-MIR)/(Green+MIR) (1)

Mid-infrared (MIR) is used because the wavelengths in this 

range, 1.55–1.75 micrometers, are particularly informative and 

discriminatory with regard to categorizing land and water. The 

green wavelength range is used as it maximizes reflectance of 

water and vegetation reflects MIR light more than green light (Xu, 

2006). Initial mNDWI values were assigned to their appropriate 

land/water categories by thresholds. Supervised and unsupervised 

classification was then used to account for areas that may be 

incorrectly classified by the mNDWI alone. This step focuses 

on error-prone classes such as floating aquatic vegetation (FAV), 

shadows, and bridges. These areas were manually recoded to their 

appropriate land/water categories by expert analyses.

Land area estimates can vary substantially because of 

environmental variations such as seasonality and, most notably, 

water-level variations on the date of acquisition (DOA) (table 2) 

of the data (Morton and others, 2005; Bernier and others, 2006). 

A composite coastal land-water dataset was developed from the 

source-classified satellite data for each time period and was then 

used to derive the land area by date, basin area, and coastal area 

(table 1). To reduce some of the errors associated with apparent 

land change caused by misclassification or transient environmental 

phenomena, two approaches were utilized. Changes must persist 

for at least two consecutive time periods (of variable length, 

depending upon availability of cloud-free imagery) following 

the initial change, and anomalous changes among land and 

water categories can only occur in less than 20 percent of the 

remaining datasets. The mapped dataset identifies only these 

areas of persistent and consistent change. For this reason, recent 

land changes (2015 to early 2016) cannot yet be defined as 

persistent because data are not available to confirm that these 

changes have persisted into the two following analysis periods. 

These unconfirmed changes are instead referred to as “new land 

investigation areas” and “new water investigation areas.” 

The resulting land area data were summarized by time period 

and by basin (fig. 3, table 1). Figures 2 and 3 display the long-term 

trend in land area over the period of observation. The function of 

change in land area over time was fit with a smoothing method 

called penalized regression splines by using the Mixed GAM 

Computation Vehicle (MGCV) package (Wood, 2000) on the R 

statistical platform (R Core Team, 2014). This method fits land 

change as a set of polynomials in a piecewise-continuous fashion 

across the span of years. Trends computed from this spline 

technique are smoothed and, as such, generalize trends over time. 

The intent of this study is to provide long-term, coarse-scale rates 

of change, and therefore these change rates are not appropriate 

at finer spatial and temporal scales. The complexity smoothing 

spline, as determined by the dimensionality of its basis function, 

was set to a maximum of five, but its final value was determined 

by cross-validation to prevent overfitting. 

One of the assumptions of these modeling methods is that 

each data point provides equally precise information about the 

response variable. When this assumption is violated, there is nonzero covariance between the model error and the independent 

variables, which leads to biased estimates. These data clearly 

violate this assumption because of differences in data-collection 

methods. To account for differences in methods across years, 

points were weighted differently on the basis of expert opinion of 

the uncertainty inherent in each method. The weights affect how 

strongly each point contributes to estimates of total deviation. Data 

before 1973 were given half the weight of data after 1984. Data 

between 1973 and 1984 were given three-quarters weight of data 

after 1984. The goodness of fit is described by the coefficient of 

determination (R2) statistic. Effective degrees of freedom (edf) can 

be interpreted as being related to the degree of the polynomial fit 

of the spline (edf = 1 is linear, edf = 2 is quadratic, and so on). We 

fit the models with and without weighting the data. As suspected, 

failing to weight the data resulted in overfitting. Specifically, nonweighted models were more complex (larger edf) and had higher 

R2 values but generated slightly larger out-of-sample prediction 

error on average. This suggests that weighting data successfully 

reduced bias and improved the robustness of the models.

Figures 2 and 3 display the change in land area over time, 

whereas figures 4 and 5 and table 3 display approximations of 

the rates of land area change over time. Rates of change were 

calculated from the fitted models by using finite-difference 

approximation. The uncertainty of the finite differences was 

calculated by posterior simulations. Each fitted model was 

simulated 10,000 times, and the finite-difference estimates of 

the rates were calculated by using the function derivSimulCI() 

(Simpson, 2016). The 15.9 and 84.1 percentiles (representing the 

expected value +/- 1 standard deviation) of each rate estimate were 

calculated and displayed on the graphs.

Results

Land Area Changes 

These analyses show that coastal Louisiana has experienced 

a net land area change of approximately -4,833 km2 (modeled 

estimate: -5,197 +/- 443 km2) from 1932 to 2016 (fig. 2, table 1), 

roughly the size of the land area of the State of Delaware. This 

net change in land area amounts to a decrease of approximately 

25 percent of the 1932 land area. Although this decrease in net 

land area is pervasive throughout much of the coast and the time 

period analyzed, the land area of recent data points (2013–16) is 

approximately equivalent and, in some cases, slightly greater than 

that of the 2010 estimate (Couvillion and others, 2011). 

The model which best approximated the coastwide net land 

area change is similar to that of a third order polynomial (edf = 

2.903) (R2=0.923) (fig. 2). Within these data, 2005 and 2008 land 

area data from hurricane-induced effects are evident as low outliers. 

Also evident is a perceived “stability” in coastwide net land area in 

recent years. 

All basins, with the exception of Atchafalaya Delta Basin, have 

experienced a net decrease in wetland area during the period of 

observation (1932–2016) (fig. 3). In terms of total area, Terrebonne 

Basin has experienced the greatest decrease in wetland area 

(-1,302 km2 observed; -1,352 +/- 136 km2 modeled), followed by 

Barataria Basin (-1,120 km2 observed; -1,177 +/- 106 km2 modeled), 

Calcasieu-Sabine Basin (-517 km2 observed; -578 +/- 100 km2

modeled), Mermentau Basin (-488 km2 observed; -500 +/- 90 km2

modeled), Pontchartrain Basin (-472 km2 observed; -529 +/- 30 km2

modeled), Breton Sound Basin (-426 km2 observed; -489 +/- 49 km2

modeled), Mississippi River Delta Basin (-375 km2 observed; 

-401 +/- 75 km2 modeled), and Teche-Vermilion Basin (-149 km2

observed; -174 +/- 37 km2 modeled) (table 1). Atchafalaya Delta 

Basin experienced an increase (+16 km2 observed; +51 +/- 39 km2

modeled) in total land area across the entire period of record (table 

1). As a percent of starting area, Mississippi River Delta Basin has 

experienced the greatest percentage decrease in wetland area (-55 

percent observed; -57 percent modeled), followed by Breton Sound 

Basin (-38 percent observed; -43 percent modeled), Barataria Basin 

(-29 percent observed; -31 percent modeled), Terrebonne Basin (-29 

percent observed; -30 percent modeled), Calcasieu-Sabine Basin 

(-24 percent observed; -27 percent modeled), Mermentau Basin (-20 

percent observed and modeled), Pontchartrain Basin (-17 percent 

observed; -18 percent modeled), and Teche-Vermilion Basin (-10 

percent observed; -12 percent modeled). Atchafalaya Delta Basin 

was the only basin to experience an increase (+3 percent observed; 

+9 percent modeled) in land area, as a percentage of starting area, 

across the entire period of record (table 1).

