OCS Study 

MMS 2005-047 

 

Coastal Marine Institute 

Feasibility of Using Remote-Sensing 

Techniques for Shoreline Delineation 

and Coastal Habitat Classification 

for Environmental Sensitivity Index (ESI) 

Mapping 

 

 

Cooperative Agreement 

Coastal Marine Institute 

Louisiana State University 

U.S. Department of the Interior 

Minerals Management Service 

Gulf of Mexico OCS Region 
U.S. Department of the Interior Cooperative Agreement Minerals Management Service Coastal Marine Institute 

Gulf of Mexico OCS Region Louisiana State University 

OCS Study 

 MMS 2005-047 

 

Coastal Marine Institute 

Feasibility of Using Remote-Sensing 

Techniques for Shoreline Delineation 

and Coastal Habitat Classification 

for Environmental Sensitivity Index (ESI) 

Mapping 

Authors 

Katherine Born-Phillips 

Chris Locke 

Jacqueline Michel 

DeWitt Braud 

August 2005 

Prepared under MMS Contract 

1435-01-99-CA-30951-85250 

by 

Coastal Studies Institute 

Louisiana State University 

Baton Rouge, Louisiana 70803 

Published by 
iii

DISCLAIMER 

This report was prepared under contract between the Minerals Management Service (MMS) and 

the Louisiana State University. This report has been reviewed technically by the MMS, and it 

has been approved for publication. Approval does not signify that the contents necessarily reflect 

the views and policies of the MMS, nor does mention of trade names or commercial products 

constitute endorsement or recommendation for use. It is, however, exempt from review and 

compliance with the MMS editorial standards. 

REPORT AVAILABILITY 

Extra copies of this report may be obtained from the Public Information Office (Mail Stop 5043) 

at the following address: 

 U.S. Department of the Interior 

 Minerals Management Service 

 Gulf of Mexico OCS Region 

 Public Information Office (Mail Stop 5043) 

 1201 Elmwood Park Boulevard 

 New Orleans, Louisiana 70123-2394 

 Telephone: (504) 736-2519 or 

 1-800-200-GULF 

CITATION 

Suggested citation: 

Phillips-Born, K., C. Locke, J. Michel, and D. Braud. 2005. Feasibility of using remote-sensing 

techniques for shoreline delineation and coastal habitat classification for environmental 

sensitivity index (ESI) mapping. U.S. Dept. of the Interior, Minerals Management Service, 

Gulf of Mexico OCS Region, New Orleans, LA. OCS Study MMS 2005-047. 45 pp. + 

appendices. 
v

ACKNOWLEDGMENT 

 This report was prepared under a cooperative agreement between the Coastal Marine 

Institute (CMI) at Louisiana State University (LSU) and the U.S. Minerals Management Service 

(MMS). The Coastal Marine Institute was formed under a cooperative agreement between LSU 

and MMS, Department of the Interior. The CMI Program is coordinated through the 

Environmental Studies Program at the Gulf of Mexico Regional Office of MMS. DeWitt Braud 

in the Coastal Studies Institute at LSU was the Principal Investigator for the project. Chris Locke 

was the project coordinator at Research Planning, Inc (RPI). The senior authors were Katherine 

Born-Phillips and Jacqueline Michel of RPI. Jacqueline Michel and Colin Plank at RPI were the 

senior coastal geologists. 

 The Louisiana Universities Marine Consortium (LUMCON) provided lodging and boat 

service for the field work. Many thanks to Sam LeBouef for safe small craft piloting. Charlie 

Henry at NOAA and John Barras at the USGS are acknowledged for field work support. Steve 

Schill and David Grigg with GeoMetrics, Inc. assisted with imagery acquisition. 
vii

TABLE OF CONTENTS

 

Page 

List of Figures ................................................................................................................................ ix 

List of Tables ................................................................................................................................ xi 

Abbreviations, Acronyms, and Symbols ..................................................................................... xiii 

1.0 INTRODUCTION ...............................................................................................................1 

2.0 METHODS OF STUDY......................................................................................................2 

2.1 Introduction..............................................................................................................2

2.2 Image Acquisition ...................................................................................................2 

2.3 Generalization of Land/Water Interface .................................................................3 

2.4 In situ Data Collection .............................................................................................4 

2.5 Classification Methodology.....................................................................................4 

2.6 Shoreline Transfer....................................................................................................7 

2.7 Validation Overflights .............................................................................................9 

2.8 Accuracy Assessment ..............................................................................................9 

3.0 RESULTS ..........................................................................................................................10 

4.0 SUMMARY AND RECOMMENDATIONS FOR ADDITIONAL RESEARCH ...........41 

 4.1 Mud and Tidal Flats...............................................................................................41 

 4.2 Beaches and Manmade Structures .........................................................................42 

 4.3 Scrub-Shrub ...........................................................................................................43 

 4.4 Salt Marsh ..............................................................................................................43 

5.0 REFERENCES CITED......................................................................................................44 

APPENDIX A Classification Methodology .............................................................................. A-1 

APPENDIX B Detailed Classification Methodology for Vegetation Classes............................B-1 

APPENDIX C Accuracy Assessment.........................................................................................C-1 
ix

LIST OF FIGURES 

 

Figure Page 

 1 Entire study area and image swaths..................................................................................2

 2 IKONOS satellite image overlaid with vectorized land/water interface derived from the 

image.................................................................................................................................5 

 3 Image classification methodology ....................................................................................7 

 4 Generalization of the shoreline.........................................................................................8 

 5 Shoreline sampling intervals and buffers used to attribute the shoreline with the habitat 

classification. ....................................................................................................................8 

 6 Habitat characterization for the sample intervals and buffers ..........................................9 

7-a-d ESI 2A, Exposed wave-cut platforms in clay .................................................................11 

8-a-d ESI 2B, Exposed scarps and steep slopes in clay ...........................................................14 

 9-a-d ESI 3A, Fine- to medium-grained sand beaches.............................................................17 

10-a-d ESI 6A, Gravel (shell) beaches.......................................................................................20 

11-a-d ESI 6B, Riprap................................................................................................................23 

12-a-d ESI 7, Exposed tidal flats................................................................................................26 

13-a-d ESI 8B, Sheltered man-made structures .........................................................................29 

14-a-d ESI 9A, Sheltered tidal flats ...........................................................................................32 

15-a-d ESI 10A, Salt marshes ....................................................................................................35 

16-a-d ESI 10D, Scrub-shrub wetlands......................................................................................38 

17 Spectral plots for sand beaches, gravel beaches, and manmade structures ....................43 

B-1 Spectral plots for scrub-shrub and salt marshes in the four IKONOS bands ...............B-4 

B-2 False color image bands 4,3,2.......................................................................................B-5 

B-3 IKONOS Band 4 shown as 5 natural breaks.................................................................B-5 
x

B-4 NDVI of IKONOS imagery shown as 5 natural breaks................................................B-6 

B-5 IKONOS tasseled cap band 2 (greenness) shown as 5 natural breaks..........................B-6 

B-6 All four bands of the tasseled cap image ......................................................................B-7 

B-7 The brightness and greenness bands of the tasseled cap image....................................B-8 

B-8 Attributes of classified GVI..........................................................................................B-9 

B-9 Attributes of classified 4-band Image...........................................................................B-9 
xi

LIST OF TABLES 

Table Page 

 1 Acquisition details for the archived IKONOS imagery.......................................................3

 2 Standard ESI shoreline types mapped during the field work to collect in situ data ............5 

 3 Modified shoreline classification.........................................................................................6 

 4 Summary of the classification accuracy assessment..........................................................10 

 C-1 Overall error matrix .........................................................................................................C-4 

 C-2 Seaward error matrix .......................................................................................................C-6 

 C-3 Landward error matrix .....................................................................................................C-6 
xiii

ABBREVIATIONS, ACRONYMS, AND SYMBOLS 

cm centimeter 

CASI Compact airborne spectrographic Imager 

DGPS Differential global positioning system 

ESI Environmental Sensitivity Index 

GIS Geographic Information Systems 

GVI Greenness Vegetation Index 

G-WIS Gulf-Wide Information System 

km kilometer 

LDWF Louisiana Department of Wildlife and Fisheries 

LiDAR Light Detecting and Ranging 

LSU Louisiana State University 

LUMCON Louisiana Universities Marine Consortium 

m meter 

mm millimeter 

MMS Minerals Management Service 

NDVI Normalized Difference Vegetation Index 

NOAA National Oceanic and Atmospheric Administration 

OCS Outer Continental Shelf 

RPI Research Planning, Inc. 

T-Cap Tasseled Cap Transformation 
1

1.0 INTRODUCTION 

Environmental Sensitivity Index (ESI) mapping refers to a shoreline classification and 

sensitivity ranking system that has been a vital component of oil spill contingency planning and 

marine environmental assessment programs nation wide for 25 years (Halls et al., 1997). The 

U.S. Minerals Management Service (MMS) currently uses ESI data and the ESI classification 

scheme for environmental assessment studies related to Outer Continental Shelf (OCS) activities. 

Traditional ESI data development includes the interpretation of aerial photographs and 

mapped observations by coastal geologists during overflights. This method has been applied 

successfully to the majority of the U.S. coastline. The complex, rapidly changing shoreline of 

Louisiana, however, has made ESI mapping extremely difficult using traditional techniques. As a 

result, a coast-wide ESI shoreline classification has never been developed for Louisiana. This 

represents a major information gap, as oil spill risk and environmental consequences in 

Louisiana are great. 

