U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2020–1030

Prepared in cooperation with the Louisiana Coastal Protection and Restoration Authority

Louisiana Barrier Island Comprehensive Monitoring 

Program: Mapping Habitats in Beach, Dune, and Intertidal 

Environments Along the Louisiana Gulf of Mexico Shoreline, 

2008 and 2015–16
Cover: Examples of various habitats found on Louisiana’s barrier islands, headlands, and along coastal shorelines.

Top row from left to right: estuarine emergent marsh and water; estuarine emergent marsh, intertidal, and water habitats 

being utilized by shorebirds; beach transitioning into unvegetated flat and meadow. 

Middle row from left to right: estuarine emergent marsh, meadow, and water; beach, unvegetated dune with sand fencing, 

unvegetated flat, and vegetated dune; meadow and scrub/shrub. 

Bottom row from left to right: unvegetated flat and meadow habitats; mangrove colonization on a recently restored backbarrier intertidal platform; meadow, scrub/shrub, estuarine emergent marsh, and mangrove habitats (listed in order from 

foreground to background).

Photographs by the Louisiana Coastal Protection and Restoration Authority (2007, 2008, and 2019), used with permission.
Louisiana Barrier Island Comprehensive 

Monitoring Program: Mapping Habitats in 

Beach, Dune, and Intertidal Environments 

Along the Louisiana Gulf of Mexico 

Shoreline, 2008 and 2015–16

By Nicholas M. Enwright, William M. SooHoo, Jason L. Dugas, 

Craig P. Conzelmann, Claudia Laurenzano, Darin M. Lee, Kelly Mouton, 

and Spencer J. Stelly

Prepared in cooperation with the Louisiana Coastal Protection and Restoration 

Authority

Open-File Report 2020–1030

U.S. Department of the Interior

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

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, 

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Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Enwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 

2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal 

environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File 

Report 2020–1030, 57 p., https://doi.org/10.3133/ofr20201030.

Associated data for this publication:

Enwright, N.M., SooHoo, W.M., Dugas, J.L., Lee, D.M., and Borrok, P.S., 2018, Louisiana Barrier Island Comprehensive 

Monitoring Program—Habitat mapping: U.S. Geological Survey data release, https://doi.org/10.5066/F7XP7440.

ISSN 2331-1258 (online)
iii

Acknowledgments

This effort was funded by the Louisiana Coastal Protection and Restoration Authority’s (CPRA) 

Barrier Island Comprehensive Monitoring program. We would like to thank Syed Khalil (CPRA) 

for numerous helpful discussions about the habitat mapping methods and analysis for this project. We thank John Barras for his assistance with the development of coastal reach boundaries 

for this effort. We are grateful to Hana Thurman (Cherokee Nation Technologies) for her assistance with designing the cover graphic. Finally, we thank Syed Khalil, Glen Curole (CPRA), Brady 

Couvillion (U.S. Geological Survey; USGS), and Richard Day (USGS) for reviewing this report.

v

Contents

Acknowledgments........................................................................................................................................iii

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Methods...........................................................................................................................................................3

Delineation and Description of Study Area ......................................................................................3

Hurricanes, Extreme Storms, and Restoration Efforts ....................................................................5

Habitat Mapping....................................................................................................................................5

Habitat Classification Schemes ................................................................................................5

Source Data and Preprocessing ...............................................................................................6

Elevation Uncertainty and Monte Carlo Simulations .............................................................9

Dune Extraction Approach .......................................................................................................10

Object-Based Image Analysis .................................................................................................10

Data Post-Processing ...............................................................................................................11

Habitat Classification Accuracy Assessment .......................................................................11

Habitat Change and Accuracy Assessment ..................................................................................12

Habitat Reporting Tool ........................................................................................................................14

Data Analyses......................................................................................................................................14

Results and Discussion ...............................................................................................................................14

Habitat and Habitat Change Data ....................................................................................................14

Habitat and Habitat Change Accuracy ...........................................................................................14

BICM Region-Wide Habitat and Habitat Change ..........................................................................19

West Chenier Plain Region Habitat and Habitat Change .............................................................23

East Chenier Plain Region Habitat and Habitat Change ...............................................................26

Acadiana Bays Region Habitat and Habitat Change ....................................................................30

Early Lafourche Delta Region Habitat and Habitat Change .........................................................32

Late Lafourche Delta Region Habitat and Habitat Change ..........................................................34

Modern Delta Region Habitat and Habitat Change .......................................................................36

Chandeleur Islands Region Habitat and Habitat Change .............................................................39

Future Efforts ................................................................................................................................................41

Summary........................................................................................................................................................42

References Cited..........................................................................................................................................42

Appendix 1.....................................................................................................................................................47

Figures

1. Map showing regions and reaches along the Louisiana Gulf of Mexico 

shoreline mapped during the 2008 and 2015–16 habitat mapping efforts for the 

Louisiana Barrier Island Comprehensive Monitoring Program ............................................4

2. Chart showing an overview of the Monte Carlo error propagation process 

for estimating the probability of a pixel being above or below a specific 

elevation threshold for digital elevation models for the Louisiana Barrier Island 

Comprehensive Monitoring Program habitat mapping effort along the Louisiana 

Gulf of Mexico shoreline ...........................................................................................................10
vi

3. Example of workflow for using geographic object-based image analyses 

for habitat mapping for the Louisiana Barrier Island Comprehensive 

Monitoring Program ...................................................................................................................11

4. Chart showing an overview of the mapping process used for the barrier island 

habitat maps for the Louisiana Barrier Island Comprehensive Monitoring 

Program for the detailed scheme ............................................................................................12

5. Maps showing Louisiana Barrier Island Comprehensive Monitoring Program 

detailed habitat classes and general habitat classes, 2016, Whiskey Island, 

Louisiana ......................................................................................................................................15

6. Map showing Louisiana Barrier Island Comprehensive Monitoring Program 

land/water-based change classes for 2008–16 for Whiskey Island, Louisiana ...............16

7. Map showing Louisiana Barrier Island Comprehensive Monitoring Program 

inundation zone-based habitat change classes for 2008–16 for Whiskey Island, 

Louisiana ......................................................................................................................................16

8. Images showing the overview of the functionality of the Louisiana Barrier 

Island Comprehensive Monitoring Program Habitat Reporting Tool .................................17

9. Graph showing percentages of habitat class areal coverage in the regions 

mapped during the Louisiana Barrier Island Comprehensive Monitoring 

Program habitat analyses, 2008 and 2015–16 ........................................................................21

10. Graph showing percentages of habitat class areal coverage in the 

West Chenier Plain region mapped during the Louisiana Barrier Island 

Comprehensive Monitoring Program, 2008 and 2015 ...........................................................23

11. Maps showing coverage of detailed habitat classes in an area of the West 

Chenier Plain region during 2008 and 2015, and paired oblique photographs 

showing Sabine Pass, 2006 and 2017 ......................................................................................25

12. Graph showing the percentages of areal coverage for land/water change 

classes, by Louisiana Barrier Island Comprehensive Monitoring Program region .........26

13. Graph showing the percentages of areal coverage for inundation zone-based 

habitat change classes, by Louisiana Barrier Island Comprehensive Monitoring 

Program region ...........................................................................................................................27

14. Graph showing percentages of habitat class areal coverage in the East Chenier 

Plain region mapped during the Louisiana Barrier Island Comprehensive 

Monitoring Program, 2008 and 2015 ........................................................................................28

15. Maps showing coverage of detailed habitat classes in an area of the East 

Chenier Plain region during 2008 and 2015, and paired oblique photographs, 

2006 and 2017 ...............................................................................................................................29

16. Graph showing percentages of habitat class areal coverage in the Acadiana 

Bays region mapped during the Louisiana Barrier Island Comprehensive 

Monitoring Program, 2008 and 2015–16 ..................................................................................30

17. Maps showing coverage of detailed habitat classes in an area of the Acadiana 

Bays region during 2008 and 2015–16 .....................................................................................31

18. Graph showing percentages of habitat class areal coverage in the Early 

Lafourche Delta region mapped during the Louisiana Barrier Island 

Comprehensive Monitoring Program, 2008 and 2016 ...........................................................32

19. Maps showing coverage of detailed habitat classes in an area of the Early 

Lafourche Delta region during 2008 and 2016, and paired oblique photographs, 

2007 and 2017 ...............................................................................................................................33

20. Graph showing percentages of habitat class areal coverage in the Late 

Lafourche Delta region mapped during the Louisiana Barrier Island 

Comprehensive Monitoring Program, 2008 and 2016 ...........................................................34
vii

21. Maps showing coverage of detailed habitat classes in an area of the Late 

Lafourche Delta region during 2008 and 2016, and paired oblique photographs, 

2007 and 2017 ...............................................................................................................................35

22. Graph showing percentages of habitat class areal coverage in the Modern 

Delta region mapped during the Louisiana Barrier Island Comprehensive 

Monitoring Program, 2008 and 2016 ........................................................................................37

23. Maps showing coverage of detailed habitat classes in an area of the Modern 

Delta region during 2008 and 2016, and paired oblique photographs, 2007 and 2017 .....38

24. Graph showing percentages of habitat class areal coverage in the Chandeleur 

Islands region mapped during the Louisiana Barrier Island Comprehensive 

Monitoring Program, 2008 and 2016 ........................................................................................39

25. Maps showing coverage of detailed habitat classes in an area of the 

Chandeleur Islands region during 2008 and 2016, and paired oblique 

photographs, 2006 and 2017 ......................................................................................................40

Tables

1. Tropical cyclones within 125 nautical miles of either Lake Charles or New 

Orleans, Louisiana, from 2008 to 2016 .......................................................................................5

2. Restoration projects along the Louisiana Gulf of Mexico shoreline, by Louisiana 

Barrier Island Comprehensive Monitoring Program region, 2009–16 ..................................6

3. Louisiana Barrier Island Comprehensive Monitoring Program detailed and 

general habitat classification schemes used in habitat mapping efforts for the 

Louisiana Gulf of Mexico shoreline ...........................................................................................7

4. Orthophotography date and lidar source data information for 2008 and 

2015–16 habitat maps, by Louisiana Barrier Island Comprehensive Monitoring 

Program region .............................................................................................................................8

5. Louisiana Barrier Island Comprehensive Monitoring Program change product 

bins for the land/water and inundation zone-based habitat change products 

with associated BICM detailed habitat classes ....................................................................13

6. Louisiana Barrier Island Comprehensive Monitoring Program change classes 

for the land/water and inundation zone-based habitat change products ........................13

7. Accuracy assessment results for detailed habitat classes for 2008 and 2015–16, 

by Louisiana Barrier Island Comprehensive Monitoring Program region ........................18

8. Fuzzy accuracy assessment results for land/water change from 2008 to 

2015–16, by Louisiana Barrier Island Comprehensive Monitoring Program region ........19

9. Fuzzy accuracy assessment results for inundation zone-based habitat 

change classes for change from 2008 to 2015–16, by Louisiana Barrier Island 

Comprehensive Monitoring Program region ..........................................................................20

10. Percent change for detailed habitat classes for 2008 and 2015–16, by Louisiana 

Barrier Island Comprehensive Monitoring Program region ................................................22
viii

Conversion Factors

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi)

meter (m) 1.094 yard (yd)

Area

square meter (m2) 0.0002471 acre

hectare (ha) 2.471 acre

hectare (ha) 0.003861 square mile (mi2)

Flow rate

meter per year (m/yr) 3.281 foot per year ft/yr)

millimeter per year (mm/yr) 0.03937 inch per year (in/yr)

Datum

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88), unless otherwise noted. 

Horizontal coordinate information is referenced to the North American Datum of 1983 Universal 

Transverse Meter Zone 15 North (NAD 83 UTM 1N). 

Elevation, as used in this report, refers to distance above the vertical datum, unless 

otherwise noted.
ix

Abbreviations

BICM Barrier Island Comprehensive Monitoring

CIMS Coastal Information Management System

CPRA Coastal Protection and Restoration Authority

DEM digital elevation model

EHWS extreme high water springs tide level

ET elevation threshold

GEOBIA geographic object-based image analysis

HRT Habitat Reporting Tool

IZB inundation zone-based

lidar light detection and ranging

MMU minimum mapping unit

MSL mean sea level

NDVI normalized difference vegetation index

NOAA National Oceanic and Atmospheric Administration

NTDE North American Tidal Datum Epoch

PCA principal components analysis

PDF Portable Document Format

SD standard deviation

TPI topographic position index

USGS U.S. Geological Survey

Louisiana Barrier Island Comprehensive Monitoring 

Program: Mapping Habitats in Beach, Dune, and Intertidal 

Environments Along the Louisiana Gulf of Mexico 

Shoreline, 2008 and 2015–16

By Nicholas M. Enwright,1 William M. SooHoo,2 Jason L. Dugas,1 Craig P. Conzelmann,1 Claudia Laurenzano,2

Darin M. Lee,3 Kelly Mouton,2 and Spencer J. Stelly4

Abstract

Barrier islands, headlands, and coastal shorelines provide 

numerous valuable ecosystem goods and services, including 

storm protection and erosion control for the mainland, habitat 

for fish and wildlife, salinity regulation in estuaries, carbon 

sequestration in marshes, and areas for recreation and tourism. 

These coastal features are dynamic environments because of 

their position at the land-sea interface. Storms, wave energy, 

tides, currents, and relative sea-level rise are powerful forces 

that shape local geomorphology and habitat distribution. In 

order to make more informed decisions, coastal resource 

managers require insights into how these dynamic systems are 

changing through time.

In 2005, Louisiana’s Coastal Protection and Restoration 

Authority, in partnership with the University of New Orleans 

and the U.S. Geological Survey, developed the Barrier Island 

Comprehensive Monitoring (BICM) Program. The goal 

of the BICM Program is to develop long-term datasets for 

habitat coverage, shoreline assessments, shoreline position, 

topobathymetric changes, and sediment characterization to 

assist with planning, designing, evaluating, and maintaining 

current and future barrier shorelines. The overall objectives 

of the study described in this report were to (1) map habitats 

for 2008 and 2015–16 for BICM coastal reaches and (2) map 

habitat change between these two time periods.

This report highlights the second phase of habitat 

analyses for the BICM Program. This work builds on a previous habitat analysis conducted by the University of New 

Orleans, which included the development of habitat maps for 

1996/1998, 2001, 2004, and 2005, along with habitat change 

maps. For this current effort, a new 15-class habitat scheme 

1U.S. Geological Survey.

2Cherokee Nation Technologies.

3Louisiana Coastal Protection and Restoration Authority.

4Stelly Consulting (for the U.S. Geological Survey).

was developed from the original BICM scheme to further 

delineate various dune habitats, including meadow habitat 

found along the backslopes of dunes, to distinguish between 

marsh and mangrove, and to distinguish between beach and 

unvegetated barrier flat habitats. Additionally, a geographic 

object-based image analysis-based mapping framework was 

used to incorporate relative topography and address elevation 

uncertainty in light detection and ranging data to assist with 

mapping dune and intertidal habitats.

For the entire BICM region, the area experiencing a 

change in a land/water category (that is, land gain or land 

loss) was 3.4 percent, of which, 59.2 percent was land gain 

and 40.8 percent was land loss. Areal coverages of meadow, 

mangrove, scrub/shrub, and vegetated dune increased from 

2008 to 2015–16, whereas areal coverages of beach, grassland, 

and intertidal decreased. The decrease in intertidal, however, 

was largely due to differing water levels in the orthophotography between the two time periods. Regional analyses of 

habitat coverage and habitat change captured the dynamic 

nature of these systems and the effects of restoration efforts, 

most notably in the Late Lafourche Delta, Modern Delta, 

and Chandeleur Islands regions. For instance, in the Modern 

Delta region there was a marked increase in unvegetated flat, 

meadow, mangrove, scrub/shrub, beach, unvegetated dune, 

and vegetated dune. As a result, this region experienced the 

highest percent change for land/water classes (6.6 percent) 

with land gain accounting for much of this change (70.8 percent). In contrast, the Acadiana Bays region had the highest 

relative percent loss of all regions. The region had a percent 

change for land/water classes of 2.8 percent, of which, 

79.7 percent was land loss.

The results of this study provide information about the 

areal coverage and distribution of habitats for two recent time 

periods and change over about an 8-year period. These data 

can be used to evaluate changes along the Louisiana Gulf of 

Mexico shoreline, including gradual changes caused by coastal 

processes, restoration actions, and (or) episodic events, such as 

hurricanes and extreme storms.
2 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

Introduction

Barrier islands, headlands, and coastal shorelines are 

dynamic coastal environments that consist of wave-, wind-, 

and (or) tide-deposited sediments located along the estuarine–

marine interface. These areas are composed of a diverse mixture of habitats, including beaches, dunes, intertidal marshes, 

intertidal flats, and coastal forests. Collectively, these coastal 

systems provide valuable ecosystem services, including storm 

protection and erosion control for the mainland, habitat for 

fish and wildlife, carbon sequestration in marshes, water catchment and purification, and areas for recreation and tourism 

(Sallenger, 2000; Feagin and others, 2010; Barbier and others, 2011). Yet, these environments face an uncertain future, 

particularly in the latter part of the 21st century, as numerous coastal hazards, including hurricanes, extreme storms, 

accelerated sea-level rise, oil spills, and anthropogenic effects, 

may influence the future of these coastal systems (Pilkey and 

Cooper, 2014). In order to make more informed decisions, 

coastal resource managers require insights into how these 

dynamic systems are changing through time.

In particular, the Louisiana Gulf of Mexico shoreline protects extensive and valuable coastal wetlands from 

the erosional forces from high-energy waves and provides 

important habitat for many wildlife species, including critically designated habitats for two species of shorebirds, piping 

plovers and red knots, which are federally listed as threatened 

species. Louisiana’s Gulf of Mexico shoreline is one of the 

fastest eroding shorelines in the United States (Byrnes and 

others, 2018), and protecting these fragile wetlands is critical 

because Louisiana is losing coastal wetlands at an alarming 

rate (Couvillion and others, 2017). Restoration efforts along 

Louisiana’s Gulf of Mexico shoreline have been extensive 

(Coastal Protection and Restoration Authority, 2017), and 

understanding the performance and effects of these efforts, 

along with future needs, is imperative.

The importance and challenge of monitoring the 

Louisiana Gulf of Mexico shoreline led to the development of 

a comprehensive and holistic monitoring approach. In 2005, 

Louisiana’s Coastal Protection and Restoration Authority 

(CPRA), in partnership with the University of New Orleans 

and the U.S. Geological Survey (USGS), developed the 

Barrier Island Comprehensive Monitoring (BICM) Program. 

The goal of the BICM Program is to provide long-term 

datasets to assist with planning, designing, evaluating, and 

maintaining current and future barrier shorelines. The program 

uses historical and contemporary data collections to assess 

and monitor changes in the subaerial and subaqueous extents, 

habitat types, sediment texture and geotechnical properties, 

environmental processes, and vegetation composition. Current 

examples of BICM datasets include (1) oblique photography 

along the shoreline with periodic comparisons; (2) coastwide 

shoreline delineation and change analysis; (3) topography 

data and change; (4) bathymetry data and change; (5) habitat 

delineation with habitat and land/water change; and (6) surficial sediment composition and change. The BICM Program 

is currently completing its second phase of data collection 

and analyses. For information on the initial analyses of the 

BICM Program, which spanned from about 2006 to 2010, see 

Kindinger and others (2013).

Geographers and remote sensing scientists produce maps 

to show how habitats on barrier islands, on headlands, and 

along the coastal shoreline are changing (Fearnley and others, 

2009; Lucas and Carter, 2010; Kindinger and others, 2013; 

Jeter and Carter, 2015; Zinnert and others, 2016; Campbell 

and others, 2017; Enwright and others, 2019). Barrier island 

and coastal shoreline habitat monitoring often requires custom 

habitat maps because of several factors, including island 

size and the classification of unique geomorphology-based 

habitats, such as beach, dune, and unvegetated barrier flat (for 

example, overwash areas; Enwright and others, 2019).

The initial BICM habitat mapping effort, led by the 

University of New Orleans Institute for Environmental 

Sciences, produced habitat maps with an eight-class habitat scheme for 1996/1998, 2001, 2004, and 2005 by using 

supervised and unsupervised classification of USGS digital 

orthophoto quarter quads and QuickBird satellite orthophotography (Fearnley and others, 2009). The habitat datasets from 

different years were compared to determine habitat changes. 

Pixel-based mapping approaches, such as the one used by the 

initial BICM habitat mapping effort, classify each individual 

pixel in the orthophotography and are commonly used to map 

habitats on barrier islands and along coastal shorelines (Lucas 

and Carter, 2010, 2013; Timm and McGarigal, 2012; McCarthy and Halls, 2014).

Geographic object-based image analysis (GEOBIA; 

Blaschke and others, 2014) is a mapping framework process 

that involves the development of objects from orthophotography and then classifying the orthophotography based 

on object-level statistics (for example, mean value and [or] 

standard deviation for a spectral band of the orthophotography, such as the near-infrared band). This approach is commonly used for mapping coastal habitats (Heumann, 2011; 

Dronova, 2015) and barrier islands, headlands, and coastal 

shorelines (Hantson and others 2012; Brownett and Mills, 

2017; Campbell and others, 2017; Sturdivant and others, 

2017; Enwright and others, 2019). While supervised and 

unsupervised algorithms can be used with GEOBIA (Timm 

and McGarigal, 2012; Dronova, 2015; Campbell and others, 

2017), researchers often use a rule-based approach that classifies habitat types in a stepwise fashion by using photointerpretation and expert knowledge (Gao and others, 2004; Myint and 

others, 2011; O’Neil-Dunne and others, 2014; Dronova, 2015; 

Brownett and Mills, 2017).