Change in Land Change Rates through Time

Trend analyses from model fits indicate that coastwide rates of 

wetland change have varied from -83.5 +/- 11.8 square kilometers 

per year to -28.01 +/- 16.37 square kilometers per year. Excluding 

the high rates of wetland loss due to the 2005 and 2008 hurricanes 

(Katrina, Rita, Gustav, and Ike), recent wetland loss rates have 

decreased from the peak loss rates observed in the 1970s which 

are known to have exceeded 80 square kilometers per year (km2/

yr) (Britsch and Dunbar, 1993; Barras and others, 1994, 2003). 

This analysis indicates wetland change rates averaged -83 km2/yr 

during the 1973–78 time period, followed by a steady reduction 

in the rate since that time (fig. 4). Britsch and Dunbar (1993) and 

Barras and others (2008) observed a similar trend of decreasing 

loss after 1978.

The rate of wetland change in most basins has changed 

drastically over the period of record. Only two basins, Mermentau 

and Pontchartrain, were best fit by a linear model (edf = 1), 

suggesting that the rate of change has not changed significantly 

over the period of record in those basins (fig. 5). The remaining 

basins were best fit by non-linear functions. Barataria, Terrebonne, 

and Teche-Vermilion Basins follow a similar pattern to that of the 

coastwide trend of loss rates increasing to a peak in the late 1970s, 

followed by a reduction in loss rates since that time. In Breton 

Sound Basin, wetland loss rates have continued to increase and 

have only recently begun to suggest a decrease in that rate (fig. 5). 

Calcasieu-Sabine Basin experienced the highest rates of wetland 

loss prior to the 1970s, with rates slowly decreasing since that 

time, with the exception of hurricane-induced losses in 2005 and 

2008. The Mississippi River Delta Basin experienced a reduction 

in wetland loss rate since the 1960s, reaching a point of wetland 

gain, only to be followed by a negative trend due to the effects of 

Hurricane Katrina in 2005 (Barras, 2006). Finally, the Atchafalaya 

Delta Basin experienced an increasing land area trend (positive 

land change slope) since 1961 (fig. 5). Prior to 1961, this basin was 

initially on a negative trajectory; however, following the opening 

of the Wax Lake Outlet in 1942, sediment deposition increased, 

and a subaerial active delta emerged after the flood of 1973.

Discussion 

The spatial and temporal patterns of wetland change observed 

in this assessment reveal a dynamic landscape changing as a result 

of the complex and often interactive effects of natural and humaninduced processes. Coastwide total net land area suggests an 

increase in land area by 2015 as compared to the previous estimate 

in 2010 (Couvillion and others, 2011); however, these values are 

well within the standard error associated with the datasets and, as 

such, are not necessarily indicative of a true change in the longterm trend. This increase in coastwide net land area may not yet 

be interpreted as “gain” because the persistence of said areas has 

yet to be assessed and much of this may be interpreted as recovery 

from the ephemeral lows associated with the hurricanes of 2005 

and 2008; however, specific areas of gain can be detected in the 

dataset, many of which are showing persistence throughout the 

newly added data points. 

It is important to note that there is a difference between net 

land area change (figs. 2 and 3, table 1) and persistent losses and 

gains (mapped dataset) and long term trends (figs. 4 and 5, table 

3). Classification of an area as “loss” or “gain” is dependent on 

persistence criteria, where the amount of area of persistent loss 

or gain for a particular period can be less than or greater than the 

net land area change for that period. This effect occurs because 

persistent losses in a particular location can be offset by transitory 

gains in another.

This analysis has fostered an ability to examine variability 

in rates of wetland change through time by fitting data with nonlinear models. Most of the models fit the coastwide and basin-level 

land area change data well, with a couple of notable exceptions. 

In the Atchafalaya Delta Basin, a basin in which water levels are 

known to greatly influence assessments, the R2 of the model is 

0.245 (fig. 3). Mississippi River Delta Basin, another basin in 

which water levels affect our ability to assess land area, also had 

one of the lower R2 values (R2 = 0.609) (fig. 3). The two Chenier 

Plain basins, Calcasieu-Sabine (R2 = 0.659) and Mermentau (R2 = 

0.522), contain several water-control structures and are therefore 

also subject to variability. Barataria, Breton Sound, Pontchartrain, 

and Terrebonne Basins all have an R2 value at or exceeding 0.9.

This analysis has shown decreasing rates of wetland loss after 

the 1970s. The possible causes of this decline in land change rate 

are as numerous as the causes of wetland change itself. One of 

the first possible causes relates to the loss of wetlands. Trends 

have shown that as wetlands are lost, there are fewer wetlands 

remaining to be lost, and as such, the rate of wetland loss will 

decrease. Although there is still a substantial area of wetlands 

remaining, at some point the influence of this artifact should be 

considered. Similarly, many of the wetlands in the most precarious 

locations have already been lost. What remains are wetlands in less 

“at-risk” positions in the landscape which are consequently less 

vulnerable and contribute to this reduction in loss rate; however, 

even stable wetlands may be impacted by a single event or 

cumulative episodic events over time (Morton and Barras, 2011).

The lack of major storms since 2008 is likely the most 

influential factor causing the decrease in net loss rates. This period 

of relative calm has led to fewer disturbances in the form of wave 

energy, thereby reducing erosion-induced losses. The storm hiatus 

also allowed vegetation to colonize new areas of shallow water 

bodies or to reestablish once vegetated areas. Some of these areas 

may have begun life as FAV but may have since attached to the 

substrate (Russell, 1942). 