 ESI classification efforts in Louisiana in the late 1980s relied on remotely sensed 

imagery with spatial resolution of 20-30 meters (Jensen et al., 1990). Although a useful landuse/land cover classification was achieved, the detail associated with the linear nature of 

shoreline features could not be resolved, and a true ESI shoreline classification was not possible. 

More recently, the barrier island beaches and other outer-coast features of Louisiana were 

classified using traditional ESI methods, as part of the Gulf-Wide Information System (G-WIS) 

project (MMS et al., 2001; Zengel and Hanifen, 2001; Zengel et al., 2002). This effort was 

possible due to the linear configuration of the outer coast. Beyond this, most of the Louisiana 

coast remains unclassified and cannot reasonably be completed using traditional methods. 

Newer, high-resolution satellite imagery offers the best opportunity for coast-wide ESI shoreline 

mapping and classification in Louisiana and other similar areas with highly complex, rapidly 

changing shorelines. 

 The primary objective of this research was to develop remote sensing classification 

procedures to support ESI mapping efforts in Louisiana and elsewhere. Several questions 

comparing remote sensing techniques and traditional classification methods were of interest 

including: 

• Are remote sensing methods reliable, more cost and time effective, and do they provide 

as much information as traditional methods? 

• Is the spectral and spatial resolution of the IKONOS satellite imagery chosen for the 

project appropriate? 

• Is the use of archived imagery for cost savings appropriate? 

• Can a viable land/water interface be created from the imagery? 

• Can an ESI style product useful for spill response and coastal management be produced? 

The saline areas of the Louisiana coastline from Port Fourchon to Lake Barre were 

chosen as the study site (Figure 1). Salinity boundaries were obtained from the 1997 Louisiana 

Coastal Marsh Vegetative Type Map (LDWF et al., 1997), which included values of brackish, 

fresh, intermediate, saline, and water. In this data set saline was defined as salt marsh having 

typical vegetation of oystergrass (spartina alterniflora), glasswort (avicennia germinans), and 

saltgrass (distichlis spicata). This area of coastal Louisiana has experienced high rates of land 

loss and rapid shoreline change that have made traditional ESI classification techniques difficult. 

However, the presence of extensive oil and gas infrastructure and highly sensitive habitats and 
2

natural resources in this area emphasizes the need for up-to-date maps for oil spill planning and 

response. 

2.0 METHODS OF STUDY 

2.1 Introduction 

The study methodology consisted of the following tasks: image acquisition, land/water 

interface production, in situ data collection, image classification, vector shoreline transfer, 

validation overflight, and an accuracy assessment. In contrast, a traditional ESI project 

methodology would consist of: identification of existing digital land/water or shoreline, 

overflight classification on topographic quads, field classification accuracy assessment, and onscreen ESI attributing of the digital shoreline. 

Figure 1. Entire study area and image swaths. The swaths outlined in bold comprise the 

study area for the ESI classification (see Table 1 for image details). 

2.2 Image Acquisition 

Archived IKONOS 4-band multispectral, 4-meter (m) spatial resolution imagery was 

selected for use in this study. It was hoped that the 4-meter pixel resolution of the IKONOS 
3

satellite would capture narrow features such as sand beaches that are missed by 20 and 30-meter 

resolution products. It was also hoped that archived imagery would prove cost-effective for a 

large study area and could thus allow for more frequent ESI updates. Seventeen images were 

collected for the study area and used for the creation of the land/water interface; three were 

replaced and not used because of extensive cloud cover. These three images are not labeled in 

Figure 1. A subset of the saline areas of eight images was chosen for the ESI classification. 

These eight images were acquired during different dates, seasons, tidal cycles, winds, and 

different atmospheric conditions (Table 1). The effects of these variables are discussed 

throughout this report. 

Table 1 

Acquisition Details for the Archived IKONOS Imagery 

Tile Index Acquisition Date Acquisition 

Time (GMT) 

Acquisition Time 

(Central 

Standard) am 

Tide 

A 14 1/7/2002 16:53 10:53 Mid 

B 13 1/7/2002 16:53 10:53 Mid 

C 12 12/27/2001 16:52 10:52 Low 

D 11 11/5/2001 16:56 10:56 Low 

E 9 3/11/2002 16:49 10:49 Low 

F 8 3/22/2002 16:50 10:50 High 

G 7 3/22/2002 16:50 10:50 High 

H 5 4/24/2002 16:52 10:52 High 

I 17 9/25/2003 17:03 11:03 

J 6 3/22/2002 16:50 10:50 

K 2 1/7/2002 16:53 10:53 

L 1 12/27/2001 16:52 10:52 

M 15 9/25/2003 17:03 11:03 

N 16 9/25/2003 17:02 11:02 

2.3 Generation of the Land/Water Interface 

 For planning and logistics the land/water interface provides a useful tool for coastal 

management and oil spill response. In the production of an ESI atlas, the land/water interface is 

an important component that represents the shoreline on which the habitat classifications are 

attributed and assists in data summary and legibility of the cartographic product. 

 Infrared light is strongly reflected by healthy vegetation and is highly absorbed by water, 

making it excellent for developing a land/water interface. The Landsat Thematic Mapper satellite 

has three infrared bands ranging from near to mid-infrared, and has been effectively used for 

developing water indices and models for land/water interface delineation. The mid-IR bands are 

particularly good for detection of wetness and water. The IKONOS satellite however, has but 

one infrared band in the near region, and consequently is less effective for water detection and 

land/water interface development. In addition, models developed using multiple IR bands for the 

Thematic Mapper cannot be directly applied to IKONOS. 
4

 The single IKONOS infrared band was initially level-sliced to derive a land/water 

threshold, but the result was not adequate. A normalized difference vegetative index (NDVI) was 

produced using the standard ratio of visible-red and infrared bands. The level-slice and threshold 

of the NDVI also proved inadequate for clearly separating land and water in the complex, low 

marsh environment of Louisiana. 

 Therefore, we resorted to a proven but time-consuming method -- the ISODATA 

unsupervised classification algorithm was employed using the green, red, and infrared bands of 

the IKONOS imagery with 75 initial classes. The ISODATA signatures were inspected for 

homogeneity and normality, and then processed with the Maximum Likelihood Decision Rule to 

classify the image. Probabilities were assigned based on class counts. This was completed 

independently for each image since they were acquired on different dates and had different water 

levels. The individual spectral classes were categorized into land or water to create a thematic 

land/water binary image. Confused classes (transition classes showing both land and water) were 

masked and differentiated by running ISODATA on the subset of image pixels assigned to the 

class - separating them into discrete spectral categories that were further defined as land or 

water. Islands of land or water smaller than .5 acres were eliminated from the classified image. A 

recursive cross-majority filter was applied to smooth the land/water interface edges and to 

minimize acute spurs. 

 The land/water classified raster images were converted to vector polygons using a rasterto-vector algorithm with a weed tolerance slightly smaller than the inherent cell size of the 

IKONOS imagery. The polygons were attributed with a grid code and class field, each polygon 

defining either a water body or land area. Finally, the polygon files were also topologically 

converted to lines representing the shore or land/water interface. Figure 2 shows the vectorized 

land/water interface displayed over a small portion of an original satellite image. 

 Two IKONOS scenes had problems with clouds. Multi-temporal images with broken 

clouds were acquired of each scene. The clouds were removed using a replacement model when 

cloud free imagery was present from at least one image. Clouds that could not be removed were 

classified as clouds when it could not be determined what was below them. 

2.4 In situ Data Collection 

During October 12-17, 2003, a team consisting of a coastal geologist and a remote 

sensing analyst visited 211 field sites to assist in the habitat imagery classification. Data 

collected included a GPS location, photos of the site, and a description of the morphology and 

vegetation. During the field work, the coastal geologist identified thirteen standard ESI classes 

(Table 2) that were present within the study area. Sites were chosen to cover all of the classes 

identified and were primarily visited by boat. 

2.5 Classification Methodology 

A series of image analysis techniques were applied to the imagery to test whether the 

standard ESI classifications could be extracted from the IKONOS imagery. After a series of 

algorithms and methodologies were tested and applied it was concluded that the same level of 

classification detail in the standard ESI could not be extracted from the IKONOS imagery. Based 

on this assessment, the coastal geologist created the modified shoreline classification (Table 3) 

that was used in the methodology below to create the final classifications. 
5

Figure 2. IKONOS satellite image overlaid with vectorized land/water interface derived 

from the image. 

Table 2 

Standard ESI Shoreline Types Mapped during the Field Work to Collect In Situ Data 

ESI Shoreline Type Definition 

1B Exposed solid man-made structures (e.g., seawall, bulkhead, etc.) 

2A Exposed wave-cut platforms in mud 

2B Exposed scarps or steep slopes in mud 

3A Fine to medium-grained sand beaches 

6A Shell beaches 

6B Exposed riprap 

7 Exposed tidal flats 

8A Sheltered scarps in mud 

8B Sheltered solid man-made structures (e.g., seawall, bulkhead, etc.)