Geomorphology is an important feature for habitat 

delineation on barrier islands and along the coastal shoreline. 

Increasingly, researchers are extracting relative topography 

information from light detection and ranging (lidar) data to 

delineate dune habitats (Wernette and others, 2016; Halls and 

others, 2018; Enwright and others, 2019) and using bareearth digital elevation models (DEMs) developed from lidar 

data and tide data for automated delineation of intertidal 
Methods 3

and supratidal/upland habitats (McCarthy and Halls, 2014; 

Brownett and Mills, 2017; Halls and others, 2018). The level 

of uncertainty from data collected with conventional aerial 

topographic lidar systems has been found to be as high as 

60 centimeters (cm) in densely vegetated emergent wetlands 

throughout the United States (Medeiros and others, 2015; 

Buffington and others, 2016; Enwright and others, 2018b). For 

habitat mapping efforts, the vertical uncertainty is often left 

unaddressed because of the lack of detailed error information. 

Yet, the level of uncertainty becomes critical when studying low-relief environments, including barrier islands, where 

centimeters can make a difference in the exposure to physically demanding abiotic conditions (for example, inundation, 

salt spray, wave energy). These challenges were addressed in 

a GEOBIA habitat mapping framework developed for a recent 

habitat mapping effort for Dauphin Island, Alabama (Enwright 

and others, 2019), which was modified for this study. For more 

details about mapping habitats on barrier islands and along 

coastal shorelines, see Enwright and others (2019).

For the second phase of the BICM habitat analyses, the 

USGS, in cooperation with the CPRA, built on the past efforts 

by the BICM Program (Fearnley and others, 2009) to develop 

habitat datasets for 2008 and 2015–2016 and assess habitat 

change between these time periods. In addition to using the 

GEOBIA habitat mapping framework (Enwright and others, 

2019), we also developed a new 15-class habitat scheme. This 

scheme expanded the eight-class scheme used in previous 

BICM habitat mapping efforts (Fearnley and others, 2009). 

The additional classes developed in the new classification 

scheme were primarily used to further delineate various dune 

habitats, including meadow habitat found along the backslopes 

of dunes, to distinguish between marsh and mangrove, and to 

distinguish between beach and unvegetated barrier flat habitats. To ensure comparability between this effort and previous 

BICM map products, we crosswalked the new habitat classification scheme to the eight-class scheme used in previous 

BICM habitat mapping efforts.

One challenge with large-scale habitat mapping efforts 

is the availability of efficient and effective data visualization and distribution. To be most effective, data products 

should be available to resource decision managers, the 

scientific community, and the general public. In response, 

the USGS Wetland and Aquatic Research Center’s Advanced 

Application Team developed a web-based reporting tool 

(https://warcapps.usgs.gov/bicm) to aid in the visualization 

and dissemination of habitat and habitat change data and maps 

via web-based visualization and reporting.

The data developed through this effort provide a snapshot 

of coastal habitats along the Louisiana Gulf of Mexico shoreline that can be combined with other maps to monitor these 

valuable natural resources over time. Specifically, the results 

of this effort will help provide information on the areal coverage and distribution of habitats for two recent time periods and 

serve as an updated baseline for evaluating changes, including gradual changes caused by coastal processes, restoration 

actions, and (or) episodic events, such as hurricanes and 

extreme storms. As previously mentioned, restoration efforts 

along Louisiana’s Gulf of Mexico shoreline have been extensive (CPRA, 2017), but monitoring these restoration efforts 

through habitat mapping is a critical part of the restoration 

process. For example, habitat maps are needed to determine 

if the efforts are meeting their intended goals (that is, delivering various ecosystem goods and services) and (or) if adaptive 

management actions are required to maintain the restoration 

benefits.

Methods

Delineation and Description of Study Area

The study area for this effort spans much of Louisiana’s 

Gulf of Mexico shoreline (fig. 1). The BICM Program has two 

nested levels for habitat analyses: BICM reaches and BICM 

regions, which are composed of a specific number of adjacent 

reaches. Specifically, the study area includes 30 reaches and 

covers 7 BICM regions: West Chenier Plain, East Chenier 

Plain, a portion of Acadiana Bays, Early Lafourche Delta, Late 

Lafourche Delta, Modern Delta, and Chandeleur Islands. For 

this effort, the BICM Program team conducted some minor 

boundary modifications of habitat mapping reaches used in 

prior efforts (Fearnley and others, 2009; Kindinger and others, 

2013) to ensure the reach extents were optimized for assessing 

change through time. Reaches were refined based on historical data (for example, shoreline data, historical topographic 

maps, historical orthophotography, and satellite orthophotography). In general, the reaches cover the nearshore water 

of the Gulf side of the shoreline to about 4 kilometers (km) 

inland. These new boundaries were created by the BICM 

Program in order to allow for potential erosion and shoreline 

change over the next 50 or so years based on BICM historical 

shoreline position, historical aerial photographs, and satellite 

orthophotography.

The Louisiana Gulf Coast has two distinct geomorphic 

zones (not shown): the Chenier Plain (fig. 1, West Chenier 

Plain region to Acadiana Bays region) and the Deltaic Plain 

(fig. 1, Early Lafourche Delta region to Chandeleur Islands 

region). In general, the Louisiana Gulf Coast geomorphology 

has largely been shaped by sea-level fluctuations and delta 

lobe switching by the Mississippi River (Penland and Suter, 

1989; Kindinger and others, 2013). The Chenier Plain consists 

of alternating sand and shell-rich ridges (that is, cheniers) and 

marshes, which were formed by periods of mudflat progradation and shoreline erosion during delta lobe switching of the 

Mississippi River, along with sedimentation from local rivers 

(Penland and Suter, 1989). Barrier islands occur along the 

Deltaic Plain of coastal Louisiana. These islands have been 

developed by episodes of delta building (that is, sedimentation) and then abandonment (Penland and Suter, 1989). Along 

the Deltaic Plain, there are examples of Penland’s threestage transgressive barrier island model (Penland and others, 
4 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

USGS The National Map: Orthoimagery. Data refreshed April, 2019.

0 20 40 KILOMETERS

0 20 40 MILES

Region

Region area

(hectares) Reach Reach area(hectares)

West Chenier Plain 25,610.50 1 Johnsons Bayou 6,910.48

2 Oceanview Beach 4,668.72

3 Holly Beach 3,528.15

4 Hackberry Beach 7,092.27

5 Mermentau River 3,410.88

East Chenier Plain 40,428.60 6 Rockefeller Refuge 22,431.98

7 Mulberry Island 4,969.52

8 Freshwater Bayou 5,151.55

9 Chenier Au Tigre 3,602.71

10 Rainey Refuge 4,272.84

Acadiana Bays 9,337.25 11 Marsh Island 9,337.25

Early Lafourche Delta 36,261.27 34 Point Au Fer Island 6,958.93

35 Oyster Bayou to Caillou Boca 13,027.66

36 Raccoon Island 2,725.30

37 Whiskey Island 4,860.11

38 Trinity Island 3,485.11

39 East Island 2,296.54

40 Wine Island 2,907.62

Late Lafourche Delta 32,786.48 41 Timbalier Island 8,374.07

42 East Timbalier Island 3,834.82

43 West Belle Pass 2,412.52

44 Caminada Headland 8,527.62

45 Grand Isle 5,118.41

46 West Grand Terre 2,145.23

47 East Grand Terre 2,373.81

Modern Delta 17,910.72 48 Chaland Headland 8,085.60

49 Shell Island 9,825.12

Chandeleur Islands 43,131.73 59 Breton Island 5,188.16

60 Grand Gosier and Curlew Islands 11,597.67

61 North Chandeleur Islands 26,345.89

Entire BICM habitat

region

205,466.55

 

Reach name 

West Chenier Plain

East Chenier Plain

Acadiana Bays

Early Lafourche Delta

Late Lafourche Delta

Modern Delta

Chandeleur Islands

Regions

EXPLANATION

8 9

60

3 4 1

11

5

7

2

10 6

8 9

60

3 4 1

11

5

7

2

10 6

39

38

36

43 46

59

44

48

47

42

45

61

35 34 49

41

37

40

39

38

36

43 46

59

44

48

47

42

45

61

35 34 49

41

37

40

93°30' 93°00' 92°30' 92°00' 91°30' 91°00' 90°30' 90°00' 89°30' 89°00'

30°00'

29°30'

!(

!(

Study area

!

LOUISIANA

MISSISSIPPI

ARKANSAS

TEXAS

GULF OF MEXICO

Sabine Pass 

North tide gauge

Grand Isle 

tide gauge

Figure 1. Regions and reaches along the Louisiana Gulf of Mexico shoreline mapped during the 2008 and 2015–16 habitat mapping 

efforts for the Louisiana Barrier Island Comprehensive Monitoring (BICM) Program.
Methods 5

1988) (1) active delta (for example, the Wax Lake Delta); 

(2) erosional headland with flanking barriers (for example, 

Caminada Headland); (3) barrier island arc (for example, 

northern Chandeleur Islands); and (4) inner shelf shoals (for 

example, Ship Shoal). For more information on the subsurface 

geologic framework of the BICM study area, see Kindinger 

and others (2013).

Hurricanes, Extreme Storms, and Restoration 

Efforts

Hurricanes can substantially change composition and 

distribution of habitats along the Louisiana Gulf of Mexico 

shoreline. Byrnes and others (2018) used a National Oceanic 

and Atmospheric Administration (NOAA) cyclone database 

(NOAA, 2019a) to identify the paths of historical hurricanes 

and tropical storms that made landfall in or near coastal 

Louisiana from 1854 to 2015. These tropical cyclones (that 

is, hurricanes and tropical storms) were generally assigned 

to BICM regions. For example, tropical cyclones that made 

landfall within about 125 nautical miles of Lake Charles were 

assigned to the regions in the Chenier Plain geomorphic zone, 

and cyclones that made landfall within 125 nautical miles 

of New Orleans were assigned to the regions in the Deltaic 

Plain geomorphic zone. No tropical cyclones occurred in 

2016 within the respective 125-nautical-mile areas (table 1; 

NOAA, 2019a).

Restoration projects on barrier islands and along 

Louisiana Gulf of Mexico shoreline have been done to help 

offset coastal forces such as extreme waves and storms. 

Table 2 lists 21 restoration projects that were constructed 

between 2009 and 2016. Information on these restoration 

projects can be found in the CPRA’s Coastal Information 

Management System (CIMS; https://cims.coastal.la.gov/). 

In this report, we will loosely connect regional habitat 

change analyses with hurricane and extreme storm events 

and restoration efforts (for example, a large increase in marsh 

habitat could be from a marsh restoration effort that occurred 

in that region).

Habitat Mapping

Habitat Classification Schemes

One challenge with a long-term monitoring program, 

such as BICM, is how to best to deal with opportunities that 

arise from technological advancements related to source data 

availability and characteristics and data processing. For this 

effort, we built on the habitat classification scheme used in 

previous BICM habitat mapping efforts (Fearnley and others, 2009) to develop a new habitat classification scheme 

(table 3). Hereinafter, the new classification scheme is referred 

to as the BICM “detailed” habitat classification scheme, and 

the original BICM habitat classification scheme is called the 

BICM “general” habitat classification scheme. The additional 

classes developed in the detailed scheme are primarily used 

to delineate dune habitats, including meadow habitat on the 

back slopes of dunes, to distinguish between marshes and 

mangroves, and to distinguish between beach and unvegetated 

barrier flat habitats. Researchers have begun extracting relative 

topography from elevation data for dune delineation (Wernette 

and others, 2016; Halls and others, 2018; Enwright and others, 2019), which has allowed the opportunity to enhance the 

BICM classification scheme by adding geomorphology-based 

habitats, such as vegetated dune and unvegetated dune, which 

were previously classified as barrier vegetation or bare land, 

respectively, for the initial BICM habitat mapping effort 

(Fearnley and others, 2009). In the absence of extreme freezes, 

black mangroves can often outcompete and expand at the 

expense of graminoids (for example, Spartina alterniflora or 

Juncus roemerianus) in tidal saline wetland systems (Osland 

and others, 2013), which provided critical rationale for delineating mangroves and marshes as separate classes. Currently, 

Table 1. Tropical cyclones (that is, hurricanes and tropical storms) within 125 nautical miles of either Lake Charles or New Orleans, 

Louisiana, from 2008 to 2016 (National Oceanic and Atmospheric Administration, 2019a). Table adapted from Byrnes and others (2018)

with permission.

[WC, West Chenier Plain; AB, Acadiana Bays; CI, Chandeleur Islands; --, not applicable]

Regions

(fig .1) Cyclone name Year Date(s) of cyclone within buffer

Saffir-Simpson hurricane wind scale 

category at landfall

WC to AB Tropical Storm Edouard 2008 August 3–6 --

Hurricane Gustav 2008 August 25–September 5 2

Hurricane Ike 2008 September 1–15 2

Tropical Storm Lee 2011 September 2–6 --

AB to CI Hurricane Gustav 2008 August 25–September 5 2

Tropical Storm Lee 2011 September 2–6 --

Hurricane Isaac 2012 August 20–September 1 1
6 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

no standardized black mangrove habitat data have been 

developed for coastal Louisiana, so these data can also help fill 

an important data gap. To ensure comparability between this 

effort and previous BICM map products, we have crosswalked 

the detailed habitat classes to general habitat classes previously used by Fearnley and others (2009). In other words, the 

results with the general habitat classes included in this report 

and associated products were not directly interpreted by using 

the methodology discussed below, but instead were derived 

from a direct linkage to detailed classes (table 3).

Source Data and Preprocessing

Orthophotography served as the primary data source for 

BICM habitat maps. We used USGS 1-meter (m) four-band 

color-infrared orthophotography acquired in 2008, 2015, 

and 2016 (table 4). Orthophotography from 2008 covered 

the entire study area, whereas orthophotography from 2015 

covered the West Chenier Plain, East Chenier Plain, and the 

western half of Acadiana Bays. The orthophotography from 

2016 covered the eastern half of Acadiana Bays eastward 

through the Chandeleur Islands (fig. 1). As a result, the later 

dates of the habitat maps were 2015 for the West Chenier Plain 

and East Chenier Plain regions, 2015–16 for the Acadiana 

Bays region, and 2016 for the remaining regions to the east.

Seamless mosaics were developed for each reach. 

Because our effort included spatial change detection, it was 

important to ensure that orthophotography for each date was 

co-registered (that is, locations in one image are in the same 

location as in another image from a different date). We used 

image matching to register the 2015 and 2016 orthophotography to 2008. This is a challenging process on barrier islands 

and coastal settings because of the limited amount of fixed 

or nonmoving features, such as roads or structures. To ensure 

that we had enough well-spaced control points, we often used 

areas with similar texture and shape, such as the center of a 

circular patch of vegetation that can be easily detected in the 

orthophotography for both time periods. On average, we used 

30 control points and either second- or third-order polynomial 

models to georeference the 2015 and 2016 orthophotography 

to match the 2008 with an average root mean square error 

of 0.93 m.

We applied a local pass filter (that is, 3 pixels by 3 pixels) 

to reduce noise in the orthophotography. Next, we used the 

filtered orthophotography to calculate a normalized difference 

Table 2. Restoration projects along the Louisiana Gulf of Mexico shoreline, by Louisiana Barrier Island Comprehensive Monitoring 

Program region, 2009–16. Table adapted from Byrnes and others (2018) with permission.

 [WC, West Chenier Plain; EC, East Chenier Plain; ELD, Early Lafourche Delta; LLD, Late Lafourche Delta; MD, Modern Delta; CI, Chandeleur Islands]

Region Project name (project number) Construction 

date

WC Cameron Parish Shoreline Restoration (CS-33) 2013–14

EC Rockefeller Refuge Gulf Shoreline Protection Demonstration Project (0018-EB) 2010

ELD Whiskey Island Back Barrier Marsh Creation (TE-50) 2009

BIMP 2009 Sand Fencing (LA-0246) 2009

Enhancement of Barrier Island and Salt Marsh Vegetation DEMO (TE-0053) 2012

Raccoon Island Shoreline Protection/Marsh Creation (TE-48) 2013

LLD BIMP 2009 Sand Fencing (LA-0246) 2009

East Grand Terre Island Restoration (BA-30) 2011

West Belle Pass Barrier Headland Restoration (TE-52) 2012

Bayside Segmented Breakwaters at Grand Isle (BA-50) 2012

Grand Isle - Fifi Island Restoration (BA-0155) 2015

Grand Isle - Fifi Island Breakwater (BA-0168) 2015

Caminada Headland Beach and Dune Restoration (BA-45) 2015

Caminada Headland Beach and Dune Restoration, Increment 2 (BA-143) 2016

MD BIMP 2009 Sand Fencing (LA-0246) 2009

Pass Chaland to Grand Bayou Pass Barrier Shoreline Restoration (BA-35) 2009

Emergency Berms W8, W9, W10 2010–11

Barataria Barrier Island Complex Project: Pelican Island and Pass (BA-38, part 2) 2012

Riverine Sand Mining/Scofield Island Restoration (BA-40) 2013

Shell Island East Berm (BA-110) 2013

CI Eastern Berm Reach E4 2010
Methods 7

Table 3. Louisiana Barrier Island Comprehensive Monitoring (BICM) Program detailed and general habitat classification schemes 

used in habitat mapping efforts for the Louisiana Gulf of Mexico shoreline.

[--, not applicable]

Detailed

class Description Description source

General class 

(Fearnley and 

others, 2009)

Beach Beach habitat includes supratidal bare or sparsely vegetated areas (that is, above the 

extreme high water springs tide level) located along coastlines with high wave energy 

(that is, gulf-facing shorelines). Vegetation cover is generally less than 30 percent. 

Beach transitions into dunes, meadow, or unvegetated flat where overwash is evident. 

Beach includes the backshore zone of a beach.

Modified from 

Cowardin and others 

(1979)

Beach

Unvegetated 

dune

Dunes are supratidal features (that is, above the extreme high water springs tide level) developed via Aeolian processes. Dunes are often located above typical storm water levels 

and have a well-defined relative elevation (that is, upper slope or ridge). Unvegetated 

dune includes dune habitat that has less than 10 percent vegetation cover.

Modified from Psuty 

(1989)

Bare land

Vegetated dune Dunes are supratidal features (that is, above the extreme high water springs tide level) developed via Aeolian processes. Dunes are often located above typical storm water levels 

and have a well-defined relative elevation (that is, upper slope or ridge). Vegetated dune 

includes dune habitat that has greater than 10 percent vegetation cover.

Modified from Psuty 

(1989)

Barrier vegetation

Unvegetated flat Unvegetated barrier flat includes flat or gently sloping supratidal unvegetated or sparsely 

vegetated areas (that is, areas located above extreme high water springs tide level) that 

are located on the backslope of dunes, unvegetated washover fans, and along lowenergy shorelines. Vegetation cover should be generally less than 30 percent.

Modified from 

Leatherman (1979)

Beach

Meadow Meadow includes supratidal areas (that is, above the extreme high water springs tide 

level) with sparse to dense herbaceous vegetation located in areas leading up to dunes 

and on the barrier flat (that is, backslope of dunes and supratidal, back-barrier habitat). 

Vegetation coverage should generally be greater than 30 percent. Classification of 

meadow habitat is restricted by geomorphic settings. Meadow is reserved for areas 

located on barrier flats of barrier islands, backslopes of dunes, or transitional vegetated 

areas in dune/beach habitats.

Modified from Lucas 

and Carter (2010)

Barrier vegetation

Intertidal Intertidal includes bare or sparsely vegetated areas located between the extreme low water 

springs and extreme high water springs tide levels. Vegetation cover should generally 

be less than 30 percent. Intertidal includes the foreshore zone of a beach.

Cowardin and others 

(1979)

Intertidal

Estuarine emergent marshEstuarine emergent marsh includes intertidal saline emergent marsh (that is, located above 

extreme low water springs and below extreme high water springs tide levels) and 

supratidal brackish emergent marsh. Vegetation cover should be generally 30 percent 

or greater cover by erect, rooted, herbaceous hydrophytes. Note, supratidal emergent 

vegetation that is located on the backslopes of dunes will be classified as meadow.

Cowardin and others 

(1979)

Estuarine vegetated wetland

Mangrove Mangrove habitat includes areas with black mangrove (Avicennia germinans). Mangrove 

vegetation coverage should generally be greater than 30 percent.

-- Estuarine vegetated wetland

Bare land Bare land includes bare or sparsely vegetated areas that are often located above typical storm water levels and are associated with unvegetated spoil or inland ridges. 

Vegetation cover should generally be less than 30 percent.

Modified from Fearnley 

and others (2009)

Bare land

Grassland Grassland includes upland areas covered by herbaceous vegetation often located above 

typical storm water levels and associated with inland spoil banks with herbaceous vegetation, freshwater emergent marsh, and upland areas along the mainland in the BICM 

regions along the Chenier Plain geomorphic zone.

Modified from Homer 

and others (2015)

Barrier vegetation

Scrub/shrub Scrub/shrub includes areas where woody vegetation height is greater than about 0.5 meter, 

but less than 6 meters. Woody vegetation coverage should generally be greater than 

30 percent.