Oil and gas extraction in the Louisiana coastal zone began in 

1926 and peaked in 1969 (Ko and Day, 2004). Infrastructure, such 

as canals, pipelines, and other features, contributes to wetland loss 

not only through direct removal of material but also by altering 

hydrologic flow. This altered hydrology can lead to indirect 

effects such as the propagation of wave energy and salinity into 

interior wetland areas. Following this peak, oil and gas activities 

started to move from the shallow wetland areas in one of two 

directions: inland or far offshore. As a result of this shift, Morton 

and others (2005) concluded that the decrease in the rate of land 

loss in Barataria Basin was attributable to a decrease in the rate of 

hydrocarbon production in that area after 1974. 

Another contributing factor involves a reduction in tectonic 

activity and subsidence due to active faulting (Dokka, 2006; 

Kolker and others, 2011). The extent to which submergence due to 

natural versus anthropologically influenced subsidence is poorly 

understood. Regardless of the causes, some evidence is beginning 

to point toward a reduction in the rate of subsidence. Kolker 

and others (2011) observed a decrease in subsidence rates which 

corresponds with the decrease in the rate of wetland loss. 

Finally, restoration activities, such as marsh creation and 

barrier island nourishments, can be observed in the datasets which 

constitute this analysis. Although these projects may have small 

effects in the coastwide total land area numbers, in specific areas, 

they have substantially changed the composition and configuration 

of the local landscape. These projects are in many cases building 

new land or sustaining existing land, again contributing to a 

reduction in loss rates. It is important to note that the effects of 

restoration projects are not always visible in the persistent change 

map because areas which were lost and then regained as a result of 

restoration do not meet the persistent change criteria. 

Conclusions

This assessment provides a comprehensive analysis of 

historical trends and rates of land area change in coastal Louisiana 

from 1932 to 2016. The primary improvements over past efforts 

are an improved statistical analysis allowing for the observation 

of rates other than linear and the inclusion of additional datasets 

for assessing coastal wetland change since 2010 (Couvillion 

and others, 2011). This dataset provides the ability to better 

quantify the amount of wetland loss and the time period in which 

it was lost. This information will allow gradual loss patterns 

to be distinguished from losses due to episodic events such as 

hurricanes. The observed spatial and temporal patterns reveal a 

dynamic landscape changing as a result of the complex and often 

interactive effects of natural and human-induced processes.

The temporal frequency of land change provided by this study 

allows for further investigation of the causal mechanisms of the 

loss that has occurred. These investigations will provide improved 

information to develop tools to monitor ecosystems, forecast 

possible impacts, and plan protection and restoration activities. 

These data will support the development of strategies to adapt to 

our changing environment.

While recent trends have shown a reduction in the rate of 

wetland loss, it is important to note that past trends are not 

necessarily indicative of future change. Future disturbance events 

such as a major hurricane impact could change the trajectory of the 

rates. Sea-level rise is projected to increase at an exponential rate, 

and that would also expedite the rate of wetland loss. (IPCC, 2013; 

Doyle and others, 2015).

CAMERON

PLAQUEMINES

ST BERNARD

IBERIA

TERREBONNE

VERMILION

ST MARY

LAFOURCHE

ST

TAMMANY

LIVINGSTON

ORLEANS

ST 

CHARLES

ASSUMPTION

ST JAMES

ST JOHN 

THE BAPTIST

Area enlarged

Path 24

Path 23

Path 22

Path 21 EXPLANATION

Modified Coastal Zone Boundary: 37,780 km2

Landsat Worldwide Reference System-2 (WRS-2)

Missing from 1932 and 1956 data: 1,494 km2 within modified Coastal Zone Boundary

Missing from 1932 data: 2,888 km2 within modified Coastal Zone Boundary

Missing from 1956 data: 2,383 km2 within modified Coastal Zone Boundary N

Figure 1. Study area in coastal Louisiana. The coastal zone boundary, the zone for which statistics were calculated, isolates coastal regions while excluding fastlands (defined as developed, agricultural, and other protected areas) from statistical analysis 

of coastal land change. Of particular note are the areas of non-overlapping data coverage in the 1932 and 1956 datasets (based on survey and aerial data) and in the 1973–2016 datasets (based on satellite imagery data). Because no data exist for these nonoverlapping areas, an assumption of no change in those areas has been used for those time periods in the analyses (a reasonable assumption because a majority of these data voids occurs in areas dominated by forested wetlands, a habitat type known to 

have been particularly stable in past observations). Areas are presented in square kilometers (km2).

Table 2. Water levels at a selected gage (Station ID: 8761724, located near Grand Isle, Louisiana) on the date of acquisition for imagery used in 

the land area estimates. Elevations provided in meters (m) relative to station datum.

Path 21* Path 22* Path 23* Path 24*

Year Date Gauge height (m) Date Gauge height (m) Date Gauge height (m) Date Gauge height (m)

1932 Variable Not Available Variable Not Available Variable Not Available Variable Not Available

1956 Variable Not Available Variable Not Available Variable Not Available Variable Not Available

1973.9 12/5/1973 Not Available 2/3/1973 Not Available 12/7/1973 Not Available 12/9/1973 Not Available

1975.7 10/11/1975 Not Available 4/15/1975 Not Available 9/25/1975 Not Available 9/26/1975 Not Available

1977.4 7/2/1977 Not Available 6/3/1977 Not Available 1/23/1977 Not Available 4/6/1977 Not Available