8C Sheltered riprap 

9A Sheltered tidal flats 

10A Salt- to brackish-water marshes (including intermediate marshes) 

10D Scrub-shrub wetlands (including mangroves) 
6

Table 3 

Modified Shoreline Classification

Class ESI Class Examples 

Beaches and manmade 

structures 

1B, 3A, 6A, 6B, 8B, 8C Seawalls, urban infrastructure, 

sand beaches, gravel beaches, 

riprap 

Mud and tidal flats 2A, 2B, 8A, 9A, 7 Wave cut scarps in mud, 

exposed tidal flats, sheltered 

tidal flats, oyster beds 

Salt marsh 10A Spartina alterniflora, Spartina 

patens 

Shrub-scrub 10D Mangroves, vegetated banks, 

scrub-shrub 

The basic methodology for the modified shoreline classification is outlined in Figure 3. 

In Step 1, the image classification methodology was to clip the 4-band image to the saline area. 

In Step 2, an unsupervised ISODATA classification was run on the saline area of the image to 

separate the areas of land from areas of water. The ISODATA classification separated the pixels 

into spectrally similar groups. The pixels classified as land were masked out of the original 4-

band image, creating a new image with only the saline land areas. In Step 3, a Tasseled Cap 

Transformation (T-Cap) was run on the saline land image. The T-Cap is a vegetation index that 

is effective at distinguishing between vegetation and non-vegetation classes (Jensen, 1996; 2000; 

Horne, 2003). An unsupervised ISODATA classification was run on the T-Cap image to group 

the vegetation and nonvegetation classes. The areas of vegetation were masked out of the T-Cap 

image to create a vegetation image. The areas of nonvegetation were masked out of the original 

4-band image to create a nonvegetation image. In Step 4, an unsupervised ISODATA 

classification was run on the nonvegetation image. Results from this classification were used to 

determine the beaches and manmade structures class and the mud and tidal flats class. In Step 5, 

an unsupervised ISODATA classification was run on the vegetation image. Results from this 

classification were used to determine the scrub-shrub class and the salt marsh class. The in situ 

data points, field notes, and photographs were used to assign habitat values to the spectrally 

similar classes in steps three, four and five. The final step in the classification methodology was 

to merge the individual classification images back into a single image. 

 After the classification methodology was complete, a 3 by 3 smoothing algorithm was 

applied to the classification to reduce the amount of speckle. The smoothing algorithm was not 

applied to the beaches and manmade structures because these features are often narrow, short, 

and might be eliminated during smoothing. Refer to Appendix A and B for a detailed 

explanation of the classification procedures. 
7

Figure 3. Image classification methodology. 

2.6 Shoreline Transfer 

 The imagery classification results are inherently area classifications that place all pixels 

in the image into one of the defined classes. In the traditional ESI approach, classifications are 

attributed directly to the linear shoreline feature. This approach provides summarized data in an 

easy to interpret format for the spill responder and aids in creating cartographic products (e.g., an 

image can be placed underneath the linear classification giving further locational information to 

the responder). To emulate the traditional product, the area/polygonal image classification was 

transferred to the linear land/water interface created previously in the project. 

 The general methodology is presented in this section. First, the land/water interface was 

generalized to remove the "stair stepping" from the pixelated imagery. The generalization 

procedure removes line vertices within a specified tolerance to reduce the complexity of the 

shoreline. A tolerance of 5 meters was selected for the generalization, which produced a 

shoreline sufficient for viewing at the typical ESI scales between 1:10,000 and 1:24,000 (Figure 

4). Next, sample points were generated at 10 m intervals along the shoreline. Area of influence 

or Thiessen polygons were then generated for each sample point, and the shoreline was buffered 

at a 5 m interval in the water and 5, 10, and 20 meter intervals on the land (Figure 5). These 

Thiessen buffers were then overlaid with the polygonal ESI classification and were characterized 

by the maximum area of ESI type in each buffer (Figure 6). The results were then collapsed to 

the shoreline based on a locational ID on the Thiessen polygons. Finally, the collapsed results 

were formatted to the same style as the traditional ESI product. 
8

1:3,000

1:10,000

Figure 4. Generalization of the shoreline. The 1:3,000 frame shows the original land/water 

interface in black and the generalized shoreline in red. The 1:10,000 frame shows the 

same area at a smaller scale. The generalization removed the “stair step” effect by 

reducing shoreline detail. This reduction in detail is difficult to notice at the scales at 

which ESI maps are typically produced and used. 

Figure 5. Shoreline sampling intervals and buffers used to attribute the shoreline with the 

habitat classification. 
9

Figure 6. Habitat characterization for the sample intervals and buffers. 

2.7 Validation Overflights 

Overflights were conducted during 6-8 February 2004 to test the accuracy of the 

shoreline habitat classification. These flights covered images A through H, exclusive of E, which 

had a large amount of cloud cover (Figure 1). The overflights were conducted within four hours 

of low tide. The Traditional ESI classification (Table 1) were mapped during the flights onto 

plots of the images that had the modified classes of sand beach/man-made structures, salt marsh, 

and scrub-shrub attributed to the shoreline and the modified class of mud plotted as polygons. 

Image A and part of Image B were mapped on 6 February as a front was starting to come 

through and the water levels in the study area reflected normal lunar tidal influences. Images B, 

C, and D were mapped on 7 February under strong north wind conditions that created a “wind 

tide” that was markedly lower than the normal lunar tide. Many more tidal flats and wave-cut 

platforms were mapped during these lower water levels. Images F, G, and H were mapped on 8 

February, after the front had passed and the normal southeast wind pattern had returned, along 

with normal water levels. 

 

2.8 Accuracy Assessment 

An error matrix based on the length of shoreline in each class was created to assess the 

accuracy of the image classification against the classification mapped during the overflights and 

is shown in Appendix C. The overall accuracy of the classification was 98.02%. This accuracy 

assessment indicates that the methodologies outlined in this research are effective at classifying 

broad ESI classes. However, it is important to note that this is an assessment of the 

generalized/modified ESI categories, which contributed significantly to the high accuracy rating.
10

The classification accuracy assessment is summarized (Table 4) as the percentage of 

over- and under-classified modified classes on both the seaward and landward sides of the 

shoreline. The over and under classification refers to the percentage of error that was either over 

classified (user’s accuracy and commission error) or under classified (producer’s accuracy and 

omission error). Commission error is over inclusion of a particular class. Omission error is 

exclusion of a particular class (Jensen, 1996). A brief explanation of the classification error is 

listed in Table 4. Refer to Appendix C for the detailed accuracy assessment. 

Table 4 

Summary of the Classification Accuracy Assessment 

Modified Class Classification Seaward Landward Explanation 

Beaches and 

manmade structures 

Over 26.63% 7.60% Over classification was expected because 

this class was not filtered or smoothed 

 Under 8.56% 16.48% Features smaller than 4 meters could not be 

classified using the IKONOS imagery.* 

Mud and tidal flats Over 1.55% 3.23% Wet sand and “coffee ground” type features 

over classified as mud and tidal flats. 

 Under 1.20% 0.22% Under classification was expected from 

image tiles collected at higher tides. 

Scrub-shrub Over 16.38 % 14.47% Over classification is likely due to mixed 

classes of scrub-shrub and salt marsh. Small 

patches of salt marsh, other than the 

Spartina, such as fragmities, classified more 

like scrub-shrub than salt marsh. 

 Under 2.07% 2.69% Areas of under classified scrub-shrub 

occurred in the northern reaches of the 

imagery. Differences in species type could 

lead to under classification. 

Salt marsh Over 0.52% 0.19% A relatively small length of salt marsh was 

over classified on either seaward or 

landward sides of the shoreline 

 Under 1.63% 0.68% Small patches of salt marsh grow on the 

seaward side of other classification types. 

These small patches are too small to be 

classified from the IKONOS imagery. 

*Since the resolution of IKONOS multispectral imagery is 4 meters, it is not generally possible to develop 

spectral signatures for land cover classes smaller than the inherent resolution of the sensor. In fact, to develop 

statistically reliable signatures requires 25 or more pixels (samples). Individual isolated pixels are mixed with 

surrounding classes too much to reliably identify. These are usually misclassified. 

3.0 RESULTS 

The results section describes ten ESI habitat types that were observed during the field 

work and traditional overflights. For each ESI habitat type, the habitat characteristics are 

described, and the technical performance of the imagery and classifications techniques are 

discussed. The text is followed by a subset of the IKONOS imagery where the ESI habitat is 

present, a second set of images shows the imagery with the shoreline classification. An oblique 

aerial photograph, taken during the overflights, is shown for the same general area of the 

imagery, and finally a representative ground photograph is provided for each ESI habitat. 
11

EXPOSED WAVE-CUT PLATFORMS IN CLAY ESI = 2A

Habitat Description 

• The intertidal zone is a flat, muddy platform or bench of variable width. 

• The sediments may incorporate a high percentage of organic material and root masses 

from eroding salt marsh vegetation. Often the platform is covered by a thick 

accumulation of loose organic particles known as “coffee grounds.” 

• They are regularly exposed to moderate to high wave energy from wind-generated waves 

where they occur along water bodies with significant fetch towards the south and 

southeast. They most commonly occur along the outer Gulf coast or the seaward 

headlands of marsh islands on the north side of the larger bays. 

• There is always at least one other habitat present on the landward side, usually a sand 

beach, gravel (shell) beach, salt marsh, or scrub-shrub vegetation. Sometimes, there are 

two landward habitats present. Very occasionally there is an exposed tidal flat seaward of 

the platform. 

