Bryan M. Costa 

Laurie J. Bauer 

Timothy A. Battista 

Peter W. Mueller 

Mark E. Monaco 

NOAA CCMA Biogeography Branch 

NOAA TECHNICAL MEMORANDUM NOS NCCOS 105 

Project Report Produced by NOAA’s Biogeography Branch in Cooperation with U.S. National Park Service 

Moderate-Depth Benthic Habitats

 of St. John, U.S. Virgin Islands 

U.S. DEPARTMENT OF COMMERCE

NAT OI NAL OCEANIC AND ATMOSPHERIC ADMINISTRAT OI N
Mention of trade names or commercial products does not constitute endorsement or recommendation for their 

use by the United States Government. 

Citation: 

Costa, B.M., L.J. Bauer, T.A. Battista, P.W. Mueller and M.E. Monaco. 2009. Moderate-Depth Benthic Habitats of 

St. John, U.S. Virgin Islands. NOAA Technical Memorandum NOS NCCOS 105. Silver Spring, MD. 57 pp. 
 

Moderate-Depth Benthic Habitats of St. John, U.S. Virgin Islands
 

Biogeography Branch 

Center for Coastal Monitoring and Assessment (CCMA) 

NOAA/NOS/NCCOS 

1305 East-West Highway, SSMC4, N/SCI-1 

Silver Spring, MD 20910 

NOAA Technical Memorandum NOS NCCOS 105 

November 2009 

Authors: 

Bryan M. Costa 

Laurie J. Bauer

 Timothy A. Battista 

Peter W. Mueller 

Mark E. Monaco 

U.S. DEPARTMENT OF COMMERCE

NAT OI NAL OCEANIC AND ATMOSPHERIC ADMINISTRAT OI N

United States Department National Oceanic and National Ocean Service 

of Commerce Atmospheric Administration 

Gary Locke Jane Lubchenco John H. Dunnigan 

Secretary Administrator Assistant Administrator 
This page intentionally left blank. 
ABOUT THIS DOCUMENT
 

This report describes the creation and assessment of a moderate-depth benthic habitat map for the Virgin Islands 

Coral Reef National Monument (VICRNM) south of St. John, U.S. Virgin Islands. The objective of this effort, conducted by NOAA’s Center for Coastal Monitoring and Assessment’s Biogeography Branch in partnership with the 

U.S. National Park Service (NPS), was to provide spatially-explicit information describing the moderate-depth 

(30 - 60 m) benthic habitat types and live coral cover present in and around VICRNM’s southern boundaries. The 

resulting fine-scale habitat map, generated using a combination of semi-automated classification and visual interpretation techniques, represents the first acoustically-generated digital map of these moderate-depth areas. 

This report consists of three primary components: 1) a description of the classification scheme used to categorize the different seafloor habitats, 2) a discussion of the techniques used to create the habitat map, and 3) an 

assessment of the habitat map’s thematic accuracies. This habitat map will be used by the U.S. National Park 

Service and other local partners for planning research and monitoring activities, and will support the management and conservation of St. John’s VICRNM. 

This work is part of NOAA Coral Reef Conservation Program’s national coral reef ecosystem integrated mapping 

and monitoring studies throughout the U.S. Caribbean (Monaco et al., 2001). 

For more information on this effort please visit: 

http://ccma.nos.noaa.gov/ecosystems/coralreef/usvi_nps.html 

Direct questions or comments to: 

Bryan M. Costa 

GIS and Remote Sensing Specialist 

NOAA/NOS/NCCOS/CCMA/Biogeography Branch 

1305 East West Highway 

SSMC4, N/SCI-1, 9th floor, #9232 

Silver Spring, MD 20910 

Phone: (301) 713-3028 x146 

Email: Bryan.Costa@noaa.gov 

Or 

Timothy A. Battista 

Chief Scientist 

NOAA/NOS/NCCOS/CCMA/Biogeography Branch 

1305 East West Highway 

SSMC4, N/SCI-1 

Silver Spring, MD 20910 

Phone: (301) 713-3028 x171 

Email: Tim.Battista@noaa.gov 

U.S. DEPARTMENT OF COMMERCE

NAT OI NAL OCEANIC AND ATMOSPHERIC ADMINISTRAT OI N

page 

i 

All photographs provided in this document were taken by NOAA/NOS/NCCOS/Center for Coastal Monitoring 

and Assessment Biogeography Branch in St. John, USVI. 
page 

ii 

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EXECUTIVE SUMMARY 

Shallow-water (< 30 m) and moderate-depth 

(30 - 60 m) coral reef ecosystems in the surrounding waters of St. John, U.S. Virgin Islands are unique natural resources that must 

be preserved. The mosaic of habitats, including hard and soft corals, are home to a diversity of marine organisms, which provide many 

important ecosystem services to the community of St. John, including fishing, tourism and 

shoreline protection. However, coral reef ecosystems throughout the U.S. Caribbean are 

under increasing pressure from environmental and anthropogenic stressors that threaten 

to destroy these valuable marine communities. Mitigating these threats requires that resource managers first understand the spatial 

distribution of these resources, making benthic habitat mapping an integral component to 

any effective ecosystem-based approach to management. 

With this goal in mind, the National Oceanic and Atmospheric Administration’s (NOAA) Center for Coastal Monitoring and Assessment’s (CCMA) Biogeography Branch and the U.S. National Park Service (NPS) have completed mapping the moderate-depth marine environment south of St. John. This work is an expansion of ongoing 

mapping and monitoring efforts conducted by NOAA and NPS in the U.S. Caribbean. The standardized protocols 

used in this effort will enable scientists and managers to quantitatively compare moderate-depth coral reef ecosystems around St. John to those throughout the U.S. Territories. These protocols and products will also help 

support the effective management and conservation of the marine resources within the National Park system. 

This report documents the process and methods used to create benthic habitat maps of the areas in and around 

the Virgin Islands Coral Reef National Monument (VICRNM). Chapter 1 provides a overview of the benthic habitat classification scheme used to partition the different habitats into ecologically relevant groups. Chapter 2 describes the steps required to create a benthic habitat map using a combination of semi-automated classification 

and visual interpretation techniques. Chapter 3 details the steps used in the accuracy assessment and reports on 

the thematic accuracy of the final map. Finally, Chapter 4 summarizes the type and size of habitats found outside 

and inside the southern boundaries of the VICRNM. 

A habitat classification scheme allows scientists to systematically group habitat types based on common ecological characteristics. The habitat classification scheme used to map moderate-depth habitats south of St. John 

grouped benthic communities based on four primary coral reef ecosystem attributes: 1) broad geographic zone, 

2) geomorphological structure, 3) dominant biological cover, and 4) amount of live coral cover. Every polygon in 

the benthic habitat map was assigned a structure and cover type within a geographic zone (e.g., Aggregate Reef 

dominated by Algae on the Bank/Shelf). These polygons were delineated and attributed using a combination of 

semi-automated classification and visual interpretation techniques of acoustic imagery. The acoustic imagery, 

which was collected using a multibeam echosounder (MBES), proved to be an excellent source from which to 

derive the location, extent and attributes of moderate-depth marine habitats. In total, 90.2 km2 of the seafloor 

south of St. John was mapped using the acoustic imagery. Approximately half of this area (43.2 km2) fell within 

the boundaries of the VICRNM. To date, 93% of the VICRNM has been mapped using acoustic and optical imagery. 

An independent accuracy assessment was conducted to evaluate the thematic accuracy of the fi nal moderate-depth benthic habitat map. Thematic accuracy was characterized for major and detailed geomorphological 

structure types, major and detailed biological cover types, and percent coral cover. This accuracy assessment 

Underwater photo depicting hard corals, soft corals and sponges at moderatedepths. 

page 

iii 
page 

iv 

revealed overall map accuracies of >95% for 

major structure and cover classes, 88.7% for 

detailed structure, 74.2% for detailed cover 

classes, and 88.3% for live coral cover. The 

88.7% accuracy achieved for detailed structure in NOAA’s moderate-depth benthic habitat map is similar to the accuracies of recent 

NOAA shallow-water benthic habitat map for 

St. John (85.7%), as well as for the Florida 

Keys (86.2%), Palau (90.0%), and the Main 

Eight Hawaiian Islands (90.0%). Given these 

high thematic accuracies, the digital map 

products originating from this project may be 

used with confidence by scientists and resource managers for a variety of different applications. 

Several patterns emerged from the summary 

map statistics for the total mapped area, as 

well as for the mapped area inside and outside the VICRNM park boundaries. In particular, Coral Reef and Hardbottom constituted the majority of the total mapped area, as well as the majority of the 

mapped areas inside and outside the VICRNM boundaries. Coral Reef and Hardbottom constituted the majority 

of these three areas because the Rhodoliths habitat type dominated the entire moderate-depth region south of 

St. John. If the Rhodoliths category is excluded, Coral Reef and Hardbottom only accounted for 14.9% of the 

total mapped area, as well as 14.6% and 15.4% of the mapped area outside and inside the VICRNM, respectively. After Rhodoliths, Sand was the second most dominant detailed structure type for all three areas. Although 

ecologically significant, Individual Patch Reefs and Aggregated Patch Reefs comprised just over 4% of the total 

mapped area, 3% of the mapped area outside the VICRNM, and 5% of the mapped area inside the VICRNM. 

In looking at major biological cover, the three mapped areas followed the same general trends for major and 

detailed biological cover types. Namely, all three areas were dominated by continuous, high density algae (i.e., 

Algae 90% - 100%), followed respectively by Algae 50% - <90%, No Cover 90% - 100% and Algae 10% - <50%. 

In terms of coral cover, the majority (>96%) of all three areas were colonized by 0% - <10% live scleractinian 

and/or soft corals. It is important to note, however, that the mapped area outside the VICRNM had one 0.25 km2 

polygon dominated by live coral (i.e., Live Coral 50% - <90%), whereas the mapped area inside the VICRNM did 

not. In addition to this one polygon, the mapped area outside the park was found to have 1.9% more Live Coral 

10% - <50% habitat than the mapped area inside the VICRNM. This difference suggests that there is slightly 

more live coral outside (than inside) the current VICRNM boundaries. 

In 2009, NOAA’s Biogeography Branch has successfully mapped the majority of the shallow-water (< 30m) benthic habitats and a significant portion of the moderate-depth (30 – 60 m) benthic habitats around St. John in the 

U.S. Virgin Islands. The moderate-depth benthic habitat map begins at the optical limit of the shallow-water map, 

and continues to the edge of the acoustic imagery. The integration of these two maps is possible, given that the 

same general habitat classification schemes and MMUs were applied to both the shallow-water and moderatedepth maps. That being said, the differences between these two maps must be thoroughly understood, in order 

to recognize the limitations associated with using the maps in an integrated fashion. Integration of the shallowwater and moderate-depth maps will provide NPS and others with a seamless benthic habitat map, extending 

from the shoreline of St. John southward to the 55 m isobath. 

Underwater photograph of Staghorn Coral (Acropora cervicornis), which is 

listed as “threatened” under the Endangered Species Act. 
TABLE OF CONTENTS 

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

CHAPTER 1: BENTHIC HABITAT CLASSIFICATION SCHEME .......................................................................3
 

1.1. COMPARISON TO PREVIOUS NOAA HABITAT CLASSIFICATION SCHEMES .......................................3
 

1.2. GEOGRAPHIC ZONES.................................................................................................................................4
 

1.3. GEOMORPHOLOGICAL STRUCTURE TYPES ..........................................................................................4
 

1.4. BIOLOGICAL COVER CLASSES ..............................................................................................................10
 

1.5. LIVE CORAL COVER CLASSES ...............................................................................................................15
 

LITERATURE CITED .........................................................................................................................................17
 

CHAPTER 2: BENTHIC HABITAT MAP CREATION ........................................................................................19
 

2.1. GENERAL MAPPING APPROACH ............................................................................................................19
 

2.2. REMOTELY SENSED IMAGERY................................................................................................................20
 

2.3. HABITAT BOUNDARY DELINEATION AND CLASSIFICATION ...............................................................24
 

2.4. GIS QUALITY CONTROL...........................................................................................................................37
 

LITERATURE CITED .........................................................................................................................................38
 

CHAPTER 3: CLASSIFICATION ACCURACY ASSESSMENT........................................................................41
 

3.1. FIELD DATA COLLECTION .......................................................................................................................41
 

3.2. EVALUATION OF ASSESSMENT DATA....................................................................................................41
 

3.3. ANALYSIS OF THEMATIC ACCURACY....................................................................................................42
 

3.4. ACCURACY ASSESSMENT RESULTS AND DISCUSSION .....................................................................43
 

3.5. CONCLUSIONS..........................................................................................................................................46
 

LITERATURE CITED .........................................................................................................................................48
 

CHAPTER 4: CONCLUSION.............................................................................................................................49
 

4.1. MAP SUMMARY STATISTICS ....................................................................................................................49
 

4.2. INTEGRATION WITH SHALLOW-WATER NOAA HABITAT MAPS OF ST. JOHN .................................53 

4.3 NEXT GENERATION OF MAPPING ..........................................................................................................54
 

LITERATURE CITED .........................................................................................................................................55
 

ACKNOWLEDGEMENTS ..................................................................................................................................57
 

page 

v 
List of Tables 

page 

vi 

Table 1.1. Changes made to the 2009 NOAA shallow-water classifi cation scheme. ......................................3
 

Table 2.1. Morphometrics used to characterize the complexity of the seafl oor. ............................................22
 

Table 2.2. Amount (%) of variance explained by a single complexity surface in a principal component.......23
 

Table 2.3. Spatial metrics calculated for each habitat polygon identified during the edge-detection

 process. ........................................................................................................................................25
 

Table 2.4. Textual, ratio, hue saturation and intensity (HSI), and spectral metrics calculated for
 

each habitat polygon identified during the edge-detection process..............................................26
 

Table 2.5. Input parameters used when building the QUEST classifi cation trees. ........................................34
 

Table 2.6. Polygon metrics identified as being significant for classifying coral reef habitats and
 

soft bottom/rhodolith habitats........................................................................................................35
 

Table 2.7. Estimated number of polygons that were manually deleted, added or merged............................36
 

Table 2.8. Estimated number of polygons that were manually reattributed...................................................36
 

Table 3.1. Error matrix for major geomorphological structure........................................................................43
 

Table 3.2. Error matrix for major geomorphological structure using individual cell probabilities. ..................44
 

Table 3.3. Error matrix for detailed geomorphological structure....................................................................44
 

Table 3.4. Error matrix for detailed geomorphological structure using individual cell probabilities................45
 

Table 3.5. Error matrix for major biological cover. .........................................................................................45
 

Table 3.6. Error matrix for major biological cover using individual cell probabilities......................................46
 

Table 3.7. Error matrix for detailed biological cover. ......................................................................................46
 

Table 3.8. Error matrix for detailed biological cover using individual cell probabilities. .................................47
 

Table 3.9. Error matrix for major geomorphological structure and percent live coral cover. ..........................47
 

Table 4.1. Final deliverable items for NOAA’s moderate-depth benthic habitat map of St. John. .................49
 

Table 4.2. Statistics describing the mapped area by major and detailed geomorphological

 structure types. .............................................................................................................................49
 

Table 4.3. Statistics describing the mapped area by major and detailed biological cover type, as well
 

as by amount of live coral cover....................................................................................................50
 

Table 4.4. Estimated time required to map St. John’s shallow-water habitats using the heads-up
 

digitizing and attribution method, and St. John’s moderate-depth habitats using the 

semi-automated delineation and classification technique. ...........................................................54
 
List of Figures 

Figure 0.1. Overview of the benthic habitat maps produced in 2009 for St. John, U.S. Virgin Islands. .........1
 

Figure 0.2. Optical and acoustic imagery sources that were used to produce the NOAA 2009
 

shallow-water and moderate-depth benthic habitat maps. ..........................................................2
 

Figure 1.1. Cross-section of geographic zones where no emergent reef crest is present.............................4
 

Figure 1.2. Underwater photograph of Aggregate Reef.................................................................................5
 

Figure 1.3. Underwater photograph of Individual Patch Reef........................................................................5
 

Figure 1.4. Underwater photograph of Aggregated Patch Reefs...................................................................6
 

Figure 1.5. Underwater photograph of Pavement..........................................................................................6
 

Figure 1.6. Underwater photograph of Pavement with Sand Channels.........................................................7
 

Figure 1.7. Underwater photograph of Rhodoliths.........................................................................................7
 

Figure 1.8. Underwater photograph of Rhodoliths with Scattered Coral and Rock. ......................................8
 

Figure 1.9. Underwater photograph of Sand. ................................................................................................8
 

Figure 1.10. Underwater photograph of Sand with Scattered Coral and Rock................................................9
 

Figure 1.11. Examples of artifacts or noise in the acoustic imagery. ...............................................................9
 

Figure 1.12. Description of how biological cover modifier represents a confounded measure of
 

density and patchiness. .............................................................................................................10
 

Figure 1.13. Underwater photographs of Lesser Starlet Coral (Siderastrea radians) and 

Groved Brain Coral (Diploria labyrinthiformis). ..........................................................................10
 

Figure 1.14. Underwater photos and associated polygons depicting the three different densities
 

of biological cover. ..................................................................................................................... 11
 

Figure 1.15. Underwater photos and associated polygons depicting the three different degrees
 

of biological cover patchiness. ..................................................................................................12
 

Figure 1.16. Underwater photographs of Algae.............................................................................................13
 

Figure 1.17. Underwater photographs of Seagrass.......................................................................................13
 

Figure 1.18. Underwater photographs of Live Coral......................................................................................14
 

Figure 1.19. Underwater photographs of No Cover.......................................................................................14
 

Figure 1.20. Underwater photographs of scleractinian (reef-building) corals and octocorals 

 (non-reef building). ....................................................................................................................15
 

Figure 1.21. Underwater photograph of 0% - <10% live coral cover. ............................................................16
 

Figure 1.22. Underwater photograph of 10% - <50% live coral cover. ..........................................................16
 

Figure 1.23. Underwater photograph of 50% - <90% live coral cover. ..........................................................16
 

Figure 2.1. Field maps were used to collect drop camera points. ...............................................................19
 

Figure 2.2. Process used to collect the data for a moderate-depth benthic habitat map.............................20
 