Cowardin and others 

(1979)

Barrier vegetation

Forest Forest includes areas where woody vegetation height is greater than 6 meters. Woody 

vegetation coverage should generally be greater than 30 percent.

Cowardin and others 

(1979)

Barrier vegetation

Shoreline protectionShoreline protection includes any material used to protect shorelines against erosion (for 

example, breakwater, groins, and jetties).

Fearnley and others 

(2009)

Rip-rap

Developed Developed includes areas dominated by constructed materials (that is, transportation infrastructure and residential and commercial areas) and open developed areas.Modified from Homer 

and others (2015)

Structure

Water Water includes areas of open water with vegetation cover generally less than 30 percent. Modified from 

Cowardin and others 

(1979)

Water
8 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

vegetation index (NDVI; eq. 1; Rouse and others, 1974), 

which is an index for vegetation greenness. The equation for 

NDVI is as follows:

 (1)

where

NIR is the near-infrared band of the 

orthophotography, and

RED is the red band of the orthophotography.

We also conducted principal components analyses (PCA) 

of the filtered four-band aerial orthophotography. The application of PCA to nonspatial data produces “new” orthophotography that is a linear combination of the input data. In other 

words, this process creates a “new” four-band raster dataset in 

which the first one or two bands account for the most variance 

in the original image.

As previously mentioned, lidar data provided important information for coastal geomorphology. For each reach 

and period (that is, 2008, 2015, and 2016), we used the best 

available bare-earth lidar-based DEMs with relation to the 

orthophotography date. The spatial resolution for the majority of the DEMs was 3 m; however, some 1-m DEMs were 

used for the Chenier Plain and Acadiana Bays regions for the 

2015–16 habitat mapping effort. We transformed the elevation 

datasets from the North American Vertical Datum of 1988 to 

local mean sea level by using NOAA’s VDatum version 3.1 

(Parker, 2003). Extensive coastal habitat mapping efforts, such 

as this one, require the use of the best available data. One challenge with lidar data is that over time lidar data acquisitions 

may be captured with an increasingly higher sampling density, 

which may result in incremental quality increases over time 

(Heidemann, 2018). Additionally, lidar data are often collected 

less frequently than orthophotography. As a result, there may 

sometimes be temporal lags between the orthophotography, 

the primary data source for this effort, and other ancillary 

data, such as lidar data (table 4). In these cases, we assumed 

there were no major differences in topography between the 

two time periods unless we noticed obvious changes between 

the orthophotography and the DEM (for example, major 

beach erosion or spit migration). We used photointerpretation 

of the orthophotography for mapping habitats in areas with 

obvious disagreement between the orthophotography and the 

DEM. A potential implication of this approach is that some 

areas that were mapped as a certain habitat may be incorrect 

if there were substantial changes in elevation and relative 

topography that could not be detected by photointerpretation. 

In other words, an area may be mapped as unvegetated dune 

based on analysis of the orthophotography and lidar data; 

however, an overwash event that occurred since the lidar data 

were acquired could have resulted in the area actually being 

unvegetated flat and not unvegetated dune (that is, commission 

errors for unvegetated dune and omission errors for unvegetated flat). For mapping habitats for the Caminada Headland 

= NIR RED NDVI

NIR RED

−

+

Table 4. Orthophotography date and lidar source data information (that is, date, digital elevation model [DEM] spatial resolution, and 

source) for 2008 and 2015–16 habitat maps, by Louisiana Barrier Island Comprehensive Monitoring (BICM) Program region.

[lidar, light detection and ranging; WC, West Chenier Plain; m, meter; USGS, U.S. Geological Survey; CoNED, Coastal National Elevation Database; EC, East 

Chenier Plain; USACE, U.S. Army Corps of Engineers; JALBTCX, Joint Airborne Lidar Bathymetry Technical Center for Expertise; AB, Acadiana Bays; ELD, 

Early Lafourche Delta; LLD, Late Lafourche Delta; MD, Modern Delta; CI, Chandeleur Islands. For full lidar DEM details, see metadata for BICM regional 

habitat classification datasets (Enwright and others, 2018a)]

Region

(fig. 1)

2008 Habitat maps 2015–16 Habitat maps

Orthophotography

date

Lidar data 

date(s)

DEM spatial 

resolution Lidar data source

Orthophotography 

date(s)

Lidar data 

date

DEM spatial 

resolution

Lidar data 

source

WC 2008 1999–2009 3-m USGS CoNED 2015 2017 1-m USGS

EC 2008 1999–2011 3-m USGS CoNED 2015 2017 1-m USGS

2009 3-m USACE JALBTCX

AB 2008 2011 3-m USGS 2015–16 2017 1-m USGS

ELD 2008 2010 3-m USACE JALBTCX 2016 2015 3-m USGS

2011 3-m USGS

LLD 2008 2002–13 3-m USGS CoNED 2016 2013 3-m USGS

2010 3-m USACE JALBTCX 2015 3-m USGS

MD 2008 2002–13 3-m USGS CoNED 2016 2013 3-m USGS

2010 3-m USACE JALBTCX

CI 2008 2005–12 3-m USGS CoNED 2016 2015 3-m USGS
Methods 9

reach for 2016, we also used restoration project as-built data 

(that is, engineering design details) for information on the 

beach/dune restoration engineering design whenever restoration features were not included in the best available DEM. Esri 

ArcMap 10.5–10.7 (Redlands, California) was used for spatial 

analyses for this effort.

Elevation Uncertainty and Monte Carlo 

Simulations

Several elevation derivatives were developed to assist 

with habitat mapping of various classes, including intertidal 

zone habitats (estuarine emergent marsh, mangrove, and intertidal detailed classes) and dune habitats. Studies have found 

that elevation data uncertainty in densely vegetated areas such 

as marsh can have a vertical error as high as 60 cm (Medeiros 

and others, 2015; Buffington and others, 2016; Enwright and 

others, 2018b). One traditional approach for addressing vertical uncertainty in DEMs is the use of Monte Carlo simulations 

(Hunter and Goodchild, 1995; Wechsler and Kroll, 2006). For 

example, Monte Carlo simulations can be used to propagate 

error and determine the probability that the elevation is below 

a specific threshold for the set of iterations. Liu and others 

(2007) used Monte Carlo simulations and error reported from 

lidar metadata to delineate the mean high water shoreline on 

the Bolivar Peninsula in Texas.

While vertical accuracy estimates are contained within 

the DEM metadata, these estimates are often representative 

of the entire acquisition footprint, which includes a large area 

and many different land cover types and geomorphic settings. 

As a result, this can be an underestimate of the DEM vertical 

uncertainty for specific coastal habitat types, such as vegetated 

intertidal wetlands and dunes. For this reason, we used a relative accuracy assessment of a 2015 1-m lidar-based DEM for 

Dauphin Island, Ala., for error and bias estimates for intertidal and wetland vegetation and dunes (Enwright and others, 

2018b, 2019).

Intertidal wetlands are situated above the extreme low 

water spring and below the extreme high water springs 

(EHWS) tidal datum (Cowardin and others, 1979). We defined 

EHWS as the highest astronomical tide for a given tide 

gauge during the most recent North American Tidal Datum 

Epoch (NTDE; 1983–2001) or the 5-year epoch for gauges 

in areas with rapid relative sea-level rise. We used data from 

NOAA tide gauges to determine the EHWS from the Sabine 

Pass North gauge (Station ID: 8770570; fig. 1; EHWS = 

0.40 m relative to mean sea level [MSL] based on observations from the recent NTDE) for West Chenier to Acadiana 

Bays regions and the Grand Isle gauge (Station ID: 8761724; 

fig. 1; EHWS = 0.39 m relative to MSL based on observations 

during 2012–16) for Early Lafourche Delta to Chandeleur 

Islands regions.

Storms play a large role in the regulation of morphology 

and habitats along the Louisiana Gulf of Mexico shoreline, 

which consists of wave-dominated barrier islands, headlands, 

and shorelines (McBride and others, 2013). We used NOAA’s 

extreme water analyses for estimates on reoccurrence intervals for extreme water levels for the two tide gauges (Zervas, 

2013). These analyses developed reoccurrence intervals for 

the tide gauges based on observations between 1966 and 2010. 

Similar to efforts for Dauphin Island, Ala. (Enwright and others, 2019), we used the extreme water level with a 10-percent 

annual exceedance probability for NTDE or recent 5-year tidal 

datum epoch (1.20 m relative to MSL for Sabine Pass North 

and 1.11 m relative to MSL for Grand Isle) for the 2008 habitat mapping effort and an updated estimate for 2015 for the 

2015–16 habitat mapping effort (1.30 m relative to MSL for 

Sabine Pass North and 1.16 m relative to MSL for Grand Isle) 

to account for relative sea-level rise (NOAA, 2016a, b).

To address elevation uncertainty, we used Monte Carlo 

simulations to estimate the probability of a pixel being above 

or below the previously mentioned elevation thresholds. The 

results of this process were probabilistic rasters that estimated 

the probability of a pixel being intertidal by using the EHWS 

elevation threshold with relative vertical error and bias for 

intertidal areas from Enwright and others (2019). Additionally, 

probabilistic rasters were developed to estimate the probability of an area being above the elevation associated with an 

extreme storm water level with a 10-percent annual exceedance by using extreme storm water levels from NOAA and the 

dune relative vertical error and bias estimates from Enwright 

and others (2019). Figure 2 shows the workflow used for the 

Monte Carlo simulations for each reach. The first step of the 

Monte Carlo simulations was to develop a random field with 

a mean = 0 and a standard deviation of 0.5. Next, a local filter 

was used to incorporate spatial autocorrelation. We multiplied 

the filtered random field by the error and bias estimates for 

the desired outcome (that is, intertidal relative vertical error 

for determining the probability of an area being intertidal and 

dune relative vertical error for the probability of an area being 

above the extreme storm water level). Next, we ran a logical 

operator to create a binary presence/absence raster based on 

whether pixels met the specific criteria related to the elevation 

threshold. This process was repeated 500 times for each Monte 

Carlo simulation to determine the probability of an area meeting the desired outcome for that simulation (that is, probability 

of being intertidal or probability of being above extreme storm 

water levels). Originally, we used 1,000 iterations for each 

Monte Carlo simulation; however, we reduced this number 

to 500 to increase computation efficiency, especially for large 

reaches, and to be consistent with the iteration count that was 

used for similar research on Dauphin Island, Ala. (Enwright 

and others, 2018b). For the Monte Carlo simulations, we 

resampled all 1-m DEMs that were used for the 2015–16 

habitat maps to 3-m DEMs by using bilinear interpolation. The 

rationale for this was for consistency with the 2008 habitat 
10 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

map DEMs and for enhanced computational efficiency. For 

more information on the Monte Carlo analyses, see Enwright 

and others (2017, 2018b, 2019).

Dune Extraction Approach

As previously mentioned, relative topography is helpful for delineating dunes. We used an approach similar to that 

of Wernette and others (2016) to help guide dune delineation 

by using relative relief. The topographic position index (TPI) 

was developed by comparing the elevation for a pixel with the 

mean for a user-defined neighborhood (Weiss, 2001; De Reu 

and others, 2013). We estimated the TPI for a circular 30-m 

neighborhood. The optimal neighborhood size was determined 

through trial and error and visual interpretation of the TPI 

outputs and cross-shore topographic profiles. Additionally, 

this neighborhood size was used for similar analyses on 

Dauphin Island, Ala. (Enwright and others, 2019). The mean 

and standard deviation (SD) for the region of interest (that is, 

the land portion of a given reach) of the relative difference 

between the center pixel and the mean neighborhood value 

were used to identify upper slopes and ridges. Upper slopes 

had a value between one-half of a SD and one SD from the 

regional mean, and ridges had a value that is greater than one 

SD from the regional mean (Weiss, 2001; De Reu and others, 

2013). We eliminated ridges and upper slopes from the TPI 

data that had a probability at or below 0.5 for being below the 

extreme storm water level. Next, we used photointerpretation 

to manually edit the ridges and upper slopes to remove areas 

with disagreement between the DEM and orthophotography 

because of temporal differences or areas that were above 

extreme storm water levels and had relative topography, but 

were not dunes (for example, spoil banks). For more information on this process, see Enwright and others (2019).

Object-Based Image Analysis

We used a semiautomated approach to classify habitats 

on barrier islands, on headlands, and along coastal shorelines. We used multiresolution segmentation (Trimble, 2016) 

in Trimble eCognition 9.2 to segment the orthophotography 

into objects based on spectral similarities with regard to the 

orthophotography, derivatives of orthophotography, including the first two components of the PCA, NDVI, and spatial 

similarities based on elevation data. As done in similar studies, 

such as Myint and others (2011), we determined the optimal segmentation parameters (that is, bands, weights, scale 

of objects) by using a trial-and-error approach. We used a 

hierarchical approach to classify the image objects based on 

object-level statistics (fig. 3) and photointerpretation. First, 

we classified the objects as land or water. We then classified 

land objects into vegetated and unvegetated categories by 

trial and error by using level-slice thresholds for NDVI and 

near-infrared spectral object-level statistics (fig. 4). We used 

general knowledge-based thresholds and photointerpretation to 

further subdivide vegetated and unvegetated areas into habitats 

according to habitat definitions (table 3). For a given step, 

threshold-based rules were used to minimize omission and 

Random field

(normal,

μ = 0, σ = 0.5)

with local filter

DEM

Error

with bias

constraint X +

PA:

≤ ET

(1, 0)

Repeat 500 times = 500 binary outputs

Sum

binary outputs

Divide 

by 500

Probability

≤ ET

PA:

probability

≤ ET > 0.5 

(1, 0)

Figure 2. An overview of the Monte Carlo error propagation process for estimating the probability of a pixel 

being above or below a specific elevation threshold (that is, the elevation of extreme high water spring or the 

elevation of extreme water level with a 10-percent annual exceedance probability) for digital elevation models 

(DEMs) for the Louisiana Barrier Island Comprehensive Monitoring Program habitat mapping effort along the 

Louisiana Gulf of Mexico shoreline. [Modified from Enwright and others (2018b) with permission; μ, mean; σ, 

standard deviation; PA, presence-absence; ET, elevation threshold]
Methods 11

commission errors. We used the intertidal probabilistic rasters 

as a guide for habitat mapping for intertidal zone habitats. 

Generally, we considered areas with a probability of being 

below EHWS greater than 0.5 to have an intertidal hydrologic 

regime. In some cases, however, we also assigned areas to 

supratidal/upland habitats based on the visual inspection of the 

aerial photography. Because the DEMs we used were limited to only topographic lidar data, our elevation analyses for 

identifying intertidal areas were focused on identifying areas 

located between the MSL and the EHWS. We used photointerpretation to map intertidal areas below the MSL. Similarly, the 

edited TPI-based ridges and upper slopes were used as a guide 

for dune delineation. After applying general decision rules for 

each habitat in a stepwise fashion, we used photointerpetation 

to refine habitats through manual editing.

Data Post-Processing

Reach-specific habitat maps were converted from image 

objects to a 1-m raster to increase efficiency of manual editing. The map was reviewed for errors and manually edited, 

as needed. ERDAS Imagine 2017 and 2018 (Hexagon 

Geospatial, Madison, Ala.) were used for manually editing 

rasters. To generalize the map, we applied a majority filter for 

a 3-pixel by 3-pixel neighborhood and then applied a 0.01 acre 

(that is, about 40 square meters [m2]) minimum mapping 

unit (MMU). Note, the MMU was not applied to the developed class. We selected this MMU as a reasonable balance 

between noise reduction and loss of detail. This MMU is well 

below the smallest MMU (that is, 2,500 m2) suggested by the 

USGS and the National Park Service for mapping vegetation 

in national parks (Lea and Curtis, 2010). The reach-specific 

raster-based habitat maps were mosaicked into 

a region-wide habitat map. We converted these 

region-wide maps to vector data by using the 

simplify polygon option. The rationale for using 

the simplify polygon option was to create more 

aesthetically pleasing habitat maps like other 

detailed habitat maps, such as the U.S. Fish and 

Wildlife Service’s National Wetlands Inventory. 

At this point, all attribution was completed, and 

the product was finalized.

Habitat Classification Accuracy 

Assessment

We assessed the habitat map accuracy by 

using photointerpretation of source data following general guidelines by Congalton and Green 

(2009). For each reach, we attempted to generate 30 random points per class with a minimum 

distance of 5 m. In some cases, we were not able 

to obtain the desired number of points per class 

because of limited areal coverage of habitat. We 

buffered the random points by 2 m. We identified the habitat class that covered the majority 

of each buffered area via photointepretation of 

orthophotography, elevation data, and relative topography. Likewise, we determined the 

mapped habitat class that covered the majority 

of each random buffer. These reference data 

were used to assess the overall accuracy and the 

user's and producer's accuracies of each class. 

We excluded the Kappa statistic (Cohen, 1960) 

based on guidance by Pontius and Millones 

(2011) and Stehman and Foody (2019). Stehman 

and Foody (2019) reviewed research regarding the usage of the Kappa statistic for remote 

sensing accuracy assessments and confirmed 

prior guidance by Pontius and Millones (2011) 

that the Kappa statistic should be omitted from 

Imagery (pixels)

Image segmentation 

Object statistics

Classification

Figure 3. Example of workflow for using geographic object-based image 

analyses for habitat mapping for the Louisiana Barrier Island Comprehensive 

Monitoring Program.
12 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

accuracy assessments because of several factors, including the 

high degree of correlation with the overall accuracy statistic 

(Lu and Weng, 2007). Also, the Kappa statistic rarely changes 

the interpretation of the map when compared to the overall 

accuracy (Pontius and Millones, 2011), and a map that is created from a random classification, which is the reference point 

for the Kappa statistic, is unrealistic (Ye and others, 2018).

Habitat Change and Accuracy Assessment

The habitat change analyses depict and summarize 

change between habitat maps from 2008 and 2015–16 per 

BICM reach. The results from these analyses highlighted 

changes based on land/water classes and classes related to 

general inundation frequency (table 5). The land/water change 

classification scheme bins change into 4 classes, whereas the 

inundation zone-based (IZB) change classification scheme 

bins change into 15 different classes (table 6).

Change was determined by intersecting the habitat maps 

from each date. Similar to the approach used by Zhou and 

others (2008), we applied two constraints on the changed 

polygons to reduce noise in the maps and issues associated 

with image misregistration: (1) the changed polygon must be 

greater than the MMU, and (2) the changed polygon must have 

a minimum width of 2 m. In other words, polygons that experienced change (for example, land gain), but had a total area 

of less than 40 m2 and (or) narrow portions of a polygon that 

were less than 2 m were assigned to the appropriate unchanged 

class (for example, water unchanged). We explored the use of 

increased width constraints; however, we decided to use 2 m 

to be consistent with the work of Zhou and others (2008) and 

Segment

image

NDVI

and 

NIR

Unvegetated

NDVI and NIR

PCA, NDVI, NIR, and DEM

Land Water

NDVI,

NIR, 

and VI

DEM, TPI, 

and PS

PI, VI, and 

NDVI

DEM, NDVI, and 

VI

Vegetated

PI

DEM, TPI, 

and PS

DEM, PI, 

and PS

DEM, TPI, 

PI, and PS

VI

NDVI, NIR

Other

Shoreline 

protection

Developed

NDVI and 

NIR

NDVI

and

NIR

Beach

Intertidal

Unvegetated

dune

Unvegetated

flat

Meadow, 

Mangrove,

Estuarine 

emergent

marsh

Vegetated

dune

Scrub/shrub

and Forest

DEM, NDVI, and Grassland

VI

DEM, PS, Bare land

and VI

Figure 4. An overview of the mapping process used for the barrier island habitat maps for the Louisiana 

Barrier Island Comprehensive Monitoring Program for the detailed scheme. The italic text indicates data 

or techniques used for classifying each habitat. Modified from Enwright and others (2019) with permission. 

[PCA, principal component analysis; NDVI, normalized difference vegetation index; NIR, near infrared 

band; DEM, digital elevation model; PI, probability of being intertidal; VI, photointerpretation; PS, probability 

of being above water levels during extreme storms; TPI, topographic position index. VI was used for all 

mapping steps and classes.]
Methods 13

because of the ~1-m root mean square error for image matching the 2015–16 orthophotography to 2008 orthophotography. 

The two constraints were applied in a stepwise fashion. First, 

we applied the MMU and recoded small polygons to the 

appropriate unchanged class for the land/water change dataset 

and the unchanged class for the inundation-based zone change 

dataset. Next, we applied the width constraint and removed 

areas within changed polygons that were generally less than 

2 m wide. Finally, we reapplied the MMU constraint because 

the width constraint can reintroduce small change polygons 

that are below the MMU.

Even with the application of constraints to reduce noise, 

it is important to assess the accuracy of the change map data. 

We assessed the change map accuracy by using photointerpretation of source data following general guidelines by 

Congalton and Green (2009). For each region, we generated 

100 random points per class. For this assessment, we placed 

equal portions of randomly selected points within interior 

and edge areas (that is, areas within a 4-m interior buffer). A 

comparison between accuracy results of points sampled from 

both interior and edge areas, and excluding points from the 

edge area, respectively, showed that targeting samples in the 

edge may lead to a lower accuracy result. However, highlighting habitat change along transition areas was important for this 

effort. Similar to the assessment of habitat maps, we assessed 

the habitat change class at each point via photointerpretation 

of orthophotography, elevation data, and relative topography. 