• In the aerial photograph in figure ESI 2A, note that the wave-cut platforms occur along 

the most exposed front sides of the marsh islands. With a paucity of sediments, the 

landward side of the platform consists of eroding salt marsh and scrub-shrub. 

Technical Performance of the Imagery Classification 

• The ability to accurately classify exposed wave-cut platforms in clay from IKONOS 

imagery is dependent on the tidal stage at which the imagery is acquired. The importance 

of tidal range is evident when comparing IKONOS imagery acquired at high vs. low tides 

and in comparison to aerial overflights at low tide (see appendices for details). The 

imagery shown in figure ESI 2A (A & B) was acquired at a higher tide than the 

validation overflights, which were conducted during a north wind event that created an 

even lower “wind tide.” 

• Based on the results from other image tiles acquired at low tides, it is reasonable to 

conclude that had this image been acquired at a lower tide, the tidal flats would have 

been accurately classified. 

• Recommendation: acquire imagery at the lowest tide possible. 

Figure 7(a-d) illustrates ESI 2A, Exposed wave-cut platforms in clay. 

 
12

Figure 7-a. False-color IKONOS imagery acquired April 24, 2002 (Figure 1, Tile H). 

Figure 7-b. The imagery classification was both mud and scrub-shrub. Where the platform was wide, it could 

be identified using the 4 meter imagery, but narrow platforms were classified as the landward 

vegetation. 
13

Figure 7-c. Aerial photograph of the area in figure A, but looking to the west, taken on 8 February 2004. 

Figure 7-d. Ground photograph (October 2003) of the area just west of the Port Fourchon jetties. Here sand 

beaches are eroding, exposing the salt marsh peat sediments and young mangrove vegetation. Also 

refer to figure ESI = 3A for the aerial photograph and imagery for this location. 
14

EXPOSED SCARPS AND STEEP SLOPES IN CLAY ESI = 2B

Habitat Description 

• Intertidal zone is a vertical scarp that is generally more than 1-1.5 m high and composed 

of muddy, consolidated sediments. 

• The sediments may incorporate a high percentage of organic material and root masses 

from eroding marsh vegetation. 

• They are regularly exposed to moderate to high wave energy, either from wind-generated 

waves where they occur along waterbodies with significant fetch towards the southeast or 

north, or from boat wakes in high-traffic corridors. 

• They are usually backed by scrub-shrub vegetation, although salt marshes and gravel 

(shell) beaches can also occur landward of the scarp. There is no shoreline type seaward 

of the scarp. 

• They are only mapped as a separate habitat type when the scarp is at least 1 m high. 

• They are uncommon along the coast of Louisiana. Along the most exposed shorelines, 

the waves cut a flat bench or platform rather than a vertical scarp, so the scarps tend to 

occur on the edges of marsh islands, along inner waterbodies, and along high-traffic 

canals. 

• In Figure ESI 2B, the tip of the small island was mapped as a double shoreline, with 

scrub-shrub fronted by exposed scarps and steep slopes in clay (10D/2B). 

Technical Performance of the Imagery Classification 

• The IKONOS imagery could not extract vertical features, such as ESI class 2B. Rather, 

exposed scarps are classified as the habitat type on top of the scarp. 

• In instances where the scarp is sloping and/or not covered by vegetation, that area could 

conceivably be classified. 

• This ESI class is less dependent on the tidal stage for accurate mapping. 

• Recommendations: To accurately classify ESI 2B, a methodology that can extract 

vertical features is necessary. It is possible that the addition/use of ancillary elevation 

data or imagery that contains a vertical component could improve the classification. With 

the advent of LIDAR derived elevations for coastal Louisiana, this is highly possible. 

Figure 8(a-d) illustrates ESI 2B, Exposed scarps and steep slopes in clay. 
15

Figure 8-a. False-color IKONOS imagery, obtained on March 22, 2002 (Figure 1, Tile F). 

Figure 8-b. The imagery classification was scrub-shrub. Because of the narrow, vertical scarp, this habitat type 

cannot be identified using the 4 m imagery. 
16

Figure 8-c. Aerial photograph taken on 8 February 2004. The arrow points to the small island that was mapped 

during the overflights as both shrub-shrub and exposed scarps (10D/2B). 

Figure 8-d. Ground photograph of an exposed scarp along a waterway (October 2003). The scarp is generally 

vertical, composed of consolidated, muddy sediments. Note the eroding blocks of mud at the base 

of the scarp and the exposed roots of live vegetation, indicating active erosion. No habitat type 

occurs seaward of the scarp. Scrub-shrub habitat usually occurs on the landward side of the scarp. 
17

FINE- TO MEDIUM-GRAINED SAND BEACHES ESI = 3A

Habitat Description 

• Intertidal zone is composed of a wide, flat, and hard-packed sand beach. 

• The sediments are very mature, consisting mostly of quartz grains and shell fragments. 

• They are exposed to moderate to high wave energy. 

• They are usually backed by low dune vegetation because, in most places, they are 

actively eroding across an older marsh surface. 

• In areas of high erosion, they are fronted by a wave-cut platform in clay (ESI = 2A). 

• They are common along the outer coast of Louisiana near sources of sand sediments, 

such as on barrier island or downdrift of headlands created by active or relict 

distributaries. 

Technical Performance of the Imagery Classification 

• Dry sand beaches exhibit spectrally bright values similar to manmade structures (asphalt 

and concrete) and clouds. IKONOS imagery is able to distinguish between spectrally 

bright features from all other feature types. However, the 4-band multispectral resolution 

of the IKONOS imagery is not high enough to distinguish between geographic features of 

similar spectral qualities, such as sand, manmade structures, and clouds (see section 4.0 

for details). 

• Narrow beaches were not picked up by the imagery and classified as the surrounding 

habitats. 

• Submerged or damp sand were sometimes classified as mud and tidal flat. 

• Recommendations: More robust sensors, such as hyperspectral and/or airborne sensors, 

may be able to distinguish between different substrate types such as course sand, mud, 

and gravel beaches and manmade features. 

Figure 9(a-d) illustrates ESI 3A, Fine- to medium-grained sand beaches. 
18

Figure 9-a. False-color IKONOS imagery, acquired on January 7, 2002 (Figure 1, Tile A). 

Figure 9-b. The imagery classification was sand beaches and man-made structures. Sand beaches appear very bright 

and are readily mapped using the imagery. This area was mostly classified as beaches and manmade 

structures. The jetty to the right was not classified and some areas of damp sand were classified as mud 

and tidal flat. 
19

Figure 9-c. Aerial photograph taken on 6 February 2004. The beach occurs as a thin layer of sand that is being 

washed onto the old marsh surface as the shoreline erodes. 

Figure 9-d. Ground photograph of the beach west of Port Fourchon (October 2003). This area was mapped as a 

double shoreline, with a fine-grained sand beach fronted by a wave-cut platform in clay (3A/2A). The 

width of both features makes them readily mapped using the IKONOS imagery.
20

GRAVEL (SHELL) BEACHES ESI = 6A 

Habitat Description 

• Gravel beaches are composed of sediments larger than 2 mm that are mostly made up of 

shell (oyster) fragments in Louisiana. 

• The slope of the intertidal zone is intermediate to steep, compared to sand beaches. 

• Along the outer coast, they occur as multiple, wave-built berms and washovers usually 

forming the upper beach. Washovers are mounds of shells deposited by storm waves on 

top of the land surface above the normal high tide. 

• Along inner water bodies they are common as perched berms and washovers on scarps 

along high-traffic channels and canals where boat wakes provide the wave energy. 

Technical Performance of the Imagery Classification 

• Gravel Beaches reflect brightness values that are similar to sand and shell beaches and 

riprap and other man-made structures. The spectral signature of these ESI types can be 

identical, making distinguishing between the features unfeasible with the 4-band, 4-meter 

IKONOS imagery (see section 4.0 for details.) 

• Comparing the unclassified image (Figure A) with the classified image (Figure B), the 

long beach was classified correctly. However, the patches of beach to the right of the 

long beach, did not classify as accurately. 

• Recommendations: More robust sensors, such as hyperspectral and/or airborne sensors, 

may be able to distinguish between different substrate types such as course sand, mud, 

and gravel beaches. A sensor with a higher spatial resolution may be able to pick up the 

smaller beaches that were missed by the IKONOS imagery.

Figure 10(a-d) illustrates ESI 6A, Gravel (shell) beaches. 
21

Figure 10-a. False color IKONOS imagery, acquired on January 7, 2002 (Figure 1, Tile A). 

Figure 10-b. The imagery classification was beaches and man-made structures. The long beach correctly 

classified as a beach or manmade structure. 
22

Figure 10-c. Aerial photograph taken on 6 February 2004 of a gravel (shell) beach along Bayou Lafourche. 

The bright white oyster shells occur as washover berm on the marsh surface created by large boat 

wakes along waterways with heavy traffic by large vessels. 

Figure 10-d. Ground photograph (October 2003) showing the steep nature of the intertidal zone typical of 

gravel beaches. 
23

RIPRAP ESI = 6B 

Habitat Description 

• Riprap consists of man-made structures composed of cobble- to boulder-sized rock 

fragments or other miscellaneous debris. 