1985.1 10/11/1985 2.07 1/19/1985 1.77 1/26/1985 1.81 12/3/1985 1.92

1988.1 12/4/1987 1.97 1/28/1988 1.53 12/2/1987 1.93 1/26/1988 1.82

1990.8 2/14/1991 1.82 11/1/1990 2.00 11/24/1990 1.95 10/30/1990 2.04

1995.7 10/7/1995 2.10 9/28/1995 2.09 10/5/1995 2.11 9/10/1995 2.08

1998.2 2/17/1998 1.84 2/24/1998 1.72 3/3/1998 1.87 3/10/1998 1.88

1999.9 11/27/1999 1.94 11/18/1999 1.97 11/16/1999 1.98 10/24/1999 2.05

2002.2 1/3/2002 1.83 2/27/2002 1.66 2/2/2002 1.82 1/8/2002 1.83

2004.9 10/15/2004 2.06 11/7/2004 1.97 10/13/2004 2.07 11/5/2004 2.01

2005.8 10/18/2005 2.05 10/25/2005 1.86 10/16/2005 2.03 10/23/2005 2.04

2006.8 1/25/2007 1.82 10/28/2006 1.99 11/4/2006 2.03 12/13/2006 1.90

2008.8 10/26/2008 2.08 10/1/2008 2.10 10/8/2008 2.10 9/29/2008 2.10

2009.8 11/14/2009 2.03 11/5/2009 2.04 11/12/2009 2.03 11/3/2009 2.05

2010.8 10/16/2010 1.99 10/7/2010 1.92 10/14/2010 1.94 10/5/2010 2.09

2013.9 10/24/2013 1.96 12/18/2013 1.76 2/27/2014 1.83 12/16/2013 1.80

2014.8 12/14/2014 1.90 11/19/2014 1.99 11/26/2014 1.81 11/1/2014 2.06

2015.8 10/14/2015 2.05 11/22/2015 2.02 12/15/2015 1.78 11/20/2015 1.99

2016.1 1/18/2016 1.88 2/10/2016 1.80 2/17/2016 1.88 2/8/2016 1.75

14,000

15,000

16,000

17,000

18,000

19,000

20,000

Coastal Louisiana

Year

Land area, in square kilometers

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

EXPLANATION

edf = 2.903

R2

 = 0.923

Penalized Regression Spline, 1932–2016

95 percent confidence band

Note:

Figure 2. Land area in coastal Louisiana, 1932–2016. (See table 1 for specific area measurements.) The red line represents a penalized regression spline which approximates 

the data. The blue dotted lines represent a 95-percent confidence interval. Effective degrees of freedom (edf) can be interpreted as being related to the degree of the 

polynomial fit of the spline (edf = 1 is linear, edf = 2 is quadratic, and so on). The goodness of fit is described by the coefficient of determination (R2) statistic.

Figure 3. Land area by hydrologic basin in coastal Louisiana, 1932–2016. (See table 1 for specific area measurements.) The red line represents a penalized regression spline which 

approximates the data. The blue dotted lines represent a 95-percent confidence interval. Effective degrees of freedom (edf) can be interpreted as being related to the degree of the polynomial 

fit of the spline (edf = 1 is linear, edf = 2 is quadratic, and so on). The goodness of fit is described by the coefficient of determination (R2) statistic.

laf170791_fig 03 500 550 600 Land area, in square kilometers

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Atchafalaya Delta

2,600 2,800 3,000 3,200 3,400 3,600 3,800

Barataria

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

edf = 3.378

R2

 = 0.937

700 800 900 1000 1100

Breton Sound

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

edf = 2.008

R2

 = 0.897

1,500 1,600 1,700 1,800 1,900 2,000 2,100

Calcasieu-Sabine

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Land area, in square kilometers

edf = 2.206

R2 = 0.659

1,900 2,000 2,100 2,200 2,300 2,400 2,500

Mermentau

edf = 1

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

R2

 = 0.522

edf = 3.252

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

200 300 400 500 600

Mississippi River Delta

R2 = 0.609

2,400 2,500 2,600 2,700 2,800

Pontchartrain

Year

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

edf = 1

Land area, in square kilometers

R2 = 0.922

1,200 1,250 1,300 1,350 1,400

Teche Vermilion

Year

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

edf = 3.06

R2

 = 0.719

3,200 3,400 3,600 3,800 4,000 4,200 4,400

Terrebonne

Year

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

edf = 3.14

R2

 = 0.915

EXPLANATION

Penalized Regression Spline, 1932–2016

95 percent confidence band

edf = 1.77

R2

 = 0.245

Table 1. Observed and modeled land area, including Standard Error (SE) in hydrologic basins and coastal Louisiana, 1932–2016. Units are in square kilometers.

Atchafalaya Delta Basin Barataria Basin Breton Sound Basin Calcasieu-Sabine Basin Mermentau Basin

Julian

date

Land area 

(observed)

Land area 

(modeled)

SE Land area 

(observed)

Land area 

(modeled)

SE Land area 

(observed)

Land area 

(modeled)

SE Land area 

(observed)

Land area 

(modeled)

SE Land area 

(observed)

Land area 

(modeled)

SE

1932 550.58 521.57 37.33 3,832.61 3,837.59 100.40 1,107.56 1,125.39 46.62 2,136.71 2,160.27 94.71 2,481.92 2,464.65 84.07

1956 511.70 514.08 21.01 3,580.92 3,629.09 76.41 974.02 1,025.31 26.36 2,099.23 1,929.75 54.67 2,379.63 2,321.90 55.97

1973.9 510.33 516.82 14.06 3,468.16 3,323.22 40.76 1,023.26 937.85 17.50 1,790.73 1,758.19 35.79 2,302.35 2,215.43 36.72

1975.7 545.24 517.82 13.54 3,172.13 3,282.24 38.65 900.69 927.70 16.83 1,807.18 1,743.12 34.35 2,338.63 2,204.73 34.98

1977.4 493.43 518.92 13.08 3,361.50 3,241.84 37.05 923.31 917.81 16.23 1,533.22 1,729.53 33.06 2,071.71 2,194.62 33.38

1985.1 483.82 525.67 11.16 2,977.82 3,047.84 31.52 864.71 869.40 13.83 1,593.98 1,676.63 28.02 2,021.58 2,148.82 27.05

1988.1 533.26 529.06 10.51 3,031.61 2,973.17 29.46 880.24 849.00 13.05 1,648.17 1,660.18 26.46 2,035.11 2,130.98 25.14

1990.8 515.21 532.43 9.98 2,859.62 2,909.55 27.90 810.68 829.96 12.43 1,707.41 1,647.40 25.29 2,164.47 2,114.92 23.78

1995.7 583.21 539.23 9.19 2,659.18 2,808.59 26.88 756.71 794.04 11.54 1,650.64 1,628.64 23.67 2,200.89 2,085.77 22.42

1998.2 468.64 542.97 8.88 2,829.30 2,766.69 26.94 811.51 775.19 11.15 1,544.00 1,620.92 22.96 1,911.53 2,070.90 22.33

1999.9 598.89 545.60 8.70 2,734.30 2,742.54 26.81 777.68 762.23 10.89 1,669.00 1,616.24 22.43 2,160.14 2,060.79 22.52

2002.2 548.25 549.23 8.52 2,802.67 2,715.65 26.10 788.86 744.55 10.58 1,631.37 1,610.50 21.69 2,023.27 2,047.11 23.07

2004.9 566.94 553.59 8.49 2,715.75 2,692.30 24.35 766.29 723.65 10.37 1,693.97 1,604.42 20.98 2,114.67 2,031.05 24.14

2006.8 554.23 556.70 8.65 2,683.07 2,680.72 22.88 684.02 708.89 10.44 1,619.11 1,600.42 20.88 1,894.03 2,019.75 25.14