We did not buffer the habitat change accuracy assessment 

points. The rationale for this was due to the complexity of 

determining the majority change for an area, especially for 

buffered points located along the edge of a polygon.

The accuracy assessment included both deterministic 

and fuzzy accuracy. We used a fuzzy accuracy assessment 

approach developed by Woodcock and Gopal (2000), which 

allowed for the classification of (1) exact match (for example, 

area is determined to be the same change class in both the 

Table 5. Louisiana Barrier Island Comprehensive Monitoring (BICM) Program change product bins for the land/water and inundation 

zone-based habitat change products with associated BICM detailed habitat classes.

BICM habitat

change product

Generalized classes for 

change BICM detailed habitat classes

Land/water Land Bare land, beach, developed, estuarine emergent marsh, forested, grassland, mangrove, scrub/shrub, shoreline protection, unvegetated dune, unvegetated flat, 

vegetated dune

Water Water, intertidal

Inundation zone-based Water Water

Intertidal-unvegetated Intertidal

Intertidal-vegetated Estuarine emergent marsh, mangrove

Supratidal Bare land, beach, forest, grassland, meadow, scrub/shrub, unvegetated dune, 

unvegetated flat, vegetated dune

Developed/shoreline protectionDeveloped, shoreline protection

Table 6. Louisiana Barrier Island Comprehensive Monitoring (BICM) Program change classes for the land/water and inundation 

zone-based habitat change products.

BICM habitat

change product Habitat change classes

Land/water 1. Land gain 3. Land unchanged

2. Land loss 4. Water unchanged

Inundation zonebased1. Intertidal-vegetated to supratidal/upland 9. Water to intertidal-unvegetated

2. Intertidal-unvegetated to supratidal/upland 10. Supratidal/upland to water

3. Water to supratidal/upland 11. Intertidal-vegetated to water

4. Supratidal/upland to intertidal-vegetated 12. Intertidal-unvegetated to water

5. Intertidal-unvegetated to intertidal-vegetated 13. Developed/shoreline protection gain

6. Water to intertidal-vegetated 14. Developed/shoreline protection loss

7. Supratidal/upland to intertidal-unvegetated 15. Unchanged

8. Intertidal-vegetated to intertidal-unvegetated
14 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

change map and for reference data); (2) acceptable match 

because of a high level of subjectivity at the location or different water levels between the two maps used to detect change; 

or (3) unacceptable/error (for example, mapping an area as 

land gain when it is water for both dates). Fuzzy accuracy is 

well suited for assessing barrier island habitats because of 

dynamic transitions such as water and intertidal classes, which 

are dependent on water level, or along the transition zones of 

supratidal and intertidal zones (for example, beach to intertidal 

or meadow to estuarine emergent marsh); therefore, we present the fuzzy accuracy assessment results in this report. For 

deterministic accuracy assessment results, see the metadata for 

regional datasets (Enwright and others, 2018a). The accuracy 

assessment includes deterministic accuracy and fuzzy accuracy estimates, including overall accuracy and the user's and 

producer's accuracies for each class. Again, we excluded the 

Kappa statistic (Cohen, 1960) based on guidance by Pontius 

and Millones (2011) and Stehman and Foody (2019). Despite 

the application of these constraints, errors identified in the 

accuracy assessment of the change data could still be from 

localized misregistration, fuzzy areas along habitat transitions, 

and habitat map discrepancies based on subjectivity.

Habitat Reporting Tool

The BICM Program developed a custom web-enabled 

mapping and reporting application called the “Habitat 

Reporting Tool” (HRT). The goal of this tool was to allow the 

habitat and habitat change datasets to be broadly accessed (that 

is, visualized) by natural resource managers, researchers, and 

the general public without the need for specialized technical 

software packages. The application is an Internet Information 

Services-hosted C#.NET application with backend dependencies on Microsoft SQL Server for tabular data and Esri ArcGIS 

Server 10.7 for spatial geometry, layer symbology, and 

performance caching. The backend is additionally dependent 

on PDFSharp (Empire Software, Germany) to develop reachspecific Portable Document Format (PDF) maps. Lastly, the 

application depends on several client-side libraries, including 

Bootstrap, jQuery, and the Esri ArcGIS Javascript application 

programming interface.

Data Analyses

For the entire BICM study area and each region, we highlighted the top five nonwater detailed habitat classes in terms 

of areal coverage. We summarized percent change by detailed 

habitat classes from 2008 to 2015–16 by region. We did not 

analyze the percent change for general habitats because these 

were not explicitly mapped, but instead were developed by 

using a crosswalk. As previously mentioned, these data were 

developed for anyone that is interested in comparing 2008 and 

2015–16 with historical BICM habitat data (that is, 1997/1998, 

2001, 2004, and 2005). We binned the percent decrease and 

percent increase estimates into quantiles.

We determined the percentage of the total for each of 

the land/water and IZB habitat change classes, excluding the 

developed/shoreline protection change classes. To do this, we 

excluded the land unchanged and water unchanged classes for 

the land/water change data and the unchanged class for the 

IZB change data from the total changed estimates. As previously mentioned, we considered general linkages to change in 

a region with hurricanes, extreme storms, shoreline changes 

(Byrnes and others, 2018), and restoration efforts that occurred 

between 2008 and 2015–16. SigmaPlot 14.0 (Systat Software, 

Inc., San Jose, Calif.) was used to produce plots.

Results and Discussion

Habitat and Habitat Change Data

Habitat maps were developed by the BICM Program 

to show the detailed and general habitat classes for the 

Whiskey Island reach (fig. 5). Maps of the Whiskey Island 

reach showing the land/water and IZB habitat changes are 

shown in figures 6 and 7, respectively. All the habitat datasets and habitat change datasets have been published in a 

USGS data release (Enwright and others, 2018a). These 

data include recommended symbology files and Federal 

Geographic Data Committee-compliant metadata. These data 

are also available on the BICM project page on the CIMS 

(https://cims.coastal.louisiana.gov/outreach/projects/bicm).

Data produced through this effort are available in the 

BICM HRT (https://warcapps.usgs.gov/bicm). As previously 

mentioned, the HRT allows users to visualize the BICM 

habitat and habitat change datasets and develop standardized reach-specific maps as PDF files without the need of a 

geographic information system software package (fig. 8). The 

HRT includes a wizard that guides the user through a series of 

questions to develop either a single- or multi-pane web map. 

The resulting map includes a user-specified combination of 

BICM habitat maps and habitat change maps developed by the 

USGS. Users can also use the tool to create reach-specific habitat maps and habitat change maps as downloadable PDF files.

Habitat and Habitat Change Accuracy

Detailed accuracy assessment results are listed in table 7. 

The overall accuracy for the detailed habitat maps from 2008 

for the seven BICM regions ranged from 89.2 to 96.7 percent (mean = 93.4 percent, SD = 2.6 percent). The overall 

accuracy for the detailed habitat maps from 2015–16 for the 

seven BICM regions ranged from 94.6 to 97.4 percent (mean 

= 95.9 percent, SD = 0.9 percent). For all habitat maps, the 

lowest producer’s accuracy (that is, omission error) was for 

unvegetated dune with a mean of 89.2 percent and an SD of 

11.2 percent, whereas estuarine emergent marsh had the lowest user’s accuracy (that is, commission error) with a mean of 

92.6 percent and an SD of 4.9 percent. The higher accuracies 
90°52' 90°51' 90°50' 

29°03'30"

29°03'

29°02'30"

GULF 

B

A

0 1 0 0.5 

90°52' 90°51' 90°50' 

29°03'30"

29°03'

29°02'30"

GULF O

BeachUnvegetated duneVegetated duneUnvegetated flatMeadow

IntertidalEstuarine emergent marshMangroveBare landGrassland

Scrub/shrubForestShoreline protectionDevelopedWater

Detailed habitat classes (2016)

BeachBare landBarrier vegetationEstuarine vegetated wetland

IntertidalRip-rapStructureWater

General habitat classes (2016)

EXPLANATION

EXPLANATION

Figure 5. Louisiana Barrier Island Comprehensive Monitoring Program A, detailed habitat classes and B, general habitaIsland, Louisiana.
16 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

associated with these habitat data (that is, often greater than 

90 percent) were likely caused by the use of a semiautomated 

process that included manual editing of the products, which 

took a substantial amount of time.

The fuzzy accuracy assessment results for the land/water 

change datasets are listed in table 8. The fuzzy overall accuracy for these data for the seven BICM regions ranged from 

93.0 to 99.5 percent (mean = 96.6 percent, SD = 2.0 percent). 

The lowest fuzzy producer’s accuracy was for land unchanged 

with a mean of 94.7 percent and an SD of 4.0 percent, whereas 

land gain had the lowest fuzzy user’s accuracy with a mean of 

94.3 percent and an SD of 5.6 percent.

The fuzzy accuracy assessment results for the IZB change 

datasets are listed in table 9. The fuzzy overall accuracy for 

these data for the seven BICM regions ranged from 87.0 

to 99.2 percent (mean = 92.1 percent, SD = 4.3 percent). 

90°52' 90°51' 90°50' 90°49'

29°03'30"

29°03'

29°02'30"

GULF OF MEXICO

0 1 2 KILOMETERS

0 0.5 1 MILE

Land gain

Land loss

Land unchanged

Water unchanged

Land/water-based change classes

(2008–16)

EXPLANATION

Figure 6. Louisiana Barrier Island Comprehensive Monitoring Program land/water-based change classes for 2008–16 for 

Whiskey Island, Louisiana.

90°52’ 90°51’ 90°50’ 90°49’

29°03'30"

29°03'

29°02'30"

0 1 2 KILOMETERS

0 0.5 1 MILE

GULF OF MEXICO

Water to supratidal

Water to intertidal-vegetated

Intertidal-unvegetated to supratidal

Intertidal-unvegetated to intertidalvegetated

Water to intertidal-unvegetated

Intertidal-vegetated to supratidal

Supratidal to intertidal-vegetated

Intertidal-vegetated to intertidalunvegetated

Supratidal to intertidal-unvegetated

Intertidal-vegetated to water

Supratidal to water

Intertidal-unvegetated to water

Developed/shoreline protection 

gain

Developed/shoreline protection 

loss

Unchanged

Inundation zone-based habitat change classes (2008–16)

EXPLANATION

Figure 7. Louisiana Barrier Island Comprehensive Monitoring Program inundation zone-based habitat change classes for 

2008–16 for Whiskey Island, Louisiana.
Results and Discussion 17

The “wizard” included with the 

BICM HRT is a graphical 

assistant that helps the user 

configure the mapping 

environment for individual or 

multiple mapping panels. It also 

helps the user choose the data 

to populate the mapping panels 

including data type, year, and 

location along the coast.

The web mapping environment of the BICM HRT supports 

single or multiple mapping panels that can be spatially 

synchronized to help when investigating multiple datasets. As 

can be seen on the zoomed-in inset, the spatial synchronization simplifies focusing on the same area of interest across 

the two maps. The legend units can be toggled from acres to 

hectares, and legend switching is supported when multiple 

datasets are loaded. Lastly, the tool supports creating printerfriendly Portable Document Format (PDF) files.

Beyond the mapping setup and side-by-side investigative 

features of the BICM HRT, there is also a print to PDF feature 

that auto adjusts the data view into an 8.5 x 11 layout view that 

ensures map printing will retain spatial relevance like scale 

and orientation. Most BICM reaches will print in landcape 

mode, but the system has been configured to produce portrait 

maps for those reaches that are more vertically oriented to 

maximize the data on the printed media. 

Figure 8. Overview of the functionality of the Louisiana Barrier Island Comprehensive Monitoring (BICM) Program Habitat Reporting 

Tool (HRT).
Table 7. Accuracy assessment results for detailed habitat classes for 2008 and 2015–16, by Louisiana Barrier Island Comprehensive Monitoring Prog[OA, overall accuracy; %, percent; BL, bare land; B, beach; DV, developed; EM, estuarine emergent marsh; F, forest; GL, grassland; I, intertidal; MG, mangrove; MW, meadowprotection; UD, unvegetated dune; UF, unvegetated flat; VD, vegetated dune; W, water; WC, West Chenier Plain; --, not applicable; EC, East Chenier Plain; AB, Acadiana Bays; LLD; Late Lafourche Delta; MD, Modern Delta; CI, Chandeleur Islands] Region (fig. 1) Map date OA (%) Class-specific accuracy (Per map date: first row, producer’s accuracy [%] and second row, user’s accuracy [%]) BL B DV EM F GL I MG MW SS SP UD 

WC 2008 89.2 68.5 88.7 97.7 94.2 91.2 71.1 95.0 -- 73.1 89.2 93.7 87.9 

96.1 96.6 97.7 82.6 83.0 90.9 87.4 -- 75.4 89.2 100.0 85.1 

2015 96.2 94.1 96.0 98.7 100.0 94.9 95.4 98.0 -- 99.3 90.1 89.3 96.5 

100.0 99.3 98.0 91.0 94.9 93.6 96.7 -- 93.7 98.3 100.0 97.2 

EC 2008 96.7 94.2 98.6 94.0 100.0 96.7 97.4 94.6 -- 94.8 97.2 98.7 98.5 

100.0 95.9 99.0 91.8 97.8 94.8 100.0 -- 96.7 97.2 100.0 94.8 

2015 95.8 100.0 90.8 94.9 98.4 96.6 96.6 97.2 -- 95.2 96.8 -- 94.1 

100.0 99.2 98.9 92.8 98.3 100.0 91.5 -- 96.6 96.0 -- 99.1 

AB 2008 96.1 85.7 92.9 93.3 100.0 -- 97.2 96.8 -- 100.0 96.2 -- 100.0 

96.0 100.0 100.0 93.9 -- 97.2 93.8 -- 96.9 86.2 -- 100.0 

2015–16 95.4 90.3 96.7 100.0 100.0 100.0 96.6 89.7 -- 100.0 100.0 -- 80.0 

96.6 96.7 100.0 100.0 100.0 96.6 89.7 -- 100.0 100.0 -- 96.0 

ELD 2008 92.7 96.5 95.2 95.4 93.7 -- 100.0 91.4 90.1 91.7 90.7 88.0 85.0 

98.2 91.9 100.0 86.2 -- 96.0 91.9 95.5 92.2 92.9 100.0 90.5 

2016 95.4 100.0 96.0 83.9 96.7 -- 100.0 96.6 97.2 93.1 97.7 93.8 92.2 

100.0 99.0 100.0 96.2 -- 98.4 97.0 95.6 93.1 99.2 100.0 92.2 

LLD 2008 94.0 96.5 94.2 99.0 96.3 96.7 94.4 94.3 91.3 88.7 95.5 98.3 89.9 

98.2 96.2 99.0 89.4 100.0 97.1 88.8 98.5 90.3 95.1 100.0 91.0 

2016 97.4 100.0 96.9 94.8 95.1 96.8 100.0 99.1 99.0 99.6 94.9 98.8 92.9 

97.6 97.3 98.2 99.1 96.8 94.0 96.6 99.0 97.2 96.5 95.2 97.5 

MD 2008 93.6 96.9 96.6 92.6 95.4 -- 91.3 94.9 94.1 91.5 80.6 91.8 89.4 

100.0 98.3 98.0 92.0 -- 84.0 93.3 89.8 91.5 96.7 100.0 97.7 

2015 96.2 98.3 98.3 96.6 100.0 -- 88.9 100.0 100.0 100.0 98.3 93.3 96.4 

100.0 100.0 100.0 97.4 -- 100.0 95.3 100.0 98.5 98.3 100.0 98.2 

CI 2008 91.4 100.0 92.1 66.7 83.0 -- -- 93.7 -- 84.7 93.3 90.9 -- 

81.8 95.1 100.0 89.3 -- -- 89.3 -- 95.3 82.4 100.0 -- 

2016 94.6 -- 94.5 100.0 97.8 -- -- 98.9 93.4 93.4 97.1 83.3 56.8 

-- 98.9 100.0 95.7 -- -- 96.8 -- 96.6 100.0 83.3 95.5 
Results and Discussion 19

While the fuzzy overall accuracy is still high, some classspecific accuracy assessments were low. The two lowest 

fuzzy producer’s accuracies were for unchanged with a mean 

of 65.0 percent and an SD of 17.5 percent and intertidalvegetated to supratidal/upland with a mean of 76.8 percent and 

an SD of 21.8 percent. The two fuzzy lowest user’s accuracies 

were for intertidal-vegetated to supratidal/upland with a mean 

of 56.7 percent and an SD of 26.2 percent and developed/

shoreline protection gain with a mean of 82.4 percent and an 

SD of 22.5 percent. As previously mentioned, the accuracy 

assessment targeted edges and interior areas equally. The rationale for this was that edges (that is, transition areas between 

habitats) could be areas where errors were concentrated. Yet, 

for the habitat change classes, the edges of polygons (that is, 

transition areas) can be difficult to assess because a point could 

be ambiguous and not clearly in a specific class. Thus, we also 

presented the accuracy assessment results for all points (that 

is, interior and exterior) as well as the accuracy assessment 

results for the interior points in the metadata for the regional 

change datasets (Enwright and others, 2018a). While image 

matching and constraints were used to reduce issues with 

image registration differences between the dates, it is still 

possible to have issues with false change from registration 

in areas where the registration error is especially high. One 

downside of a semiautomated approach is that methods, such 

as photointerpretation, introduce subjectivity into the mapping 

process, which can lead to change error. One example of this 

type of error could be inconsistent mapping of small ponds 

within estuarine emergent marsh between both dates. These 

results highlight the importance of ensuring that the mapping 

process takes advantage of as much automation as possible to 

enhance the consistency of the results.

BICM Region-Wide Habitat and Habitat Change

The focus of this section is the habitat and habitat 

change results across the entire BICM region. Regional 

results will be discussed in subsequent sections of this 

report. The areal coverages for detailed and general habitat maps by reach are reported in the appendix (table 1–1; 

table 1–2). The five nonwater detailed habitat classes in 2008 

with the highest areal coverages were (1) estuarine emergent marsh (54,803.30 hectares [ha]; 26.7 percent of total 

area [that is, including water]); (2) intertidal (4,391.48 ha; 

2.1 percent); (3) grassland (2,368.55 ha; 1.2 percent); 

(4) meadow (1,628.61 ha; 0.8 percent); and (5) unvegetated 

flat (1,279.99 ha; 0.6 percent) (table 1–1; fig. 9). The five nonwater detailed habitat classes with the highest areal coverage 

in 2015–16 were (1) estuarine emergent marsh (54,238.80 ha; 

26.4 percent); (2) meadow (2,319.98 ha; 1.1 percent); 

(3) mangrove (1,937.03 ha; 0.9 percent); (4) grassland 

(1,880.14 ha; 0.9 percent); and (5) scrub/shrub (1,801.88 ha; 

0.9 percent) (table 1–1; fig. 9).

Table 10 lists the percent change between 2008 and 

2015–16 per class by region. Habitats that increased throughout the entire BICM region were vegetated dune, scrub/shrub, 

Table 8. Fuzzy accuracy assessment results for land/water change from 2008 to 2015–16, by Louisiana Barrier Island Comprehensive 

Monitoring Program region.