• They are called revetments when used onshore for shoreline protection, groins when used 

to trap sediments, jetties when used for inlet stabilization, breakwaters when used 

offshore, and canal plugs when used to close off pipeline and other types of canals 

(Figure C). 

• Riprap forms a steep slope (Figure D), the structures are usually large (several meters 

wide) to be effective. 

• Riprap is relatively common in developed areas, particularly along the larger navigational 

channels and associated with industrial sites (e.g., ports and oil facilities) 

Technical Performance of the Imagery Classification 

• The classification procedures were able to accurately classify these features shown in 

images below, as noted by the double-ended arrow. The single arrow is pointing to a thin, 

linear riprap feature, similar to that shown in the ground photo, but not shown in the 

unclassified imagery. Thus, the classification procedures inaccurately mapped a linear 

beach and man-made structure that does not appear in the unclassified imagery. 

• The narrow width of the features, algal growth on such features, and the vertical 

characteristic are possible reasons for the misclassification of these features. 

• Recommendation: A sensor with higher spatial and spectral properties may improve the 

classification results of riprap (refer to Section 4.0 for details). 

Figure 11(a-d) illustrates ESI 6B, Riprap. 
24

Figure 11-a. False color IKONOS Imagery, acquired on January 7, 2002 (Figure 1, Tile A). 

Figure 11-b. The imagery classification was beaches and man-made structures. The section of riprap in this 

figure was correctly classified as beaches or man-made structures, denoted by the double arrow. 
25

Figure 11-c. Aerial photograph taken on 7 February 2004 of riprap used to plug the pipeline canals. Note the 

gravel beach to the east (right) of the structure, and that it was not possible to differentiate 

between the riprap and gravel using the IKONOS imagery classification. 

Figure 11-d. Ground photograph of riprap along Bayou Lafourche, a heavily trafficked waterway (October 

2003). Note the steepness of the intertidal zone and the typical width of the structure. 
26

EXPOSED TIDAL FLATS ESI =7 

Habitat Description 

• These are flat, unvegetated intertidal areas, composed primarily of sand with some mud 

and shell, that vary in width from a few to hundreds of meters. 

• The presence of sand indicates that tidal or wind-driven currents and waves are strong 

enough to mobilize the sediments. Therefore, they are located mostly along the outer 

coast where they are exposed to waves (Figure C). 

• They are usually associated with another shoreline type on the landward side of the flat. 

• Exposed tidal flats are widest where they are associated with sand beaches, such near 

Port Fourchon (Figure D). 

Technical Performance of the Imagery Classification 

• The largest influence on the mapping of flats was the tidal stage at which the imagery 

was acquired. 

• This imagery was acquired during a tidal cycle low enough to accurately classifying the 

tidal flats. 

• Recommendations: When the imagery is acquired during lower tides, the classification 

process is effective at accurately classifying tidal flats. For consistent mapping between 

imagery tiles, it is important to collect imagery within the same tidal range. 

Figure 12(a-d) illustrates ESI 7, Exposed tidal flats. 
27

Figure 12-a. False Color IKONOS imagery, acquired on November 5, 2001(Figure 1, Tile D). 

Figure 12-b. The imagery classification was mud and tidal flats. The classification correctly mapped the 

extensive flats in this figure. 
28

Figure 12-c. Aerial photograph of exposed tidal flats along a marsh island on the outer coast, taken on 7 

February 2004. The view is looking northeast. Exposed tidal flats are widest on either side of the 

small peninsula. 

Figure 12-d. Ground photograph of the wide sandy tidal flat near the jetties at Port Fourchon (October 2003). 

Such wide flats are associated with barrier islands and headlands. 
29

SHELTERED MAN-MADE STRUCTURES ESI = 8B 

Habitat Description 

• Sheltered man-made structures include revetments, seawalls, piers, and docks typically 

constructed of impermeable materials such as concrete and wood. 

• They occur inside harbors and in front of developed properties (camps and oil facilities) 

along canals and waterways. 

• They are usually vertical structures and do not occur with other shoreline types. 

 

Technical Performance of the Imagery Classification 

• Seawalls such as those shown in the overflight and field photos are not easily 

distinguishable in IKONOS imagery. The widths of seawalls are too narrow to be 

identified using 4 m spatial resolutions. 

• The classification procedures were unable to accurately detect these features. 

• Recommendation: A sensor with higher a spatial resolution may enable these linear 

seawalls to be classified correctly. 

Figure 13(a-d) illustrates ESI 8B, Sheltered man-made structures. 
30

Figure 13-a. False Color IKONOS imagery, acquired on January 7, 2002 (Figure 1, Tile B). 

Figure 13-b. The imagery classification was beaches and man-made structures. The classification procedures 

did not accurately classify the seawalls. The seawalls were classified as the neighboring habitats. 
31

Figure 13-c. Aerial photograph taken on 6 February 2004, with a view that is very similar to B. Vertical 

seawalls and docks have been built in front of the houses. There is water on the seaward side and 

developed land on the landward side.

Figure 13-d. Ground photograph (October 2003) of a seawall that is part of a structure to plug a pipeline canal. 

In this case, there is salt marsh behind the seawall. Seawall and riprap often intermixed along 

developed areas
32

SHELTERED TIDAL FLATS ESI = 9A 

Habitat Description 

• Sheltered tidal flats are composed primarily of mud with minor amounts of sand and 

shell. 

• They are present in calm-water habitats, sheltered from waves and strong tidal currents. 

They occur along channels in salt marshes and as open, unvegetated areas within salt 

marsh habitat. 

• They can have heavy wrack deposits along the upper fringe. 

• The intertidal exposure of the tidal flat is strongly dependent on water level, which is 

affected by both astronomical and wind-generated tides. During strong north wind 

events, water is driven out of the shallow bays, exposing wide tidal flats. During normal 

southeast winds, most of the flats are covered by water. 

Technical Performance of the Imagery Classification 

• The classification of shallow water tidal flats is dependent on the tidal stage during which 

the imagery was acquired. 

• The sheltered (9A) and exposed (7) tidal flats classified the same. An exposure index 

applied to the classified image and/or shoreline can refine this broad mud and tidal flat 

class into exposed or sheltered tidal flats. 

• Oyster beds embedded in the mud and tidal flats can exhibit brighter spectral properties 

and lead to mud and tidal flats classified as beaches and man-made structures. 

• Recommendation: When feasible, acquire the imagery during the low tides. Use an 

exposure index to further define the tidal classes. 

Figure 14(a-d) illustrates ESI 9A, Sheltered tidal flats. 
33

Figure 14-a. False color IKONOS imagery, acquired on January 7, 2002 (Figure 1, Tile B), at low tide. 

Figure 14-b. The imagery classification was mud and tidal flats. The classification procedures successfully 

classified the muddy tidal flats on this low-tide imagery. 
34

Figure 14-c. Aerial photograph taken on 7 February 2004 during low tide and a strong north wind weather 

condition. Note that extensive areas of sheltered tidal flats are exposed. For orientation, note the 

canal in the right center of both the imagery and oblique aerial photograph. 

Figure 14-d. Ground photograph (october 2003) of a sheltered tidal flat fringing salt marsh. The sediments are 

generally dark and organic rich. There can contain scattered patches of oysters. 
35

SALT MARSHES ESI = 10A 

Habitat Description 

• These are intertidal wetlands consisting of emergent, herbaceous vegetation that is salt 

tolerant. 

• Marshes vary in distribution from extensive areas of dense vegetation to broken marsh to 

fringing marshes. 

• Near the coast, small mangroves occur along the marsh fringe, where mangrove seedlings 

have stranded and taken root. Mangroves are generally killed every few years when a big 

frost occurs. 

• Marsh soils range from fine sands to mud, to organic-rich peats. 

Technical Performance of the Imagery Classification 

• Overall, the classification of salt marsh was effective, except in the highly broken up 

areas of marsh. The imagery below shows areas of broken marsh that were misclassified 

as mud and tidal flats. 

• Vegetation indices were used to classify the vegetated areas. The Greenness Vegetation 

Index, band 2 from the Tasseled Cap transformation, successfully distinguished salt 

marsh habitat from scrub-shrub habitat. 

• The salt marsh habitat is the dominant habitat throughout the study area. 

• Areas of exposed salt marsh and/or areas where the edge of the salt marsh had been 

flattened were more difficult to classify than the other areas of salt marsh habitat. 

• Recommendations: Acquire imagery at the height of the phenological cycle for the 

dominant vegetation. Apply additional algorithms to areas of broken marsh to improve 

the classification. 

Figure 15(a-d) illustrates ESI 10A, Salt marshes. 
36

Figure 15-a. False Color IKONOS Imagery, acquired on January 7, 2002 (Figure 1, Tile A). 

Figure 15-b. The imagery classification was salt marsh. The classification of salt marsh in this area was 

successful. Some of the areas of broken marsh were misclassified as mud and tidal flats. 
37

Figure 15-c. Aerial photograph taken on 7 February 2004 showing areas of extensive and broken marsh. For 

orientation, the small pond with a narrow channel on the east (lower) side of the channel in the 

middle of the imagery is also visible in the middle of the photograph, though the view is much 

more oblique. 

Figure 15-d. Ground photograph (October 2003) of salt marsh habitat. 
38

SCRUB-SHRUB WETLANDS ESI = 10D 

Habitat Description 

• Scrub-shrub wetlands consist of salt-tolerant woody shrubs that grow 1-3 meters high. 