2008.8 526.17 559.99 9.04 2,632.37 2,672.25 21.90 622.62 693.33 10.83 1,502.79 1,596.37 21.47 1,884.73 2,007.86 26.37

2009.8 568.12 561.65 9.33 2,618.09 2,669.21 21.97 628.07 685.55 11.17 1,473.35 1,594.38 22.11 1,893.36 2,001.91 27.05

2010.8 593.91 563.30 9.69 2,652.65 2,666.83 22.58 656.31 677.77 11.63 1,583.57 1,592.40 23.02 2,080.01 1,995.96 27.77

2013.9 619.64 568.45 11.23 2,656.36 2,662.38 28.31 647.23 653.65 13.68 1,605.89 1,586.31 27.43 2,033.72 1,977.52 30.21

2014.8 551.35 569.94 11.77 2,656.79 2,661.55 30.89 652.70 646.65 14.44 1,622.24 1,584.55 29.10 2,077.78 1,972.17 30.97

2015.8 553.12 571.60 12.42 2,679.08 2,660.72 34.08 646.26 638.87 15.34 1,621.26 1,582.59 31.11 1,996.29 1,966.22 31.84

2016.1 566.90 572.10 12.62 2,712.53 2,660.48 35.09 682.01 636.54 15.63 1,619.01 1,582.01 31.73 1,993.69 1,964.44 32.11

Mississippi River Delta Basin Pontchartrain Basin Teche-Vermilion Basin Terrebonne Basin Coastwide Totals

Julian

date

Land area 

(observed)

Land area 

(modeled)

SE Land area 

(observed)

Land area 

(modeled)

SE Land area 

(observed)

Land area 

(modeled)

SE Land area 

(observed)

Land area 

(modeled)

SE Land area 

(observed)

Land area 

(modeled)

SE

1932 678.75 702.35 70.42 2,862.43 2,873.36 27.67 1,421.74 1,419.80 34.53 4,471.55 4,490.11 128.40 19,543.84 19,658.05 419.20

1956 626.31 486.15 51.80 2,689.07 2,722.32 18.42 1,347.43 1,377.98 24.12 4,191.70 4,246.20 91.66 18,400.01 18,271.63 280.89

1973.9 208.08 335.04 28.45 2,666.67 2,609.67 12.08 1,372.11 1,329.91 13.82 4,139.91 3,883.05 51.61 17,481.59 16,897.92 166.25

1975.7 244.16 326.14 26.99 2,587.61 2,598.35 11.51 1,367.83 1,323.47 13.13 3,961.86 3,838.01 48.99 16,925.32 16,748.29 158.09

1977.4 359.27 319.42 25.84 2,573.73 2,587.65 10.99 1,295.86 1,317.09 12.56 3,615.95 3,794.44 46.88 16,227.97 16,606.40 151.25

1985.1 315.01 308.05 21.85 2,536.17 2,539.19 8.90 1,275.20 1,286.24 10.54 3,433.10 3,593.55 39.45 15,501.38 15,976.75 126.60

1988.1 334.27 310.59 20.46 2,567.17 2,520.31 8.27 1,262.45 1,274.44 9.91 3,609.35 3,518.61 37.02 15,901.61 15,747.99 119.18

1990.8 357.39 315.08 19.45 2,514.53 2,503.32 7.83 1,237.77 1,264.54 9.46 3,425.47 3,455.23 35.30 15,592.54 15,555.21 114.13

1995.7 354.60 325.11 18.77 2,420.37 2,472.48 7.38 1,235.07 1,249.57 9.13 3,271.51 3,353.72 34.08 15,132.18 15,243.84 109.91

1998.2 295.37 329.39 18.75 2,491.55 2,456.75 7.35 1,255.36 1,243.95 9.07 3,281.10 3,309.97 33.93 14,888.36 15,106.21 108.66

1999.9 393.70 331.41 18.61 2,452.49 2,446.05 7.41 1,267.35 1,241.04 8.97 3,291.05 3,283.62 33.59 15,344.58 15,021.13 107.09

2002.2 346.00 332.60 18.06 2,446.46 2,431.58 7.59 1,258.42 1,238.35 8.67 3,351.74 3,252.32 32.53 15,197.04 14,916.65 103.35

2004.9 308.25 331.41 16.86 2,426.08 2,414.59 7.95 1,228.45 1,236.97 8.11 3,245.28 3,221.54 30.40 15,065.67 14,808.26 96.97

2006.8 274.49 328.83 15.91 2,362.91 2,402.63 8.27 1,236.53 1,237.05 7.71 3,215.95 3,203.29 28.80 14,524.36 14,739.97 92.80

2008.8 294.57 324.63 15.34 2,333.54 2,390.04 8.68 1,203.24 1,237.96 7.52 3,119.94 3,186.61 27.96 14,119.96 14,673.89 91.47

2009.8 281.50 322.02 15.44 2,339.36 2,383.75 8.90 1,219.41 1,238.67 7.61 3,119.51 3,179.07 28.24 14,140.76 14,642.67 92.96

2010.8 357.50 319.11 15.91 2,359.36 2,377.46 9.14 1,221.36 1,239.54 7.87 3,161.68 3,171.96 29.15 14,666.35 14,612.41 96.31

2013.9 306.83 308.79 19.88 2,364.85 2,357.95 9.94 1,289.79 1,242.86 9.77 3,213.18 3,151.80 36.31 14,737.51 14,522.81 118.92

2014.8 324.68 305.57 21.64 2,366.89 2,352.28 10.19 1,232.92 1,243.94 10.59 3,139.95 3,146.25 39.41 14,625.29 14,497.48 128.39

2015.8 315.79 301.96 23.80 2,364.48 2,345.99 10.48 1,234.85 1,245.14 11.59 3,142.53 3,140.14 43.23 14,553.67 14,469.43 139.99

2016.1 303.98 300.87 24.48 2,390.08 2,344.10 10.57 1,272.90 1,245.51 11.91 3,169.56 3,138.31 44.44 14,710.66 14,461.03 143.65

References Cited

Barras, J.A., 2006, Land area change in coastal Louisiana after the 2005 hurricanes—A series 

of three maps: U.S. Geological Survey Open-File Report 2006–1274. 

Barras, J.A., Bernier, J.C., and Morton, R.A., 2008, Land area change in coastal Louisiana—A 

multidecadal perspective (from 1956 to 2006): U.S. Geological Survey Scientific 

Investigations Map 3019, scale 1:250,000, 14 p. pamphlet. [Also available at http://pubs.

usgs.gov/sim/3019/.]