[OA, overall accuracy; %, percent; PA, producer’s accuracy; UA, user’s accuracy; LG, land gain; LL, land loss; LU, land unchanged; WU, water unchanged; 

WC, West Chenier Plain; EC, East Chenier Plain; AB, Acadiana Bays; ELD, Early Lafourche Delta; LLD, Late Lafourche Delta; MD, Modern Delta; CI, 

Chandeleur Islands]

Region

(fig. 1) OA (%)

Class-specific accuracy

(Per map date: first row, PA [%] and second row, UA [%])

LG LL LU WU

WC 96.5 100.0 100.0 100.0 100.0

100.0 100.0 100.0 100.0

EC 97.8 100.0 99.0 92.8 100.0

94.0 99.0 100.0 98.0

AB 97.5 100.0 100.0 96.6 94.9

92.0 98.0 100.0 100.0

ELD 93.0 100.0 97.0 90.0 89.0

84.0 91.0 98.1 99.0

LLD 95.5 100.0 100.0 90.4 93.9

93.0 90.0 99.0 100.0

MD 96.5 98.0 100.0 94.1 94.6

97.0 92.0 98.0 99.0

CI 99.5 100.0 100.0 99.0 98.0

100.0 98.0 100.0 100.0
Table 9. Fuzzy accuracy assessment results for inundation zone-based habitat change classes for change from 2008 to 2015–16, by Louisiana BarrieMonitoring Program region. [OA, overall accuracy; %, percent; S-IV, supratidal/upland to intertidal-vegetated; S-IUV, supratidal/upland to intertidal-unvegetated; S-W supratidal/upland to water; IV-S, interupland; IV-IUV, intertidal-vegetated to intertidal-unvegetated; IV-W, intertidal-vegetated to water; IUV-S, intertidal-unvegetated to supratidal/upland; IUV-IV, intertidal-unvegetIUV-W, intertidal-unvegetated to water; W-S, water to supratidal/upland; W-IV, water to intertidal-vegetated; W-IUV, water to intertidal-unvegetated; DSPG, developed/shorelindeveloped/shoreline protection loss; U, unchanged; WC, West Chenier Plain; EC, East Chenier Plain; AB, Acadiana Bays; ELD, Early Lafourche Delta; LLD, Late Lafourche DChandeleur Islands] Region (fig. 1) OA (%) Class-specific accuracy (Per map date: first row, producer’s accuracy [%] and second row, user’s accuracy [%]) S-IV S-IUV S-W IV-S IV-IUV IV-W IUV-S IUV-IV IUV-W W-S W-IV W-IUV DS

WC 87.0 100.0 100.0 100.0 100.0 98.0 100.0 98.0 99.0 100.0 100.0 97.9 99.0 10

23.0 95.0 96.0 44.0 100.0 98.0 98.0 97.0 99.0 98.0 99.0 97.0 8

EC 93.5 100.0 100.0 100.0 100.0 98.0 100.0 100.0 77.1 99.0 100.0 100.0 100.0 10

34.0 99.0 100.0 87.0 100.0 98.0 98.0 100.0 100.0 97.0 98.0 100.0 10

AB 95.1 100.0 98.1 100.0 97.3 98.0 100.0 100.0 97.9 100.0 100.0 100.0 100.0 10

66.0 100.0 100.0 96.0 100.0 96.0 100.0 96.0 100.0 100.0 100.0 100.0 10

ELD 89.0 100.0 100.0 96.4 98.6 100.0 97.9 98.2 98.8 95.5 100.0 100.0 100.0 9

68.0 97.2 99.1 83.7 95.0 96.0 97.2 88.7 98.0 100.0 86.7 100.0 3

LLD 88.1 100.0 100.0 99.0 90.5 98.7 100.0 97.9 90.6 97.2 99.0 96.2 100.0 9

40.0 96.0 98.0 50.0 86.0 93.0 97.0 98.0 100.0 98.0 96.0 96.0 8

MD 92.5 98.3 98.7 96.4 93.9 98.9 94.8 100.0 93.4 98.2 99.0 96.9 96.9 10

66.0 88.0 96.0 77.0 97.0 95.0 95.0 98.0 98.0 100.0 98.0 99.0 9

CI 99.2 99.0 100.0 100.0 100.0 100.0 100.0 100.0 99.0 100.0 100.0 99.0 100.0 10

100.0 99.0 100.0 100.0 100.0 99.0 99.0 100.0 99.0 100.0 98.0 100.0 6
Results and Discussion 21

meadow, and mangrove (table 1–1). Vegetated dune increases 

were largely attributed to habitat succession on unvegetated 

dunes. One of the causes for scrub/shrub increase was habitat 

succession along spoil banks, but also along the backslopes 

of dunes (that is, meadow habitat). One dynamic aspect of 

barrier islands, headlands, and coastal shorelines is vegetation 

burial from overwash during extreme storms and subsequent 

vegetation recovery (Lucas and Carter, 2013; Brantley and 

others, 2014). Vegetation burial during extreme storms (that 

is, Hurricanes Katrina and Rita in 2005 and Hurricanes Gustav 

and Ike in 2008) and subsequent recovery (that is, habitat 

succession on unvegetated flats) is one of the main causes for 

meadow increases. As previously noted, mangrove expansion can occur in the absence of extreme winter temperatures. 

Recent expansion and contraction of mangroves has occurred 

in the Barataria Basin (not shown; spans Late Lafourche 

Delta and Modern Delta regions in fig. 1) (Osland and others, 

2017). From 2008 to 2015–16, the intertidal and grassland 

habitat classes tended to decrease more than the other classes 

(table 1–1). The change in the intertidal habitat class tended to 

vary by region; some causes of the changes include water level 

changes, restoration efforts, and dynamic coastal processes, 

such as waves and currents, occurring during the time between 

mapping efforts. Grassland loss can largely be attributed to 

addition of new spoil along spoil banks (that is, change from 

grassland to bare land) or habitat succession (that is, change 

from grassland to scrub/shrub). The other classes either had a 

mix of increases and decreases by region, or the change was in 

the lower two quantiles for increase or decrease.

The areal coverage for land/water change and IZB habitat 

change by reach are reported in tables 1–3 and 1–4, respectively. For the entire BICM region, the area experiencing a 

change in a land/water category (that is, land gain or land loss) 

was 3.4 percent (table 1–3). Of this change, 59.2 percent of the 

change was land gain, and 40.8 percent was land loss. For the 

IZB change (that is, excluding developed/shoreline protection 

gain or loss), the changed classes accounted for 6.0 percent 

of the BICM region (table 1–4). The reason for the difference 

Bare land*

Beach

Developed

Estuarine emergent marsh

Forest

Grassland

Intertidal

Mangrove

Meadow

Scrub/shrub

Shoreline protection*

Unvegetated dune

Unvegetated flat

Vegetated dune

Water

EXPLANATION

Habitat class areal coverage, 2008, in percent

Habitat class areal coverage, 2015–16, in percent

1:1 line

*, not shown due to low percentage

Figure 9. Percentages of habitat class areal coverage in the regions mapped during the Louisiana Barrier Island 

Comprehensive Monitoring Program habitat analyses (fig. 1), 2008 and 2015–16. Points that are off the 1:1 line 

represent change in coverage between the time periods.
Table 10. Percent change for detailed habitat classes for 2008 and 2015–16, by Louisiana Barrier Island Comprehensive Monitoring Program region. [The color indicates the quantile of percent increase or percent decrease, respectively. PDQ1, percent decrease quantile 1; PDQ2, percent decrease quantile 2; PDQ3, percent decdecrease quantile 4; PIQ1, percent increase quantile 1; PIQ2, percent increase quantile 2, PIQ3, percent increase quantile 3; PIQ4, percent increase quantile 4; %, percent; BL, boped; EM, estuarine emergent marsh; F, forest; GL, grassland; I, intertidal; MG, mangrove; MW, meadow; SS, scrub/shrub; SP, shoreline protection; UD, unvegetated dune; UF, dune; W, water; WC, West Chenier Plain; EC, East Chenier Plain; AB, Acadiana Bays; ELD, Early Lafourche Delta; LLD, Late Lafourche Delta; MD, Modern Delta; CI, Chand

EXPLANATION PDQ1 PDQ2 PDQ3 PDQ4 PIQ1 PIQ2 PIQ3 PIQ4 Region (fig. 1) Percent change (%; rounded to nearest integer) BL B DV EM F GL I MG MW SS SP UD 

WC −74 −60 10 5 −18 −12 −61 0 45 48 −24 −49 −EC 67 −77 −11 0 47 −52 −47 0 −18 110 83 −9 −AB 853 21 67 −4 0 −71 −85 0 18 94 0 −80 ELD −31 −74 −23 −5 0 −54 −93 99 139 43 −10 −97 −LLD −59 −37 2 −23 54 −40 −78 80 62 49 −13 169 −MD 32 68 −9 −12 0 474 −88 78 565 31 −30 1,008 1CI −100 331 74 88 0 0 −22 74 209 4,420 −43 −41 1
Results and Discussion 23

between the two change assessments is that intertidal is 

lumped in with water for the land/water change classification 

scheme, but not within the IZB change classification scheme 

(table 5). The IZB change classes with a “high” coverage (that 

is, greater than ~10.0 percent) were (1) intertidal-unvegetated 

to water (21.9 percent); (2) water to intertidal-vegetated 

(15.2 percent); (3) intertidal-vegetated to water (15.2 percent); 

and (4) intertidal-vegetated to supratidal/upland (9.9 percent). 

The high amount of intertidal-unvegetated to water change 

was likely caused by water levels and dynamic coastal 

processes. Water and intertidal-vegetated areas had a high 

degree of transfer between classes (that is, areas swapped 

values), which was likely caused by restoration efforts that 

include marsh planting and (or) natural habitat succession on 

intertidal areas, such as the berm restoration on Chandeluer 

Island (table 2; FitzGerald and others, 2015), and marsh 

loss (Couvillion and others, 2017). Intertidal-vegetated to 

supratidal/upland change could have been caused by restoration efforts or overwash events that increased the elevation of 

intertidal areas (Walters and Kirwan, 2016).

West Chenier Plain Region Habitat and Habitat 

Change

For the West Chenier Plain region, the five nonwater 

detailed habitat classes with the highest areal coverages 

in 2008 were (1) estuarine emergent marsh (13,533.61 ha; 

52.8 percent); (2) grassland (1,858.67 ha; 7.3 percent); 

(3) intertidal (608.62 ha; 2.4 percent); (4) developed 

(552.41 ha; 2.2 percent); and (5) scrub/shrub (508.15 ha; 

2.0 percent) (table 1–1; fig. 10). The five nonwater detailed 

habitat classes with the highest areal coverages in 2015 were 

(1) estuarine emergent marsh (14,183.90 ha; 55.4 percent); 

(2) grassland (1,626.44 ha; 6.4 percent); (3) scrub/shrub 

Bare land*

Beach

Developed

Estuarine emergent marsh

Forest

Grassland

Intertidal

Mangrove*

Meadow

Scrub/shrub

Shoreline protection*

Unvegetated dune

Unvegetated flat

Vegetated dune

Water

EXPLANATION

Habitat class areal coverage, 2008, in percent

Habitat class areal coverage, 2015, in percent

*, not shown due to low percentage

1:1 line

Figure 10. Percentages of habitat class areal coverage in the West Chenier Plain region mapped during the 

Louisiana Barrier Island Comprehensive Monitoring Program, 2008 and 2015. Points that are off the 1:1 line represent 

change in coverage between the time periods.
24 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

(750.82 ha; 2.9 percent); (4) developed (608.12 ha; 

2.4 percent); and (5) meadow (409.77 ha; 1.6 percent) ( ; 

fig. 10). Vegetated dune had a “high” percent increase (that 

is, either quantile 3 or 4 for percent increase) between these 

two dates (table 10), mainly because of habitat succession 

associated with the Cameron Parish Shoreline Restoration 

project (table 2). Bare land, beach, intertidal, and unvegetated dune classes had “high” percent decreases (table 10; 

that is, either quantile 3 or 4 for percent decrease). As previously discussed, bare land includes areas with spoil banks, 

inland ridges, or upland bare areas (table 3). In some cases, 

these bare areas could be in an early phase of development 

(for example, land clearing phase) or become vegetated over 

time (for example, spoil banks). The decrease in beach was 

due to a wider beach zone in 2008 compared to 2015. Some 

of the decrease in beach could be attributed to the shoreline 

restoration effort in 2014, which converted some of this area 

to dune, and (or) insufficient time for shoreface adjustment 

to occur after sediment placement. Collectively, this creates 

an elevation high enough to reduce exposure to high energy 

waves and to become transitional areas in front of a dune 

(that is, unvegetated flat or meadow; table 3). Additionally, 

this localized beach loss is also the result of coastal processes 

and coastal erosion. BICM shoreline analyses by Byrnes and 

others (2018) found that the West Chenier region had the 

lowest shoreline loss rate from 2004 to 2012; however, the 

analyses did point to spatial variability with high amounts 

of accretion in the Johnsons Bayou (fig. 11), Holly Beach, 

and Mermentau River reaches. Decrease of intertidal area for 

this region was largely due to changes in water levels, which 

reduced the ability to detect intertidal flats in 2015 compared 

to 2008 in the Johnsons Bayou and Mermentau River reaches. 

In the accreting western portion of the Johnsons Bayou reach 

(Byrnes and others, 2018), a large amount of intertidal area 

became estuarine emergent marsh (fig. 11). Finally, some of 

the unvegetated dune decrease can be attributed to vegetation 

succession on the dune associated with the Cameron Parish 

Shoreline Restoration project.

The area experiencing a change in a land/water category 

for this region was 4.7 percent (table 1–3). Of this change, 

78.8 percent was land gain, and 21.2 percent was land loss 

(fig. 12). This amount of change is supported by the very 

low rate of shoreline erosion (that is, −0.7 meter per year [m/

yr]) for this reach from 2004 to 2012, relative to other BICM 

reaches (Byrnes and others, 2018). For the IZB change (that 

is, excluding developed/shoreline protection gain or loss), 

the changed classes accounted for 8.3 percent of the region 

(table 1–4). These IZB change classes had a “high” coverage: 

(1) water to intertidal-vegetated (30.3 percent); (2) Supratidal/

upland to intertidal-vegetated (14.5 percent); (3) intertidalunvegetated to water (13.7 percent); and (4) intertidalvegetated to supratidal/upland (13.0 percent) (fig. 13). Water 

to intertidal-vegetated change was largely due to expansion of 

emergent marsh at the expense of marsh ponds in the western 

half of the region and shoreline accretion in the western portion of the Johnsons Bayou reach, which was likely driven 

by recovery after Hurricanes Gustav and Ike (table 1). The 

change from supratidal/upland to intertidal-vegetated could 

be from degradation of spoil banks or changes in shrub/scrub 

cover (that is, scrub/shrub is considered supratidal/upland 

and does not account for estuarine scrub/shrub wetlands). 

However, some changes could be caused by the higher quality 

lidar data for the 2015 mapping effort (that is, a recent 2017 

1-m lidar-based DEM collection; table 4). The changes from 

intertidal-unvegetated to water for this region were previously 

discussed. The change from intertidal-vegetated to supratidal/

upland could be a result of the Cameron Parish Shoreline 

Restoration project increasing elevations in localized areas 

or overwash increasing the elevation of these areas; however, lidar quality differences between the dates may also be 

a factor.
93°49'30"

29°41'40"

29°41'30"

29°42' 93°50'

93°49'30" 29°41'40"

29°41'30"

29°42' 93°50'

N

Area shown 

on maps

GULF OF MEXICO

B

A

0 0.2 0.4 KILOMETER0 0.2 0.4 MILE

GULF OF MEXICO

C

Location marker for paired photo comparison

West 

BeachUnvegetated duneVegetated dune

Unvegetated flatMeadowIntertidal

Estuarine emergent marshMangroveBare land

GrasslandScrub/shrubForest

Detailed habitat classes

Approximate vantage point and bearing for oblique photo (C)

EXPLANATION

Figure 11. Coverage of detailed habitat classes in an area of the West Chenier Plain region during A, 2008 and B, 2015, and C, paired obliquSabine Pass, 2006 and 2017, used with permission from Westphal (2018).
26 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

East Chenier Plain Region Habitat and Habitat 

Change

For the East Chenier Plain region, the five nonwater 

detailed habitat classes with the highest areal coverages 

in 2008 were (1) estuarine emergent marsh (22,974.94 ha; 

56.8 percent); (2) meadow (543.53 ha; 1.3 percent); (3) grassland (371.40 ha; 0.9 percent); (4) unvegetated flat (296.99 ha; 

0.7 percent); and (5) scrub/shrub (292.38 ha; 0.7 percent) 

(table 1–1; fig. 14). The five nonwater detailed habitat classes 

with the highest areal coverages in 2015 were (1) estuarine 

emergent marsh (23,005.50 ha; 56.9 percent); (2) scrub/shrub 

(614.22 ha; 1.5 percent); (3) meadow (446.89 ha; 1.1 percent); 

(4) unvegetated flat (180.20 ha; 0.4 percent); and (5) grassland (177.63 ha; 0.4 percent) (table 1–1; fig. 14). Scrub/shrub, 

shoreline protection, and vegetated dune all had a “high” 

percent increase (table 10). Increases in scrub/shrub are 

likely due to post-storm habitat recovery and (or) succession 

along spoil banks, dune backslopes, and in marshes (fig. 15). 

The increase in shoreline protection was associated with the 

Rockefeller Refuge Gulf Shoreline Protection Demonstration 

Project (table 2) and some additional breakwater construction 

projects in the Chenier Au Tigre and Rainey Refuge reaches. 

The increase in vegetated dune appears to be associated with 

habitat succession in the eastern part of the region (that is, the 

Chenier Au Tigre and Rainey Refuge reaches). Beach, grassland, intertidal, and unvegetated flat all had a “high” percent 

decrease (table 10). Similar to the beach in the West Chenier 

region, the beach was wider in 2008 than in 2015 (fig. 15). The 

shoreline for this region rapidly receded from 2004 to 2012 

(−7.3 m/yr), which is similar to some of the regions in the 

Deltaic Plain (Byrnes and others, 2018). Additionally, some of 

the areas that were beach in 2008 have converted to intertidal 

or estuarine emergent marsh, which is common for post-storm 

recovery with an extended period of quiescent conditions 

(fig. 15). The coastal processes of erosion and post-storm 

succession, along with a lack of sediment supply to offset the 

erosion, are expected to be the major factors leading to the 

WC

EC

AB

ELD

LLD

MD

CI

0 10 20 30 40 50 60 70 80 90 100

Percentage

EXPLANATION

Land gain

Land loss

Land/water change classes

Unchanged

Figure 12. The percentages of areal coverage for land/water change classes (table 5), by Louisiana Barrier 

Island Comprehensive Monitoring Program region. For each region, the first bar shows the percentage of change 

made up by each change class, and the second bar shows the percentage of cover for changed and unchanged 

classes. [WC, West Chenier Plain; EC, East Chenier Plain; AB, Acadiana Bays; ELD, Early Lafourche Delta; LLD, 

Late Lafourche Delta; MD, Modern Delta; CI, Chandeleur Islands]
Results and Discussion 27

loss of beach habitat in this region. The decline in grassland 

habitat seems to be largely due to habitat succession. The bulk 

of the intertidal change is along the shoreline, with some areas 

concentrated in the parts of the Rockefeller Refuge reach. It 

can be assumed that these decreases are largely due to coastal 

processes (that is, shoreline erosion) and water level differences between the two images (that is, surf level along the 

shoreline and water level in Flat Lake [not shown]). Finally, 

the unvegetated flat decreases seem to be largely due to habitat 

succession on overwash areas along the shoreline, especially for 

a large area in the eastern part of the Mulberry Island reach.

The area experiencing a change in a land/water category for this region was 3.3 percent (table 1–3). Of this 

change, 45.7 percent was land gain, and 54.3 percent was 

land loss (fig. 12). This change is supported by the high rate 

of shoreline erosion for this reach from 2004 to 2012 (Byrnes 

and others, 2018). For the IZB change, the changed classes 

accounted for 5.9 percent of the region (table 1–4). These IZB 

change classes had a “high” coverage (1) water to intertidalvegetated (23.8 percent); (2) intertidal-vegetated to supratidal/upland (21.5 percent); (3) supratidal/upland to water 

(16.3 percent); (4) intertidal-vegetated to water (10.8 percent); 

0 10 20 30 40 50 60 70 80 90 100

Percentage

EXPLANATION

IUV-IV

IUV-S

IUV-W

IV-IUV

IV-S

IV-W

S-IUV

S-IV

S-W

W-IUV

W-IV

W-S

IZB habitat change classes

Unchanged

WC

EC

AB

ELD

LLD

MD

CI

Figure 13. The percentages of areal coverage for inundation zone-based (IZB) habitat change classes (table 6), 

by Louisiana Barrier Island Comprehensive Monitoring Program region. For each region, the first bar shows the 

percentage of change made up by each change class, and the second bar shows the percentage of cover for 

changed and unchanged classes. [WC, West Chenier Plain; EC, East Chenier Plain; AB, Acadiana Bays; ELD, Early 

Lafourche Delta; LLD, Late Lafourche Delta; MD, Modern Delta; CI, Chandeleur Islands IUV-IV, intertidal-unvegetated 

to intertidal-vegetated; IUV-S, intertidal-unvegetated to supratidal/upland; IUV-W, intertidal-unvegetated to 

water; IV-IUV, intertidal vegetated to intertidal-unvegetated; IV-S, intertidal-vegetated to supratidal/upland; 

IV-W, intertidal-vegetated to water; S-IUV, supratidal/upland to intertidal-unvegetated; S-IV, supratidal/upland 

to intertidal-vegetated; S-W, supratidal/upland to water; W-IUV, water to intertidal-unvegetated; W-IV, water to 

intertidal-vegetated; W-S, water to supratidal/upland]
28 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

(5) intertidal-unvegetated to water (10.5 percent); and 

(6) supratidal/upland to intertidal-vegetated (10.0 percent) 

(fig. 13). The water to intertidal-vegetated change was 

largely due to expansion of emergent marsh at the expense 

of marsh ponds, and to a lesser extent, terrace development 

in Rockefeller State Wildlife Refuge and Game Preserve 

in Round Lake (not shown). The change from intertidalvegetated to supratidal/upland seemed to be from increased 

detection of linear spoil bank features. As previously stated, 

these “new” features could be detected because of actual 

change or increased lidar quality for the 2015 habitat map 

(table 4). Similarly, some areas with habitat succession 

and scrub/shrub development would change the area from 

intertidal-vegetated to supratidal/upland based on the IZB 

change classification scheme. The supratidal/upland to water 

change was largely due to the high shoreline erosion rate. As 

an example, a spit in front of Flat Lake on the eastern portion 

of the Rockefeller Refuge reach was eroded between 2008 and 

2015. Intertidal-vegetated to water change was also highest 

in the Rockefeller Refuge reach. As previously mentioned, 

the intertidal-unvegetated to water change was largely due 

to water level differences between 2008 and 2015. Finally, 

the supratidal/upland to intertidal-vegetated change occurred 

along spoil banks, the previously mentioned overwash area in 

Mulberry Island, near chenier ridges, along the landward edge 

of meadow (that is, backslopes), and near some developed 

areas. The changes along spoil banks could indicate spoil bank 

degradation, yet, besides the change related to the overwash 

area in Mulberry Island, much of all the change for this class 

could be due to differences in lidar quality between the dates.