• Their lower limit is above the high-tide line because they do not tolerate regular saltwater inundation. 

• The width of the vegetation can range from one tree to a many kilometers. 

• They are common along canals on the side where the spoil was broadcast because the 

land elevation is higher. 

Technical Performance of the Imagery Classification 

• Overall, scrub-shrub vegetation was relatively easy to extract from the IKONOS imagery, 

however some images produced better results than others. The spectral signatures of 

vegetation are distinctly different from non-vegetation types. Within the study area in 

Louisiana, the main vegetation present was shrub-scrub and salt marsh. These two 

vegetation types have similarly shaped spectral profiles, with the main distinguishing 

factor being the near-infrared band (band 4). Refer to Appendix B for details. 

• Recommendations: Identify the vegetation cycles of the dominant vegetation types (saltmarsh and shrub-scrub) and acquire imagery when the vegetation cycles are the most 

different. Use additional image processing techniques, such as texture or height analysis 

to distinguish between salt marsh and scrub-shrub. Further analysis may be able to 

distinguish between mangroves and other types of scrub-shrub. Elevation data may be 

useful at distinguishing between scrub-shrub wetlands and vegetated banks. The 

classification could also be improved by using additional infrared spectral bands, 

applying a different classification algorithm, or by using all the T-Cap bands in the 

classification. 

Figure 16(a-d) illustrates ESI 10D, Scrub-shrub wetlands. 
39

Figure 16-a. False color IKONOS image, acquired on January 7, 2002 (Figure 1, Tile A). 

Figure 16-b. The imagery classification was scrub-shrub. Scrub-shrub areas are easily identified in the imagery 

and appear and brighter reds at higher elevations. 
40

Figure 16-c. Aerial photograph taken on 6 February 2004. The riprap canal plug in the distance in the 

photograph is visible as a bright line in the middle of the imagery. Scrub-shrub wetlands were 

readily differentiated from other wetland types, as can be seen by comparing the classified 

imagery and the aerial photograph. 

Figure 16-d. Ground photograph (October 2003) showing the slightly supratidal elevation of the scrub-shrub 

vegetation and the typical mix of some salt marsh on the seaward side. 
41

4.0 SUMMARY AND RECOMMENDATIONS FOR ADDITIONAL RESEARCH 

The spatial and spectral resolutions of IKONOS imagery adequately captured four broad 

habitat classes, but did not extract the same level of detail as traditional ESI mapping methods. 

The usefulness and applicability of these broad ESI classifications are dependent on the question 

at hand. For oil spill response, the relatively current imagery provides a valuable base map that is 

greatly improved over other available maps. This feature is particularly important in coastal 

areas experiencing rapid shoreline change. One of the biggest challenges of responders in these 

areas is access to maps that accurately reflect current conditions and shoreline locations so that 

equipment and personnel can be deployed to appropriate cleanup sites. Currently, responders 

obtain oblique digital photography during overflights, then print out the photographs to use as 

base maps for deploying equipment and mapping the distribution of oil. Recent imagery would 

provide a more uniform base map. Creation of an actual shoreline would also be of value to spill 

responders, since miles of shoreline oiled is a common metric used to measure the spill impact 

on linear shoreline types and track cleanup progress. 

 Both archived and new-collect imagery were used in this study. The average price for 

archived imagery was $6.85 per km2 whereas the average price for new-collect imagery was 

$12.89 per km2. The archived imagery is significantly cheaper, however, the seasons and tides 

cannot be chosen and fieldwork cannot coincide with the collection of the imagery. On the other 

hand, it is still difficult to predict and plan for new-collect satellite imagery to coincide with the 

desired seasons, tides and cloud free days, and the small swath width of the IKONOS satellite 

cannot collect imagery for a large area in a single pass. 

 General recommendations to improve the classification of all ESI habitat types include 

the use of more robust sensors, sensors that can extract vertical structures, ancillary elevation 

data, texture analysis, and an exposure index. The Compact Airborne Spectrographic Imager 

(CASI and CASI 2) and Light Detecting and Ranging (LiDAR) are robust airborne sensors that 

have the potential to extract ESI classes with greater detail. Larsen and Erickson (1998) used the 

CASI sensor to distinguish between substrate types such as course sand, mud, and gravel 

beaches and man-made features. LiDAR collects elevation data, which may be useful in 

classifying vertical structures. Applying an exposure index to the shoreline can further define the 

modified classes into sheltered and exposed. 

4.1 Mud and Tidal Flats 

The tide at which the imagery was acquired significantly affected the accuracy of the 

mud and tidal flat mapping. IKONOS imagery accurately classified mud and tidal flats when the 

imagery was acquired at low tide. When the imagery was acquired at high tides, few flats were 

classified. The validation overflights, conducted during low tide, confirmed the extensive areas 

of mud and tidal flats found within the coastal region of Louisiana. 

As identified in this research, imagery acquired at the lowest tide possible will improve 

the classification of mud and tidal flats. When using archived imagery, there is less option for 

choosing the tide. Commissioning imagery at specific dates and/or time, or collecting archived 

imagery based on tidal stage predictions would be difficult to implement. Within the study area, 

a north-south tidal gradient of ebb and flow was evident in the imagery. Thus, acquiring imagery 

where the entire image is at low tide may not be possible. 

One of the objectives of this study was to determine the feasibility of using archived 

imagery since it is cheaper. The limited selection available with regard to tidal conditions is 
42

obviously a drawback. It would be better to use recently collected or commissioned imagery to 

target low tides, however, this is more expensive. There will be more options available in the 

near future as more high-resolution satellites are being developed and digital photography is 

becoming a competitive alternative. These platforms will provide pricing competition, better 

selection, improved capability, and more opportunity to acquire imagery at desired conditions. 

Future missions should consider these possibilities. 

4.2 Beaches and Manmade Structures 

The modified ESI class of beaches and man-made structures was the most difficult and 

least accurate classification type. The spatial and spectral resolutions of IKONOS imagery are 

not as effective at distinguishing between spectrally bright features and/or narrow or vertical 

structures. Gravel and sand beaches, urban infrastructures, riprap, and seawalls all exhibit similar 

reflectance values within the multi-spectral IKONOS imagery. Figure 7 shows the overlapping 

spectral curves of known sand beaches, gravel beaches, and solid man-made structures in Tile A 

of the study area (Figure1). These overlapping spectral values are one reason these ESI classes 

could not be distinguished from each other. 

Another problem with classifying this habitat type was the suspended sediment in 

shallow water. Suspended sediment in shallow water has a higher radiant flux back to the 

atmosphere than nonturbid, clear water. Water with high concentrations of plankton and/or other 

organic matter also has a higher radiant flux than nonturbid water (Jensen, 2000). This higher 

radiant flux can lead to misclassification of the shallow water and/or turbulent water as a mud or 

tidal flat or beaches and man-made structures. In addition, the glare that reflects off the top of 

mud and tidal flats appears bright and is misclassified as beaches and man-made structures. 

The 4 m spatial resolution was not precise enough to consistently distinguish the linear 

characteristics of small beaches, riprap and other manmade structures. Some areas of riprap, 

usually multiple pixels in length and width, were classified as the modified ESI class of beaches 

and manmade structures while many areas of small beaches and/or manmade structures were not 

classified at all. Because these small habitats were not detected, they were classified as the 

dominant class surrounding them. 

Because the spectral properties alone cannot distinguish between the beaches and 

manmade structures, other methods should be tested to improve the classification of beaches and 

manmade sturctures: 

• Higher spectral and spatial resolutions 

• Texture analysis 

• Ancillary data 

Orun (2004) applied a texture analysis to distinguish between manmade and natural 

features using 5 m imagery. Initial investigation indicates that a texture analysis would increase 

the ability to distinguish between beaches and manmade structures, and potentially could 

distinguish between sand beaches and gravel beaches. Research into this approach may result in 

a methodology to distinguish between gravel beaches, sand beaches, and manmade features. A 

geometry analysis to distinguish between beaches and manmade features was unsuccessful 

because many small, linear beaches had similar dimensions as riprap and seawalls. 
43

Figure 17. Spectral plots for sand beaches, gravel beaches, and manmade structures. 

4.3 Scrub-Shrub 

Overall, the habitat class of scrub-shrub was easily identified and classified from the 

IKONOS imagery. Recommendations to refine the classification include: 

• Identifying the vegetation cycles of the dominant vegetation types (salt marsh and 

shrub-scrub) 

• Acquire imagery when the vegetation cycles are the most different 

• Applying a texture algorithm or elevation data may assist in further distinguishing 

between salt marsh and vegetated banks, mangroves and other scrub-shrub. 

4.4 Salt Marshes 

 Salt marsh was the dominant habitat class found within the study area. Overall, the 

classification of salt marsh habitats was successful. Research into the phenological cycles of 

scrub-shrub and salt marshes in Louisiana may be able to identify the month in which the 

phenological cycles are the most different. The classification of areas of broken marsh was less 

accurate than areas of solid salt marsh. Adapting a new methodology for areas of broken marsh 

may improve the classification results, particularly using all T-Cap bands or using more spectral 

bands. In addition, broken marsh areas could be defined by patch analysis. 
44

5.0 REFERENCES CITED 

Halls, J., J. Michel, S. Zengel, J.A. Dahlin and J. Petersen. 1997. Environmental sensitivity index 

guidelines, version 2.0. NOAA Technical Memorandum NOS ORCA 115, Seattle, 

Washington. 79 pp. plus appendices. 