Barras, J.A., Bourgeois, P.E., and Handley, L.R., 1994, Land loss in coastal Louisiana, 

1956–1990: Lafayette, La., National Biological Survey, National Wetlands Research Center 

Open-File Report 94-01.

Bernier, J.C., Morton, R.A., and Barras, J.A., 2006, Constraining rates and trends of historical 

wetland loss, Mississippi River delta plain, south-central Louisiana, in Xu, Y.J., and Singh, 

V.P., eds., Coastal environment and water quality, proceedings of the AIH 25th Anniversary 

Meeting and International Conference Challenges in Coastal Hydrology and Water Quality: 

Highlands Ranch, Colo., Water Resources Publications, LLC, p. 371–373.

Britsch, L.D., and Dunbar, J.B., 1993, Land-loss rates—Louisiana coastal plain: Journal of 

Coastal Research, v. 9, p. 324–338.

Couvillion, B.R., Barras, J.A., Steyer, G.D., Sleavin, W., Fischer, M., Beck, H., Trahan, N., 

Griffin, B., and Heckman, D., 2011, Land area change in coastal Louisiana from 1932 to 

2010: U.S. Geological Survey Scientific Investigations Map 3164, scale 1:265,000, 12 p. 

pamphlet.

Costanza, R.; P ́erez-Maqueo, O.; Martinez, M.L.; Sutton, P.; Anderson, S.J., and Mulder, K., 

2008. The value of coastal wetlands for hurricane protection. Ambio, 37, 241–248.

Dokka, R.K., 2006, Modern-day tectonic subsidence in coastal Louisiana: Geology, v. 34, no. 

4, p. 281–284, http://dx.doi.org/10.1130/G22264.1.

Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—

Resource guide for coastal land managers, engineers, and scientists: U.S. Geological 

Survey Professional Paper 1815, 76 p., http://dx.doi.org/10.3133/pp1815.

Dunbar, J.B., Britsch, L.D., and Kemp, E., III, 1990, Land loss rates. Report 2. Louisiana 

Chenier Plain: Vicksburg, Miss., Army Engineer Waterways Experiment Station 

Geotechnical Lab 2, 38 p., http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=Get

TRDoc.pdf&AD=ADA229640.

Dunbar, J., Britsch, L., and Kemp, E.B., 1992, Land loss rates. Report 3. Louisiana Coastal 

Plain: Vicksburg, Miss., Army Engineer Waterways Experiment Station Geotechnical 

Lab 3, 66 p., http://www.dtic.mil/cgi-bin/GetTRDoc?Location=U2&doc=GetTRDoc.

pdf&AD=ADA256591.

Feagin, R.A.; Mukherjee, N.; Shanker, K.; Baird, A.H.; Cinner, J.; Kerr, A.M.; Koedam, 

N.; Sridhar, A.; Arthur, R.; Jayatissa, L.P.; Lo Seen, D.; Menon, M.; Rodriguez, S.; 

Shamsuddoha, M., and Dahdouh-Guebas, F., 2010. Shelter from the storm? Use and misuse 

of coastal vegetation bioshields for managing natural disasters. Conservation Letters, 3, 

1–11.

Fisk, H.N., and McFarlan, E., Jr., 1955, Late Quaternary deltaic deposits of the Mississippi 

River, in Poldervaart, A., ed., Crust of the Earth: Geological Society of America Special 

Paper 62, p. 279–302.

Gedan, K.B.; Kirwan, M.L.; Wolanski, E.; Barbier, E.B., and Silliaman, B.R., 2011. The 

present and future role of coastal wetland vegetation in protecting shorelines: answering 

recent challenges to the paradigm. Climatic Change, 106(1), 7–29.

Hexagon Geospatial, 2016, ERDAS IMAGINE: Norcross, Ga., Hexagon Geospatial web page, 

accessed April 25, 2017, at http://www.hexagongeospatial.com/.

Intergovernmental Panel on Climate Change (IPCC)—Stocker, T.F., Qin, D., Plattner, G.K., 

Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.M., 2013, 

Climate change 2013—The physical science basis: Contribution of working group I to the 

Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, 

U.K., and New York.

Kesel, R.H., and others, 1992, An approximation of the sediment budget of the lower 

Mississippi River prior to major human modification: Earth Surface Processes and 

Landforms, v. 17, p. 711. 

Ko, J.Y., and Day, J.W., 2004, A review of ecological impacts of oil and gas development on 

coastal ecosystems in the Mississippi Delta: Ocean & Coastal Management, v. 47, no. 11, 

p. 597–623.

Kolker, A.S., Allison, M.A., and Hameed, S., 2011, An evaluation of subsidence rates and 

sea-level variability in the northern Gulf of Mexico: Geophysical Research Letters, v. 38, 

L21404, https://doi.org/10.1029/2011GL049458.

Louisiana Coastal Wetlands Planning, Protection and Restoration Act Program, n.d., Coastal 

Louisiana basins: Louisiana Coastal Wetlands Planning, Protection and Restoration Act 

Program website, accessed May 5, 2011, at http://lacoast.gov/new/About/Basins.aspx.

Meade, R.H., Yuzyk, T.R., and Day, T.J., 1990, Movement and storage of sediment in rivers 

of the United States and Canada, in Wolman, M.G., and Riggs, H.C., eds., Surface water 

hydrology: Geological Society of America, Geology of North America Series, v. 0-1, p. 

255–280.

Morton, R.A., Bernier, J.C., Barras, J.A., and Fernia, N.F., 2005, Rapid subsidence and 

historical wetland loss in the Mississippi Delta Plain, likely causes and future implications: 

U.S. Geological Survey Open-File Report 2005–1216, 124 p. [Also available at http://pubs.

usgs.gov/of/2005/1216/.]

Morton, R.A. and J. A. Barras (2011) Hurricane Impacts on Coastal Wetlands: A HalfCentury Record of Storm-Generated Features from Southern Louisiana. Journal of Coastal 

Research: Volume 27, Issue 6A: pp. 27 – 43. 

National Oceanic and Atmospheric Administration (NOAA), 2010. Annual Commercial 

Landing Statistics. http://www.st.nmfs.noaa.gov/st1/commercial/landings/annual_landings.

html.