Bare land

Beach

Developed

Estuarine emergent marsh

Forest

Grassland

Intertidal

Mangrove*

Meadow

Scrub/shrub

Shoreline protection*

Unvegetated dune

Unvegetated flat

Vegetated dune*

Water

EXPLANATION

Habitat class areal coverage, 2008, in percent

Habitat class areal coverage, 2015, in percent

1:1 line

*, not shown due to low percentage

Figure 14. Percentages of habitat class areal coverage in the East Chenier Plain region mapped during the 

Louisiana Barrier Island Comprehensive Monitoring Program, 2008 and 2015. Points that are off the 1:1 line represent 

change in coverage between the time periods.
N Area shown 

on maps

 92°58'

29°43'10"

29°43'20"

29°43'20" 29°43'10"

92°58'

29°43'10"

29°43'20"

B 29°43'20" 29°43'10"

A

0 0.2 0.4 KILOMETER0 0.2 0.4 MILE

GULF OF MEXICO

C

Location marker for paired photo comparison

GULF OF MEXICO

BeachUnvegetated duneVegetated dune

Unvegetated flatMeadowIntertidal

Estuarine emergent marshMangroveBare land

GrasslandScrub/shrubForest

Detailed habitat classes

Approximate vantage point and bearing for oblique photo (C)

EXPLANATION

East Chen

#29°42’30” N / 92°57’44” W. View 

Figure 15. Coverage of detailed habitat classes in an area of the East Chenier Plain region during A, 2008 and B, 2015, and C, paired oblique 

2017, used with permission from Westphal (2018).
30 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

Acadiana Bays Region Habitat and Habitat 

Change

For the Acadiana Bays region, the five nonwater detailed 

habitat classes with the highest areal coverages in 2008 were 

(1) estuarine emergent marsh (5,745.33 ha; 61.5 percent); 

(2) meadow (362.11 ha; 3.9 percent); (3) intertidal (90.83 ha; 

1.0 percent); (4) grassland (22.86 ha; 0.2 percent); and 

(5) scrub/shrub (12.65 ha; 0.1 percent) (table 1–1; fig. 16). 

The five nonwater detailed habitat classes with the highest 

areal coverages in 2015–16 were (1) estuarine emergent marsh 

(5,525.91 ha; 59.2 percent); (2) meadow (426.56 ha; 4.6 percent); 

(3) scrub/shrub (24.58 ha; 0.3 percent); (4) vegetated dune 

(13.90 ha; 0.1 percent); and (5) intertidal (13.81 ha; 

0.1 percent) (table 1–1; fig. 16). Bare land and scrub/shrub had 

“high” percent increases (table 10). The bare land and scrub/

shrub increases were due to spoil bank maintenance along 

several canals and vegetation succession along undisturbed 

canals, respectively. Grassland, intertidal, and unvegetated 

dune had “high” percent decreases (table 10). Mirroring the 

increase in scrub/shrub, grassland areas changed to scrub/

shrub along some spoil banks. The intertidal decrease (fig. 17) 

is largely due to water level differences between the dates 

because many mudflats were evident in marsh ponds for 2008, 

but not for 2015–16. The decrease in unvegetated dune was 

largely due to habitat succession on dunes in the western portion of the Marsh Island reach.

The area experiencing a change in a land/water category 

for this region was 2.8 percent (table 1–3). Of this change, 

20.3 percent was land gain, and 79.7 percent was land loss 

(fig. 12). This region experienced the highest relative percent 

loss of land for all regions. Some of this change is from a 

low rate (0 to 3 m/yr) of shoreline erosion for this reach from 

2004 to 2012 (Byrnes and others, 2018), although most of the 

change is from loss of emergent marsh to open water. For the 

IZB change, the changed classes accounted for 5.5 percent of 

the region (table 1–4). These IZB change classes had a “high” 

coverage (1) intertidal-vegetated to water (32.3 percent); 

1:1 line

*, not shown due to low percentage

Bare land*

Beach

Developed*

Estuarine emergent marsh

Forest*

Grassland

Intertidal

Mangrove*

Meadow

Scrub/shrub

Shoreline protection*

Unvegetated dune*

Unvegetated flat

Vegetated dune

Water

EXPLANATION

Habitat class areal coverage, 2008, in percent

Habitat class areal coverage, 2015–16, in percent

Figure 16. Percentages of habitat class areal coverage in the Acadiana Bays region mapped during the Louisiana 

Barrier Island Comprehensive Monitoring Program, 2008 and 2015–16. Points that are off the 1:1 line represent 

change in coverage between the time periods.
N 

91°48'

29°29'30"

91°48'30” 91°48' 91°47'30”

91°48'30” 91°47'30”

29°29'30"

29°29'25"29°29'35"

29°29'25"29°29'35"

B

A

0 0.2 0.4 KILOMETER0 0.2 0.4 MILE

BeachUnvegetated duneVegetated dune

Unvegetated flatMeadowIntertidal

Estuarine emergent marshMangroveBare land

GrasslandScrub/shrubForest

Detailed habitat classes

EXPLANATION

Acadia

Figure 17. Coverage of detailed habitat classes in an area of the Acadiana Bays region during A, 2008 and B, 2015–16.
32 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

(2) intertidal-vegetated to supratidal/upland (27.6 percent); 

and (3) intertidal-unvegetated to water (13.4 percent) (fig. 13). 

As previously mentioned, a large amount of the change in this 

reach/region was attributed to loss of marsh to open water. The 

change from intertidal-vegetated to supratidal/upland occurred 

along the landward edge of meadow habitat. This could have 

been a result of overwash; however, the limited number of 

extreme storms suggests that these differences could be largely 

based on differences in lidar data quality. Finally, the changes 

from intertidal-unvegetated to water were largely due to water 

level variability in marsh ponds.

Early Lafourche Delta Region Habitat and 

Habitat Change

For the Early Lafourche Delta region, the five nonwater detailed habitat classes with the highest areal coverages 

in 2008 were (1) estuarine emergent marsh (8,436.06 ha; 

23.3 percent); (2) intertidal (1,328.15 ha; 3.7 percent); 

(3) mangrove (340.03 ha; 0.9 percent); (4) unvegetated flat 

(200.40 ha; 0.6 percent); and (5) beach (96.76 ha; 0.3 percent) 

(table 1–1; fig. 18). The five nonwater detailed habitat classes 

with the highest areal coverages in 2016 were (1) estuarine 

emergent marsh (8,037.72 ha; 22.2 percent); (2) mangrove 

(677.45 ha; 1.9 percent); (3) meadow (184.73 ha; 0.5 percent); 

(4) intertidal (89.76 ha; 0.2 percent); and (5) scrub/shrub 

(83.19 ha; 0.2 percent) (table 1–1; fig. 18). Meadow, vegetated 

dune, and mangrove had “high” percent increases (table 10). 

The increase in meadow and vegetated dune was largely due 

to habitat succession associated with restoration efforts for 

Raccoon Island (fig. 19), Whiskey Island, Trinity Island, and 

East Island reaches where unvegetated flat and unvegetated 

dune changed to meadow and vegetated dune, respectively. 

Mangrove expansion into estuarine emergent marsh accounted 

for the increase in mangrove habitat (fig. 19). Beach, intertidal, grassland, unvegetated dune, and unvegetated flat had 

“high” percent decreases (table 10). Similar to the beaches 

in regions to the west, the beach was wider in 2008 than in 

2016, which was likely due to the system being out of equilibrium after the extreme storms (table 1). The westernmost 

1:1 line

*, not shown due to low percentage

Bare land*

Beach

Developed*

Estuarine emergent marsh

Forest*

Grassland

Intertidal

Mangrove

Meadow

Scrub/shrub

Shoreline protection*

Unvegetated dune

Unvegetated flat

Vegetated dune

Water

EXPLANATION

Habitat class areal coverage, 2008, in percent

Habitat class areal coverage, 2016, in percent

Figure 18. Percentages of habitat class areal coverage in the Early Lafourche Delta region mapped during the 

Louisiana Barrier Island Comprehensive Monitoring Program, 2008 and 2016. Points that are off the 1:1 line represent 

change in coverage between the time periods.
90°56' 90°55'

29°03'10"

29°03'20"

90°56' 90°55'

29°03'10"

29°03'20"

B

A

0 0.2 0.4 KILOMETER0 0.2 0.4 MILE

GULF OF MEXICO

C

Location marker for paired photo comparison

GULF OF MEXICO

BeachUnvegetated duneVegetated dune

Unvegetated flatMeadowIntertidal

Estuarine emergent marshMangroveBare land

GrasslandScrub/shrubForest

Detailed habitat classes

Approximate vantage point and bearing for oblique photo (C)

EXPLANATION

Early Lafourche Delta region

Figure 19. Coverage of detailed habitat classes in an area of the Early Lafourche Delta region during A, 2008 and B, 2016, and C, paired obli2017, used with permission from Westphal (2018).
34 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

reaches have a marsh-dominated shoreline (that is, Point Au 

Fer Island and Oyster Bayou to Caillou Boca), whereas the 

remainder of the reaches are wave-dominated barrier islands. 

The decline of beach habitat for these barrier island reaches 

(that, is Raccoon, Whiskey, Trinity, East, and Wine Islands) 

is not surprising given the high shoreline erosion rates from 

2004 to 2012, which ranged from 2.7 m/yr to 26.8 m/yr, with 

a median of 5.7 m/yr (Byrnes and others, 2018). The main 

reason for the decline of intertidal area was due to water level 

differences leading to mudflats within marsh ponds and tidal 

creeks throughout the region being exposed in 2008, but not 

in 2016. However, some other causes of the intertidal decline 

were erosion of part of the western spit of Raccoon Island 

and restoration along the western spit of Whiskey Island. 

The decline in grassland appeared to be largely associated 

with habitat succession along spoil banks. The reason for the 

decline in unvegetated dune and unvegetated flat were previously discussed with regard to the increase in meadow and 

vegetated dune habitat (fig. 19).

The area experiencing a change in a land/water category 

for this region was 3.1 percent (table 1–3). Of this change, 

41.7 percent was land gain, and 58.3 percent was land loss 

(fig. 12). For the IZB change, the changed classes accounted 

for 6.1 percent of the region (table 1–4). These IZB change 

classes had a “high” percent change (1) intertidal-unvegetated 

to water (42.7 percent); (2) intertidal-vegetated to water 

(19.7 percent); and (3) intertidal-unvegetated to intertidalvegetated (10.4 percent) (fig. 13). The intertidal-unvegetated 

to water change was previously discussed. The intertidalvegetated to water change was largely due to shoreline erosion throughout the whole region, but especially along the 

marsh shorelines of the westernmost reaches. The intertidalunvegetated to intertidal-vegetated change was largely a result 

of the marsh restoration effort on Whiskey Island in 2009 

(table 2).

Late Lafourche Delta Region Habitat and Habitat 

Change

For the Late Lafourche Delta region, the five nonwater detailed habitat classes with the highest areal coverages in 2008 were (1) estuarine emergent marsh (2,551.91 

ha; 7.8 percent); (2) intertidal (797.97 ha; 2.4 percent); 

(3) developed (680.23 ha; 2.1 percent); (4) mangrove 

(582.36 ha; 1.8 percent); and (5) unvegetated flat (323.79 ha; 

1.0 percent) (table 1–1; fig. 20). The five nonwater detailed 

habitat classes with the highest areal coverages in 2016 were 

(1) estuarine emergent marsh (1,952.63 ha; 6.0 percent); 

(2) mangrove (1,048.20 ha; 3.2 percent); (3) developed 

(695.02 ha; 2.1 percent); (4) meadow (478.79 ha; 1.5 percent); 

and (5) scrub/shrub (265.20 ha; 0.8 percent) (table 1–1; 

fig. 20). Unvegetated dune and mangrove had “high” percent 

increases (table 10). The reason for the high increase in unvegetated dune was the Caminada Headland beach and dune restoration (table 2; fig. 21). Mangrove expansion occurred in most 

Figure 20. Percentages of habitat class areal coverage in the Late Lafourche Delta region mapped during 

the Louisiana Barrier Island Comprehensive Monitoring Program, 2008 and 2016. Points that are off the 1:1 line 

represent change in coverage between the time periods.

1:1 line

Bare land

Beach

Developed

Estuarine emergent marsh

Forest

Grassland

Intertidal

Mangrove

Meadow

Scrub/shrub

Shoreline protection

Unvegetated dune

Unvegetated flat

Vegetated dune

Water

EXPLANATION

Habitat class areal coverage, 2008, in percent

Habitat class areal coverage, 2016, in percent
90°06' 29°10'

29°09'45"

90°06'

29°09'45"

90°06'

29°11' 29°10'15" 29°10'30" 29°10'45"

90°06' 29°10' 29°11' 29°10'15" 29°10'30" 29°10'45" B

A

GULF OF MEXICO

0 0.3 0.6 KILOMETER0 0.3 0.6 MILE

C

Location marker for paired photo comparison

GULF OF MEXICO

#

BeachUnvegetated duneVegetated dune

Unvegetated flatMeadowIntertidal

Estuarine emergent marshMangroveBare land

GrasslandScrub/shrubForest

Detailed habitat classes

Approximate vantage point and bearing for oblique photo (C)

EXPLANATION

Late Lafourche De

#

Figure 21. Coverage of detailed habitat classes in an area of the Late Lafourche Delta region during A, 2008 and B, 2016, and C, paired obli

2017, used with permission from Westphal (2018).
36 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

of the reaches, with the largest increase along the Caminada 

Headland (fig. 21). This finding is consistent with a recent 

study by Osland and others (2017) for this area. Intertidal, bare 

land, and grassland had “high” percent decreases (table 10). 

The main reasons for the decline of intertidal area were water 

level differences leading to mudflats within marsh ponds and 

tidal creeks throughout the region being exposed in 2008, 

but not in 2016. Additionally, some intertidal loss was likely 

driven by recovery after Hurricanes Gustav and Ike (table 1; 

fig. 21). Habitat succession was the reason for the bulk of the 

loss of grassland and bare land area (fig. 21).

The area experiencing a change in a land/water category for this region was 4.0 percent (table 1–3). Of this 

change, 55.1 percent was land gain, and 44.9 percent was 

land loss (fig. 12). Despite a high shoreline erosion rate 

from 2004 to 2012 of 6.2 m/yr (Byrnes and others, 2018), 

restoration efforts were likely the major reason for the land 

gain in this region. For the IZB change, the changed classes 

accounted for 5.8 percent of the region (table 1–4). These IZB 

change classes had a “high” percent change (1) intertidalvegetated to water (21.6 percent); (2) intertidal-unvegetated 

to water (19.9 percent); and (3) water to supratidal/upland 

(10.6 percent) (fig. 13). The change from intertidal-vegetated 

to water was largely due to marsh loss to open water and erosion along the back-barrier shorelines. The Grand Isle area has 

one of the highest relative sea-level rise trends in the United 

States at about 9.1 millimeters per year based on observations from 1947 to 2018 (Sweet and others, 2017; NOAA, 

2019b), which is likely to be a major factor in the change for 

both intertidal classes to water. Another likely cause for these 

changes was erosion of Timbalier and East Timbalier Islands 

from Hurricane Isaac. Water level differences in marsh ponds 

and tidal creeks accounted for a large amount of the change 

from intertidal-unvegetated to water. The change from water 

to supratidal/upland was likely due to restoration projects on 

West Belle Pass, East Grand Terre Island, and the previously 

mentioned Caminada Headland (table 2), along with some 

localized accretion along the shoreline, especially along island 

spits and tips.

Modern Delta Region Habitat and Habitat 

Change

For the Modern Delta region, the five nonwater detailed 

habitat classes with the highest areal coverages in 2008 were 

(1) estuarine emergent marsh (1,404.89 ha; 7.8 percent); 

(2) intertidal (718.41 ha; 4.0 percent); (3) unvegetated flat 

(143.84 ha; 0.8 percent); (4) mangrove (69.83 ha; 0.4 percent); 

and (5) scrub/shrub (47.08 ha; 0.3 percent) (table 1–1; 

fig. 22). The five nonwater detailed habitat classes with the 

highest areal coverages in 2016 were (1) estuarine emergent marsh (1,238.67 ha; 6.9 percent); (2) unvegetated flat 

(322.63 ha; 1.8 percent); (3) meadow (304.48 ha; 1.7 percent); (4) mangrove (124.29 ha; 0.7 percent); and (5) intertidal (84.51 ha; 0.5 percent) (table 1–1; fig. 22). Grassland, 

meadow, unvegetated dune, and vegetated dune all had “high” 

percent increases (table 10). The grassland increase was 

largely associated with herbaceous vegetation growing on containment dikes (that is, spoil banks) from the various restoration efforts in the Chaland Headland and Shell Island reaches 

(table 2; fig. 23). These restoration efforts were also likely 

responsible for the high increases in meadow, unvegetated 

dune, and vegetated dune because these efforts all included 

these features in their designs. In addition to the restoration 

efforts, habitat succession in areas restored in 2007 in Chaland 

Headland, as well as some other nonrestored areas, may have 

also been responsible for the increase in meadow habitat. 

Intertidal was the only class with a “high” percent decrease 

(table 10). Most of the intertidal changes were due to mudflats 

being exposed by lower water levels in 2008; however, some 

change was from restoration efforts throughout the region 

(table 2), which changed intertidal habitat to supratidal/upland 

habitats.

The area experiencing a change in a land/water category 

for this region was 6.6 percent, which was the highest of all 

regions (table 1–3). Of this change, 70.8 percent was land 

gain, and 29.2 percent was land loss (fig. 12). Restoration 

efforts led to a net gain of land despite the high shoreline erosion rate from 2004 to 2012 of 6.4 m/yr (Byrnes and others, 

2018). For the IZB change, the changed classes accounted 

for 10.3 percent of the region (table 1–4). These IZB change 

classes had a “high” percent change (1) intertidal-unvegetated 

to water (25.8 percent); (2) water to supratidal/upland 

(24.8 percent); and (3) intertidal-vegetated to water (16.2 percent) (fig. 13). The intertidal-unvegetated to water change was 

previously discussed. The water to supratidal/upland change 

likely resulted from several restoration efforts (table 2) and 

was responsible for the high amount of net gain in this region. 

Finally, the intertidal-vegetated to water change was from 

estuarine emergent marsh loss to water throughout the region, 

but particularly along marsh edges.
Results and Discussion 37

% cover 2008

012345678

8687

	


0

1

2

3

4

5

6

7

8

86

87

Bare land

Beach

Developed*

Estuarine emergent marsh

Forest*

Grassland*

Intertidal

Mangrove

Meadow

Scrub/shrub

Shoreline protection*

Unvegetated dune

Unvegetated flat

Vegetated dune

Water

1:1 line

*, not shown due to low percentage

Bare land

Beach

Developed*

Estuarine emergent marsh

Forest*

Grassland*

Intertidal

Mangrove

Meadow

Scrub/shrub

Shoreline protection*

Unvegetated dune

Unvegetated flat

Vegetated dune

Water

EXPLANATION

Habitat class areal coverage, 2008, in percent

Habitat class areal coverage, 2016, in percent

Figure 22. Percentages of habitat class areal coverage in the Modern Delta region mapped during the Louisiana 

Barrier Island Comprehensive Monitoring Program, 2008 and 2016. Points that are off the 1:1 line represent change 

in coverage between the time periods.
89°33' 29°14'40" 29°14'30" 89°33'20" 89°33'10" 89°33' 89°32'50" 89°32'40" 89°33'20" 89°33'10" 89°32'50" 89°32'40" 29°14'40" 29°14'30" BA GULF OF MEXICO 0 0.2 0.4 KILOMETER 0 0.2 0.4 MILE C Location marker for paired photo comparison GULF OF MEXICO # Beach Unvegetated dune Vegetated dune Unvegetated flat Meadow Intertidal Estuarine emergent marsh Mangrove Bare land Grassland Scrub/shrub Forest S

DW

Detailed habitat classes Approximate vantage point and bearing for oblique photo (C) EXPLANATION Modern Delta region #

Figure 23. Coverage of detailed habitat classes in an area of the Modern Delta region during A, 2008 and B, 2016, and C, paired oblique photogused with permission from Westphal (2018).
Results and Discussion 39

Chandeleur Islands Region Habitat and Habitat 

Change

For the Chandeleur Island region, the five nonwater 

detailed habitat classes with the highest areal coverages in 

2008 were (1) intertidal (565.50 ha; 1.3 percent); (2) estuarine 

emergent marsh (156.54 ha; 0.4 percent); (3) unvegetated flat 

(129.11 ha; 0.3 percent); (4) mangrove (50.01 ha; 0.1 percent); 

and (5) meadow (22.23 ha; 0.1 percent) (table 1–1; fig. 24). 

The five nonwater detailed habitat classes with the highest areal coverages in 2016 were (1) intertidal (443.33 ha; 

1.1 percent); (2) unvegetated flat (301.74 ha; 0.7 percent); 

(3) estuarine emergent marsh (294.45 ha; 0.7 percent); 

(4) mangrove (87.09 ha; 0.2 percent); and (5) meadow 

(68.76 ha; 0.2 percent) (table 1–1; fig. 24). Beach, meadow, 

scrub/shrub, unvegetated flat, developed, estuarine emergent marsh, and mangrove all had “high” percent increases 

(table 10). The increases in beach and unvegetated flat habitats 

could be attributed to restoration actions in this region in 

addition to continued recovery from Hurricane Katrina, which 

severely impacted the Chandeleur Islands (table 2; fig. 25). 