Figure 2.3. Two types of imagery (i.e., bathymetry and intensity) collected using an MBES sensor. ..........21
 

Figure 2.4. Photo of the NOAA ship Nancy Foster. .....................................................................................21
 

Figure 2.5. Photo of the RESON SeaBat 8101 ER (240 kHz) MBES sensor. .............................................21
 

Figure 2.6. Diagram illustrating the process used to create the moderate-depth benthic habitat
 

south of St. John........................................................................................................................24
 

Figure 2.7. Habitat objects that were exported from ENVI Fx as a single ESRI shapefile. .........................26
 

Figures 2.8a. Screenshots of the ENVI Fx 4.6 feature extraction process......................................................27
 

Figures 2.8b. Screenshot showing the scale level used to identify coral reef habitat features. ......................27
 

Figures 2.8c. Screenshot showing the merge level used to aggregate coral reef habitat features into

 larger objects. ............................................................................................................................27
 

Figures 2.8d. Screenshot showing the step where the spatial, textual, HSI, ratio and spectral metrics
 

were calculated for each coral reef habitat polygons. ...............................................................27
 

Figure 2.9. Process used to manually edit habitat polygons to remove acoustic noise within

 their boundaries. ........................................................................................................................28
 

Figure 2.10. Photograph of the Spectrum Phantom S2 ROV, which collected underwater video
 

and high resolution photographs of the seafloor along 13 transects. ........................................28
 

Figure 2.11. Photograph of the SeaViewer Sea-Drop 950 camera, which collected underwater
 

video of the seafloor at 117 discrete locations. ..........................................................................29
 

Figure 2.12. Photograph of the U.S. National Park Service vessel used to collect underwater video...........29
 

Figure 2.13. Photograph of the field equipment used to collect underwater video........................................30
 

page 

vii 
page 

viii 

Figure 2.14. Map illustrating how the drop camera occasionally drifted over habitat transitions...................30
 

Figure 2.15. Process used to create a fi nal classified set of GV points.. .......................................................31
 

Figure 2.16. Process used to convert fi nal classified GV points to ENVI 4.6 ROIs. ......................................31
 

Figure 2.17. T Screenshot of ENVI 4.6’s RuleGen 1.02 add-on. ...................................................................32
 

Figure 2.18. Underwater photograph of the Great Star Coral (Montastraea cavernosa)...............................33
 

Figure 2.19. Description of how QUEST uses binary decisions to split an image into classes. ....................33
 

Figure 2.20. Description of how QUEST determines where to split super classes into two groups. .............33
 

Figure 2.21. Process used to merge polygons < 1,000 m2

 with neighboring polygons. ................................35
 

Figure 2.22. Process used to smooth the pixilated appearance of the polygon lines....................................36
 

Figure 4.1. Map showing the major and detailed geomorphological structure types south of St. John.......50
 

Figure 4.2. Map showing the major and detailed biological cover types south of St. John. ........................51
 

Figure 4.3. Map showing the amount of live coral cover south of St. John. ................................................51
 
INTRODUCTION 

NOAA CCMA’s Biogeography Branch has developed analytical protocols to map benthic habitats throughout all 

U.S. jurisdictions, States, and Territories, including the U.S. Caribbean. NOAA, in partnership with the U.S. National Park Service, has generated both shallow-water and moderate-depth benthic habitat maps of the coral reef 

ecosystems surrounding St. John, U.S. Virgin Islands (Figure 0.1). The synthesis of existing geospatial data and 

collection of new data provides the most contemporary compilation of remotely-sensed and in situ data within the 

network of NPS-managed marine Ocean Parks. These products provide a fine-scale assessment of the status, 

abundance, and distribution of marine habitats in and around the VICRNM, giving the NPS an increased technical capacity for ocean exploration, management, and stewardship. Direct management implications specifically 

include: (1) evaluating the efficacy of management actions, (2) improving the spatial framework for monitoring 

activities, (3) enhancing the assessment of human-use impacts, and (4) enabling the use of marine spatial planning to support protected area boundary alternatives. 

0 1 2 3 km 

Protected Area Boundaries 

Virgin Islands National Park 

VI Coral Reef National Monument 

Benthic Habitat Maps 

Shallow-water Habitat Map 

Moderate-depth Habitat Map 

Figure 0.1. Overview of the benthic habitat maps produced in 2009 for St. John, U.S. Virgin Islands. 

As a result of the U.S. Ocean Action Plan, the National Park Service developed an Ocean Park Stewardship 

Action Plan to focus organizational and scientific capacity on conserving marine, estuarine, and Great lakes resources. The Ocean Park Stewardship Action Plan aims to prevent the loss of productive fisheries, habitats, and 

wildlife, and continue to conserve ocean resources and recreational activities for park visitors. The National Park 

Service manages and protects more than 250,000 acres of coral reef in ten National Park units, two of which are 

located in St John. The Virgin Islands Coral Reef National Monument includes 12,722 acres (51.4 km2

) of federal 

submerged lands off the coast of St. John. These waters contain some of the most biologically rich and economically important coral ecosystems in U.S. waters, supporting a diverse and complex system of coral reefs, shoreline mangrove forests, and seagrass beds. Additionally, the Virgin Islands National Park includes 5,637 acres 

(22.8 km2

) of submerged federal lands to protect and conserve a rich but fragile coral reef seascape. As part of 

the ocean stewardship effort, the Ocean Park Stewardship Action Plan serves to improve scientific capacity in 

order to better understand ocean ecosystems and the impact of humans on ecosystems. This includes providing 

improved spatial products to better inform resource managers of the current distribution of benthic habitats. 

Introduction 

page 

1 
 

 

Introduction 

page 

2 

Figure 0.2. Optical and acoustic imagery sources that were used to produce the NOAA 2009 shallow-water and moderate-depth benthic 

habitat maps (respectively) for St. John, U.S. Virgin Islands. 

NOAA’s effort to map the moderate-depth benthic habitats of St. John has resulted in a suite of spatial products. 

The project deliverables specifi cally include: 

• Primary data sources, including acoustic imagery, ground validation field data, and 

accuracy assessment fi eld data
 

• Derived datasets, including GIS files of benthic habitats 

• Classifi cation manual 

• Description of the methods used to create the habitat maps 

• Assessment of the thematic accuracy of the maps 

This moderate-depth mapping effort compliments a recently completed NOAA project, which mapped the shallow-water (< 30 m) benthic habitats of St. John. These two benthic habitat maps represent one of the fi rst attempts to spatially integrate products developed from acoustic and optical imagery (Figure 0.2), as well as from 

using manual and semi-automated classifi cation techniques. 
CHAPTER 1: BENTHIC HABITAT CLASSIFICATION SCHEME 

A habitat classification scheme allows scientists to systematically group habitat types based on common ecological characteristics. The initial task in any mapping effort is to create such a scheme by clearly identifying and 

defining discrete habitat classes. This scheme is subsequently used to guide the delineation and attribution of 

polygons throughout the mapping process. It is, consequently, critical for map users to have an understanding 

of the classification system, its structure and its definitions. This understanding allows users to decide on the 

appropriate uses and limitations of the map. 

The St. John moderate-depth habitat classification scheme defines benthic communities on the basis of four 

primary coral reef ecosystem attributes: 1) broad geographic zone, 2) geomorphological structure, 3) dominant 

biological cover, and 4) amount of live coral cover. Habitat features are described by varying levels of detail (i.e. 

at the major and minor levels), so that users can refine the information depicted by the habitat map to best suit 

their research and management needs. Every polygon in the benthic habitat map was assigned a geomorphological structure, biological cover type and live coral cover percentage within a geographic zone. For example, 

many areas along the Mid Shelf Reef (MSR), which is located on the Bank/Shelf, are comprised of Aggregate 

Reef that is colonized by continuous, high density Algae 90% - 100% and low density live coral 0% - <10%. 

1.1. COMPARISON TO 2009 NOAA SHALLOW-WATER HABITAT CLASSIFICATION SCHEME 

Many important factors were considered in the development of the St. John moderate-depth habitat classification 

scheme. These factors specifically included: (1) existing shallow-water classification schemes for coastal ecosystems, (2) quantitative in situ habitat data, (3) minimum mapping unit (MMU), (4) and limitations of the acoustic 

imagery (particularly, the intensity imagery). 

Table 1.1. This table depicts the changes made to the 2009 NOAA shallow-water classifica- In order to simplify this process, the tion scheme, in order to tailor it to the habitats seen at deeper depths (> 30 m) on the shelf St. John moderate-depth habitat south of St. John. Classes with a line through them were not present at moderate depths.

classification scheme was based The class with a star was added to the moderate-depth classifi cation scheme. Italicized 

classes had their definitions changed slightly from those used in the shallow-water scheme. on the recently updated classification scheme developed by NOAA 

to map shallow-water (< 30 m) 

benthic habitats around St. John 

(Zitello et al., 2009). Specifi cally, 

the geographic zones, major and 

detailed geomorphological structure types, biological cover types 

and live coral cover classes used 

in the moderate-depth scheme 

were based on the same groupings developed for the shallowwater scheme. Also, a consistent 

MMU (i.e., 1,000 m2

) was used in 

both the shallow-water and moderate-depth classifi cation schemes. 

However despite the similarities, 

there were also some differences 

between the two schemes because 

not all of the habitats found in shallow-waters are present at deeper 

depths. Specifically, these differences included: (1) changing the 

defi nitions of Pavement and Algae 

slightly to broaden their meanings, 

(2) removing 11 geographic zones, 

7 geomorphological structure and 

2 biological cover types because 

they were not present in the modGEOGRAPHIC GEOMORPHOLOGICAL BIOLOGICAL 

ZONE STRUCTURE COVER 

Land Coral Reef and Hard Bottom (Hard) Major Cover 

Salt Pond Rock Outcrop Algae 

Shoreline Intertidal Boulder Live Coral 

Lagoon Aggregate Reef Coralline Algae 

Reef Flat Individual Patch Reef Mangrove 

Back Reef Aggregated Patch Reefs Seagrass 

Reef Crest Spur and Groove No Cover 

Fore Reef Pavement Unknown 

Bank/Shelf Pavement with Sand Channels Percent Major Cover 

Escarpment Reef Rubble 10% - <50% 

Channel Rhodoliths 50% - <90% 

Dredged Rhodoliths with Scattered Coral & Rock* 90% - 100% 

Unknown Unknown Unknown 

Unconsolidated Sediment (Soft) Percent Coral Cover 

Sand 0% - <10% 

Mud 10% - <50% 

Sand with Scattered Coral and Rock 50% - <90% 

Unknown 90% - 100% 

Other Delineations Unknown 

Land 

Artificial 

Unknown 

Chapter 1: Benthic Habitat Classi

fi cation Scheme 

page 

3 
Chapter 1: Benthic Habitat Classifi cation Scheme 

page 

4 

erate-depth area, and (3) adding 1 new geomorphological structure type to accommodate a new habitat seen at 

these deeper depths (Table 1.1). 

The other difference between the St. John shallow-water and moderate-depth classification schemes is how the 

two systems quantified percent biological cover. Specifically, in the shallow-water scheme, percent biological 

cover represents a measure of patchiness of the biological cover at the scale of the ≥ 1,000 m

2

 habitat feature. 

It does not represent the density of biological cover observed by divers in the water. For example, a seagrass 

bed maybe described as covering 90% - 100% of a given polygon, but may have sparse shoot densities when 

observed by divers. The moderate-depth classification scheme, on the other hand, quantified percent biological 

cover by accounting for both the density of biological cover at the scale of the 4 m

2

 pixel and the patchiness of 

biological cover at the scale of the ≥ 1,000 m

2

 habitat feature. For example, a habitat feature defined as having 

10% - <50% biological cover may have continuous algal cover at low densities, or it may have small, patchy 

areas of moderately dense algal cover scattered within large areas with no biological cover. Percent biological 

cover differs between the two schemes due to inherent differences in the technological approaches. Further research is being conducted to reconcile this difference in the future. 

1.2. GEOGRAPHIC ZONES 

One geographic zone (i.e., Bank/Shelf) was identified in the moderate-depth area, corresponding to typical insular shelf and coral reef geomorphology. Figure 1.1 illustrates zone types across typical cross-sections when 

the reef feature is fringing the shore. Zone only refers to the location of a benthic habitat feature and does not 

address the habitat feature’s substrate or biological cover types. For example, the Bank/Shelf zone may include 

patch reefs and pavement; however, these are considered structural elements that may or may not occur within 

the Bank/Shelf zone and therefore, are not used to define it (Kendall et al., 2001). A brief description of the Bank/ 

Shelf zone is provided in the proceeding text. 

Figure 1.1. Cross-section of zone types where no emergent reef crest is present. 

Bank/Shelf 

Deeper water area (usually >30 m) extending offshore from the seaward edge of the Fore Reef or shoreline to 

the beginning of the bank/shelf escarpment where the insular shelf drops off into deep, oceanic water. 

Unknown 

Zone indistinguishable due to random or systematic noise in the bathymetry and/or backscatter, or other interference with an acoustic signature of the seafl oor. 

1.3. GEOMORPHOLOGICAL STRUCTURE TYPES 

Thirteen distinct and non-overlapping geomorphological structure types were mapped using a combination of 

semi-automated classification and visual interpretation of acoustic imagery. Geomorphological structure refers 

to a habitat feature’s dominate physical composition and does not address its location (e.g., on the Bank/Shelf). 

The structure types are defined in a collapsible hierarchy ranging from three major classes (Coral Reef and Hard-
bottom, Unconsolidated Sediment, and Unknown), to ten detailed classes (Individual Patch Reef, Aggregated 

Patch Reefs, Aggregate Reef, Pavement, Pavement with Sand Channels, Rhodoliths, Rhodoliths with Scattered 

Coral and Rock, Sand, Sand with Scattered Coral and Rock and Unknown). Habitat features with areas smaller 

than the map’s minimum mapping unit or MMU (1,000 m2

) were not considered. For example, sand halos surrounding individual patch reefs were often too small to be mapped independently. 

Coral Reef and Hardbottom 

Areas on the seafloor with solid substrates including bedrock, boulders and/or the deposition of calcium carbonate by reef or nodule building organisms. Substrates typically have no sediment cover, but a thin veneer of 

sediment may be present at times. Detailed structure classes include Individual Patch Reef, Aggregated Patch 

Reefs, Aggregate Reef, Pavement, Pavement with Sand Channels, Rhodoliths and Rhodoliths with Scattered 

Coral and Rock. 

Aggregate Reef

Continuous, high-relief coral formation of variable shapes lacking sand channels. Includes linear coral formations 

that are oriented parallel to the shelf edge (Figure 1.2). 

0 100 200

Meters

VICRNM

Figure 1.2. Underwater photograph (left) depicting the detailed structure type: Aggregate Reef. Red polygons outline the features in 

the acoustic imagery (right). 

Individual Patch Reef 

Individual Patch Reefs are coral formations with a circular or oblong shapes, and vertical reliefs of one meter 

or more in relation to the surrounding seafloor. They are isolated from other coral reef formations by bare sand, 

seagrass, rhodoliths or other habitats, and have no organized structural axis relative to the contours of the insular 

shelf edge. Individual Patch Reefs are larger than or equal to the map’s MMU (Figure 1.3). 

0 50 100

Meters

VICRNM 

Chapter 1: Benthic Habitat Classi

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Figure 1.3. Underwater photograph (left) depicting the detailed structure type: Individual Patch Reef. Red polygons outline the 

features in the acoustic imagery (right). 
Aggregated Patch Reefs

Aggregated Patch Reefs have the same defining characteristics as an Individual Patch Reef. This class, however, refers to clustered patch reefs that cover ≥ 10% of an entire polygon’s area, but are too small (less than the 

MMU) or are too close together to map individually. Where aggregated patch reefs share sand halos, the halo 

is included in the polygon (Figure 1.4). If the density of small or aggregated coral heads is < 10% of the entire 

polygon, then the structure type is described as either Rhodoliths with Scattered Coral and Rock or Sand with 

Scattered Coral and Rock. 

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0 125 250

Meters

VICRNM 

Figure 1.4. Underwater photograph (left) depicting the detailed structure type: Aggregated Patch Reefs. Red polygons outline the 

features in the acoustic imagery (right). 

Pavement 

Flat, low-relief or sloping solid carbonate rock with little or no fine-scale rugosity that is covered with algae, hard 

coral, gorgonians, zooanthids or other sessile vertebrates that are dense enough to partially obscure the underlying surface. On less colonized Pavement features, rock may be covered by a thin sand veneer (Figure 1.5). 

0 280 560

Meters

VICRNM

Figure 1.5. Underwater photograph (left) depicting the detailed structure type: Pavement. Red polygons outline the features in the 

acoustic imagery (right). 
Chapter 1: Benthic Habitat Classifi cation Scheme 

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7 

Pavement with Sand Channels 

Pavement with Sand Channels have the same defining characteristics as Pavement, in addition to having periodic sand/surge channels oriented perpendicular to the Bank/Shelf Escarpment. The sand/surge channels of this 

feature have low vertical relief and are typically erosional in origin. This habitat type occurs in areas exposed to 

moderate wave surge, such as the Bank/Shelf zone (Figure 1.6). 0 130 260

Meters

VICRNM

Figure 1.6. Underwater photograph (left) depicting the detailed structure type: Pavement with Sand Channels. Red polygons outline 

the features in the acoustic imagery (right). 

Rhodoliths 

Areas on the seafloor that are covered by ≥ 10% rhodoliths. Rhodoliths are cylindrical, discoidal, or irregular 

shaped calcareous nodules averaging approximately 6 cm in diameter (Foster 2001). These unattached nodules are colonized by successive layers of coralline red algae, and are commonly found in offshore topographic 

depressions (Figure 1.7). Since Rhodoliths are unattached to the seafloor and mobile, their distributions can 

change quantifiably from year to year. 

0 40 80

Meters

Figure 1.7. Underwater photograph (left) depicting the detailed structure type: Rhodoliths. Red polygons outline the features in the acoustic 

imagery (right). 
Rhodoliths with Scattered Coral and Rock 

Areas on the seafl oor where ≥ 10% of the entire polygon is covered by rhodoliths, and < 10% of the entire polygon is covered by scattered rocks or isolated coral heads that are too small to be delineated individually (Figure 

1.8). If the density of the rocks and/or coral heads is ≥ 10% of the entire polygon’s area, then the structure type 

is described as Aggregated Patch Reefs. 