Penland, S., Wayne, L., Britsch, L.D., Williams, S.J., Beall, A.D., and Butterworth, V.C., 2001, 

Geomorphic classification of coastal land loss between 1932 and 1990 in the Mississippi 

River Delta Plain, southeastern Louisiana: U.S. Geological Survey Open-File Report 

00-417.

R Core Team, 2014, R—A language and environment for statistical computing: Vienna, 

Austria, R Foundation for Statistical Computing web page, accessed September 22, 2016, at 

http://www.R-project.org/.

Russell, R.J., 1942, Flotant: Geographical Review, v. 32, no. 1, p. 74–98. [Also available at 

http://www.jstor.org/stable/210360.]

Simpson, G., 2016, derivSimulCI.R: GitHub Repository, accessed September 22, 2016, at 

https://gist.github.com/gavinsimpson/ca18c9c789ef5237dbc6.

Twilley, R.R., 2007. Coastal Wetlands and Global Climate Change: Gulf Coast Wetland 

Sustainability in a Changing Climate. Arlington, Virginia: Pew Center on Global Climate.

Wood, S.N., 2000, Modelling and smoothing parameter estimation with multiple quadratic 

penalties: Journal of the Royal Statistical Society Series B, v. 62, no. 2, p. 413–428.

Xu, H., 2005, Using the improved normalized difference water index (MNDWI) to extract 

water body information research: International Journal of Remote Sensing, v. 9, p. 

589–595.

Xu, H., 2006, Modification of normalised difference water index (NDWI) to enhance open 

water features in remotely sensed imagery: International Journal of Remote Sensing, v. 27, 

no. 14, p. 3025–3033.

Figure 4. Finite-difference approximation of land area change rates through time in coastal Louisiana, 1932–2016; negative measurements indicate a net land loss rate, 

whereas positive measurements indicate net land gain rates. See table 3 for specific values.

EXPLANATION

Finite Difference Slope Approximation, 1932–2016

95 percent confidence band

Coastal Louisiana

Land area change rate, in square kilometers per year

-100

-80

-60

-40

-20

0

Year

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

laf170791_fig 05

EXPLANATION

Finite Difference Slope Approximation, 1932–2016

95 percent confidence band

Atchafalaya Delta

Land area loss rate,

in square kilometers per year

-2 -1 0 1 2 3 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Barataria

-30 -25 -20 -15 -10 -5 0 5 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Breton Sound

-10 -9 -8 -7 -6 -5 -4 -3 -2

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Calcasieu/Sabine

-15 -10 -5 0 5 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Land area loss rate,

in square kilometers per year

Mermentau

-10 -8 -6 -4 -2 0 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Mississippi River Delta

-15 -10 -5 0 5 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Pontchartrain

-10 -8 -6 -4 -2 0

Land area loss rate,

in square kilometers per year

Year

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Teche/Vermilion

-6 -4 -2 0 2 4

Year

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Terrebonne

-30 -25 -20 -15 -10 -5 0 5

Year

1930 1940 1950 1960 1970 1980 1990 2000 2010 2020

Figure 5. Finite-difference approximation of land area change rates 

through time in coastal Louisiana by hydrologic basin (as defined by 

the Louisiana Coastal Wetlands Planning, Protection and Restoration 

Act Program, n.d.), 1932–2016; negative measurements indicate land 

loss, whereas positive measurements indicate land gain. See table 3 for 

specific values

Table 3. Finite-difference approximation estimates of long-term wetland change rates including standard error (SE) estimates for each hydrologic basin and coastal Louisiana from 1932 to 2016. Units are in square kilometers per year.

Atchafalaya Delta Basin Barataria Breton Sound Calcasieu Mermentau Basin Mississippi River Delta Basin Pontchartrain Basin Teche/Vermilion Basin Terrebonne Basin Coastal Louisiana

Year Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low 1 SE high Change rate

per year

1 SE low

1932 -0.40 -1.69 0.88 -6.82 -13.28 -0.16 -4.05 -5.82 -2.31 -9.45 -13.26 -5.51 -5.95 -7.18 -4.72 -8.71 -13.09 -4.38 -6.29 -6.69 -5.88 -1.57 -3.61 0.45 -7.62 -15.30 0.00 -52.66 -75.13 -29.82

1956 -0.13 -0.97 0.71 -12.43 -15.14 -9.75 -4.42 -5.48 -3.34 -9.92 -12.21 -7.67 -5.95 -7.18 -4.72 -9.61 -11.44 -7.78 -6.29 -6.69 -5.88 -2.09 -2.99 -1.19 -15.25 -18.58 -11.84 -67.98 -78.71 -57.05

1973 0.47 -0.26 1.16 -21.66 -25.56 -17.87 -5.47 -6.41 -4.48 -8.72 -10.91 -6.55 -5.95 -7.18 -4.72 -5.91 -8.43 -3.34 -6.29 -6.69 -5.88 -3.38 -4.54 -2.22 -24.29 -28.73 -19.82 -82.47 -95.42 -69.23

1975 0.57 -0.13 1.23 -22.89 -26.53 -19.41 -5.66 -6.57 -4.71 -8.33 -10.46 -6.25 -5.95 -7.18 -4.72 -4.84 -7.18 -2.45 -6.29 -6.69 -5.88 -3.59 -4.69 -2.50 -25.10 -29.26 -20.91 -83.19 -95.38 -70.74

1977 0.67 -0.01 1.31 -24.01 -27.31 -20.79 -5.86 -6.74 -4.93 -7.90 -9.95 -5.90 -5.95 -7.18 -4.72 -3.70 -5.85 -1.47 -6.29 -6.69 -5.88 -3.80 -4.81 -2.78 -25.76 -29.60 -21.89 -83.51 -94.87 -71.87

1985 1.06 0.43 1.67 -25.34 -27.96 -22.71 -6.65 -7.47 -5.81 -5.91 -7.74 -4.10 -5.95 -7.18 -4.72 0.26 -1.51 2.07 -6.29 -6.69 -5.88 -4.02 -4.88 -3.18 -25.61 -28.84 -22.43 -78.54 -88.54 -68.64

1988 1.19 0.57 1.80 -24.36 -27.04 -21.66 -6.93 -7.74 -6.11 -5.11 -6.89 -3.33 -5.95 -7.18 -4.72 1.31 -0.49 3.14 -6.29 -6.69 -5.88 -3.83 -4.70 -2.98 -24.33 -27.62 -21.14 -74.05 -83.90 -64.16