The increase in estuarine emergent marsh was likely an indirect response of these various previous actions and processes 

(that is, berms created sheltered areas where habitat succession 

can occur in back-barrier intertidal flats; fig. 25) along with 

habitat succession in overwash areas from hurricanes (table 1). 

Developed and scrub/shrub had a high percent increase, but 

the magnitude increase was small. The Breton Island reach had 

a small increase in developed area (that is, 0.14 ha) because of 

expansion of an oil and gas facility offshore of Breton Island. 

Habitat succession likely resulted in the increase in meadow 

and scrub/shrub habitat (fig. 25). Mangrove expansion into 

estuarine emergent marsh was the likely cause of the increase 

in mangrove habitat. Bare land, shoreline protection, and 

unvegetated dune had “high” percent decreases (table 10). 

Both bare land and shoreline protection had a “high” percent decrease, but the magnitude of change was very low 

(that is, 0.04 ha and 0.06 ha, respectively). The decrease in 

1:1 line

*, not shown due to low percentage

Bare land*

Beach

Developed*

Estuarine emergent marsh

Forest*

Grassland*

Intertidal

Mangrove

Meadow

Scrub/shrub*

Shoreline protection*

Unvegetated dune*

Unvegetated flat

Vegetated dune

Water

EXPLANATION

Habitat class areal coverage, 2008, in percent

Habitat class areal coverage, 2016, in percent

Figure 24. Percentages of habitat class areal coverage in the Chandeleur Islands region mapped during the 

Louisiana Barrier Island Comprehensive Monitoring Program, 2008 and 2016. Points that are off the 1:1 line represent 

change in coverage between the time periods.
Area sho

on map

29°29'30" 29°29' 89°11' 89°10'30” 89°10' 29°30' 29°29'30" 29°29' 89°11' 89°10'30” 89°10' A B 29°30' 0 0.3 0.6 KILOMETER 0 0.3 0.6 MILE # C Location marker for paired photo comparison Chan

Beach Unvegetated dune Vegetated dune Unvegetated flat Meadow Intertidal Estuarine emergent marsh Mangrove Bare land Grassland Scrub/shrub Forest 

W

Detailed habitat classes Approximate vantage point and bearing for oblique photo (C) EXPLANATION # GULF OF MEXICO GULF OF MEXICO 

N

Figure 25. Coverage of detailed habitat classes in an area of the Chandeleur Islands region during A, 2008 and B, 2016, and C, paired oblique pused with permission from Westphal (2018).
Future Efforts 41

unvegetated dune was largely due to habitat succession and the 

dynamic nature of conditions in the Chandeluer Island reach. 

For example, as overwash fans became vegetated and cut off 

the nearby sand supply to some of the more landward dunes, 

these areas changed to meadow per the BICM classification 

scheme (table 3; fig. 25). The shoreline protection decrease is 

likely related to the degradation and (or) burial of engineering 

structures in the Breton Island reach.

The area experiencing a change in a land/water category 

for this region was 1.5 percent (table 1–3), which was the 

lowest of all regions. Of this change, 84.3 percent was land 

gain, and 15.7 percent was land loss (fig. 12). Similar to the 

Late Lafourche Delta and Modern Delta regions, restoration 

efforts and natural recovery have led to a net gain of land but 

also the highest shoreline erosion rate of all the BICM regions 

from 2004 to 2012 at 41.2 m/yr (Byrnes and others, 2018). For 

the IZB change, the changed classes accounted for 3.1 percent 

of the region (table 1–4). These IZB change classes had a 

“high” percent change (1) water to intertidal-unvegetated 

(25.2 percent); (2) intertidal-unvegetated to water (21.9 

percent); (3) water to supratidal/land (17.7 percent); and (4) 

water to intertidal-vegetated (10.2 percent) (fig. 13). The water 

to intertidal-unvegetated change was likely due to natural 

recovery and overwash associated with Hurricane Isaac in the 

Breton, Grand Gosier, and Curlew Island reaches because no 

major restoration efforts occurred in these areas between 2008 

and 2016. The intertidal-unvegetated to water change was 

largely due to shoreline erosion moving the island chain westward. This change was most notable in the Breton Island reach 

and the north Chandeleur Islands reach, but this may also be 

due to water levels at the time of photo acquisition, as noted 

for other regions. In addition to loss of intertidal-unvegetated 

area along the high energy shoreline, there was also substantial 

loss along the back-barrier shoreline in the north Chandeleur 

Islands reach. Water to supratidal/upland changes are a result 

of the berm restoration efforts in this region (table 2). As 

previously mentioned, the water to intertidal-vegetated change 

was largely due to increases in emergent marsh and mangrove 

along the back-barrier side of the Chandeleur Island.

Future Efforts

While the habitat mapping framework used in this effort 

includes approaches that help reduce the subjectivity and 

enhance repeatability by using relative topography, elevation 

uncertainty, and probabilistic rasters in the mapping process, 

some areas could be improved upon in future efforts. First, 

while habitat maps provide important snapshots of habitat 

coverage and are a critical component of monitoring barrier 

islands and gulf shorelines, targeted multitemporal analyses on 

barrier islands, such as those used by Zinnert and others (2011, 

2016), may help capture and provide more information on the 

dynamic nature of these systems. Periodic assessments with 

orthophotography, such as the ones used in this study, only 

provide a single snapshot of water level, whereas multitemporal analyses can capture a wider range of water levels over 

a specified time period, which could help minimize issues 

associated with false change in intertidal areas as noted for this 

study. In addition to providing more information concerning 

habitat dynamics, multitemporal satellite analyses could also 

help enhance the approaches related to elevation uncertainty 

used in this study. For example, the approach to address elevation uncertainty by using Monte Carlo simulations applies 

the relative error for vegetated areas regardless of vegetative 

cover. Yet, multitemporal satellite analyses would allow for a 

more targeted application of relative error by using a map of 

deterministic or fractional vegetation cover produced during 

the time of lidar data acquisition. Collectively, periodic maps 

and multitemporal analyses could provide information that 

is critical for adaptive management of these coastal environments by using an approach similar to that of Dalyander and 

others (2016).

Second, for this current effort, the mapping framework 

was rerun to completely remap areas for both years. Satellitebased land use/land cover mapping efforts, such as the USGS 

National Land Cover Database, use change analyses to target 

areas that changed since the last mapping effort and then focus 

the mapping effort on the changed areas (Homer and others, 

2015). This process can be difficult with orthophotography 

because of different camera calibrations and settings; however, as a first pass, coarser satellite imagery (for example, 

10-m Sentinel-2 satellite imagery) could be used to identify 

changed areas. These data could be paired with morphological 

changes in lidar data by using a process similar to the one used 

by Liu and others (2010). Collectively, these steps could help 

increase habitat mapping efficiency and reduce false change 

detection based on subjective differences or omission errors 

between maps.

Third, we expect that lidar data acquisition will become 

more frequent in the future. Over time, technological advancements may allow for more regular topographic data collection via earth-orbiting satellites (Patterson and others, 2019). 

If freely available, these data may enhance coastal habitat 

mapping analyses by advancing the understanding of topographic changes, and, similar to multitemporal analyses, by 

minimizing issues with water level variation between periodic 

habitat maps.

Fourth, as more data are added to the time series, the 

persistence of changes should also be factored into change 

analyses by using approaches similar to one developed by 

Pontius and others (2004).

Fifth, the habitat reporting tool produced for this effort 

could be enhanced to move beyond reach-specific reporting 

to custom reporting for user-defined areas (that is, custom 

polygons or polygons developed by using mapping tools in the 

web mapping interface).

Finally, the previous BICM habitat maps (that is, 

1996/1998, 2001, 2004, and 2005) could be vectorized and 

analyzed for change with these new habitat data.
42 Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16

Summary

In 2005, Louisiana’s Coastal Protection and Restoration 

Authority, in partnership with the University of New Orleans 

and the U.S. Geological Survey, developed the Barrier Island 

Comprehensive Monitoring (BICM) Program to monitor and 

assess changes along the Louisiana Gulf of Mexico shoreline. This report covers the second phase of the BICM habitat 

analyses. This effort built on the initial BICM habitat mapping 

efforts, which developed habitat and habitat change maps for 

1996/1998, 2001, 2004, and 2005. The overall objectives of 

the study described in this report were to (1) map habitats 

for 2008 and 2015–16 for BICM coastal reaches and (2) map 

habitat change between these two time periods. For the current 

study, a new detailed 15-class habitat scheme was developed 

from the original BICM scheme. The new habitat scheme 

included additional classes developed to delineate various 

dune habitats, to distinguish between marsh and mangrove, 

and to distinguish between beach and unvegetated barrier flat 

habitats. These habitat data were developed by using a geographic object-based image analysis habitat mapping framework that was previously applied to map barrier island habitats 

on Dauphin Island, Alabama. This approach reduces the subjectivity and enhances the repeatability of habitat delineation 

by using relative topography and elevation uncertainty.

For the entire BICM region, the area experiencing a 

change in a land/water category (that is, land gain or land loss) 

was 3.4 percent, of which, 59.2 percent was land gain and 

40.8 percent was land loss. The areal coverages of meadow, 

mangrove, scrub/shrub, and vegetated dune increased from 

2008 to 2015–16, whereas the areal coverages of beach, 

grassland, and intertidal decreased during this period. The 

decrease in intertidal was largely due to differing water levels 

in the orthophotography between 2008 and 2015–16. Regional 

analyses of habitat coverage and habitat change captured 

both the dynamic nature of these systems and the effects of 

restoration efforts, most notably in the Late Lafourche Delta, 

Modern Delta, and Chandeleur Islands regions. In the Modern 

Delta region, there was a marked increase in unvegetated flat, 

meadow, mangrove, scrub/shrub, beach, unvegetated dune, 

and vegetated dune. As a result, this region experienced the 

highest percent change for land/water classes (6.6 percent) 

with land gain accounting for much of this change 

(70.8 percent). In contrast, the Acadiana Bays region had the 

highest relative percent loss for all regions. The Acadiana 

Bays region had a percent change for land/water classes of 

2.8 percent, of which, 79.7 percent was land loss.

Natural resource managers can use the results of these 

mapping efforts to provide insights into the response of these 

systems to storms and restoration efforts. Further research 

could explore the use of a multitemporal approach that could 

provide valuable monitoring information, especially related 

to informing adaptive management of these systems, between 

detailed BICM habitat mapping efforts.

References Cited

Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, 

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For more information about this publication, contact

Director, Wetland and Aquatic Research Center

U.S. Geological Survey

700 Cajundome Blvd. 

Lafayette, LA 70506-3152

For additional information, visit

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

Publishing support provided by

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Appendix 1
Table 1–1. Areal coverage (hectares) for detailed habitat classes for 2008 and 2015–16, by Louisiana Barrier Island Comprehensive Monitoring (BICM) for reach location and name.—Continued [BL, bare land; B, beach; DV, developed; EM, estuarine emergent marsh; F, forest; GL, grassland; I, intertidal; MG, mangrove; MW, meadow; SS, scrub/shrub; SP, shoreline proteunvegetated flat; VD, vegetated dune; W, water; RT, region total; --, not applicable] Reach Date BL B DV EM F GL I MG MW SS SP UD UF V

West Chenier Plain region 1 2008 5.62 112.80 209.84 3,909.17 11.46 609.23 164.47 0.00 118.78 201.13 1.13 40.54 23.19 35

2015 3.55 30.55 212.54 4,160.80 20.31 550.49 116.47 0.00 144.42 210.94 0.96 22.68 26.36 104

2 2008 0.40 49.58 72.87 2,885.15 0.88 274.06 91.43 0.00 5.62 60.56 3.39 14.45 47.04 6

2015 0.00 18.23 77.95 2,981.56 4.60 262.03 28.92 0.00 25.42 105.26 1.94 4.08 8.16 40

3 2008 0.85 31.32 49.43 1,183.01 4.78 102.79 26.12 0.00 44.09 10.33 0.84 14.22 26.86 14

2015 0.20 15.57 61.74 1,371.34 3.89 80.09 15.45 0.00 53.42 36.98 0.73 18.88 45.95 26

4 2008 7.27 35.78 218.77 3,857.04 64.61 814.43 162.47 0.00 75.49 218.13 0.78 77.10 51.58 63

2015 0.00 24.26 253.96 3,935.72 38.22 689.44 43.89 0.00 155.82 346.94 0.85 37.49 26.14 114

5 2008 0.35 13.34 1.50 1,699.24 1.44 58.16 164.13 0.00 38.89 18.00 0.51 23.33 20.32 0

2015 0.07 8.64 1.93 1,734.48 1.31 44.39 31.14 0.00 30.69 50.70 0.55 4.18 7.01 14

RT 2008 14.49 242.82 552.41 13,533.61 83.17 1,858.67 608.62 0.00 282.87 508.15 6.65 169.64 168.99 119

2015 3.82 97.25 608.12 14,183.90 68.33 1,626.44 235.87 0.00 409.77 750.82 5.03 87.31 113.62 300

East Chenier Plain region 6 2008 1.17 106.70 5.38 11,523.53 0.00 135.59 229.39 0.00 85.94 89.07 0.41 18.31 156.86 0

2015 16.00 21.53 4.87 11,325.36 0.03 63.90 120.36 0.00 81.48 170.43 0.45 14.11 126.58 5

7 2008 0.51 8.45 0.14 3,111.34 6.88 21.05 28.37 0.00 38.99 10.52 0.00 7.42 83.39 0

2015 6.36 3.18 0.11 3,185.11 12.73 3.84 8.01 0.00 39.54 55.54 0.00 0.02 8.87 0

8 2008 2.13 0.00 19.93 3,278.14 1.99 41.22 4.00 0.00 396.02 61.83 0.00 0.00 17.29 0

2015 0.07 0.08 17.64 3,410.11 12.51 19.29 2.11 0.00 302.41 139.98 0.26 0.00 18.84 0

9 2008 9.60 11.21 0.64 2,150.10 67.22 140.33 12.87 0.00 6.16 93.99 0.26 5.43 26.24 0

2015 0.00 2.61 0.64 2,173.96 93.89 76.06 7.78 0.00 12.46 205.50 0.46 7.05 11.96 3

10 2008 0.00 6.95 0.02 2,911.83 43.25 33.21 7.37 0.00 16.42 36.97 0.59 12.96 13.21 5

2015 0.00 3.43 0.03 2,910.96 55.70 14.54 10.80 0.00 11.00 42.77 1.13 18.99 13.95 3

RT 2008 13.41 133.31 26.11 22,974.94 119.34 371.40 282.00 0.00 543.53 292.38 1.26 44.12 296.99 5

2015 22.43 30.83 23.29 23,005.50 174.86 177.63 149.06 0.00 446.89 614.22 2.30 40.17 180.20 12

Acadiana Bays region

11 2008 0.87 5.64 0.09 5,745.33 0.00 22.86 90.83 0.00 362.11 12.65 0.00 10.70 10.87 13

2015–16 8.29 6.82 0.15 5,525.91 0.01 6.65 13.81 0.00 426.56 24.58 0.00 2.15 10.30 13
Table 1–1. Areal coverage (hectares) for detailed habitat classes for 2008 and 2015–16, by Louisiana Barrier Island Comprehensive Monitoring (BICMfor reach location and name.—Continued[BL, bare land; B, beach; DV, developed; EM, estuarine emergent marsh; F, forest; GL, grassland; I, intertidal; MG, mangrove; MW, meadow; SS, scrub/shrub; SP, shoreline protunvegetated flat; VD, vegetated dune; W, water; RT, region total; --, not applicable]

Reach Date BL B DV EM F GL I MG MW SS SP UD UF V

Early Lafourche Delta region

34 2008 0.56 11.30 1.34 3,622.43 0.00 6.99 295.16 1.57 5.00 41.17 1.96 17.98 55.69 

2016 0.00 7.53 0.82 3,645.54 0.00 11.68 10.39 6.59 49.31 29.78 1.96 1.55 16.94 

35 2008 0.00 5.27 0.68 4,604.92 0.00 25.08 786.01 109.17 7.46 5.77 0.32 3.67 24.06 

2016 0.10 2.20 0.40 4,166.76 0.00 3.46 21.48 339.60 4.31 28.52 0.23 0.24 15.12 

36 2008 0.00 4.76 0.00 2.12 0.00 0.00 45.67 1.98 2.81 0.12 1.57 1.43 5.14 

2016 0.86 6.03 0.00 4.88 0.00 0.00 20.57 2.21 12.31 0.14 1.10 0.03 11.03 

37 2008 0.00 12.45 0.45 143.05 0.00 0.00 128.28 196.44 25.74 0.10 0.00 10.36 32.30 

2016 0.00 4.03 0.43 200.91 0.00 0.00 15.83 261.18 33.01 0.00 0.28 0.00 12.83 

38 2008 0.84 43.63 0.08 56.51 0.00 1.00 47.46 30.65 18.49 11.06 0.00 11.98 50.73 

2016 0.00 3.89 0.08 17.02 0.00 0.00 15.01 64.84 60.86 17.00 0.00 0.03 7.24 2

39 2008 0.00 18.96 0.29 6.62 0.00 0.00 22.82 0.22 17.02 0.04 0.00 15.48 31.49 

2016 0.00 1.00 0.16 2.61 0.00 0.00 4.44 3.03 24.93 7.75 0.00 0.21 5.73 1

40 2008 0.00 0.39 0.00 0.41 0.00 0.00 2.75 0.00 0.67 0.00 0.37 0.00 0.99 

2016 0.00 0.57 0.30 0.00 0.00 0.00 2.04 0.00 0.00 0.00 0.24 0.00 0.00 

RT 2008 1.40 96.76 2.84 8,436.06 0.00 33.07 1,328.15 340.03 77.19 58.26 4.22 60.90 200.40 2

2016 0.96 25.25 2.19 8,037.72 0.00 15.14 89.76 677.45 184.73 83.19 3.81 2.06 68.89 4

Late Lafourche Delta region

41 2008 0.00 34.11 1.07 81.85 0.00 0.00 113.00 42.70 78.81 68.65 0.59 31.51 23.47 1

2016 0.92 10.84 0.95 57.28 0.00 0.00 29.95 84.13 116.95 51.14 0.38 0.16 7.86 2

42 2008 0.00 2.99 3.52 10.13 0.00 0.27 46.43 11.42 7.01 0.62 1.45 0.75 19.30 

2016 0.00 1.49 3.58 5.78 0.00 0.00 12.04 8.32 6.45 2.15 0.13 0.01 6.49 

43 2008 10.98 2.96 0.22 112.76 0.00 3.54 86.23 85.07 4.08 1.20 1.61 0.22 11.89 

2016 7.92 3.73 0.15 62.76 0.00 8.24 24.96 177.88 11.97 1.86 1.32 0.39 102.54 

44 2008 4.71 58.11 189.18 1,896.93 0.00 65.97 314.54 360.24 16.80 49.05 3.68 14.44 175.79 1

2016 2.51 33.81 188.32 1,354.55 0.00 37.16 66.85 641.83 73.72 110.18 2.86 150.46 90.36 

45 2008 12.63 39.46 483.84 247.63 6.34 6.52 139.73 39.34 155.17 55.14 8.51 7.14 58.51 1

2016 0.23 32.59 500.46 245.09 9.78 0.22 19.10 76.97 197.39 88.76 9.17 1.54 16.74 3

46 2008 0.15 5.35 2.40 109.91 0.00 5.36 26.38 0.83 29.70 3.66 2.10 2.60 23.44 

2016 0.00 5.90 1.56 88.66 0.00 2.45 9.20 23.34 48.88 8.08 1.72 0.00 5.44 
Table 1–1. Areal coverage (hectares) for detailed habitat classes for 2008 and 2015–16, by Louisiana Barrier Island Comprehensive Monitoring (BICM) for reach location and name.—Continued [BL, bare land; B, beach; DV, developed; EM, estuarine emergent marsh; F, forest; GL, grassland; I, intertidal; MG, mangrove; MW, meadow; SS, scrub/shrub; SP, shoreline proteunvegetated flat; VD, vegetated dune; W, water; RT, region total; --, not applicable] Reach Date BL B DV EM F GL I MG MW SS SP UD UF V

Late Lafourche Delta region—Continued 47 2008 0.00 4.32 0.00 92.70 0.00 0.00 71.66 42.76 3.35 0.00 0.00 0.14 11.39 0

2016 0.00 3.98 0.00 138.51 0.00 1.10 15.02 35.73 23.43 3.03 0.00 0.00 32.05 0

RT 2008 28.47 147.30 680.23 2,551.91 6.34 81.66 797.97 582.36 294.92 178.32 17.94 56.80 323.79 57

2016 11.58 92.34 695.02 1,952.63 9.78 49.17 177.12 1,048.20 478.79 265.20 15.58 152.56 261.48 79

Modern Delta region 48 2008 8.83 21.70 2.49 918.64 0.00 0.89 541.83 65.46 27.35 46.15 0.11 5.84 120.59 5

2016 5.56 18.51 2.20 738.07 0.00 0.10 27.91 110.08 159.52 59.44 0.00 1.12 117.23 30

49 2008 0.00 9.11 0.69 486.25 0.00 0.00 176.58 4.37 18.41 0.93 1.15 1.37 23.25 0

2016 6.06 33.27 0.68 500.60 0.00 5.01 56.60 14.21 144.96 2.17 0.88 78.77 205.40 12