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0 130 260 

Meters 

VICRNM

Figure 1.8. Underwater photograph (left) depicting the detailed structure type: Rhodoliths with Scattered Coral and Rock. Red polygons outline 

the features in the acoustic imagery (right). 

Unconsolidated Sediment 

Areas on the seafloor consisting of small particles (< 256 mm) with less than 10% cover of rhodoliths or large 

stable substrate. Detailed structure classes include: Sand and Sand with Scattered Coral and Rock. 

Sand 

Areas on the seafloor covered by coarse sediments (Figure 1.9). Particle sizes for these coarse sediments range 

from 0.0625 – 256 mm, including pebbles and cobbles (Wentworth 1922). This structure type is typically found 

in areas exposed to currents or moderate wave energy. 

0 240 480

Meters

VICRNM

Figure 1.9. Underwater photograph (left) depicting the detailed structure type: Sand. Red polygons outline the features in the acoustic 

imagery (right). 
 

VICRNM

Sand with Scattered Coral and Rock 

Areas on the seafloor covered by sand, and < 10% of the entire polygon is covered by scattered rocks or isolated 

coral heads that are too small to be delineated individually (Figure 1.10). If the density of small coral heads is ≥

10% of the entire polygon, then the structure type is described as Aggregated Patch Reefs. If rhodoliths cover 

≥ 10% of the entire polygon, and < 10% of the entire polygon is covered by scattered rocks and isolated coral 

heads, then the structure type is described as Rhodoliths with Scattered Coral and Rock. 

0 40 80

Meters

VICRNM

Figure 1.10. Underwater photograph (left) depicting the detailed structure type: Sand with Scattered Coral and Rock. A red polygon outlines the 

feature in the acoustic imagery (right). 

Unknown 

Major structure indistinguishable due to random or systematic noise in the bathymetry and/or backscatter, or 

other interference with an acoustic signature of the seafloor (Figure 1.11). 

Figure 1.11. These red polygons are highlighting artifacts or noise in the acoustic imagery. This noise is depicted both in the intensity imagery 

(left) and the PCA imagery (right). This type of noise is akin to turbidity, wave action and/or sunglint in optical imagery, making it difficult for the 

cartographer to determine the habitat type underneath. 

Chapter 1: Benthic Habitat Classi

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9 
Chapter 1: Benthic Habitat Classifi cation Scheme 

page 

10 

1.4 BIOLOGICAL COVER CLASSES 

Five distinct and non-overlapping biological 

cover classes were mapped using a combination of semi-automated classification and visual interpretation of acoustic imagery. Cover 

classes refer to the dominant biological component colonizing the surface of the habitat 

feature, and does not address its location (e.g., 

on the Bank/Shelf). Habitats features that 

covered areas smaller than the MMU (1,000 m2

) were not mapped independently. Biological cover types were grouped into fi ve major 

classes (i.e., Algae, Seagrass, Live Coral, No 

Cover and Unknown), and were combined 

with a modifier describing the density and distribution of the dominant cover type within the 

habitat feature (i.e., 10%-<50%, 50%-<90%, 

and 90%-100%). It is important to emphasize 

that the percent biological cover modifi er represents a combined measure of density at the 

scale of the 4 m

2

 pixel and of patchiness at 

the scale of the ≥ 1,000 m

2

 habitat feature. For 

example, a habitat feature defined as having 

10%-50% biological cover may have continuous algal cover at low densities, or it may be 

covered by small, patchy areas of moderately 

dense algae scattered within large areas with 

no biological cover. Figures 1.12, 1.14 and 

1.15 are helpful for understanding how density 

within a pixel and patchiness within a habitat 

feature affected the assignment of percent 

cover. 

Figure 1.12. Graph denoting how the biological cover modifier represents a 

confounded measure of density at the scale of the 4 m

2

 pixel and of patchiness 

at the scale of the ≥ 1,000 m

2

 habitat feature. The dashed red circle in the 

middle denotes how the size of the patches of biological cover can influence 

whether a polygon is moved down into the 10% - 50% or up into the 50% – 

90% category. 

Figure 1.13. Underwater photographs of Lesser Starlet Coral (Siderastrea radians) (left) and Groved Brain Coral (Diploria labyrinthiformis) 

(right). 
High Density 

0 25 50 100

Meters

Moderate Density 

0 25 50 100

Meters

Low Density 

0 25 50 100

Meters

Figure 1.14. Underwater photos (left) and associated polygons highlighted in red (right) depicting the three different densities (high, moderate 

and lowe) of biological cover. 

Chapter 1: Benthic Habitat Classi

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11 
Continuous 

0 40 80 

Meters 

Patchy 

0 30 60 

Meters 

Sparse 

0 30 60 

Meters 

Chapter 1: Benthic Habitat Classi

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12 

Figure 1.15. Underwater photos (left) and associated polygons highlighted in red (right) depicting the three different degrees (continuous, 

patchy and sparse) of biological cover patchiness. 
Chapter 1: Benthic Habitat Classifi cation Scheme 

page 

13 

Major Cover 

Algae (1) 

Substrates dominated by any combination of numerous species of red, green, or brown algae. These algae 

may be turf, fl eshy, filamentous or crustose coralline species. This cover type occurs throughout many zones, 

especially on hard bottoms with low coral densities and soft bottoms in deeper waters on the Bank/Shelf zone. 

In some instances, habitat features with 10% - <50% Algae were attributed as such, albeit the habitat feature 

had more uncolonized area than colonized area. This 10% - <50% modifier was retained to explicitly denote that 

some Algae existed within a habitat feature (Figure 1.16). 

0 25 50

Meters

Figure 1.16. Underwater photographs depicting the major biological cover type: Algae. This class includes any combination of numerous species 

turf, fl eshy, filamentous or crustose coralline species. A red polygon outlines the feature in the acoustic imagery (right). 

Seagrass (2) 

Habitat dominated by any single species of seagrass (e.g. Syringodium sp., Thalassia sp., and Halophila sp.) or 

a combination of several species (Figure 1.17). 

0 50 100 

Meters 

Figure 1.17. Underwater photographs depicting the major biological cover type: Seagrass. Turtle Grass (Thalassia testudinum) (left) and Manatee 

Grass (Syringodium filiforme) (middle) are both common in shallow-waters, but not in waters deeper than approximately 30 m. Red polygons outline 

the features in the acoustic imagery (right). 
Live Coral (3)
 

Habitat dominated by live reef building corals, including scleractinian (e.g., Acropora sp.) and octocorals (e.g., 

Briareum sp.) (Figure 1.18). 

Chapter 1: Benthic Habitat Classi

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14 

0 220 440 

Meters 

Figure 1.18. Underwater photographs depicting the major biological cover type: Live Coral. This biological cover type includes both scleractinian 

corals (left) and octocorals (middle). A red polygon outlines the feature in the acoustic imagery (right). 

No Cover (4)
 

Substrates (usually Sand) covered with < 10% of any of the other biological cover types. In some instances, 

habitat features with 10% - <50% biological cover were attributed as such, albeit the habitat feature had more 

uncolonized area than colonized area. This 10% - <50% modifier was retained to explicitly denote that some 

biological cover existed within a habitat feature. The user may assume that the remainder of these polygons 

(50% - <90%) has no biological cover (Figure 1.19).
 

0 150 300

Meters

VICRNM

Figure 1.19. Underwater photographs depicting the major biological cover type: No Cover. Red polygons outline the features in the acoustic 

imagery (right). 

Unknown (5)
 

Biological cover is indistinguishable due to random or systematic noise in the bathymetry and/or backscatter, or 

other interference with an acoustic signature of the seafl oor.
 
VICRNM

Percent Major Cover 

10% - <50% (1)
 

Continuous, low-density biological cover or patchy, moderate-density biological cover with uncolonized breaks 

in coverage that are too small (< 1,000 m2

) to be mapped as discrete habitat features. These uncolonized areas 

make up 90% or less of the habitat feature’s total area. The overall combined density/patchiness of the major 

biological cover type is estimated to be 10% - <50% of the polygon feature (Figures 1.14 and 1.15).
 

50% - <90% (2)
 

Continuous, moderate-density biological cover or patchy, high-density biological cover with uncolonized breaks 

in coverage that are too small (< 1,000 m2

) to be mapped as discrete habitat features. These uncolonized areas 

make up 50% or less of the habitat feature’s total area. The overall combined density/patchiness of the major 

biological cover type is estimated to be 50% - <90% of the polygon feature (Figures 1.14 and 1.15).
 

90% - 100% (3) 

Continuous, high-density biological cover with uncolonized breaks in coverage that are too small (< 1,000 m2

) 

to be mapped as discrete habitat features. These small uncolonized areas make up 10% or less of the habitat 

feature’s total area. The overall combined density/patchiness of the major biological cover type is estimated to 

be 90% - 100% of the polygon feature (Figures 1.14 and 1.15). 

Unknown (4)
 

Percent estimate of the biological cover is indistinguishable due to random or systematic noise in the bathymetry 

and/or backscatter, or other interference with an acoustic signature of the seafl oor.
 

1.5. LIVE CORAL COVER CLASSES 

Four distinct and non-overlapping percent live coral classes were mapped using a combination of semi-automated classification and visual interpretation of acoustic imagery. This attribute is an additional biological cover 

modifier used to denote information about the percent cover of live coral (both scleractinian and octocorals) 

(Figure 1.20), when it was not the dominant cover type. In order to provide resource managers with additional information about corals, four range classes were used (0% - <10%, 10% - <50%, 50% - <90%, and 90% - 100%). 

Unlike the biological cover modifi er, live coral cover simply describes the density of coral on hardbottom features 

at the 4 m2

 pixel scale. It does not denote patchiness of coral at the scale of the habitat feature. For this reason, 

extensive in situ data is critical to correct attribution of the live coral cover modifier. As a result of these varying 

scales of interpretation, the percent biological cover and percent live coral cover modifiers are not additive properties within the same mapping unit. In many cases, they will sum to greater than 100%. For example, an Aggregate Reef may be covered by Algae 90% - 100%, as well as have 10% - <50% coral densities at the fine-scale. 

Chapter 1: Benthic Habitat Classi

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page 

15 

Figure 1.20. Both scleractinian (hard) corals (left) and octocorals (soft) corals (right) are aggregated together when classifying live coral cover. Typical corals of St. John include the scleractinian boulder coral (Montastraea annularis) and several octocorals including sea fans (Gorgonia sp.). 
Chapter 1: Benthic Habitat Classifi cation Scheme 

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16 

0% - <10% (1)
 

Live coral covering of less than 10% of the seafl oor at 

the the 4 m

2

 pixel scale (Figure 1.21). 

10% - <50% (2)
 

Live coral covering 10% to 50% of the seafloor at the 

4 m

2

 pixel scale (Figure 1.22). 

50% - <90% (3)
 

Live coral covering 50% to 90% of the seafloor at the 

4 m

2

 pixel scale (Figure 1.23). 

90% - 100% (4)
 

Continuous live coral covering 90% or more of the 

seafloor at the 4 m

2

 pixel scale.
 

Unknown (5)
 

Percent estimate of coral cover is indistinguishable 

due to random or systematic noise in the bathymetry and/or backscatter, or other interference with an 

acoustic signature of the seafl oor.
 

Figure 1.21. Photograph depicting the presence of live coral in the 

0% - 10% cover range. 

Figure 1.22. A photograph depicting the presence of live coral in the 

10% - 50% cover range. 

Figure 1.23. A photograph depicting the presence of live coral in the 

50% - 90% cover range. 
LITERATURE CITED 

Foster, M.S. 2001. Rhodoliths: Between Rocks and Soft Places. Journal of Phycology. 37, 659-667. 

Kendall, M.S., C.R. Kruer, K.R. Buja, J.D. Christensen, M. Finkbeiner, R.A. Warner and M.E. Monaco. 2001. 

Methods Used to Map the Benthic Habitats of Puerto Rico and the U.S. Virgin Islands. NOAA Technical Memorandum NOS NCCOS CCMA 152. Silver Spring, MD. 

Wentworth, C.K. 1922. A Scale of Grade and Class Terms for Clastic Sediments. Journal of Geology. 30 (5): 

377-392. 

Zitello, A.G., L.J. Bauer, T.A. Battista, P.W. Mueller, M.S. Kendall and M.E. Monaco. 2009. Benthic Habitats of St. 

John, U.S. Virgin Islands. NOAA Technical Memorandum NOS NCCOS 96. Silver Spring, MD. 53 pp. 

Chapter 1: Benthic Habitat Classi

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Chapter 1: Benthic Habitat Classifi cation Scheme 

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CHAPTER 2: BENTHIC HABITAT MAP CREATION 

Benthic habitats of the 

moderate-depth marine 

environment in and around 

the Virgin Islands Coral 

Reef National Monument 

were mapped using a combination of semi-automated 

classification and visual 

interpretation of acoustic 

imagery. Acoustic imagery has been successfully 

used to derive the location, 

extent and attributes of marine habitats (Prada et al., 

2008; Kendall et al., 2005; 

Kostylev et al., 2001). 

NOAA scientists were able 

to accurately and reliably 

delineate the boundaries of 

features in the imagery using a combination of Geographic Information System 

(GIS) and remote sensing 

software. Field investigations were conducted on 

both small and large vessels in order to understand and validate the acoustic signatures created by habitat features on the seafloor (Figure 2.1). Spatially and thematically accurate habitat maps were developed using this 

process, providing researchers and natural resource managers with a quantitative understanding of the benthic 

resources in and around the VICRNM. 

Figure 2.1. Laminated maps were used in the field to help keep track of which drop camera points 

had already been collected. 

2.1 GENERAL MAPPING APPROACH 

NOAA’s approach to moderate-depth benthic habitat mapping was a six-step process: 

1. Imagery Acquisition and Processing – The first step in this process was to acquire and process high-resolution acoustic imagery. Acoustic imagery was collected during two separate years, in order to map the 

full geographic extent of the VICRNM’s southern boundaries. Several metrics were derived from the depth 

imagery, in order to describe the complexity of the seafloor in different ways. Principal components analysis 

was then used to reduce the redundancy of information contained in these metrics. 

2. Habitat Boundary Delineation – A first draft benthic habitat map was generated using edge detection algorithms to delineate features on the seafloor with discrete acoustic signatures. During the creation of this first 

draft map, the cartographer placed discrete points on features in the map that had unknown acoustic signatures. Points were also placed on features with known acoustic signatures (evenly distributed throughout 

the geographic extent of the map) to confirm that the habitats associated with these signatures remained 

consistent through the area of interest. These two types of points were labeled as “ground validation” sites 

and were visited in the field. 

3. Ground Validation (GV) – NOAA scientists explored these ground validation locations using two different 

sampling techniques and two different pieces of equipment. Specifically, underwater video was collected 

along 13 transects using a remotely operated vehicle (ROV), and at 117 discrete points using a manually 

operated drop camera. The resulting GPS and video information was processed, analyzed and used to train 

the classification algorithm that was used to generate the second draft map. 

Chapter 2: Benthic Habitat Map Creation 

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19 
 

Chapter 2: Benthic Habitat Map Creation 

page 

20 

4. Habitat Classification – A CART-like (Classification and Regression Tree) algorithm was used to classify 

each habitat feature delineated by the edge-detection algorithms described in step 2. To simplify this classification process, coral reef habitat features, and soft bottom and rhodolith habitat features were classified 

separately. The classification algorithm separated these two habitat feature types into different major and 

detailed structure, biological cover and lives coral classes. The two, resulting classifications were merged 

together and manually edited to create a final seamless habitat map. 

5. Accuracy Assessment (AA) – AA sites were generated using a stratified random sampling design (based 

on detailed structure type) that allowed for a statistically rigorous assessment of map accuracy. Underwater 

video was collected at these 299 sites using a manually operated drop camera. 

6. Final Product Creation – A final benthic habitat map for the VICRNM was generated by correcting inaccuracies identified by the accuracy assessment. Additionally, all associated datasets, including GIS files, field 

video and metadata were packaged and provided to project partners. 

2.2 REMOTELY SENSED IMAGERY 

Multibeam (MBES) Sound Navigation and Ranging 

(SoNAR) sensors actively emit sound to measure the 

depth, hardness/softness and roughness/smoothness 

of the seafloor (Figure 2.2). Depth is measured by determining the time required for an individual ping of 

sound to travel from the sensor to the seafl oor and 

back again. These individual measurements were 

used to create seamless images of the seafl oor’s 

depth, which will be referred to hereafter as “bathymetry” (Figure 2.3). Hardness/softness and roughness/ 

smoothness are measured by calculating the intensity of an individual ping of sound scattered from the 

seafloor. These individual measurements were used 

to create continuous images of the seafl oor’s physical structure, which will be referred to hereafter as 

“intensity” (Figure 2.3). The resulting acoustic images 

(i.e., bathymetry and intensity) are valuable tools for 

natural resource managers and researchers because 

they provide baseline information on the location and 

extent of seafloor habitats in turbid waters and in deep 

waters beyond the limits of optical imagery (i.e., approximately 30 m). 