1990 1.27 0.66 1.87 -23.25 -25.95 -20.50 -7.10 -7.90 -6.29 -4.59 -6.34 -2.83 -5.95 -7.18 -4.72 1.79 -0.04 3.65 -6.29 -6.69 -5.88 -3.60 -4.48 -2.75 -23.14 -26.46 -19.89 -70.38 -80.37 -60.55

1995 1.44 0.85 2.04 -18.96 -21.49 -16.40 -7.44 -8.23 -6.66 -3.45 -5.12 -1.80 -5.95 -7.18 -4.72 2.02 0.31 3.74 -6.29 -6.69 -5.88 -2.71 -3.52 -1.93 -19.28 -22.35 -16.27 -59.66 -68.97 -50.59

1998 1.52 0.93 2.12 -15.51 -17.68 -13.31 -7.59 -8.36 -6.83 -2.91 -4.51 -1.32 -5.95 -7.18 -4.72 1.49 -0.04 2.99 -6.29 -6.69 -5.88 -1.99 -2.70 -1.29 -16.51 -19.19 -13.82 -52.55 -60.94 -44.43

1999 1.54 0.94 2.15 -14.27 -16.35 -12.17 -7.63 -8.40 -6.85 -2.76 -4.35 -1.17 -5.95 -7.18 -4.72 1.21 -0.24 2.64 -6.29 -6.69 -5.88 -1.72 -2.41 -1.05 -15.55 -18.14 -12.99 -50.16 -58.36 -42.24

2002 1.60 0.95 2.24 -10.51 -12.53 -8.49 -7.71 -8.56 -6.88 -2.39 -4.10 -0.67 -5.95 -7.18 -4.72 0.17 -1.23 1.57 -6.29 -6.69 -5.88 -0.91 -1.59 -0.24 -12.73 -15.31 -10.16 -43.32 -51.83 -35.21

2004 1.62 0.93 2.32 -8.11 -10.39 -5.85 -7.75 -8.67 -6.84 -2.21 -4.13 -0.33 -5.95 -7.18 -4.72 -0.62 -2.21 0.96 -6.29 -6.69 -5.88 -0.40 -1.17 0.37 -11.01 -13.88 -8.14 -39.23 -48.78 -30.09

2006 1.64 0.89 2.40 -5.93 -8.67 -3.23 -7.77 -8.77 -6.77 -2.09 -4.26 0.01 -5.95 -7.18 -4.72 -1.42 -3.30 0.46 -6.29 -6.69 -5.88 0.08 -0.81 0.97 -9.49 -12.84 -6.12 -35.67 -46.81 -25.17

2008 1.65 0.83 2.47 -4.05 -7.35 -0.84 -7.78 -8.88 -6.67 -2.02 -4.44 0.33 -5.95 -7.18 -4.72 -2.18 -4.40 0.06 -6.29 -6.69 -5.88 0.49 -0.56 1.55 -8.21 -12.17 -4.26 -32.75 -45.57 -20.51

2009 1.65 0.80 2.50 -3.25 -6.83 0.22 -7.78 -8.93 -6.63 -2.00 -4.55 0.46 -5.95 -7.18 -4.72 -2.52 -4.93 -0.09 -6.29 -6.69 -5.88 0.67 -0.46 1.81 -7.68 -11.96 -3.43 -31.54 -45.28 -18.56

2010 1.66 0.78 2.53 -2.56 -6.41 1.15 -7.78 -8.98 -6.59 -1.98 -4.66 0.56 -5.95 -7.18 -4.72 -2.82 -5.39 -0.24 -6.29 -6.69 -5.88 0.82 -0.38 2.03 -7.23 -11.79 -2.66 -30.52 -45.09 -16.80

2013 1.66 0.73 2.59 -1.18 -5.55 3.05 -7.78 -9.07 -6.51 -1.96 -4.86 0.82 -5.95 -7.18 -4.72 -3.45 -6.35 -0.53 -6.29 -6.69 -5.88 1.13 -0.22 2.49 -6.34 -11.45 -1.20 -28.53 -44.62 -13.08

2014 1.66 0.72 2.61 -0.96 -5.42 3.35 -7.78 -9.08 -6.50 -1.96 -4.90 0.85 -5.95 -7.18 -4.72 -3.55 -6.50 -0.58 -6.29 -6.69 -5.88 1.18 -0.19 2.56 -6.20 -11.41 -0.96 -28.22 -44.57 -12.54

2015 1.66 0.72 2.61 -0.85 -5.34 3.51 -7.78 -9.09 -6.49 -1.96 -4.92 0.87 -5.95 -7.18 -4.72 -3.61 -6.59 -0.60 -6.29 -6.69 -5.88 1.21 -0.18 2.60 -6.12 -11.37 -0.84 -28.06 -44.58 -12.26

U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Map 3381

Land Area Change in Coastal Louisiana (1932 to 2016) 

By

Brady R. Couvillion, Holly Beck, Donald Schoolmaster, and Michelle Fischer

2017

Publishing support provided by Lafayette Publishing Service Center

For more information concerning this publication contact: Director, USGS, 

Wetland and Aquatic Research Center 700 Cajundome Blvd. Lafayette, LA 70506

https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc.

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

Earth, its natural and living resources, natural hazards, and the environment—

visit https://www.usgs.gov/ or call 1–888–ASK–USGS (1–888–275–8747).

For an overview of USGS information products, including maps, imagery, and 

publications, visit http://store.usgs.gov/.

Any use of trade, firm, or product names is for descriptive purposes only and 

does not imply endorsement by the U.S. Government.

Not for navigational use.

This map or plate is offered as an online-only, digital publication. Users should be 

aware that, because of differences in rendering processes and pixel resolution, 

some slight distortion of scale may occur when viewing it on a computer screen 

or when printing it on an electronic plotter, even when it is viewed or printed at its 

intended publication scale.

Suggested citation: Couvillion, B.R., Beck, Holly, Schoolmaster, Donald, and 

Fischer, Michelle, 2017, Land area change in coastal Louisiana 1932 to 2016: U.S. 

Geological Survey Scientific Investigations Map 3381, 16 p. pamphlet,

https://doi.org/10.3133/sim3381.

ISSN 2329-132X (online)

https://doi.org/10.3133/sim3381

Base from USGS Center for Earth Resources Observation and Science (EROS) 

landsat imagery. Analyses presented utilized imagery from Landsat Multispectral 

Scanner System (MSS), Thematic Mapper (TM), and Operational Land Imager 

(OLI) satellites.

Background imagery consists of average band 6 (mid-infrared) values from early 

2016 Landsat Operational Land Imager (OLI) satellite imagery.