RT 2008 8.83 30.81 3.18 1,404.89 0.00 0.89 718.41 69.83 45.76 47.08 1.26 7.21 143.84 5

2016 11.62 51.78 2.88 1,238.67 0.00 5.11 84.51 124.29 304.48 61.61 0.88 79.89 322.63 43

Chandeleur Islands region 59 2008 0.04 0.47 0.14 1.06 0.00 0.00 53.13 1.78 1.48 0.01 0.14 0.00 3.70 0

2016 0.00 4.48 0.29 8.67 0.00 0.00 32.31 1.56 8.01 0.09 0.08 0.00 20.35 0

60 2008 0.00 0.00 0.02 0.00 0.00 0.00 5.14 0.00 0.00 0.00 0.00 0.00 0.00 0

2016 0.00 8.45 0.01 1.89 0.00 0.00 77.31 0.00 3.13 0.00 0.00 0.00 24.47 0

61 2008 0.00 14.10 0.03 155.48 0.00 0.00 507.23 48.23 20.75 0.04 0.00 4.01 125.41 9

2016 0.00 49.83 0.03 283.89 0.00 0.00 333.71 85.53 57.62 2.17 0.00 2.36 256.92 15

RT 2008 0.04 14.57 0.19 156.54 0.00 0.00 565.50 50.01 22.23 0.05 0.14 4.01 129.11 9

2016 0.00 62.76 0.33 294.45 0.00 0.00 443.33 87.09 68.76 2.26 0.08 2.36 301.74 15

BICM region-wide (fig. 1) -- 2008 67.51 671.21 1,265.05 54,803.28 208.85 2,368.55 4,391.48 1,042.23 1,628.61 1,096.89 31.47 353.38 1,273.99 233

-- 2015–16 58.70 367.03 1,331.98 54,238.78 252.98 1,880.14 1,193.46 1,937.03 2,319.98 1,801.88 27.68 366.50 1,258.86 514
Table 1–2. Areal coverage (hectares) for general habitat classes for 2008 and 2015–16, by Louisiana Barrier Island Comprehensive MonitoProgram reach. See figure 1 for reach location and name.—Continued[BL, bare land; BV, barrier vegetation; B, beach; EW, estuarine vegetated wetland; I, intertidal; RR, rip-rap; ST, structure; W, water; RT, region total]

Reach Date BL BV B EW I RR ST W 

West Chenier Plain region

1 2008 46.16 975.80 135.99 3,909.17 164.47 1.13 209.84 1,467.92 

2015 26.23 1,030.50 56.90 4,160.8 116.47 0.96 212.54 1,306.08 

2 2008 14.85 347.38 96.62 2,885.15 91.43 3.39 72.87 1,157.03 

2015 4.08 437.91 26.38 2,981.56 28.92 1.94 77.95 1,109.98 

3 2008 15.07 176.57 58.18 1,183.01 26.12 0.84 49.43 2,018.93 

2015 19.08 201.16 61.52 1,371.34 15.45 0.73 61.74 1,797.13 

4 2008 84.37 1,235.72 87.36 3,857.04 162.47 0.78 218.77 1,445.75 

2015 37.49 1,344.87 50.39 3,935.72 43.89 0.85 253.96 1,425.09 

5 2008 23.69 117.27 33.66 1,699.24 164.13 0.51 1.50 1,370.88 

2015 4.25 141.43 15.65 1,734.48 31.14 0.55 1.93 1,481.44 

RT 2008 184.14 2,852.74 411.81 13,533.61 608.62 6.65 552.41 7,460.51 2

2015 91.13 3,155.87 210.84 14,183.90 235.87 5.03 608.12 7,119.72 2

East Chenier Plain region

6 2008 19.47 310.91 263.56 11,523.53 229.39 0.41 5.38 10,079.32 2

2015 30.11 321.06 148.10 11,325.36 120.36 0.45 4.87 10,481.67 2

7 2008 7.93 77.50 91.84 3,111.34 28.37 0.00 0.14 1,652.40 

2015 6.38 112.11 12.05 3,185.11 8.01 0.00 0.11 1,645.76 

8 2008 2.13 501.06 17.30 3,278.14 4.00 0.00 19.93 1,329.00 

2015 0.07 474.20 18.91 3,410.11 2.11 0.26 17.64 1,228.25 

9 2008 15.03 307.89 37.45 2,150.10 12.87 0.26 0.64 1,078.48 

2015 7.05 391.22 14.57 2,173.96 7.78 0.46 0.64 1,007.04 

10 2008 12.96 135.22 20.16 2,911.83 7.37 0.59 0.02 1,184.69 

2015 18.99 127.31 17.39 2,910.96 10.80 1.13 0.03 1,186.25 

RT 2008 57.52 1332.58 430.31 22,974.94 282.00 1.26 26.11 15,323.89 4

2015 62.60 1425.90 211.02 23,005.50 149.06 2.30 23.29 15,548.97 4

Acadiana Bays region

11 2008 11.57 411.04 16.52 5,745.33 90.83 0.00 0.09 3,061.88 

2015–16 10.44 471.70 17.12 5,525.91 13.81 0.00 0.15 3,298.14 
Table 1–2. Areal coverage (hectares) for general habitat classes for 2008 and 2015–16, by Louisiana Barrier Island Comprehensive MonitorinProgram reach. See figure 1 for reach location and name.—Continued [BL, bare land; BV, barrier vegetation; B, beach; EW, estuarine vegetated wetland; I, intertidal; RR, rip-rap; ST, structure; W, water; RT, region total] Reach Date BL BV B EW I RR ST W T

Early Lafourche Delta region 34 2008 18.54 53.55 66.99 3,624.00 295.16 1.96 1.34 2,897.39 6

2016 1.55 93.38 24.46 3,652.13 10.39 1.96 0.82 3,174.25 6

35 2008 3.67 38.31 29.32 4,714.09 786.01 0.32 0.68 7,455.26 13

2016 0.35 36.34 17.32 4,506.36 21.48 0.23 0.40 8,445.19 13

36 2008 1.43 7.83 9.90 4.10 45.68 1.57 0.00 2,654.80 2

2016 0.89 16.24 17.06 7.10 20.57 1.10 0.00 2,662.35 2

37 2008 10.36 31.04 44.75 339.49 128.28 0.00 0.45 4,305.75 4

2016 0.00 35.55 16.86 462.10 15.83 0.28 0.43 4,329.08 4

38 2008 12.82 34.94 94.36 87.16 47.46 0.00 0.08 3,208.29 3

2016 0.32 102.44 11.15 81.85 15.01 0.00 0.08 3,274.26 3

39 2008 15.48 23.59 50.45 6.84 22.82 0.00 0.29 2,177.07 2

2016 0.21 48.94 6.73 5.64 4.44 0.00 0.16 2,230.42 2

40 2008 0.00 0.67 1.38 0.41 2.75 0.37 0.00 2,902.04 2

2016 0.00 0.00 0.57 0.00 2.04 0.24 0.30 2,904.47 2

RT 2008 62.30 189.93 297.15 8,776.09 1,328.16 4.22 2.84 25,600.60 36

2016 3.32 332.89 94.15 8,715.18 89.76 3.81 2.19 27,020.02 36

Late Lafourche Delta region 41 2008 31.51 165.26 57.58 124.56 113.00 0.59 1.07 7,880.49 8

2016 1.08 79.90 18.70 258.37 29.95 0.38 0.95 7,984.76 8

42 2008 0.75 8.45 22.29 21.55 46.43 1.45 3.52 3,730.37 3

2016 0.01 2.29 7.98 20.55 12.04 0.13 3.58 3,788.25 3

43 2008 11.21 10.46 14.86 197.83 86.23 1.61 0.22 2,090.10 2

2016 8.32 10.96 106.26 252.62 24.96 1.32 0.15 2,007.93 2

44 2008 19.15 146.92 233.89 2,257.10 314.54 3.68 189.18 5,363.09 8

2016 152.97 156.76 124.17 2,070.10 66.85 2.86 188.32 5,765.61 8

45 2008 19.77 235.81 97.97 286.98 139.73 8.51 483.84 3,845.80 5

2016 1.77 130.99 49.33 519.46 19.10 9.17 500.46 3,888.13 5

46 2008 2.75 48.07 28.79 110.74 26.38 2.10 2.40 1,924.01 2

2016 0.00 18.18 11.34 160.88 9.20 1.72 1.56 1,942.36 2
Table 1–2. Areal coverage (hectares) for general habitat classes for 2008 and 2015–16, by Louisiana Barrier Island Comprehensive MonitorinProgram reach. See figure 1 for reach location and name.—Continued[BL, bare land; BV, barrier vegetation; B, beach; EW, estuarine vegetated wetland; I, intertidal; RR, rip-rap; ST, structure; W, water; RT, region total]

Reach Date BL BV B EW I RR ST W T

Late Lafourche Delta region—Continued

47 2008 0.14 3.54 15.70 135.46 71.66 0.00 0.00 2,147.31 2

2016 0.00 27.57 36.04 174.23 15.02 0.00 0.00 2,120.95 2

RT 2008 31.51 165.26 57.58 124.56 113.00 0.59 1.07 7,880.49 32

2016 1.08 79.90 18.70 258.37 29.95 0.38 0.95 7,984.76 32

Modern Delta region

48 2008 14.67 79.50 142.29 984.09 541.83 0.11 2.49 6,320.61 8

2016 6.68 249.82 135.75 848.15 27.91 0.00 2.20 6,815.09 8

49 2008 1.37 19.92 32.36 490.62 176.58 1.15 0.69 9,102.42 9

2016 84.83 164.76 238.67 514.81 56.60 0.88 0.68 8,763.89 9

RT 2008 16.04 99.42 174.65 1,474.71 718.41 1.26 3.18 15,423.03 17

2016 91.51 414.58 374.42 1,362.96 84.51 0.88 2.88 15,578.98 17

Chandeleur Islands region

59 2008 0.04 1.65 4.17 2.84 53.13 0.14 0.14 5,126.06 5

2016 0.00 8.33 24.83 10.23 32.31 0.08 0.29 5,112.08 5

60 2008 0.00 0.00 0.00 0.00 5.14 0.00 0.02 11,592.51 11

2016 0.00 3.13 32.92 1.89 77.31 0.00 0.01 11,482.41 11

61 2008 4.01 30.92 139.51 203.72 507.23 0.00 0.03 25,460.48 26

2016 2.36 75.30 306.75 369.42 333.71 0.00 0.03 25,258.33 26

RT 2008 4.05 32.57 143.68 206.56 565.50 0.14 0.19 42,179.05 43

2016 2.36 86.76 364.50 381.54 443.33 0.08 0.33 41,852.82 43

BICM region-wide (fig. 1)

-- 2008 420.90 5,536.79 1,945.20 55,845.54 4,391.49 31.47 1,265.05 136,030.13 205-- 2015–16 425.51 6,314.35 1,625.87 56,631.20 1,193.46 27.68 1,331.98 137,916.64 205
Table 1–3. Areal coverage (hectares) for land/water change between 2008 and 2015–16, by Louisiana Barrier Island Comprehensive Monitoring (BICM) figure 1 for reach location and name. [LG, land gain; LL, land loss; LU, land unchanged; WU, water unchanged; RT, region total; --, not applicable] Reach LG LL LU WU 

West Chenier Plain region 1 309.58 104.49 5,173.60 1,322.81 

2 145.82 41.68 3,378.58 1,102.64 3 237.10 13.82 1,469.28 1,807.95 4 192.32 57.57 5,426.48 1,415.91 5 57.83 36.16 1,839.70 1,477.18 

RT 942.65 253.72 17,287.64 7,126.49 

East Chenier Plain region 6 271.54 563.29 11,559.98 10,037.18 

7 65.35 42.19 3,246.56 1,615.42 8 147.36 57.26 3,761.29 1,185.64 9 95.10 23.80 2,487.56 996.25 

10 23.85 31.49 3,049.29 1,168.21 

RT 603.20 718.03 24,104.68 15,002.70 

Acadiana Bays region 11 53.24 209.53 5,975.01 3,099.47 

Early Lafourche Delta region 34 94.49 102.42 3,663.96 3,098.06 

35 170.93 385.57 4,400.82 8,070.34 36 25.10 7.49 17.33 2,675.38 37 161.23 74.49 351.60 4,272.80 38 17.55 51.74 177.62 3,238.19 39 4.55 39.54 57.12 2,195.33 40 0.86 2.54 0.29 2,903.94 

RT 474.71 663.79 8,668.74 26,454.04 

Late Lafourche Delta region 41 57.07 77.81 302.77 7,936.43 

42 13.33 36.75 21.27 3,763.47 43 167.62 23.66 212.52 2,008.72 44 220.97 352.56 2,497.43 5,456.66 45 130.83 50.40 1,082.47 3,854.70 
Table 1–3. Areal coverage (hectares) for land/water change between 2008 and 2015–16, by Louisiana Barrier Island Comprehensive Monitoring (BICM) figure 1 for reach location and name.—Continued[LG, land gain; LL, land loss; LU, land unchanged; WU, water unchanged; RT, region total; --, not applicable]

Reach LG LL LU WU 

Late Lafourche Delta region—Continued

46 19.58 20.01 174.84 1,930.81 47 107.89 23.47 131.37 2,111.08 

RT 717.29 584.66 4,422.67 27,061.87 

Modern Delta region

48 260.36 230.21 992.95 6,602.08 49 572.5 113.47 432.65 8,706.49 

RT 832.86 343.68 1,425.60 15,308.57 

Chandeleur Islands region

59 35.82 1.33 7.63 5,143.37 60 37.65 0.02 0.01 11,560.00 61 477.63 101.45 276.74 25,490.08 

RT 551.10 102.8 284.38 42,193.45 

BICM region-wide (fig. 1)

-- 4,175.05 2,876.21 62,168.72 136,246.59 
Table 1–4. Areal coverage (hectares) for inundation zone-based habitat change classes for change between 2008 and 20–2016, by Louisiana Barrier Monitoring (BICM) Program reach. See figure 1 for reach location and name. [DSPG, developed/shoreline protection gain; DSPL, developed/shoreline protection loss; IUV-IV, intertidal-unvegetated to intertidal-vegetated; IUV-S, intertidal-unvegetated to intertidal-unvegetated to water; IV-IUV, intertidal-vegetated to intertidal-unvegetated; IV-S, intertidal-vegetated to supratidal/upland; IV-W, intertidal-vegetated to water; S-IUVunvegetated; S-IV, supratidal/upland to intertidal-vegetated; S-W, supratidal/upland to water; U, unchanged; W-IUV, water to intertidal-unvegetated; W-IV, water to intertidal-vetidal/upland; RT, region total; --, not applicable] Reach DSPG DSPL IUV-IV IUV-S IUV-W IV-IUV IV-S IV-W S-IUV S-IV S-W U W-IUV W-

West Chenier Plain region 1 9.67 8.18 42.65 23.37 58.85 51.68 55.43 19.95 11.09 108.27 20.94 6,242.85 14.89 226.

2 10.85 6.77 8.81 13.78 58.11 8.17 31.86 14.36 8.05 19.98 9.60 4,352.85 2.49 117.3 12.46 0.67 0.41 19.08 3.79 0.42 7.31 10.48 1.11 6.44 1.65 3,235.85 11.34 191.4 50.17 15.12 39.07 58.05 42.92 7.68 156.10 32.15 6.21 160.67 11.24 6,408.80 9.41 77.5 0.66 0.22 28.05 2.39 125.16 1.72 22.87 6.26 7.89 9.65 20.12 3,144.65 14.04 26.

RT 83.81 30.96 118.99 116.67 288.83 69.67 273.57 83.20 34.35 305.01 63.55 23,385.00 52.17 640.

East Chenier Plain region 6 0.81 1.19 14.15 3.03 207.42 36.11 246.71 220.15 9.92 40.97 296.99 21,032.13 68.34 250.

7 0.01 0.01 0.62 0.58 26.48 3.08 56.18 15.04 4.08 80.05 19.98 4,699.25 0.01 63.8 1.18 2.64 1.90 0.15 1.74 0.53 88.28 7.23 1.07 69.47 48.36 4,783.81 0.15 144.9 0.36 0.36 0.06 0.31 11.49 0.33 97.88 2.75 5.62 29.76 15.09 3,343.38 0.67 92.

10 0.45 0.01 0.41 1.34 4.49 1.42 26.32 13.97 6.15 18.79 9.94 4,166.21 1.69 19.

RT 2.81 4.21 17.14 5.41 251.62 41.47 515.37 259.14 26.84 239.04 390.36 38,024.78 70.86 570.

Acadiana Bays region 11 0.04 0.00 15.78 0.84 69.34 2.20 142.61 166.94 8.99 41.21 31.39 8,820.54 0.74 36.2

Early Lafourche Delta region 34 0.50 0.85 45.07 3.80 214.66 3.08 44.89 46.56 5.17 19.00 45.97 6,486.67 0.40 40.

35 0.11 0.47 145.83 0.79 563.51 5.24 18.46 345.56 2.07 4.50 30.22 11,889.18 1.02 19.36 0.02 0.42 0.67 6.66 35.51 0.53 0.67 1.59 0.65 1.50 4.17 2,638.96 16.74 3.37 0.27 0.02 31.81 7.50 79.16 1.58 3.83 28.87 2.27 14.16 40.67 4,520.72 10.13 107.38 0.01 0.00 6.04 3.28 31.47 0.57 1.94 12.06 5.08 1.49 33.35 3,376.25 6.38 0.39 0.08 0.25 1.26 1.46 18.44 0.24 1.02 2.07 1.42 0.23 35.28 2,232.09 1.32 0.40 0.30 0.11 0.00 0.04 2.59 0.02 0.00 0.37 0.18 0.00 1.84 2,899.94 1.72 0.

RT 1.29 2.12 230.68 23.53 945.34 11.26 70.81 437.08 16.84 40.88 191.50 34,043.81 37.71 171.
Table 1–4. Areal coverage (hectares) for inundation zone-based habitat change classes for change between 2008 and 20–2016, by Louisiana Barrier Monitoring (BICM) Program reach. See figure 1 for reach location and name.—Continued[DSPG, developed/shoreline protection gain; DSPL, developed/shoreline protection loss; IUV-IV, intertidal-unvegetated to intertidal-vegetated; IUV-S, intertidal-unvegetated to intertidal-unvegetated to water; IV-IUV, intertidal-vegetated to intertidal-unvegetated; IV-S, intertidal-vegetated to supratidal/upland; IV-W, intertidal-vegetated to water; S-IUVunvegetated; S-IV, supratidal/upland to intertidal-vegetated; S-W, supratidal/upland to water; U, unchanged; W-IUV, water to intertidal-unvegetated; W-IV, water to intertidal-vetidal/upland; RT, region total; --, not applicable]

Reach DSPG DSPL IUV-IV IUV-S IUV-W IV-IUV IV-S IV-W S-IUV S-IV S-W U W-IUV W-

Late Lafourche Delta region

41 0.41 0.71 10.28 20.40 71.83 0.65 1.83 26.22 4.74 26.74 44.71 8,125.53 15.76 6.42 0.68 1.99 1.36 2.17 40.45 0.81 0.94 11.85 0.99 1.02 20.77 3,734.94 8.17 3.43 0.28 0.59 31.47 21.91 26.84 2.32 10.26 7.25 3.63 7.79 9.49 2,162.37 17.62 22.44 4.91 7.61 59.74 69.49 143.96 8.53 60.88 298.70 14.34 28.72 25.29 7,712.00 21.32 31.45 28.59 10.89 35.40 35.31 54.13 0.36 9.51 39.99 1.50 19.99 5.04 4,822.19 8.47 27.46 0.30 1.53 3.80 8.12 11.78 1.12 3.10 8.03 2.41 7.90 7.16 2,079.52 3.82 1.47 0.00 0.00 19.76 16.72 29.30 0.78 11.13 18.08 1.28 5.51 2.76 2,189.22 9.77 44.

RT 35.17 23.32 161.81 174.12 378.29 14.57 97.65 410.12 28.89 97.67 115.22 30,825.77 84.93 136.

Modern Delta region

48 0.52 0.81 53.62 59.23 398.64 5.47 60.58 194.80 5.08 35.31 24.20 7,091.97 8.23 47.49 0.04 0.24 37.28 47.55 77.39 1.97 37.80 104.06 0.84 4.50 6.40 8,973.37 46.06 130.

RT 0.56 1.05 90.90 106.78 476.03 7.44 98.38 298.86 5.92 39.81 30.60 16,065.34 54.29 177.

Chandeleur Islands region

59 0.15 0.05 2.62 13.79 27.83 0.10 0.67 0.17 0.33 1.98 0.69 5,097.39 23.14 3.60 0.01 0.02 0.09 1.32 2.44 0.00 0.00 0.00 0.00 0.00 0.00 11,481.65 75.91 1.61 0.01 0.01 77.11 88.93 257.34 2.98 17.46 40.55 17.24 23.25 40.66 25,236.81 231.97 128.

RT 0.17 0.08 79.82 104.04 287.61 3.08 18.13 40.72 17.57 25.23 41.35 41,815.85 331.02 133.

BICM region-wide (fig. 1)

-- 123.85 61.74 715.12 531.39 2,697.06 149.69 1,216.52 1,696.06 139.40 788.85 863.97 192,981.09 631.72 1,866
 

Enwright and others—Louisiana BICM Program: Mapping Habitats Along the Louisiana Gulf of Mexico Shoreline, 2008 and 2015–16—OFR 2020–1030

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20201030