Acquisition Of Remotely Sensed Imagery 

Acoustic imagery was acquired for the southern VICRNM boundaries on two separate missions onboard the 

NOAA ship, Nancy Foster (Figure 2.4). The first mission took place from 2/18 to 3/5/2004, and the second mission took place from 2/1 to 2/12/2005. On both missions, seafloor depths between 14 to 55 m were mapped 

using a RESON SeaBat 8101 ER (240 kHz) MBES sensor (Figure 2.5). This pole-mounted system measured 

water depths across a 150° swath consisting of 101 individual 1.5° x 1.5° beams. The vessel survey speed was 

between 5 and 8 knots. In 2004, the ship’s location was determined by a Trimble DSM 132 DGPS system, which 

provided a RTCM differential data stream from the U.S. Coast Guard Continually Operating Reference Station 

(CORS) at Port Isabel, Puerto Rico. Gyro, heave, pitch and roll correctors were acquired using an Ixsea Octans 

gyrocompass. In 2005, the ship’s positioning and orientation were determined by the Applanix POS/MV 320 V4, 

which is a GPS aided Inertial Motion Unit (IMU) providing measurements of roll, pitch and heading. The POS/ 

MV obtained its positions from two dual frequency Trimble Zephyr GPS antennae. An auxiliary Trimble DSM 132 

DGPS system provided a RTCM differential data stream from the U.S. Coast Guard CORS at Port Isabel, Puerto 

Rico. For both years, CTD (conductivity, temperature and depth) measurements were taken approximately every 

4 hours using a Seabird Electronics SBE-19 to correct for changing sound velocities in the water column. In 2004, 

Figure 2.2. Diagram illustrating the collection of data for moderatedepth habitat mapping. The acoustic imagery and underwater video 

datasets are integrated to create a final map product. 
Chapter 2: Benthic Habitat Map Creation 

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21 

Figure 2.3. These maps depict the two types of imagery (i.e., bathymetry and intensity) collected using an MBES sensor. The bathymetry 

or depth surface (left) was measured by determining the time required for an individual ping of sound to travel from the sensor to the 

seafloor and back again. The backscatter or intensity surface (right) was determined by measuring the strength of an individual ping of 

sound scattered from the seafloor. The black hatched polygon denotes the area mapped using optical imagery. The red polygons show 

the boundaries of the VICRNM. 

raw data were referenced to the WGS84 UTM 20 N 

horizontal coordinate system, and were logged in .xtf 

(extended triton format) using Triton ISIS® software 

6.2. In 2005, raw data were referenced to the NAD83 

UTM 20 N horizontal coordinate system, and were 

logged in .gsf (generic sensor format) using SAIC 

ISS 2000 software. Data for both years were referenced to the Mean Lower Low Water (MLLW) vertical 

tidal coordinate system. For a complete description 

of the data acquisition and processing parameters, 

please see the data acquisition and processing reports (DAPRs) for projects: NF-04-06-VI and NF-05­

-05-USVI (Monaco and Rooney, 2004; Battista and 

Lazar, 2005). 

Processing Of Remotely Sensed Imagery 

Creating Bathymetry and Intensity Surfaces 

The 2004 and 2005 MBES bathymetric data were 

corrected for sensor offsets, latency, roll, pitch, yaw, 

static draft, the changing speed of sound in the water column and the influence of tides in CARIS Hips 

and Sips 5.3 and 5.4, respectively. The 2004 data 

was then binned to create a 1 x 1 m raster surface, 

and the 2005 data was binned to a create 2 x 2 m 

raster surface. After these final surfaces were created, the datum for the 2004 bathymetric surfaces 

was transformed from WGS84 to NAD83 using the 

“Project Raster” function in ArcGIS 9.1. The 2004 

surface was transformed so that it would have the 

same datum as the 2005 surface. The 2004 bathymetric surface was then down sampled from 1 x 1 

Figure 2.4. The NOAA ship Nancy Foster was used to acquire MBES imagery and ROV data during two, two week missions in 2004 and 2005. 

Figure 2.5. The RESON SeaBat 8101 ER (240 kHz) MBES sensor was 

pole-mounted on the NOAA ship Nancy Foster during two, two week 

missions in 2004 and 2005. It was used to collect MBES bathymetry and 

intensity imagery. 
Table 2.1. Descriptions of the morphometrics used to characterize the complexity of the seafloor in and around VICRNM. The GIS tools 

used to derive these metrics from the MBES bathymetry surface are also included in the table below. 

DATASET UNIT DESCRIPTION TOOL 

Curvature 

1/100 z units 

– = concave 

+ = convex 

Rate of change in 

curvature across the 

surface highlighting ridges, 

crests and valleys 

(3 x 3 cell neighborhood) 

Curvature function in 

ArcGIS 3D Analyst 

Plan Curvature 

1/100 z units 

– = concave 

+ = convex 

Curvature of the surface 

perpendicular to the slope 

direction 

(3 x 3 cell neighborhood) 

Plan curvature function in 

ArcGIS 3D Analyst 

Profile 

Curvature 

1/100 z units 

– = convex 

+ = concave 

Curvature of the surface in 

the direction 

(3 x 3 cell neighborhood) 

Profile curvature function 

in ArcGIS 3D Analyst 

Depth (Mean) Meters Average water depth 

(3 x 3 cell neighborhood) 

Focal statistic function in 

ArcGIS Spatial Analyst 

Depth 

(Standard 

Deviation) 

Meters 

Dispersion of water depth 

values about the mean 

(3 x 3 cell neighborhood) 

Focal statistic function in 

ArcGIS Spatial Analyst 

Surface rugosity Ratio value 

Ratio of surface area to 

planar area 

(3 x 3 cell neighborhood) 

Rugosity function in the 

Benthic Terrain Modeler 

toolbox (Jenness 2002, 

2004; Wright et al., 2005) 

Slope Degrees 

Maximum rate of change 

in slope between cell 

and 8 neighbors 

(3 x 3 cell neighborhood) 

ArcGIS Spatial Analyst’s 

slope function 

Slope of the 

slope Degrees of degrees 

Maximum rate of maximum 

slope change between cell 

and eight neighbors 

(3 x 3 cell neighborhood) 

ArcGIS Spatial Analyst’s 

slope function 

page 

22 Chapter 2: Benthic Habitat Map Creation
to 2 x 2 m using the “Resample” function in ArcGIS 9.1. The 2004 surface was resampled so it would have 

the same spatial resolution as the 2005 surface. Having the same coordinate systems and spatial resolutions, 

the final 2004 and 2005 bathymetry rasters were then merged using the Raster Calculator function “Merge” in 

ArcGIS’s Spatial Analyst Extension to create a seamless bathymetry surface for the entire VICRNM area south 

of St. John. 

The 2004 and 2005 MBES intensity data were received as raw.xtf and .gsf files (respectively), which recorded 

the uncorrected intensity value from each snippet (i.e., from each beam of sound for each ping of sound). The .xtf 

and .gsf files were geometrically and radiometrically corrected using Geocoder 3.0 (Fonseca and Calder, 2005). 

In particular, the intensity surface was geometrically corrected for navigation attitude, transducer attitude and 

slant range distortion using the MBES bathymetric surface. It was radiometrically corrected for changes in acquisition gains, power levels, pulse widths, incidence angles and ensonification areas. All snippets were preserved 

during these corrections, allowing the full resolution data to be used to create the final mosaic. Systematic noise 

in 2004 intensity mosaic was then filtered in frequency space using the “fast Fourier transformation” (Mather 

2004; Lillesand and Kiefer, 2000) function in ENVI 4.5. The final 2004 and 2005 intensity rasters were converted 

to relative 8-bit (0 – 255) values, since neither intensity surface was calibrated. These 8-bit images were then 

color balanced and merged using ENVI’s “Mosaicing (Georeferenced)” function to create a seamless intensity 

surface of the entire VICRNM area south of St. John. 

Creating Derivative Surfaces 

A suite of eight metrics were derived from the final, merged bathymetry surface, in order to characterize the 

complexity and structure of the seafloor. These metrics specifically included: (1) mean depth, (2) standard deviation of depth, (3) curvature, (4) plan curvature, (5) profile curvature, (6) rugosity, (7) slope, and (8) slope of 

slope. Each of these metrics was calculated using a square 3 x 3 cell neighborhood, where the central pixel in 

the neighborhood was assigned the calculated 

value. These metrics are described in more de- Table 2.2. The amount (%) of variance in a principal component that is explained by a single complexity surface. For example, the bathymetry surface tail in Table 2.1. 

contributed 11.1%, 5.7% and 22.5% of the information contained in the transformed image’s first, second and third principal components, respectively. These eight complexity surfaces were subsequently stacked, and exported to create one 

image with several different bands (each band 

representing a specific metric). This image 

was then transformed into its first three principal components using the “Principal Components Analysis” (PCA) (Mather 2004; Lillesand 

and Kiefer, 2000) function in ENVI 4.6. This 

transformation reduced the dimensionality of 

the dataset by removing information that was 

redundant across the different bands. The resulting three band PCA image only contained 

information that uniquely described the complexity and structure of the seafloor (Table 

2.2). It is important to note that each metric contributed equally to the information contained in the fi rst principal 

component. Rugosity and slope explained the most amount of the variance contained in the second principal 

component. The three flavors of bathymetry (i.e., depth, mean depth, and standard deviation of depth) explained 

the most amount of the variance contained in the third principal component. Each of these three bands were 

converted from 16-bit, floating point values to 8-bit, integer values, so that they could be imported into ENVI 

Zoom 4.6 

PC NUMBER 

1 2 3 

Bathymetry 11.1 5.7 22.5 

Bathymetry (Mean) 11.1 5.7 22.3 

Bathymetry (Stdev) 11.1 5.4 35.7 

Curvature 11.1 5.5 2.2 

Curvature (Plan) 11.1 5.5 2.3 

Curvature (Profile) 11.1 5.5 0.3 

Rugosity 11.1 22.1 2.8 

Slope 11.1 22.1 2.3 

Slope of Slope 11.1 22.5 9.6 

Chapter 2: Benthic Habitat Map Creation 

page 

23 
Chapter 2: Benthic Habitat Map Creation 

page 

24 

2.3 HABITAT BOUNDARY DELINEATION AND CLASSIFICATION 

The majority of shallow-water coral reef habitats have been successfully characterized at high thematic resolutions (≤ 32 classes) by conducting heads-up digitizing and interpretation of high resolution (≤ 4 x 4 m) imagery 

(Kendall et al., 2001; Battista et al., 2007

a

; Battista et al., 2007

b

). These resulting maps, however, are time-intensive to produce, limited by the size of the minimum mapping unit, and ultimately subjective and irreproducible 

because they depend on the accuracy and interpretation of the person that is digitizing. 

In order to address these difficulties and increase the repeatability and efficiency with which maps are produced, 

the Biogeography Branch has been experimenting with alternative techniques to automate the process of delineating and attributing features on the seafloor. This moderate-depth map represents the Biogeography Branch’s first attempt to create a benthic habitat map from acoustic imagery using this new semi-automated methodology. 

This method specifically employs a combination of object and pixel-based classification techniques to delineate 

and classify benthic habitat features. This process is described in a general way in Figure 2.6, and in more detail 

in the following paragraphs. 1

2 

3

(Stdev)

Depth 

Depth 

Slope of Slope

Slope

Rugosity 

Curvature

(Plan)

(Profile)

(Mean) 

e 

Coral Reef Hard bottom 

2. HABITAT 

DELINEATION 1. IMAGE ACQUISITION & PROCESSING 

Soft bottom

Rhodoliths

Intensity 

ROV 

(n=13) 

Drop Camera 

(n=117) 

3. GROUND VALIDATION 4. HABITAT CLASSIFICATION 

Delineate habitat

features in imagery 

Edge Detection

Reduce data

redundancy 

PCA 

Create ROIs & overlay

on imagery from step 2 

Regions of Interest 

Train algorithm to

classify habitat features 

Ground Truthing Classification

Collect & classify

underwater video 

Manually edit &

merge 2 habitat maps 

Editing 

Figure 2.6. Diagram illustrating the process used to create the moderate-depth benthic habitat south of St. John. 

Results from this new mapping approach suggest that it is 7x more time efficient than heads-up habitat delineation and attribution. Additionally, less than 40% of the polygons created using the semi-automated method 

were manually edited, indicating that the majority of the moderate-depth map produced using this method is 

completely reproducible. These initial results indicate that this new semi-automated approach has the potential 

to increase the repeatability and efficiency with which maps are produced. 

Habitat Delineation 

Habitat features on the seafloor were identified and extracted using ENVI Zoom 4.6 Feature Extraction (Fx) Module. This module uses edge detection algorithms to detect and delineate objects in a single image or in a suite of 

spatially coincident images. An object is defined as a region of interest with unique spatial, spectral (brightness 

and color), and/or textural characteristics that make it visually distinct from its surroundings (ITT VIS, 2008

a

). 

There are four steps involved in extracting discrete objects from an image (or images). These steps specifically 

include: (1) segmenting the image, (2) merging smaller segments into larger objects, and (3) computing spatial, 

spectral, textual and custom attributes for each object. The first two steps are interactive, allowing the user to adjust the input parameters to extract the features in which they are most interested. In particular, step 1 allows the 

user to alter the “scale level” of the edge detection algorithm to determine the size of the objects to be extracted. 

Choosing a higher scale level (>75) causes a smaller number of larger segments to be defined, while choosing 

a lower scale level (<25) causes a greater number of smaller segments to be defined (ITT VIS, 2008

b

). Step 2 

allows the user to alter the “merge level” of the algorithm and to merge smaller segments into larger objects. 

Choosing a higher merge level (>75) causes segments with faded or faint edges to be merged, while choosing a 

lower merge level (<25) preserves more of these features with faded or faint edges (ITT VIS, 2008

b

; Robinson et 

al., 2002). In step 3, ENVI Fx computes 14 spatial metrics, 4 textual metrics, 1 band ratio metric, 3 hue, saturation 

and intensity (HSI) metrics and 4 spectral metrics (for each input band) for each distinct object. These different 
Table 2.3. Descriptions of the spatial metrics calculated by ENVI Fx for each habitat polygon identified during the edge-detection 

process (ITT VIS, 2008b

). 

ATTRIBUTE DESCRIPTION FORMULA 

AREA Total area of the polygon, minus the area of the holes. Values are in map units. -

LENGTH 

The combined length of all boundaries of the polygon, including the boundaries 

of the holes. This is different than the MAXAXISLEN attribute. Values are in map 

units. 

-

COMPACT 

A shape measure that indicates the compactness of the polygon. A circle is the 

most compact shape with a value of 1 / pi. The compactness value of a square is 

1 / 2(sqrt(pi)). 

= Sqrt (4*AREA/pi) / 

outer contour length 

CONVEXITY 

Polygons are either convex or concave. This attribute measures the convexity of 

the polygon. The convexity value for a convex polygon with no holes is 1.0, while 

the value for a concave polygon is less than 1.0. 

= length of convex hull / 

LENGTH 

SOLIDITY 

A shape measure that compares the area of the polygon to the area of a convex 

hull surrounding the polygon. The solidity value for a convex polygon with no 

holes is 1.0, and the value for a concave polygon is less than 1.0.

 = AREA / 

area of convex hull 

ROUNDNESS 

A shape measure that compares the area of the polygon to the square of the 

maximum diameter of the polygon. The “maximum diameter” is the length of the 

major axis of an oriented bounding box enclosing the polygon. Circle = 1 and 

square = 4/pi.

 = 4 * (AREA) / 

(pi * MAXAXISLEN2) 

FORMFACTOR A shape measure that compares the area of the polygon to the square of the total 

perimeter. The form factor value of a circle is 1, and the value of a square is pi / 4.

 = 4 * pi * (AREA) / 

(total perimeter)2 

ELONGATION 

A shape measure that indicates the ratio of the major axis of the polygon to the 

minor axis of the polygon. The major and minor axes are derived from an oriented 

bounding box containing the polygon. Square = 1 and Rectangle > 1. 

= MAXAXISLEN / 

MINAXISLEN 

RECT_FIT 

A shape measure that indicates how well the shape is described by a rectangle. 

This attribute compares the area of the polygon to the area of the oriented bounding box enclosing the polygon. Rectangle = 1 and non-rectangle < 1. 

= AREA / 

(MAXAXISLEN * MINAXISLEN) 

MAINDIR 

The angle subtended by the major axis of the polygon and the x-axis in degrees. 

The main direction value ranges from 0 to 180 degrees. 90 degrees is North/ 

South, and 0 and 180 degrees is East/West. 

-

MAJAXISLEN 

The length of the major axis of an oriented bounding box enclosing the polygon. 

Values are map units of the pixel size. If the image is not georeferenced, then 

pixel units are reported. 

-

MINAXISLEN 

The length of the minor axis of an oriented bounding box enclosing the polygon. 

Values are map units of the pixel size. If the image is not georeferenced, then 

pixel units are reported. 

-

NUMHOLES The number of holes in the polygon. Integer value. -

HOLESOLRAT The ratio of the total area of the polygon to the area of the outer contour of the 

polygon. The hole solid ratio value for a polygon with no holes is 1.0. 

= AREA / 

outer contour area 

Chapter 2: Benthic Habitat Map Creation 

page 

25 
metrics will be referred to hereafter as “Fx object attributes,”

and are described in more detail 

in Tables 2.3 and 2.4 (ITT VIS, 

2008b

). After these Fx object attributes are calculated, the user 

may export all of the objects and 

their associated spatial, textual, 

HSI, ratio and spectral attributes 

as a single ESRI shapefi le (Figure 2.7). 

Using this ENVI Fx workfl ow, 

discrete habitat features were

identifi ed and delineated in the 

acoustic imagery. Specifi cally, 

features that appeared to be

coral reef habitats (i.e., either

aggregate reef, patch reefs, aggregated patch reefs, pavement 

or pavement with sand channels) 

were extracted from the PCA image using a scale level of 75 and 

an merge level of 99.2 (Figures 

2.8 a-d). Features that appeared 

to be soft bottom habitats (i.e., 

either sand or scattered coral

and rock) or a mixture of soft bottom and rhodolith habitats were 

extracted from the intensity surface using a scale level of 25 and a merge level of 

99.1. The fi nal coral reef habitats features, as well as 

soft bottom and rhodolith habitat features, were exported from ENVI Zoom as two separate shapefiles. 

The attribute tables of these shapefi les contained 

the spatial, textual, HSI, ratio and spectral metrics 

calculated for each habitat feature. Each shapefile was then visually inspected and polygons (or 

pieces of polygons) were manually removed, if they 

appeared to be the result of acoustic noise (Figure 

2.9). Each of the Fx object attributes associated with 

each habitat feature were then converted to rasters 

using a custom script built in ArcGIS’s Model Builder. 

In total, there were 34 attribute rasters associated 

with the 1,287 coral reef features and 22 attribute 

rasters associated with the 11,421 soft bottom and 

rhodolith features. The 34 coral reef rasters and 22 

soft bottom and rhodoliths rasters were respectively 

stacked, and exported to create two separate images with several different bands (each band representing a specifi c metric). The next step in the mapping process sought to better understand, identify, 

and quantify the correlations between these objectbased metrics and specifi c habitat classes. 