Horne, J.H. 2003. A tasseled cap transformation for IKONOS images. In: ASPRS 2003 Annual 

Conference Proceedings in Anchorage, Alaska. 

Jensen, J.R., E.W. Ramsey, J.M. Holmes, J. Michel, B. Savitsky, and B.A. Davis. 1990. 

Environmental sensitivity index (ESI) mapping for oil spills using remote sensing and 

geographic information system technology. International Journal of Geographic Information 

Systems 4(2):181-201. 

Jensen, J.R. 1996. Introductory digital image processing: A remote sensing perspective. Prentice 

Hall, Upper Saddle River, New Jersey. 

Jensen, J.R. 2000. Remote sensing of the environment: An earth resource perspective. Prentice 

Hall, Upper Saddle River, New Jersey. 

Larsen, P.F. and C.B. Erickson. 1998. Applications of remote sensing and geographical 

information systems for marine resources management in Penobscot Bay, Maine: Intertidal 

habitat definition and mapping in Penobscot Bay. Report submitted to Island Institute by 

Bigelow Laboratory for Ocean Sciences. 

Louisiana Department of Wildlife and Fisheries (LDWF), Fur and Refuge Division; U.S. 

Geological Survey, Biological Resources Division's National Wetlands Research Center; and 

Department of Geography and Anthropology, Louisiana State University. 1997. 1997 

Louisiana coastal marsh vegetative type map, geographic NAD83, LDWF, NWRC, LSU 

(1997) [salinity]: Louisiana Department of Wildlife and Fisheries, Fur and Refuge Division, 

and the U.S. Geological Survey's National Wetlands Research Center, Lafayette, Louisiana. 

Minerals Management Service, Louisiana State University, Louisiana Department of Wildlife 

and Fisheries, and Research Planning Inc. 2001. Gulf-wide information system, Louisiana: 

Outer coast environmental sensitivity index (ESI). U.S. Department of the Interior, Minerals 

Management Service, New Orleans, Louisiana. Arc/Info coverage and metadata. 

Orun, A.B. 2004. Automated identification of manmade textural features on satellite imagery by 

Bayesian Networks. Photogrammetric Engineering & Remote Sensing. 70 (2):211-216. 

Zengel, S. and J. Hanifen. 2001. Louisiana G-WIS: ESI, wildlife, and fisheries components. 17th 

Annual Louisiana Remote Sensing and GIS Conference, Baton Rouge, Louisiana, April 2-4, 

2001. 
45

Zengel, S., Z. Nixon, C. Plank, J. Hanifen, D. Sa, and D. Braud. 2002. Environmental sensitivity 

mapping and GIS: The Louisiana G-WIS database. Fourth Biennial Freshwater Spills 

Symposium in Cleveland, Ohio, March 19-21, 2002. 
A- 1

APPENDIX A 

Classification Methodology 
A- 3

General Polygonal Classification methods of IKONOS imagery: 

1. Determine Study Area: 

a. Clip out original imagery to the upper salinity boundary. All imagery from here 

on out is clipped to this salinity boundary. 

2. Determine Land vs. Water: 

a. Run an unsupervised classification (3-10 cl; depended on the image) on the 

original image to classify the water vs. land areas. 

b. Once the water/land distinction is made, mask out the water and land areas to 

create three images: 

- Mask of original image of water area only. 

- Mask of original image of land area only. 

- Classified image of water area with one class: water. 

3. Using the land area mask of the original image, determine a vegetation class vs. a 

nonvegetation class. 

a. Run tasseled cap on the land area masked of the original image. 

b. Subset out band 2 (greenness) from the 4-band tasseled cap image. 

c. Run unsupervised classification (20-50 depending on the image) on band 2 

(greenness) band of the tasseled cap image to determine a vegetation class from a 

nonvegetation class. 

d. Mask out the vegetation and nonvegetation classes from the unsupervised image. 

e. This step results in the following images: 

- 4-band tasseled cap image of the masked out land area of the original 

image. 

- Subset of band 2 (greenness) from the tasseled cap image. 

- Unsupervised classification of the Subset of band 2 (greenness) from the 

tasseled cap image. 

- Mask of vegetation class from the classified image. 

- Mask of nonvegetation class from the classified image. 

4. Classify the nonvegation areas: 

a. Mask out the nonvegetation areas from the original 4-band image that was clipped 

to the saline boundary. 

b. Run an unsupervised classification (usually 20 class) on the 4-band image of the 

nonvegetation areas to determine the beaches and manmade structures class and 

the mud and tidal flats class. 

c. Use the field collected GPS points to assign class names to the unsupervised 

classification. 

d. Once the class names have been assigned, mask out the beaches and manmade 

structures class and the mud and tidal flats class. 

e. This step results in the following images: 

- Mask of the nonvegetation area from the original 4-band clipped image. 

- Classified image of the nonvegetation areas. 
A- 4

- Mask of Classified image of the beaches and manmade structures 

- Mask of Classified image of the mud and tidal flats 

5. Classify the vegetation areas: 

a. Mask out the vegetation areas from band 2 (greenness) of the tasseled cap image. 

b. Run an unsupervised classification (usually 20cl) on band2 of the tasseled cap 

image mask of the vegetation areas to determine salt marsh from shrub-scrub. 

c. Use the field collected GPS points to assign class names to the unsupervised 

classification. 

d. Once the class names have been assigned, mask out the salt marsh and shrubscrub classes. 

e. This step resulted in the following images: 

- Mask of the vegetation areas from the band 2 of the tasseled cap image. 

- Classified image of the vegetation areas. 

- Mask of classified image of the salt marshes 

- Mask of classified image of the shrub-scrub. 

6. Overlay the individual classification layers (water, beaches and manmade structures, mud 

and tidal flats, salt marsh, and shrub-scrub) into one classification image. 

7. Use the 3 by 3 neighborhood function in ERDAS to ‘smooth’ out the speckeled nature of 

the classified image, excluding the beaches and manmade structures class from the 

smoothing algorithm. The beaches and manmade structures class is not smoothed because 

these features are often small and/or narrow and this smoothing could result in the loss of 

these important features. 

8. Subset out the smoothed mud and tidal flats class. Convert the mud and tidal flats image 

into a polygon. This is the flats polygonal coverage. 
B- 1

APPENDIX B 

Detailed Classification Methodology for Vegetation Classes 
B- 3

 Initial unsupervised classification results were inconsistent in the classification of salt 

marsh and scrub-shrub habitat types. Fig. B-1 illustrates the overlapping spectral values of salt 

marsh and scrub-shrub that exist within the red and near infrared band. Healthy, green vegetation 

typically exhibits a strong spectral signal in the red and near-infrared bands. The relationship 

between the red and near-infrared band led to the development of remote sensing vegetation 

indices (Jensen, 2000). Vegetation indices are a dimensionless, radiometric measure of green 

vegetation that has been successful at classifying vegetation (Jensen, 1996; 2000). Numerous 

vegetation indices exist, including the Normalized Difference Vegetation Index (NDVI) and the 

Tasseled Cap transformation. Both the NDVI and Tasseled Cap were used on the IKONOS 

imagery to identify a methodology that would more accurately classify salt marsh and scrubshrub habitats. 

As shown in Fig. B-1, the main distinguishing factor between the scrub-shrub and salt 

marsh is the spectral reflectance in the near-IR band. The spectral values for each of the other 

three bands have relatively similar spectral profiles. Checking the imagery locations where a 

field verified scrub-shrub was classified as a salt marsh, the spectral reflectance for band 4 in 

those locations was closer to the spectral value of the salt marsh than the scrub-shrub. This event 

occurred relatively infrequently. 

Figure B-2 indicates areas of scrub-shrub and salt marsh. The scrub-shrub frequently 

occurs along the edge of the canal and the salt marsh predominately occurs in the inner area of 

the marsh and along the edge of the tidal creeks. Figure B-3 shows the near-infrared band with 

five statistical breaks, with the darkest colors mostly scrub-shrub areas. The NDVI created for 

the image, Fig. B-4, did not assist in the classification of the vegetation types. 

Kauth and Thomas developed the Tasseled Cap transformation for Landsat MMS 

imagery (Jensen, 2000). Horne (2003) derived a Tasseled Cap transformation specifically for 

use with IKONOS multi-spectral imagery. The Tasseled Cap Transformation results in four 

indexes, soil brightness index (SBI), green vegetation index (GVI), yellow stuff (YVI), and 

nonesuch index (NSI), which provide information about the separation between classes (Jensen, 

1996; Horne, 2003). 
B- 4

Figure B-1. Spectral plots for scrub-shrub and salt marshes in the four IKONOS bands. 

The four equations derived by Horne (2003) are: 

SBI = μ1= 0.326 xblue + 0.509 xgreen + 0.560 xred + 0.567 xnir

GVI= μ2= -0.311 xblue – 0.356xgreen – 0.325xred – 0.819 xnir

YVI = μ3 = -0.612 xblue – 0.312 xgreen + 0.722 xred – 0.081 xnir

NSI = μ4= -0.650 xblue + 0.719 xgreen –0.243 xred – 0.031 xnir. 