Table 2.4. Descriptions of the textual, ratio, hue saturation and intensity (HSI), and spectral 

metrics calculated by ENVI Fx for each habitat polygon identifi ed during the edge-detection 

process (ITT VIS, 2008b

).

ATTRIBUTE DESCRIPTION

TEXTUAL

TX_RANGE 

Average data range of the pixels comprising the region inside the 

kernel. A kernel is an array of pixels used to constrain an operation to 

a subset of pixels.

TX_MEAN Average value of the pixels comprising the region inside the kernel. 

TX_VARIANCE Average variance of the pixels comprising the region inside the kernel. 

TX_ENTROPY 

Average entropy value of the pixels comprising the region inside the 

kernel. ENVI Zoom computes entropy, in part, from the Max Bins in 

Histogram preference. 

RATIO BANDRATIO 

"Values range from -1.0 to 1.0.ENVI Zoom computes a normalized 

band ratio between two bands, using the following equation:

(B2 - B1) / (B2 + B1 + eps), where eps is a small number to avoid division by zero."

H S I 

HUE Hue is often used as a color fi lter and is measured in degrees from 0 

to 360. A value of 0 is red, 120 is green, and 240 is blue. 

SATURATION Saturation is often used as a color fi lter and is measured in floatingpoint values that range from 0 to 1.0. 

INTENSITY 

Intensity often provides a better measure of brightness than using the 

AVGBAND_x spectral attribute. Intensity is measured in floating-point

values from 0 to 1.0. 

SPECTRALMINBAND_X Minimum value of the pixels comprising the region in band x. 

MAXBAND_X Maximum value of the pixels comprising the region in band x. 

AVGBAND_X Average value of the pixels comprising the region in band x. 

Chapter 2: Benthic Habitat Map Creation

STDBAND_X Standard deviation value of the pixels comprising the region in band x.

0 250 500 m

Figure 2.7. This map depicts a subset of habitat objects that were exported from ENVI Fx as a single ESRI shapefi le. The pink/red objects 

are coral reef habitats features, while the green/brown objects are 

soft bottom and rhodoliths habitats features. 

page 

26 
Figure 2.8. This series of screenshots depicts (step-by-step) the ENVI Fx 4.6 feature extraction process used to identify and delineate 

visually distinct habitat features. ENVI Fx is an add-on module that is nested within ENVI Zoom. 

Figure 2.8a. Screenshot depicting the first step (i.e., choosing 

the input or source image) in the ENVI Fx feature extraction process. Specifically, this screenshot shows the source image (i.e., 

PCA image) from which coral reef habitat features were extracted. 

Bands 1, 2 and 3 in the PCA image correspond to the image’s first, 

second and third principal components, respectively. 

Figure 2.8b. Screenshot depicting the second step (i.e., choosing a scale level) in the ENVI Fx feature extraction process. Specifically, this screenshot shows the scale level (i.e., 0.0) used to 

identify coral reef habitat features. The preliminary results from 

choosing this scale level are seen in the red box in the lower right 

corner of the screenshot. 

Chapter 2: Benthic Habitat Map Creation 

page 

27 

Figure 2.8c. Screenshot depicting the third step (i.e., choosing 

a merge level) in the ENVI Fx feature extraction process. Specifically, this screenshot shows the merge level (i.e., 99.2) used 

to aggregate coral reef habitat features into larger objects. The 

preliminary results from choosing this merge level are seen in the 

red box in the lower right corner of the screenshot. 

Figure 2.8d. Screenshot depicting the fourth and final step (i.e., 

calculating polygon attributes) in the ENVI Fx feature extraction 

process. Specifically, this screenshot shows that spatial, textual, 

HSI, ratio and spectral metrics were calculated for each of the 

coral reef habitat polygons identified by the feature extraction process. After these attributes were calculated, the final coral reef 

polygons (and their associated attributes) were exported as a 

single ESRI shapefile. 
Chapter 2: Benthic Habitat Map Creation 

page 

28 

Ground Validation 

Extensive field work is needed to create high-quality 

benthic habitat maps because it enhances the accuracy of habitat attribution and (to a lesser degree) 

habitat delineation. Given the importance of field 

work, a team of NOAA scientists visited predetermined locations to explore and verify existing habitat 

information on the seafloor. These “ground validation” (GV) locations were targeted by the cartographer to satisfy the following objectives: 

1. Explore features in the imagery with unknown or 

confusing acoustic signatures 

2. Confirm that the habitat type correlated with a 

particular acoustic signature remained consistent throughout the entire study area. 

To achieve this first objective, the cartographer

manually placed GV points in, or transects that intersected, features with unknown habitat types. These

points and transects were important for understanding the habitat class associated with these distinct,

but unknown acoustic signatures. To achieve the

second objective, the cartographer manually placed 

GV points in, or transects that intersected, features

with known habitat types distributed throughout the

entire spatial extent of the mapped area. These

points and transects were important to the GV process because the same habitat type may exhibit slightly different signatures in different parts of the study area. A single habitat type may exhibit slightly different acoustic 

signatures because polygons contain some structural and biological cover heterogeneity within them (e.g., polygons attributed as “rhodoliths” contain varying amounts of sand and algae). 

Two different GV sampling methods (i.e., points and transects) were used because the GV data were collected 

during two different years (2005 and 2009) using two different pieces of equipment. In 2005, GV data were collected along 13 transects using a remotely operated vehicle or ROV (Figure 2.10) from 2/1 to 2/12 onboard the 0 50 100m

Figure 2.9. This graphic depicts how artifacts in the acoustic imagery

were manually removed from the benthic habitat map. Some habitat 

polygons (denoted by the black lines) were manually deleted or edited because they included acoustic noise within their boundaries. The 

white arrows point to artifacts in the PCA image that were falsely identi­ fied as discrete habitat features. The gray arrow points to real habitat 

features in the PCA image. The white and black dotted line denotes 

where this polygon was manually split to remove the artifacts from the

habitat polygon.

Figure 2.10. In 2005, the Spectrum Phantom S2 ROV (right) collected underwater video and high resolution photographs of the 

seafl oor along 13 transects. These datasets were used for ground validation purposes. The map (left) depicts these 13 transects 

(and their orientation) overlayed on the PCA image. The thin black, hatched polygons denote the extent of the shallow-water 

maps developed using optical imagery. The thick black polygon denotes the spatial extent of the acoustic imagery. 
Chapter 2: Benthic Habitat Map Creation 

page 

29 

Figure 2.11. In 2009, the SeaViewer Sea-Drop 950 camera (right) collected underwater video of the seafloor at 117 discrete locations. 

This dataset was used for ground validation purposes. The map (left) depicts these 117 points as white dots overlayed on the PCA image. 

The thin black, hatched polygons denote the extent of the shallow-water maps developed using optical imagery. The thick black polygon 

denotes the spatial extent of the acoustic imagery. 

NOAA ship, Nancy Foster. In 2009, GV data were collected at 117 GV sites using an underwater drop camera 

(Figure 2.11) from 1/5 to 1/16, 2/9 to 2/20 and 5/31 to 6/7 onboard small research vessels provided by the National Park Service (Figure 2.12). 

For the 2005 mission, the GV transects were systematically placed over the study area to include 

as many benthic habitat features and transition 

zones as possible. Distinct features and transitional areas were identified by visual examination 

of fine-scale multibeam bathymetry data collected 

in 2004 and moderate-scale GEODAS bathymetry data (GEODAS, 2005). The bathymetry data 

were divided into distinct benthic habitats using 

variations in depth, roughness and spatial patterning (ridges, bumps, troughs, regular undulations, 

etc.). Georeferenced underwater video and photographs were acquired using a video camera and 

high-resolution digital still camera mounted on a 

Spectrum Phantom S2 ROV. High powered strobe 

lights mounted on the ROV were used to supplement ambient light levels during the day and served 

as the only source of light during night operations. 

Data from the cameras were transmitted and immediately recorded to a computer’s hard drive. Video data were 

collected during an entire transect, and still photos were collected every 30 seconds. The forward-facing video 

camera was pointed at a 45 degree downward angle to give ROV pilots a view of upcoming obstacles and researchers a view of the benthic habitat. The ROV’s height above the substrate and speed were approximately 2 

m and 1 m/s, respectively. The ROV pilot attempted to keep the ROV height and speed as constant to standardize the field of view and spatial resolution of interpretations. Two downward pointing parallel lasers separated 

by 5 cm and the scale of habitat features and organisms were used to estimate height off the bottom. Still photo 

images were acquired using a downward pointed camera. A transducer attached to the ROV and an acoustic 

receiver (suspended off the side of the ship) were used to determine the ROV’s relative position to the ship. The 

ROV’s absolute geographic position was estimated using this relative position and the shipboard GPS. The positional accuracy was estimated to be within +/-5 m. 

Figure 2.12. A U.S. National Park Service vessel was used to collect 

underwater video in support of habitat map development. 
Chapter 2: Benthic Habitat Map Creation 

page 

30 

For the 2009 missions, GV sites were 

systematically placed in features with unknown acoustic signatures, and in parts of 

the study area that were not explored using the ROV in 2005. These GV sites were 

prioritized as high, medium and low in case 

there was not enough time to visit all of them 

during the field mission. Geographic coordinates for these sites were uploaded to a 

hand-held Garmin 76 CS WAAS-enabled 

GPS unit (Figure 2.13). At the beginning of 

every morning, a specific region was selected (depending on the weather) to work in 

during that day. Often, this meant working in 

areas with more challenging sea conditions first, and then moving inshore or to more 

protected areas as the day progressed. GV 

sites were navigated to using the hand-held 

Garmin 76 CS GPS unit. The vessel was 

maneuvered to within 5 m of the target location. Once in position, NOAA scientists would concurrently deploy a 

SeaViewer Sea-Drop 950 camera (attached to a down weight and 300 feet of line), as well as begin logging a 

waypoint on a Trimble GeoXT GPS receiver. The drop camera reached the bottom in approximately 60 - 90 seconds. While on site, the vessel’s position was captured as an epic (i.e., point) approximately every 5-10 seconds 

depending on the number of satellites detected by the GPS antennae. The underwater video was recorded onto 

mini-video tapes using a Sony Walkman video recorder. The camera operator adjusted the camera lens to get a 

downward view of the seafloor approximately 2 m from the bottom, and a side view of the seafl oor. This allowed 

for accurate measurements of percent biological cover and a broader scale understanding of the structure at 

each site. No attempt was made to standardize the amount of time the camera was on the seafloor. In fact, it was 

often advantageous to drift across habitat transitions, as 

it allowed the cartographer to understand the ecotone 

at many locations (Figure 2.14). While the camera was 

recording video of the seafloor, an observer viewed the 

video real-time on a Panasonic Toughbook, and classi­ fied the major/detailed geomorphological structure, major/detailed biological cover, and percent coral for each 

site. Water-proof field maps (depicting the draft habitat 

map and source imagery) were used to visually link signatures in the imagery with the in situ habitats at seen in 

the video (Figure 2.1). In many cases, suggestions on 

boundary delineation and habitat classifications were 

made directly on the field maps with permanent marker. 

Once back in the offi ce, Trimble Pathfi nder Offi ce software was used to post process and differentially correct 

the raw GPS data to the Continually Operating Reference System (CORS) station at St. Thomas, U.S. Virgin Islands (VITH). The underwater video was converted 

from the mini-tapes to softcopy form using Final Cut Pro 

software. The classification of each GPS location (completed in the field) was then reviewed in conjunction with 

the acoustic imagery and the associated underwater 

video, to develop a fi nal classified set of GV points (Figure 2.15). Next, the fi nal classified GV points were separated into multiple shapefiles (i.e., one file for each unique habitat class combining structure, cover, percent cover 

Figure 2.13. The equipment used in the field to collect georeferenced underwater video. The Panasonic Toughbook (left) was used to view the video in realtime. The Trimble GeoXT GPS receiver (middle) was used to record the location 

of the drop-camera. The Garmin 76 WAAS-enabled GPS unit (right) was used 

to navigate to each GV site. 

0 25 50m

GV Site 66 

Figure 2.14. This map illustrates how the drop camera (denoted 

by the white points) drifted over a habitat transition (denoted by 

the black line) at GV site 66, allowing the cartographer to understand both habitat types. 
Chapter 2: Benthic Habitat Map Creation 

page 

31 

0 50 100m

GV Site 13 

Figure 2.15. A fi nal classified set of GV points was developed by concurrently viewing the acoustic imagery and underwater video 

for a specific location. The map (left) depicts the drop camera location (denoted by the white points) for GV site 13 overlayed on 

the PCA image. The screenshot (right) depicts the underwater video associated with GV site 13. 

and live coral cover) using the “Split Layer by Attribute” toolbox in ArcGIS 9.3 (Patterson 2008). Each shapefile 

was subsequently imported into ENVI 4.6, and converted to ROIs (Regions of Interest) using the “Import Vector 

Files” and “Export Active Layer to ROIs” functions (Figure 2.16). These ROIs were needed to create a second 

draft habitat map. 

Figure 2.16. The classified GV points were converted from ESRI shapefiles to ENVI 4.6 ROIs. The table (right) shows these converted 

ROIs. The map (left) depicts the coral reef features extracted by ENVI Fx at three different scales. The location of the ROI highlighted 

in the table (i.e., 08 Ind Patch Reef Algae 90-100 10-50) is denoted by two maroon pixels overlayed on a coral reef feature in the finest 

scale window (bottom right). 
Chapter 2: Benthic Habitat Map Creation 

page 

32 

Habitat Classification 

QUEST (Quick, Unbiased, Efficient Statistical Tree) 

The ROIs created from the 

classified GV locations and 

video (above) were used to 

train the classifi cation algorithm and to develop a second draft habitat map. These 

tasks were performed using 

ENVI 4.6’s RuleGen 1.02 

add-on (Figure 2.17; Jengo 

2004). This add-on contains 

the Quick, Unbiased, Efficient 

Statistical Tree (QUEST) 

algorithm (Loh and Shih, 

1997), which is implemented 

via ENVI’s native Decision 

Tree Tool. QUEST is a type 

of Classification and Regression Tree (CART) (Breiman 

et al., 1984) that: (1) is nonparametric and nonlinear, (2) 

has negligible variable selection bias, (3) is computationally simplistic, and (4) yields 

binary splits for categorical 

predictor variables, ordinal 

predictor variables, or a mix 

of both types of predictors. 

Unlike CART however, the QUEST algorithm separates objects in an image into classes using univariate (axis-orthogonal) discriminat-based splits. 

This type of analysis separates the classification 

process into two parts at each split (or node) in the 

decision tree (Figure 2.19). The first step in this 

analysis, independent variable selection, fi nds the 

independent variable (i.e., input band) that is signifi cantly different from the other variables in order 

to create the most efficient split. The second step 

in this analysis, binary split identification, identifies the threshold at which to split the previously 

selected input band into two classes that are as 

homogenous as possible. 

More specifi cally, during independent variable se-

lection stage, QUEST uses the Pearson contingency table Chi-squared test of independence to 

approximate the statistical signifi cance (p-value) of 

each input band. If the smallest p-value for these 

input bands is less than a predefined threshold (as 

determined by the Bonferroni method for multiple 

comparisons), then the corresponding input band 

is selected to be included in the classifi cation. If 

not, then Levene’s F-test for unequal variances is 

Figure 2.17. The converted ROIs were used to create a QUEST classification tree using ENVI 

4.6’s RuleGen 1.02 add-on. The variable type declarations included all of the spatial, textual, 

ratio, HSI and spectral metrics calculated for each polygon by ENVI’s Fx module. 

Figure 2.18. Underwater photograph of the Great Star Coral (Montastraea 

cavernosa). 
Chapter 2: Benthic Habitat Map Creation 

page 

33 

calculated for each input band. If the smallest p-value from this F-test is less than another 

predefined threshold (again, as determined by 

the Bonferroni method), then the corresponding input band is selected to be included in the 

classification. If not, then the input band with 

the smallest p-value from the fi rst Chi-squared 

test is selected to be included in the classification (Loh and Shih, 1997). Thus, if all the input 

bands are uninformative with respect to partitioning an image, then each band has approximately the same chance of being selected to 

split a node. 

Once the statistically significant input band 

has been selected, this input band is then 

used during the binary split identification step 

to partition objects in the image into different 

classes. To determine the location of these 

splits, QUEST uses a 2-means clustering algorithm (Hartigan and Wong, 1979), which 

minimizes the within-cluster sum of squares, 

to preliminarily group objects in the image into 

two super classes clustered around the two 

most extreme sample means. If the two super class means are identical, then the class 

with the largest number of samples becomes 

one superclass, while the remaining classes 

are grouped into the second superclass. The 

ideal split between these two classes is then 

identified using a modified version of quadratic 

discriminant analysis. This modifi ed analytical technique accounts for unequal variances 

by only using the quadratic root closest to the 

sample mean of each class. This quadratic root 

is then used to find the ideal split between the 

two super classes at the intersection of their 

density curves (Figure 2.20). Once the split is 

made, the entire process of variable selection 

and partitioning starts over, and continues iteratively until no more useful splits are found 

in the data, or until pruning or a predefi ned rule 

or tells the algorithm to stop (Loh and Shih, 

1997). 

Classifying Objects on the Seafloor 

The QUEST algorithm was used to classify 

each habitat object delineated by ENVI Fx. 

To simplify this classification process, coral 

reef habitats as well as soft bottom and rhodolith habitats were classifi ed separately. This 

means that ROIs for coral reef features and 

ROIs for soft bottom and rhodoliths features were used to train the QUEST algorithm separately to develop two 

different classification trees. These trees were built using the same input parameters (Table 2.5), but different 

combinations of the spatial, spectral, textual, HSI and band ratio attributes. In total, the algorithm found 91 and 

Figure 2.19. This diagram illustrates how QUEST uses binary decisions to 

split an image into different classes. For example at node 1, if a habitat object’s Minimum Band value is < 7.5, then it is attributed with the Individual 

Patch Reef detailed structure type. However, if a habitat object’s Minimum 

Band value is ≥ 7.5, then QUEST moves on to node 2 and so on down the 

line. While this sample decision tree only has three nodes, the fi nal coral reef 

and soft bottom/rhodoliths decision trees had 91 and 71 nodes, respectively. 

Figure 2.20. This graph describes how QUEST determines where to split 

super classes into two groups. The density curves of Superclass 1 and 2 are 

depicted in gray and black, respectively. The ideal split between the 2 super 

classes (denoted by the vertical dashed line) is determined by using the quadratic root closest to the sample mean of each class near the intersection of 

their density curves. This split becomes a binary node in the decision tree. 
 

Chapter 2: Benthic Habitat Map Creation 

page 

34 

71 useful splits when grouping coral reef features and soft bottom/rhodoliths features, respectively. For coral 

reef habitats in particular, the algorithm found 19 (of the 34) ENVI Fx object attributes useful when splitting these 

objects into 20 distinct classes. For soft bottom and rhodoliths habitats, the algorithm found 14 (of the 22) ENVI 

Fx object attributes useful when splitting these objects into 11 distinct classes (Table 2.6). The distinct classes 

for both coral reef habitats as well as soft bottom and rhodoliths habitats were based on unique combinations of 

major structure, detailed structure, major biological cover, percent major biological cover and percent live coral 

cover classes. The final coral reef and soft bottom and rhodoliths classifications were exported from ENVI 4.6 as 

separate ESRI shapefiles. 

Table 2.5. Descriptions of the input parameters used when building the QUEST classifi cation trees. 

INPUT 

PARAMETER 

INPUT VALUES 

USED 

IMPACT OF 

PARAMETER DEFINITION 

Minimum Node 

Size 5 When to stop the tree from growing 

The smallest number of samples in a node during tree construction. The 

node will not be split if it contains fewer cases than this number. The smaller this value is, the larger the initial tree will be prior to pruning. The default 

value is max (5, n/100), where n is the total number of observations (Shih, 

2004). 

Split Method Univariate How to split input 

bands 

The user can choose to create discriminant-based splits using a single 

variable (to examine the effects of predictor variables one at time) or a 

linear combination of variables (Shih, 2004; StatSoft, 2007). 

Variable 

Selection Method Unbiased 

How to select 

important input 

bands 

The user can choose between the unbiased variable selection method described in Loh and Shih (1997) or the biased exhaustive search method 

which is used in CART described in Breiman et al. (1984) (Shih, 2004). 

The unbiased method uses discriminant-based split methods to prevent 

biased in variable selection. Thus, if all the attributes are uninformative 

with respect to the class attribute, then each has approximately the same 

chance of being selected to split a node (Lim et al., 2000). 

Alpha Value (α) 0.05 

When to consider 

a variable as 

significant 

Alpha value is a number 0 ≤α≤ 1 at which point a p-value is considered 

significant. If the unbiased variable selection method is used, then an alpha 

value is needed to conduct the tests (Shih, 2004). 

Number of SEs 

for 

pruning 

1 

How much of the 

tree to prune 

The number of SEs (standard errors) controls the size of the pruned tree. 

SE = 0 gives the tree with the smallest cross validation estimate of misclassification cost or error (Shih, 2004). 

Number of Folds 

(V) 10 

How to calculate 

SE for the tree 

The user can choose the value of V in V-fold cross-validation. 10-fold is 

usually recommended and is the default in CART (Shih, 2004). This means 

that when V = 10, the dataset is randomly divided into 10 roughly equal 

parts. One part is left out while a regression estimate is constructed using 

the 9 remaining parts. The left-out part is then used to estimate the prediction mean standard error for the tree (Loh, 2002). 

CV Tree 

Details No N/A The user can choose whether the details of the cross validation tree are reported (Shih, 2004). 

Output PStricks 

tree? No N/A 

The user can choose whether to use the PSTricks package (to access 

PostScript features that are otherwise not directly accessible from LaTeX) 

to draw the QUEST tree. LaTeX is a document preparation system for the 

TeX typesetting program, offering desktop publishing features for automating aspects of typesetting and desktop publishing. 
Classification Post Processing and Evaluation	 Table 2.6. The polygon metrics (calculated by ENVI Fx) that 

were identified by QUEST as being significant for classifying These ESRI shapefiles were then brought into ArcGIS 9.3 coral reef habitats and soft bottom/rhodolith habitats. In total, 19 for additional post processing and visual quality assurance metrics were used to classify coral reef habitats, and 14 metrics

and control. In particular, these steps included: (1) remov- were used to classify the soft bottom and rhodolith habitats. 

ing polygons smaller than the minimum mapping unit and 

filling gaps in the shapefiles, (2) merging the coral reef as 

well as soft bottom and rhodoliths maps, (3) visually evaluating and editing this merged map, and (4) smoothing the 

final habitat polygon boundaries. In step 1, habitat polygons, which were smaller than the 1,000 m2 MMU, were 

merged with adjacent polygons using ET Geowizards 

“Eliminate” function (Tchoukanski 2008). This function removed these polygons by merging them into neighboring 

polygons with which they shared their longest common 

border (Figure 2.21). In total, this function merged 2,717 

soft bottom and rhodoliths polygons into 638 soft bottom 

and rhodoliths polygons, and 698 coral reef polygons into 

290 coral reef polygons. After these polygons with < 1,000 

m2 area were removed, gaps in the soft bottom and rhodoliths shapefi le were filled using ET Geowizards “Clean 

Gaps” function (Tchoukanski 2008). This function added 

360 unattributed polygons into the soft bottom and rhodoliths shapefile. 

In step 2, the coral reef and soft bottom and rhodoliths 

shapefiles were integrated (during an active Editing session) to produce one shapefile. To do so, all 290 of the 

coral reef polygons were selected, copied and pasted into 

the soft bottom and rhodoliths shapefi le. These polygons 

were then merged and used to collectively clip the soft bottom and rhodoliths polygons, discarding the area that 

intersected. Clipping the soft bottom and rhodoliths polygons with the coral reef polygons ensured that there 

was no overlap between habitat types. After clipping, all of the coral reef polygons were then selected, copied 

and pasted again into the soft bottom and rhodoliths shapefile to create a continuous map. The ET Geowizards 

“Eliminate” function was then run again on this continuous map to remove any polygon slivers < 1,000 m2 created during the merge process. 

# of 

BANDS 

FX ATTRIBUTE 

(Coral Reef) 

FX ATTRIBUTE 

(Soft bottom & Rhodoliths) 

1 AVGBAND_1 AREA 

2 BANDRATIO AVGBAND_1 

3 COMPACT CONVEXITY 

4 FORMFACTOR HOLESOLRAT 

5 HUE MAINDIR 

6 INTENSITY MAJAXISLEN 

7 MAINDIR MAX_BAND_1 

8 MAXBAND MINBAND_1 

9 MAXBAND_2 NUMHOLES 

10 MAXBAND_3 ROUNDNESS 

11 MINAXISLEN STDBAND_1 

12 MINBAND_2 TX_ENTROPY 

13 MINBAND_3 TX_MEAN 

14 RECT_FIT TX_VARIANC 

15 SATURATION 

16 SOLIDITY 

17 TX_ENTROPY 

18 TX_MEAN 

19 TX_VARIANC 

Figure 2.21. The ET Geowizard’s “Eliminate” function was used to merge polygons < 1,000 m2 with neighboring polygons with 

which they shared their longest common border. The map on the left shows the habitat map before these polygons were merged, 

and the map on the right shows the habitat map after these polygons were merged. Polygons smaller than the map’s MMU are 

black. Coral reef features are white, and soft bottom and rhodoliths features are gray. 

Chapter 2: Benthic Habitat Map Creation 

page 

35 
 

Chapter 2: Benthic Habitat Map Creation 

page 

36 

In step 3, the merged map was visually evaluated, and polygons 

were manually edited if necessary. 

These edits included reclassifying, merging or deleting polygons 

where the cartographer disagreed 

with the algorithm’s interpretation. 

They also included manually digitizing habitat features (>1,000 m2 at 

a scale of 1:2,000) that were overlooked during the feature extraction 

process (Table 2.7). During this QA/ 

QC process, approximately 40% 

of the total number of polygons 

were manually deleted or merged 

and approximately 37% of the total 

number of polygons were manually 

added (Table 2.8). In the fi nal step, 

(step 4), ET Geowizard’s “Smooth 

Polygons” function was used to 

smooth the pixilated appearance 

of the polygon lines (Figure 2.22). 

The B-spline smoothing algorithm 

was used with the input parameters “smooth” and “freedom” set 

to 10 and 5, respectively. The end 

result was a seamless habitat map 

of the seafloor area between 14 to 

55 m deep within and around the 

VICRNM. 

Table 2.7. Estimated number of polygons that were manually deleted, added or merged. 

TOTAL # % OF POLYGONS POLYGONS 

Unedited Map 1,324* -

Final Map 1,283 -

Deleted/Merged 514 (514/1,283)*100 = 40% 

Added 473 (473/1,283)*100 = 37% 

Note: * = 7,221 polygon slivers < 1,000 m2 were subtracted from the total

number of polygons (8,545) in the unedited map to derive this number. 

Table 2.8. Estimated number of polygons that were manually reattributed because they 

were deleted, added and/or reclassified. These numbers are based on a randomly distributed sample of 1,000 points stratified by detailed structure type and weighted by area. 

Habitat classifications contained in the original map (i.e., the unedited map produced by 

QUEST) and the final map (i.e., the map that was manually edited and delivered to the 

NPS) were extracted at each of these 1,000 points, and compared to determine whether 

they had been changed. 

POLYGON 

ATTRIBUTE 

TOTAL # 

UNEDITED 

POLYGONS 

% UNEDITED 

POLYGONS 

TOTAL # 

EDITED 

POLYGONS 

% EDITED 

POLYGONS 

Major Structure 1,149 90% 134 10% 

Detailed Structure 1,106 86% 177 14% 

Major Cover 1,137 89% 146 11% 

% Cover 888 69% 395 31% 

Major + % Cover 844 66% 439 34% 

% Live Coral Cover 1,239 97% 44 3% 

Figure 2.22. ET Geowizard’s “Smooth Polygons” function was used to smooth the pixilated appearance of the polygon lines. The map 

on the left shows the habitat map before the polygon lines were smoothed, and the map on the right shows the habitat map after the 

polygon lines were smoothed. 
2.4 GIS QUALITY CONTROL 

All GIS deliverable products generated throughout the mapping process were examined for attribution and topological errors. Particular attention was given to polygon geometry and attribution of the benthic habitat map, as 

well as to the attribution of each GV point. Multipart, sliver and void polygons were all removed using standard 

ArcGIS Spatial Analyst tools. Two custom ArcGIS extensions were employed to identify the following conditions: 

1. Adjacency – polygons that shared a common boundary and exact attribute combination that were delineated separately (Buja, 2008a

) 

2. Overlap – polygons sharing the same geographic space, thus violating mutual exclusion (Buja, 2008b

) 

Errors resulting from either of these GIS routines were corrected in the draft maps and eliminated from the final 

product. A visual inspection of attributes on a feature-by-feature basis was conducted to correct for any misspellings or illogical attribute combinations. These quality assessments and controls ensured that the GIS data from 

this work were topologically clean and free of attribution errors. In addition, metadata summaries were prepared 

in an FGDC-compliant format for all GIS products that were supplied during fi nal delivery. 

Chapter 2: Benthic Habitat Map Creation 

page 

37 
Chapter 2: Benthic Habitat Map Creation 

page 

38 

LITERATURE CITED 

Battista, T. A., and J.V. Lazar. 2005. MBES data acquisition and processing report: Project NF-05-05-USVI, 

NOAA Data Acquisition and Processing Report, NOS NCCOS CCMA (Online). 

Battista, T. A., B.M. Costa and S.M. Anderson. 2007

a

. Shallow-water benthic habitats of the main eight Hawaiian 

islands. NOAA Technical Memorandum NOS NCCOS 61(Online). 

Battista, T.A., B.M. Costa and S.M. Anderson. (2007)

b

. Shallow-Water Benthic Habitats of the Republic of Palau. 

NOAA Technical Memorandum NOS NCCOS 59 (Online). 

Breiman, L., J.H. Friedman, R.A. Olshend and C.J. Stone. 1984. Classification and regression trees. Belmont, 

California: Wadsworth International Group. p 324. 

Buja, K. 2008

a

. Find adjacent features. ESRI Support Center. Online: http://arcscripts.esri.com/details. 

asp?dbid=15805. Accessed March 2009. 

Buja, K. 2008

b

. Find overlapping polygons. ESRI Support Center. Online: http://arcscripts.esri.com/details. 

asp?dbid=15198. Accessed March 2009. 

Fonseca, L., and B. Calder. 2005. Geocoder: an efficient backscatter map constructor. Proceedings of the U.S. 

Hydrographic Conference 2005, San Diego, CA. 

GEODAS. 2005. NOAA’s GEOphysical DAta System (GEODAS). Online: http://www.ngdc.noaa.gov/mgg/geo­

das/geodas.html. Accessed September, 2005. 

Hartigan, J. A. and M.A. Wong. 1979. Algorithm AS 136: A k-means Clustering Algorithm. Applied Statistics. 28, 

100-108. 

ITT VIS (Visual Information Solutions). 2008

a

. Chapter 1: Introduction. ENVI Feature Extraction Module 4.6 

User’s Guide. pp. 1−78. 

ITT VIS (Visual Information Solutions). 2008

b

. Chapter 3: Finding Objects. ENVI Feature Extraction Module 4.6 

User’s Guide. pp. 1−78. 

Jengo, C. 2004. RuleGen 1.02. ITT VIS (Visual Information Solutions), Online: http://www.ittvis.com/UserCommunity/CodeLibrary.aspx. Accessed November, 2007. 

Jenness, J. 2002. ArcView 3.x extension: Surface Areas and Ratios from Elevation Grid v1.2. Online: http://www. 

jennessent.com/arcview/surface_areas.htm. Accessed September, 2009. 

Jenness, J. 2004. Calculating landscape surface area from digital elevation models. Wildlife Society Bulletin, 32, 

829–839. 

Kendall, M.S., M.E. Monaco, K.R. Buja, J.D. Christensen, C.R. Kruer, M. Finkbeiner and R.A. Warner. 2001. 

Methods used to map the benthic habitats of Puerto Rico and the U.S. Virgin Islands. NOAA Technical Report 

(Online). 

Kendall, M.S., O.P. Jensen, C. Alexander, D. Field, G. McFall, R. Bohne and M.E. Monaco. 2005. Benthic Mapping Using Sonar, Video Transects, and an Innovative Approach to Accuracy Assessment: A Characterization of 

Bottom Features in the Georgia Bight. Journal of Coastal Research. 21:6, 1154-1165. 

Kostylev, V.E., B.J. Todd, G.B.J. Fader, R.C. Courtney, G.D.M. Cameron and R.A. Pickrill. 2001. Benthic habitat 

mapping on the Scotian Shelf based on multibeam bathymetry, surficial geology and sea floor photographs. Marine Ecology Progress Series. 219, 121-137. 
Lillesand, T.M. and R.W. Kiefer. 2000. Remote sensing and image interpretation, 4th ed. New York, U.S.A.: John 

Wiley and Sons Ltd. p 724. 

Lim, T-S., W-Y. Loh and Y-S. Shih 2000. A Comparison of Prediction Accuracy, Complexity, and Training Time of 

Thirty-three Old and New Classification Algorithms. Machine Learning, 40, 203-229. 

Loh, W-Y. 2002. Regression Trees with Unbiased Variable Selection and Interaction Detection. Statistica Sinica, 

12, 361-386. 

Loh, W-Y. and Y-S. Shih. 1997. Split Selection Methods for Classification Trees. Statistica Sinica, 7, 815-840. 

Mather, P.M. 2004. Computer processing of remotely-sensed images, 3rd ed. West Sussex, England: John Wiley 

and Sons Ltd. pp. 1−324. 

Monaco, M.E. and S.C. Rooney. 2004. MBES data acquisition and processing report: Project NF-04-06-VI, 

NOAA Data Acquisition and Processing Report, NOS NCCOS CCMA (On-line). 

Patterson, D. 2008. Split Layer by Attributes. ESRI Support Center. Online: http://arcscripts.esri.com/details. 

asp?dbid=14127. Accessed August, 2009. 

Prada, M.C., R.S. Appeldoorn and J.A. Rivera. 2008. Improving Coral Reef Habitat Mapping of the Puerto Rico 

Insular Shelf Using Side Scan Sonar. Marine Geodesy. 31, 49-73. 

Robinson, D.J., N.J. Redding and D.J. Crisp. 2002. Implementation of a fast algorithm for segmenting SAR imagery, Scientific and Technical Report, 01 January 2002. Australia: Defense Science and Technology Organization. 

Shih, Y-S. 2004. QUEST User’s Manual. National Chung Cheng University, Taiwan. p 28. Online: http://www.stat. 

wisc.edu/~loh/treeprogs/quest/questman.pdf. Accessed September 2009. 

StatSoft, Inc. 2007. Electronic Statistics Textbook (Classification Trees). Tulsa, OK. Online: http://www.statsoft. 

com/textbook/stclatre.html#computation2. Accessed September, 2009. 

Tchoukanski, I. 2008. ET Geo Wizards 9.8 for ArcGIS 9.2. ET Spatial Techniques. Pretoria, South Africa. Online: 

http://www.ian-ko.com/. Accessed September, 2009. 

Wright, D.J., E.R. Lundblad, E.M. Larkin, R.W. Rinehart, J. Murphy, L. Cary-Kothera and K. Draganov. 2005. ArcGIS Benthic Terrain Modeler (BTM). Corvallis, Oregon. Oregon State University, Davey Jones Locker Seafloor 

Mapping/Marine GIS Laboratory and NOAA Coastal Services Center. Online: http://www.csc.noaa.gov/products/ 

btm/. Accessed September, 2009. 

Chapter 2: Benthic Habitat Map Creation 

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Chapter 2: Benthic Habitat Map Creation 

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This page intentionally left blank. 
CHAPTER 3: ASSESSMENT OF CLASSIFICATION ACCURACY 

A comprehensive assessment was conducted to evaluate the thematic accuracy of the St. John benthic habitat 

map. Thematic accuracy was characterized for major and detailed geomorphological structure, major and detailed biological cover, and percent coral cover classifications (see Chapter 1 for classifi cation scheme description). 