Tasseled Cap transformation is useful at distinguishing between surface types (Jensen, 

1996; Horne, 2003). This and other vegetation indices provide additional information about a 

scene that classification procedures cannot extract. The GVI was classified to identify the 

vegetation classes of salt marsh and scrub-shrub. The GVI is particularly useful in distinguishing 

between vegetation classes. The GVI from the Tasseled Cap transformation, Fig. B-5, did the 

best job at distinguishing between the scrub-shrub and salt marsh vegetation. Based on the 

results from the unsupervised classification, NDVI and Tasseled Cap transformation, the GVI 

was the chosen approach to classifying the vegetation classes. 
B- 5

Figure B-2. False color image bands 4,3,2.

 

Figure B-3. IKONOS Band 4 shown as 5 natural breaks. 

Scrub-shrub 

Salt marsh
B- 6

Figure B-4. NDVI of IKONOS imagery shown as 5 natural breaks. 

Figure B-5. IKONOS tasseled cap band 2 (greenness) shown as 5 natural breaks. 
B- 7

An unsupervised classification was run on the GVI to extract the vegetation classes. The 

classification results during the vegetation or nonvegetation step differed depending on the 

method used. The unsupervised classification of Tasseled Cap transformation band 2 was more 

consistent than the unsupervised classification of the original 4-band image. In the GVI classified 

image, there was a threshold value that consistently separated vegetated from unvegetated 

classes (Figs. B-6 & B-7 and B-8 & B-9). Once the threshold was identified, the remaining 

unassigned classes on either side of the threshold were then easily assigned vegetation or 

nonvegetation values, depending which side of the threshold the class was on. In the 4-band 

classified image, this threshold concept was not applicable. When developing a repeatable 

methodology, finding the threshold and verifying selected pixels is more efficient and repeatable 

than checking each class to assign a class name. 

 Comparing known locations of salt marsh and scrub-shrub on band 2 of the tasseled cap 

imagery, Figs. B-6 and B-7, the tasseled Cap imagery separated the vegetation classes more 

effectively. 

Figure B-6. All four bands of the tasseled cap image. 
B- 8

Figure B-7. The brightness and greenness bands of the tasseled cap image. 

Threshold 
B- 9

 

Figure B-8. Attributes of classified GVI. Figure B-9. Attributes of classified 

 4-band Image. 

Threshold 
C- 1

APPENDIX C 

Accuracy Assessment 
C- 3

Overall Error Matrix 

The rows represent the classification derived from the IKONOS imagery and the columns 

represent the classification from the over flights. The values of 2, 3, 4, 5 represent beaches and 

manmade structures, mud and tidal flats, scrub-shrub, and salt marsh respectively. All values in 

the table C-1 represent shoreline length in meters rounded to the nearest meter. 

In Tables C-2 and C-3 the accuracy assessment was split out into the classification on the 

seaward and landward sides of the shoreline and summarized by the four modified classes. 

Values in Tables C-2 and C-3 are in kilometers. Table C-3 is the sum of the middle and landward 

features along the shoreline. The error of omission indicates the percent of shoreline type that 

was under classified and the error of commission represents the percent of shoreline type that 

was over classified. 
Image 2

2/3

2/3/2

2/3/5

2/4

2/4/2

2/4/3

2/4/5

2/5

2/5/2

2/5/3

2/5/4 3

3/2

3/5 4

4/2

4/2/3

2 59423 5 55 5 203 172/3 193 49762/3/2 222/3/5 362/4 31 1009 82/4/2 5 1752/4/3 16 922/4/5 48 1762/5 2683 10383 5 402/5/2 337 13842/5/3 76 23602/5/4 10 63

3 224 436578 5 85 3/2 12273/5 12 71974 769 18 35 15 26 176058 13 4/2 512 38 121 5614 

4/2/3 22 339

4/2/4 4/2/5 16 10 4/3 496 67 155 42 

4/3/24/3/44/3/54/5 1448 71 59 51 10 12 5 149 4/5/2 476 9 26 

4/5/3 406 62 191 

4/5/4 7 7

5 5516 197 76 931 169 381 30 2992 155 5/2 2371 7 37 43 68 5/2/3 1415/2/45/2/5 163 10 12 9

5/3 1484 10 10 262 29 1226 11 5/3/2 55/3/55/4 228 46 127 5/4/2 100 5 45

5/4/3 183 10 86 

5/4/5 248 19 40 Total 77641 5304 22 36 1009 175 262 176 11556 1384 2549 63 437259 1227 7511 181624 5861 339 

Flights

Table C-1 

Overall Error Matrix. (Error matrix of the overall classification map derived from IKONOS imagery (rows) and compared to the classification from aerial over flights (columns). The diagonal represents the length of shoreline (in meters) 

from both classification methods.)

C-4 
Image

4/3/2

4/3/4

4/3/5

4/5

4/5/2

4/5/3

4/5/4 5

5/2

5/2/3

5/2/4

5/2/5

5/3

5/3/2

5/3/5

5/4

5/4/2

5/4/3

2 13 2765 1194 2/3 262 737 2/3/2 4 

2/3/5 2/4 18 

2/4/2 2/4/3 19 2/4/5 25 2/5 6 625 90 2/5/2 463 26 2/5/3 170 125 2/5/4 3 81 6108 5 7162 3/2 469 69 3/5 140 120 4 1466 26923 5 514 4/2 18 327 17 

4/2/3 4/2/4 4/2/5 4/3 59 2734 616 

4/3/2 52 6 4/3/4 9 8 

4/3/5 84 19 9 4/5 132702 84 20753 33 638 4/5/2 19 4145 190 10 10 

4/5/3 48 16 26917 3160 738 

4/5/4 10 7 1320 351 

5 3090 8734356 164 169 29374 206 

5/2 75 47071 80478 63 3198 5/2/3 282 2716 480 

5/2/4 93 5/2/5 264 1991 20 5/3 441 30704 52 2781384 

5/3/2 654 89 1586 

5/3/5 4 140 53 6612 

5/4 605 18962 25 805 87343 15/4/2 413 7 1839 5/4/3 52 2681 270 123

5/4/5 82 6081 215 

Total 52 9 84 138771 4162 27008 1320 8907144 80771 2948 93 1991 2827964 1586 6612 87549 1839 126

Flights

Table C-1. Overall Error Matrix. (Error matrix of the overall classification map derived from IKONOS imagery 

compared to the traditional classification from aerial over flights (columns). The diagonal represent

shoreline (in meters) classified the same from both classification methods) (continued).

C-5 
C- 6

Table C-2 

Seaward Error Matrix. 

(Error matrix of the seaward shoreline classification. Values are lengths of shorelines in 

kilometers.) 

User's Results 

 Flights 

 

Beaches/ 

manmade 

Mud/ 

tidal flats Scrub-shrub Salt marsh Total 

Error of 

Omission 

Beaches/manmade 159.77 4.85 0.47 52.68 217.77 26.63%

Mud/tidal flats 3.36 3,302.63 1.74 46.97 3,354.71 1.55%

Scrub-shrub 1.09 2.61 266.13 48.41 318.24 16.38%

Salt marsh 10.50 32.50 3.43 8,951.26 8,997.69 0.52%

Total 174.72 3,342.60 271.77 9,099.32 12,888.41 

Producer's Results 

Image 

Error of

Commission 8.56% 1.20% 2.07% 1.63% 

Table C-3 

Landward Error Matrix. 

(Error matrix of landward shoreline lassification. Values are lengths of shoreline in kilometers.) 

User's Results 

 Flights 

 

Beaches/ 

manmade 

Mud/ 

tidal flats Scrub-shrub Salt marsh Total 

Error of 

Omission 

Beaches/manmade 168.35 0.02 0.57 13.26 182.20 7.60%

Mud/tidal flats 0.47 898.42 0.43 29.12 928.44 3.23%

Scrub-shrub 8.42 0.38 757.79 119.43 886.02 14.47%

Salt marsh 24.33 1.58 19.99 23,734.27 23,780.16 0.19%

Total 201.57 900.39 778.77 23,896.08 25,776.82 

Producer's Results 

Image 

Error of

Commission 16.48% 0.22% 2.69% 0.68% 
The Department of the Interior Mission

As the Nation's principal conservation agency, the Department of the Interior has responsibility 

for most of our nationally owned public lands and natural resources. This includes fostering 

sound use of our land and water resources; protecting our fish, wildlife, and biological diversity; 

preserving the environmental and cultural values of our national parks and historical places; 

and providing for the enjoyment of life through outdoor recreation. The Department assesses 

our energy and mineral resources and works to ensure that their development is in the best 

interests of all our people by encouraging stewardship and citizen participation in their care. 

The Department also has a major responsibility for American Indian reservation communities 

and for people who live in island territories under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) 

primary responsibilities are to manage the mineral resources located on the Nation's Outer 

Continental Shelf (OCS), collect revenue from the Federal OCS and onshore Federal and Indian 

lands, and distribute those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program 

administers the OCS competitive leasing program and oversees the safe and environmentally 

sound exploration and production of our Nation's offshore natural gas, oil and other mineral 

resources. The MMS Minerals Revenue Management meets its responsibilities by ensuring the 

efficient, timely and accurate collection and disbursement of revenue from mineral leasing and 

production due to Indian tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially 

affected parties and (2) carrying out its programs with an emphasis on working to enhance the 

quality of life for all Americans by lending MMS assistance and expertise to economic 

development and environmental protection.