3.1. FIELD DATA COLLECTION 

Target locations for the accuracy assessment (AA) procedure were determined by an iterative, GIS-based, stratified random sampling technique to ensure that all bottom classifications would be assessed. Points were randomly placed within each geomorphological structure class of the draft habitat map using Hawth’s Analysis Tools 

(Beyer, 2004). No buffer from polygon edges was used. A minimum of 25 points were randomly distributed within 

each detailed structure class. Classes occupying larger areas were often allocated more than 25 points. A total 

of 325 sample locations were targeted, of which 299 were sufficiently surveyed to be included in the accuracy 

assessment. 

Data were collected over a field mission from 5/31/09 to 6/7/09. Sample locations were navigated to using a 

hand-held Garmin 76 CS WAAS-enabled GPS unit. Underwater video from a SeaViewer Sea-Drop 950 camera 

was taken at each site. A weight was tied to the bottom of the camera to help lower the camera to the bottom, 

and the camera operator adjusted the camera position to get a downward and side view of the habitat at each 

location. Video length depended on the habitat type and vessel drift, ranging from approximately 30 seconds to 

two minutes. Videos of large, homogeneous sand habitats were generally short while heterogeneous coral reef 

habitats (especially edges) were typically longer. While the video was being recorded, GPS waypoints were recorded on board the vessel using a Trimble GeoXT GPS receivers. This resulted in a string of epics that tracked 

boat position at each site. An observer categorized each site according to the video for each level of the map 

classification scheme: major/detailed geomorphological structure, major/detailed biological cover, and percent 

coral. Data was entered into a custom data dictionary on the Trimble data logger and recorded on waterproof 

data sheets. Videos were recorded to tape using a Sony Walkman video recorder, and converted to digital video 

clips using Final Cut Pro software. 

3.2. EVALUATION OF ASSESSMENT DATA 

The GPS data were processed using Trimble Pathfinder software. GPS data, which were originally recorded as 

code phase signals, were differentially post-processed to the Continually Operating Reference System (CORS) 

station at St. Thomas, U.S. Virgin Islands (VITH). For each survey site, individual epics were averaged to generate an “average” GPS point. The GPS data were then exported and plotted in ArcGIS along with the corresponding field notes. In most cases, the average point was a sufficient representation of the survey site; however in 

cases where the survey was conducted along or crossed a polygon edge, the average GPS point did not always 

fall into the polygon that was assessed. In these cases, the survey point was shifted to the portion of the transect 

and polygon that was classified. 

Prior to analysis, each video clip was re-analyzed and viewed in concert with the benthic habitat map overlaid 

on the acoustic imagery. It should be noted that all analysis at this stage was made by a scientist independent 

of the cartographer who created the map. Density of the biological cover was assessed at the video level and 

patchiness of the biological cover polygon level. As a result, it was often necessary to adjust the classifications 

that were initially recorded in the fi eld to reconcile the differences between the video and map scales. Similar adjustments were sometimes necessary to correctly characterize detailed structure. For example, heterogeneous 

hardbottom classes, such as pavement with sand channels, could not always be correctly classified from the 

video alone. In other cases, additional information on the position, size and shape of hardbottom features was 

needed to determine whether the structure should be classified as aggregate reef or a patch reef (either individual or part of an aggregated patch reef feature, if below the MMU). 

Following these adjustments, data were then spatially joined to the benthic habitat layer to extract the map classification for each point. Sites that differed between field notes and map classification were evaluated both in 

GIS and from video to determine possible source of disagreement. At this stage, a couple of map-wide issues 

were identified resulting from a difference in general interpretation between the cartographer and the scientist 

conducting the accuracy assessment. For example, patch reefs and aggregate reef were not always classified 

Chapter 3: Assessment of Classi

fi cation Accuracy 

page 

41 
 

 

Chapter 3: Assessment of Classifi cation Accuracy 

page 

42 

in the same way. In addition, there were certain areas of the map that could not be suffi ciently classifi ed under 

the current scheme. Following discussion, it was decided than an additional habitat class be created called: 

Rhodoliths with Scattered Coral and Rock. The map was edited to resolve these differences before the accuracy 

assessment was conducted. 

3.3. ANALYSIS OF THEMATIC ACCURACY 

The thematic accuracy of the St. John benthic habitat map was characterized in several ways from these data. 

Error matrices were computed for the attributes major and detailed geomorphological structure, major and detailed biological cover, and percent coral cover. Overall accuracy, producer’s accuracy, and user’s accuracy were 

computed directly from the error matrices (Story and Congalton, 1986). The error matrices were constructed as 

a square array of numbers arranged in rows (map classification) and columns (accuracy assessment, or groundtruthed classification). The overall accuracy (Po

) was calculated as the sum of the major diagonal (i.e. correct 

classifications, divided by the total number of accuracy assessment samples). 

The producer’s and user’s accuracies were calculated to characterize the classification accuracy of individual 

map categories. The producer’s accuracy (omission/exclusion error) is a measure of how well the cartographer 

classified a particular habitat (e.g., the percentage of times that substrate ground-truthed as sand was correctly 

mapped as sand). The user’s accuracy (commission/inclusion error) is a measure of how often map polygons 

of a certain habitat type were classified correctly (e.g., the percentage of times that a polygon classified as sand 

was actually ground-truthed as sand). Each diagonal element was divided by the column total to yield a producer’s accuracy and by the row total to yield a user’s accuracy. 

In addition, the Tau coeffi cient (Te

), a measure of the improvement of classification accuracy over a random assignment of map units to map categories (Ma and Redmond, 1995), was calculated. As the number of categories 

increases, the probability of random agreement (P ) diminishes, and T approaches P . Values of T were calcu­ r e o e

lated as follows: 

Tau coefficient = T

e = (Po 

– P

r) / (1 

– P

r), 

where Pr = 1/r. The variance of Tau (Ma and Redmond 1995) was calculated as: 

Variance of Tau coefficient = σ

r

2

 = P

o(1 

– P

o) / 

n(1 – P

r

)

2 

Confidence intervals were then calculated for each Tau coefficient at the 95% confidence level (1-α), using the 

following generalized form: 2)

0.5 95% CI = Te ± Zα/2(σr 

While stratification ensures adequate evaluation of all map categories, it has the undesired effect of introducing 

bias into the error matrix (Hay 1979; Card 1982). A minimum number of sites were targeted within each mapping 

category, which caused rare map categories to be sampled at a greater rate than common map categories. For 

example, although Rhodoliths habitat comprised 77% of the map area, only 44% of the target points were allocated for this habitat. Conversely, Aggregated Patch Reefs comprised only 3% of the map area, but received 

13% of the allocated target sample points. The bias introduced by differential sampling rates was removed using 

the method of Card (1982), which utilizes the known map marginal proportions (i.e. the proportional areas of map 

categories relative to the total map area). The map marginal proportions were calculated as the area of each map 

category divided by the total mapped area of the St. John benthic habitat map. The map marginal proportions 

were also utilized in the computation of confidence intervals for the overall, producer’s, and user’s accuracies 

(Card 1982; Congalton and Green, 1999). This method was also used in the recent accuracy assessment of the 

NOAA Florida Keys benthic habitat map (Walker and Foster, 2009) and the NOAA shallow-water St. John habitat 

map (Zitello et al., 2009). 

The known map marginal proportions (π

j

) were computed from the GIS layer of the draft benthic habitat map 

for each of the four error matrices (major and detailed geomorphological structure, major and detailed biological 

cover), by dividing the area of each category by the total map area. Marginal proportions were not computed for 

the percent coral cover matrix, as this would have required an estimate of the percent hardbottom within each 
polygon to truly estimate the area of live coral. The map areas were exclusive to categories present in the error 

matrix. For the example of detailed structure category Rhodoliths, πj

 was 0.77 (69.9 km2/90.2 km2). The individual 

cell probabilities, i.e. the product of the original error matrix cell values and πj

, divided by the row marginal (total 

map classifications per category), were computed for the off-diagonal elements using the following equation: 

Individual cell probabilities = Pˆ

ij jnij / n j

The relative proportions of the cell values within a row of the error matrix were unaffected by this operation, but 

the row marginals were forced to the known map marginal proportions (i.e. the row total of a particular habitat 

now equaled the fraction of map area occupied by that habitat, instead of the total number of accuracy assessment points). The estimated true marginal proportions (pi

) were computed as the sum of individual cell probabilities down each column of the error matrix. 

The πj

-adjusted overall, producer’s, and user’s accuracies were then computed from the new error matrix, now 

populated by individual cell probabilities. The values of the πj

-adjusted overall and producer’s accuracies differ by design from those of the original error matrix, as they have been corrected for the areal bias introduced 

by the stratified random sampling protocol. The user’s accuracy, in contrast, is not affected. The variances and 

confidence intervals of the overall, producer’s, and user’s accuracies were then computed from the following set 

of equations (Card 1982; Walker and Foster, 2009): 

Overall Variance = V (Pˆ

c ) ( i pii ) / ni)
 pii

r

i
 1

Overall Confidence Interval = CI = Pc 2[V(Pc )]1/ 2 

Producer’s Variance = (/)([) )( /) ˆ ( 4 2

j ij ij ii i ii ji

ij

iiii i ijii npppnpppppV ]
 

Producer’s Confidence Interval = CI = 2/1 ([2 )] ii V ii

User’s Variance = iiii i ii nppV ii

2 /)() ˆ ( 

User’s Confidence Interval = CI = 2/1 )] ˆ ([2 ˆ

ii V ii

3.4 ACCURACY ASSESSMENT RESULTS AND DISCUSSION
 

Major Geomorphological Structure 

Error matrices for major geomorphological structure are 

displayed in Tables 3.1 and 3.2. The overall accuracy 

(Po

) at the major geomorphological structure level was 

96% (Table 3.1). The Tau coefficient for equal probability of group membership is 0.913 ± 0.046 (α=0.05). The 

error matrix in Table 3.2 is populated by the individual 

cell probabilities (pij), which in review are the product 

of the original error matrix cell values (Table 3.1) and 

the map marginal proportions, divided by the row marginal of the original matrix (i.e., total map classifications 

per category). The adjusted overall accuracy, corrected 

for bias using the true map marginal proportions, was 

virtually identical at 95.7 (±2.3)% (α=0.05). The user’s 

ˆ ˆ

r

ˆˆ

Table 3.1. Error matrix for major geomorphological structure. 

Accuracy Assessment (i) 

Hard Soft n-j 

User's 

Accuracy (%) 

Hard 264 11 275 96.0% 

Soft 2 22 24 91.7% 

ni­ 266 33 n=299 

Po = 95.7% Producer's 

Accuracy (%) 99.2% 66.7% 

Map data (j) 

Te = 0.913 ± 0.046 

Chapter 3: Assessment of Classi

fi cation Accuracy 

page 

43 
accuracies were similarly high for both hard Table 3.2. Error matrix for major geomorphological structure using individual 

cell probabilities. The overall accuracy and producer’s accuracy were correct- and softbottom habitats, while producer’s ac­ ed for bias using the true map marginal proportions. curacy was markedly lower for softbottom 

than hardbottom (Table 3.2). Eleven sites located in polygons that were mapped as hardbottom were determined to be softbottom in 

the accuracy assessment. This was primarily 

due to confusion between Sand and Rhodoliths (Section 1.3 Geomorphological Structure 

Types (pg. 4)). 

Detailed Geomorphological Structure 

Error matrices for detailed geomorphological 

structure are displayed in Tables 3.3 and 3.4. 

The overall accuracy (Po

) at the detailed geomorphological structure level was 88.3%, with 

a Tau coeffi cient (Te

) of 0.868 ± 0.041 (α=0.05) 

(Table 3.3). The adjusted overall accuracy, 

corrected for bias using the true map marginal proportions, was similar at 88.7 (±4.4)% 

(α=0.05), which indicated that the classes that 

covered the most area were also the most correctly interpreted. 

User’s accuracy was above 80% for all categories (Table 3.3). No systematic errors were evident. The user’s 

accuracy of the Sand with Scattered Coral and Rock class could not be assessed as no points were located 

in mapped areas of this structure type. Other categories with relatively low sampling effort include Aggregate 

Reef, Individual Patch Reef, and Rhodoliths with Scattered Coral and Rock, classes that also comprise a small 

percentage of the total map. These categories were likely undersampled due to changes in the draft map and 

classification scheme following collection of the ground-truth and accuracy assessment data. 

hard 

soft 

piProducer's 

Accuracy (%)

Producer's CI 

(±%)

Map data (j)

hard 

0.8872 

0.0063 

0.8935 

99.3% 

1.0% 

Accuracy Assessment (i)

User's User's CI soft ʌ-j Accuracy (±%) (%) 

0.0370 0.9242 96.0% 2.4%

0.0695 0.0758 91.7% 11.3%

0.1065 ʋ=1

65.3% Po = 95.7% 

13.7% CI(±) = 2.3%

Chapter 3: Assessment of Classi

fi cation Accuracy 

page 

44 

Table 3.3. Error matrix for detailed geomorphological structure. 

Accuracy Assessment (i) 

Map data (j) 

Aggregate

Reef

Aggregated

Patch Reefs

Individual

Patch Reef

PavementPavement

with Sand

Channels

RhodolithsRhodoliths

w/ SCR

Sand

Sand w/

SCR

n-j 

User's 

Accuracy (%) 

Aggregate 

Reef 10 1 11 90.9% 

Aggregated 

Patch Reefs 32 1 1 2 2 38 84.2% 

Individual 

Patch Reef 9 9 100.0% 

Pavement 3 1 1 49 3 1 58 84.5% 

Pavement with 

Sand 

Channels 

2 0 17 19 89.5% 

Rhodoliths 4 117 1 7 3 132 88.6% 

Rhodoliths w/ 

SCR 8 8 100.0% 

Sand 2 22 24 91.7% 

Sand w/ SCR 0 0 n/a 

ni­ 15 33 12 54 17 124 11 30 3 n=299 

Po = 88.3% Producer's 

Accuracy (%) 66.7% 97.0% 75.0% 90.7% 100.0% 94.4% 72.7% 73.3% 0 

Te = 0.868 ± 0.041 
Table 3.4. Error matrix for detailed geomorphological structure using individual cell probabilities. The overall accuracy and producer’s 

accuracy were corrected for bias using the true map marginal proportions. 

Accuracy Assessment (i) 

Aggreg

Aggrega

Patc

Individ

Patc

PavemPavem

with Sa

Chann

RhodoliRhodoli

w/ SC

Sand

Sand

Reef

SCR

ʌ-j 

ate

h Reefs

ual

h Reef

ent

ent

els

ths

ths

R

w/

ted

nd 

Accuracy 

(%) 

User's CI 

(±%) 

Aggregate 

Reef 0.0206 0.0021 0.0226 90.9% 17.3% 

Aggregated 

Patch Reefs 0.0313 0.0010 0.0010 0.0020 0.0020 0.0372 84.2% 11.8% 

Individual 

Patch Reef 0.0036 0.0036 100.0% 0.0% 

Pavement 

) 

0.0032 0.0011 0.0011 0.0527 0.0032 0.0011 0.0624 84.5% 9.5% 

ta (j

Pavement with

da

Sand 0.0006 0.0048 0.0054 89.5% 14.1% 

Channels

Map

Rhodoliths 0.0235 0.6868 0.0059 0.0411 0.0176 0.7748 88.6% 5.5% 

Rhodoliths w/ 

SCR 0.0183 0.0183 100.0% 0.0% 

Sand 0.0063 0.0692 0.0755 91.7% 11.3% 

Sand w/ SCR 0.0003 n/a n/a 

pi- 0.0244 0.0324 0.0077 0.0772 0.0048 0.6982 0.0261 0.1114 0.0176 ʋ=1

Producer's 

Accuracy (%) 84.4% 96.7% 46.5% 68.3% 100.0% 98.4% 70.0% 62.1% n/a Po = 88.7% 

Producer's CI 

(±%) 13.1% 12.4% 29.4% 20.7% 0.0% 1.4% 32.2% 17.2% n/a CI(±) = 4.4% 

User's 

Adjusted and un-adjusted producer’s accuracy was very high (>95%) for several classes, including Rhodoliths, 

Pavement with Sand Channels, and Aggregated Patch Reefs Categories with the lowest adjusted producer’s 

accuracy include Individual Patch Reef, Pavement, Rhodoliths with Scattered Coral and Rock, and Sand (Table 

3.4). In all cases, there was a high degree of variance. Several points ground-truthed as Sand or Sand with 

Scattered Coral and Rock were mapped as Rhodoliths. These errors were the primary reason for the lower producer’s accuracy for unconsolidated sediment at the major structure level. 

Major Biological Cover
 

Error matrices for major biological Table 3.5. Error matrix for major biological cover.
 

cover are displayed in Tables 3.5 Accuracy Assessment (i) 

and 3.6. The overall accuracy (Po

) at 

the major biological cover level was 

95.3%, with a Tau coeffi cient (Te

) of 

0.930 ± 0.036 (α=0.05). The adjusted 

overall accuracy, corrected for bias 

using the true map marginal proportions, was similar at 95.0 (±2.3)% 

(α=0.05). 

Map data (j) 

No Cover Live Coral Algae n-j 

User's 

Accuracy (%) 

No Cover 15 7 22 68.2% 

Live Coral 1 1 100.0% 

Algae 7 269 276 97.5% 

ni­ 22 1 276 n=299 

Po = 95.3% Producer's 

Accuracy (%) 68.2% 100.0% 97.5% 

Te = 0.930 ± 0.036 Both producer’s and user’s accuracy were high for Algae. The primary 

source of confusion was between Algae and No Cover, which resulted in slightly lower accuracy for the No Cover 

category. The number of accuracy assessment points for Live Coral was too few to robustly assess this category. 

The low sample size was due to the rarity of polygons mapped where coral was mapped as the dominant cover. 

However, a better assessment of the accuracy of mapped coral cover will be discussed in the subsection Percent 

Coral Cover (pg. 46). 

Chapter 3: Assessment of Classi

fi cation Accuracy 

page 

45 
Detailed Biological Cover	 Table 3.6. Error matrix for major biological cover using individual cell probabilities. The 

overall accuracy and producer’s accuracy were corrected for bias using the true map 

marginal proportions. Error matrices for detailed biological cover are displayed in Tables

3.7 and 3.8. The overall accuracy

(Po

) at the detailed biological cover 

level was 74.2%, with a Tau coeffi

cient (Te

) of 0.678 ± 0.062 (α=0.05). 

The adjusted overall accuracy, corrected for bias using the true map 

marginal proportions, was similar at 

74.0 (±5.2)% (α=0.05). 

The greatest source of confusion at 

the detailed biological cover level 

was degrees of density/patchiness 

Table 3.7. Error matrix for detailed biological cover. 

within Algae categories. For example, 

the adjusted user’s 

and producer’s accuracy of the Algae 

10% - <50% were 

68.2% and 31.1%, respectively (Table 3.8). 

Of the 21 sites interpreted to have 10% 

- <50% patchiness in 

the accuracy assessment, only three had 

been mapped as that 

category. The remaining 18 points were 

had been mapped as 

No Cover, Algae 50% 

Map data (j) 

- <90%, and Algae Te 0.678 ± 0.062 

90% - 100%. Producer’s accuracy was also low (62.2% adjusted) for Algae 50-90%, primarily due to confusion with Algae 90%-100%.
 

Percent Coral Cover 

The error matrix for percent coral cover is displayed in Table 3.9. The overall accuracy (Po

) at the detailed biological cover level was 88.3%, with a Tau coeffi cient (Te

) of 0.844 ± 0.049 (α=0.05). As mentioned previously, a 

second matrix using the true map marginal proportions, was not computed for percent coral cover. 

Two of the possible coral categories were primarily present in the map and accuracy assessment data (<10% 

and 10% - <50%) Only one location had coral cover of 50% - <90%. Accuracy was very high for the softbottom 

habitats, where a low amount of coral is to be expected. There was lower accuracy for percent coral on hardbottom habitats. The decision between <10% and 10% - <50% is often difficult to determine, especially if there is 

a mix of octocorals and sclerectinians. As such, user’s accuracy of 10% - <50% coral on hardbottom was only 

56.4%. Since percent coral cover was recorded at all sites regardless of whether it was the dominant cover type, 

this is a better measure of coral accuracy than is found under Major Cover (pg. 13). 

3.5 CONCLUSIONS 

Accuracy for detailed geomorphological structure and biological cover were both high, although accuracy was 

better for structure than cover. There are several possible reasons for this result. The first is that the poor quality of 

the intensity imagery in some areas prevented the cartographer from accurately mapping biological cover on the 

seafloor. Second, it is possible that that the semi-automated technique may be better at picking out signatures for 

Accuracy Assessment (i)

Accuracy Assessment (i)

User's User's CI No Cover Live Coral Algae ʌ-j Accuracy (±%) (%) 

No Cover 0.0456 0.0213 0.0669 68.2% 19.9%

Live Coral 0.0028 0.0028 100.0% 0.0%

Algae 0.0284 0.9019 0.9303 96.9% 2.1%

pi­ 0.0740 0.0028 0.9232 ʌ=1

Producer's 61.6% 100.0% 97.7% Po = 95.0% Accuracy (%) 

Producer's CI 17.5% 0.0% 1.4% CI(±) = 2.3% (±%)

Map data (j)

­

Chapter 3: Assessment of Classi

fi cation Accuracy 

page 

46 

No Cover 

90% - 100% 

Live Coral 

50% - <90% 

Algae 

90% - 100% 

Algae 

50% - <90% 

Algae 

10% - <50% n-j 

User's 

Accuracy (%) 

No Cover 

90% - 100% 15 7 22 68.2% 

Live Coral 

50% - <90% 0 1 1 100.0% 

Algae 

90% - 100% 3 127 45 6 181 70.2% 

Algae 

50% - <90% 3 6 76 5 90 84.4% 

Algae 

10% - <50% 1 1 3 5 60.0% 

ni­ 22 1 133 122 21 n=299 

Po = 74.2% Producer's 

Accuracy (%) 68.2% 100.0% 95.5% 62.3% 14.3% 

=
Table 3.8. Error matrix for detailed biological cover using individual cell probabilities. The overall accuracy and producer’s accuracy were corrected for bias using the true map marginal proportions. 

Accuracy Assessment (i) 

Map data (j)

User's No Cover Live Coral Algae Algae Algae User's CI ʌ-j Accuracy 90% - 100% 50% - <90% 90% - 100% 50% - <90% 10% - <50% (±%) (%) 

No Cover 0.0456 0.0213 0.0669 68.2% 19.9% 90% - 100% 

Live Coral 0.0028 0.0028 100.0% 0.0% 50% - <90% 

Algae 0.0097 0.4113 0.1457 0.0194 0.5862 70.2% 1.9% 90% - 100% 

Algae 0.0100 0.0201 0.2541 0.0167 0.3010 84.4% 3.8% 50% - <90% 

Algae 0.0086 0.0086 0.0259 0.0432 60.0% 35.8% 10% - <50% 

pi- 0.0740 0.0028 0.4314 0.4085 0.0833 ʋ=1

Producer's 61.6% 100.0% 95.3% 62.2% 31.1% Po = 74.0% Accuracy (%) 

Producer's 19.7% 0.0% 3.5% 6.6% 18.2% CI(±) = 5.2% CI (±%) 

Chapter 3: Assessment of Classi

fi cation Accuracy 

page 

47 

structure than for cov- Table 3.9. Error matrix for major geomorphological structure and percent live coral cover. 

er. In addition, the Accuracy Assessment (i) way cover was classified (i.e., as a combination of both density and polygon-wide 

distribution) could 

have infl uenced this 

outcome as well. 

Although the classification schemes are 

not directly comparable due to regionspecific categories, 

the level of accuracy 

for detailed structure 

Map data (j) 

was similar to that of Te = 0.844 ± 0.049 

recent NOAA shallow-water benthic habitat maps in St. John (85.7% [88.8% adjusted], Zitello et al., 2009), the Florida Keys (86.2% 

[91.5% adjusted], Walker and Foster, 2009), Palau (90.0%, Battista et al., 2007b), and the Main Hawaiian Islands 

(90.0%, Battista et al., 2007a

). 

Comparisons with other accuracy assessments at the biological cover level are difficult due to the differences in 

the classification scheme and how cover was assessed. With the exception of the new shallow-water map for 

St. John, previous mapping efforts utilized a hierarchical classification scheme to characterize biological cover, 

in comparison to the dominance based scheme used here. In addition, due to inherent differences in the structure and biological cover types present in shallow vs. moderate-depth environments, the classifi cation schemes 

vary accordingly. For instance, several classes that were present in the shallow-water map (e.g., Rock Outcrop, 

Boulder, Spur and Groove) were not present in the deep environment. In contrast, rhodoliths dominated the deep 

shelf environment, and were often interdispersed with patch reefs or scattered coral and rock. Further, biological 

cover levels were a product of both density and polygon-wide patchiness in the deep shelf map. 

Softbottom, 

Coral <10% 

Hardbottom, 

Coral <10% 

Hardbottom, 

Coral 

10% - <50% 

Hardbottom, 

Coral 

50% - <90% 

n-j 

User's 

Accuracy (%) 

Softbottom, 

Coral <10% 22 2 0 0 24 91.7% 

Hardbottom, 

Coral <10% 11 219 5 0 235 93.2% 

Hardbottom, 

Coral 

10% - <50% 

0 17 22 0 39 56.4% 

Hardbottom, 

Coral 

50% - <90% 

0 0 0 1 1 100.0% 

ni­ 33 238 27 1 n=299 

Po = 88.3% Producer's 

Accuracy (%) 66.7% 92.0% 81.5% 100.0% 
Chapter 3: Assessment of Classifi cation Accuracy 

page 

48 

LITERATURE CITED 

Battista, T.A., B.M. Costa, and S.M. Anderson. 2007

a

. Shallow-Water Benthic Habitats of the Main Eight Hawaiian Islands. NOAA Technical Memorandum NOS NCCOS 61, Biogeography Branch. Silver Spring, MD. 

Battista, T.A., B.M. Costa and S.M. Anderson. 2007

b

. Shallow-Water Benthic Habitats of the Republic of Palau. 

NOAA Technical Memorandum NOS NCCOS 59, Biogeography Branch. Silver Spring, MD. 

Beyer, H.L. 2004. Hawth’s Analysis Tools for ArcGIS. Online: http://www.spatialecology.com/htools. Accessed 

September 2009. 

Card, D.H. 1982. Using known map categorical marginal frequencies to improve estimates of thematic map accuracy. Photogrammetric Engineering and Remote Sensing, 48, 431-439. 

Congalton, R.G. and K. Green. 1999. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. CRC/Lewis Press, Boca Raton, FL. 137 pp. 

Hay, A.M. 1979. Sampling designs to test land use map accuracy. Photogrammetric Engineering and Remote 

Sensing, 45, 529-533. 

Ma, Z. and R.L. Redmond. 1995. Tau coefficients for accuracy assessment of classification of remote sensing 

data. Photogrammetric Engineering and Remote Sensing, 61, 435-439. 

Story, M. and R. Congalton. 1986. Accuracy assessment: A user’s perspective. Photogrammetric Engineering 

and Remote Sensing, 52, 397-399. 

Walker, B.K. and G. Foster. 2009. Final Report: Accuracy Assessment and Monitoring for NOAA Florida Keys 

mapping: AA ROI-1 (near American Shoal). National Coral Reef Institute, Nova Southeastern University, Dania 

Beach, FL. 32 pp. 

Zitello, A.G., L.J. Bauer, T.A. Battista, P.W. Mueller, M.S. Kendall and M.E. Monaco. 2009. Benthic Habitats of St. 

John, U.S. Virgin Islands. NOAA Technical Memorandum NOS NCCOS 96, Biogeography Branch. Silver Spring, 

MD. 
CHAPTER 4: CONCLUSION 

NOAA’s Biogeography Branch, with support from the U.S. National Park Service, has completed benthic habitat 

mapping and subsequent field validation and accuracy assessment of the moderate-depth marine environment 

south of St. John, U.S. Virgin Islands. An independent accuracy assessment revealed overall map accuracies to 

be over 95% for major structure and cover classes, Table 4.1. Final deliverable items for NOAA’s moderate-depth benthic 

and over 88% and 74% for detailed structure and habitat map of St. John. Additional information is provided describing the 

cover classes, respectively. As a result, these digi- item, its format and quantity. 

tal map products can be used with confi dence by 

scientists and resource managers for a multitude 

of different applications. The scientific and management communities have used previous NOAA 

benthic habitat maps to structure monitoring programs, support management decisions, and establish and manage marine conservation areas in 

coral reef ecosystems. The final delivery consisted 

of the benthic habitat maps in several formats and 

all ancillary data generated in support of map creation. These items are listed in Table 4.1 with a 

description of the format type and quantity when 

appropriate. 

4.1 MAP SUMMARY STATISTICS 

In total, 90.2 km2 of the seafloor was mapped in and around the VICRNM. Of this 90.2 km2, 43.2 km2 fell inside 

and 47.0 km2 fell outside the VICRNM boundaries. Several patterns emerged when examining the summary map 

statistics for the total mapped area, as well as the mapped area inside and outside the park boundaries. These 

patterns are discussed in more detail below. 

Total Mapped Area 

In looking at major structure, Coral Reef and Hardbottom constituted the majority of the total mapped area 

(91.8%), while Unconsolidated Sediment only comprised a small fraction of this area (Table 4.2; Figure 4.1). 

Coral Reef and Hardbottom constituted the majority of the mapped area because the Rhodoliths habitat type 

dominated the entire moderate-depth region south of St. John. If the Rhodoliths category is excluded, the Coral 

Reef and Hardbottom category only accounted for 14.9% of the total mapped area. After Rhodoliths, Sand was 

ITEM FORMAT QUANTITY 

Benthic Habitat Map GIS 1,283 polygons

Acoustic Imagery GIS 11 images 

Interactive Map Project Online -

Ground Validation Dataset GIS 117 locations & 

13 transects

Accuracy Assessment Datatset GIS 299 locations

Video of Seafloor 

.mov or 

.wmv 

432 videos

Final Report PDF 1

FGDC-compliant Metadata for GIS Files Text 17 files 

Table 4.2. Summary statistics describing the total amount (and percent) of mapped area by major and detailed geomorphological structure types. These numbers are further divided into the amount of mapped area inside and outside the 

VICRNM. 

GEOMORPHOLOGICAL STRUCTURE 

TOTAL AREA 

(km2) INSIDE 

VICRNM 

TOTAL AREA 

(km2) OUTSIDE 

VICRNM 

TOTAL 

AREA (km2) 

MAPPED 

% OF TOTAL 

MAPPED 

AREA 

Major

Structure

Coral Reef and Hardbottom 39.4 43.4 82.8 91.8 

Unconsolidated Sediment 3.83 3.59 7.4 8.23 

Detailed Structure 

Aggregate Reef 0.24 2.31 2.6 2.83 

Aggregated Patch Reefs 2.13 1.22 3.4 3.72 

Individual Patch Reef 0.21 0.13 0.3 0.39 

Pavement 3.07 2.02 5.1 5.64 

Pavement with Sand Channels 0.11 0.37 0.5 0.53 

Rhodoliths 32.8 36.6 69.4 76.9 

Rhodoliths with Scattered Coral and Rock 0.87 0.79 1.7 1.84 

Sand 3.74 3.57 7.3 8.11 

Sand with Scattered Coral and Rock 0.09 0.02 0.1 0.12 

Total Area (km2) Mapped 43.2 47.0 90.2 100 

Chapter 4: Conclusion 

page 

49 
page 

50 Chapter 4: Conclusion

0 1 2 3 km 

St. John 

Geomorphological Structure 

Hard - Aggregate Reef 

Hard - Aggregated Patch Reefs 

Hard - Individual Patch Reef 

Hard - Pavement 

Hard - Pavement w/ Sand Channels 

Hard - Rhodoliths 

Hard - Rhodoliths w/ Scattered Coral/Rock 

Soft - Sand 

Soft - Sand w/ Scattered Coral/Rock 

Boundaries 

VICRNM 

NOAA shallow-water habitat map (2009) 

Figure 4.1. This figure depicts the major and detailed geomorphological structure of the moderate-depth area that was mapped using 

acoustic imagery south of St. John. The black hatched polygon denotes the area mapped using optical imagery. The red polygons show the 

boundaries of the VICRNM. 

the second most dominant detailed structure type, followed respectively by: (3) Pavement, (4) Aggregated Patch 

Reefs, (5) Aggregate Reef, (6) Rhodoliths with Scattered Coral and Rock, (7) Pavement with Sand Channels, 

(8) Individual Patch Reef, and (9) Sand with Scattered Coral and Rock. Although ecologically significant, Individual Patch Reefs and Aggregated Patch Reefs comprised just over 4% of the moderate-depth habitat that was 

mapped south of St. John. 

In looking at major biological Table 4.3. Summary statistics describing the total amount (and percent) of mapped area by major 

and detailed biological cover type as well as by amount of live coral cover. These numbers are fur- cover, Algae colonized the ther divided into the amount of mapped area inside and outside the VICRNM. majority of the total mapped 

area (92.8%) (Table 4.3; Figure 4.2). Algae dominated 

this moderate-depth region 

because this category included any combination 

of numerous types of red, 

green, or brown algae that 

were turf, fl eshy, filamentous 

or crustose coralline species. After Algae, No Cover 

was the second most dominant major cover type, followed respectively by Seagrass and Live Coral. When 

the percent cover modifier 

is added to the calculations, 

Algae 90% - 100% colonized the majority of the total 

mapped area (54.8%), followed respectively by Algae 

50% - <90%, No Cover 90% 

- 100%, Algae 10% - <50%, 

Live Coral 50% - <90% and 

Seagrass 50% - <90% and 

90% - 100%. The area coloBIOLOGICAL COVER 

TOTAL 

MAPPED 

AREA (km2) 

INSIDE 

VICRNM 

TOTAL 

MAPPED 

AREA (km2) 

OUTSIDE 

VICRNM 

TOTAL 

MAPPED 

AREA (km2) 

% OF TOTAL 

MAPPED 

AREA 

Major Cover

Algae 39.9 43.8 83.7 92.8 

Live Coral 0.00 0.25 0.25 0.28 

No Cover 3.36 2.93 6.28 6.97 

Seagrass 0.02 0.02 0.04 0.04 

Detailed Cover 

Algae 10% - <50% 2.03 2.29 4.32 4.79 

Algae 50% - <90% 11.7 18.2 29.9 33.2 

Algae 90% - 100% 26.1 23.3 49.4 54.8 

Live Coral 50% - <90% 0.00 0.25 0.25 0.28 

No Cover 90% - 100% 3.36 2.93 6.28 6.97 

Seagrass 50% - <90% 0.02 0.01 0.02 0.02 

Seagrass 90% - 100% 0.00 0.02 0.02 0.02 

Live Coral

Cover 

0% - <10% 42.9 45.4 88.3 97.9 

10% - <50% 0.39 1.33 1.72 1.91 

50% - <90% 0.00 0.25 0.25 0.28 

Total Area (km2) Mapped 43.2 47.0 90.2 100 
nized by Live Coral 50% - <90% is the only polygon of its kind within the mapped area. This polygon is located in 

the southwestern part of the mapped area between 26 and 37 m in depth (Figure 4.3). Except for this one polygon, the majority of the mapped area (97.9%) was colonized by 0% - <10% live scleractinian and/or soft corals. 

Only 2 km2

 (or 2.2%) of the mapped area was colonized by coral covers greater than or equal to 10%. 

Total Mapped Area Outside VICRNM Boundaries 

In looking at major structure, Coral Reef and Hardbottom also constituted the majority of the mapped area inside 

the VICRNM boundaries (92.4%), while Unconsolidated Sediment only comprised a small fraction (7.6%). Coral 

Reef and Hardbottom again constituted the majority of the mapped area inside the VICRNM boundaries because 

Rhodoliths were the dominant habitat type outside the park boundaries. If the Rhodoliths category is excluded, 

Coral Reef and Hardbottom only accounts for 14.6% of the mapped area outside the VICRNM boundaries. After Rhodoliths, Sand was the second most dominant detailed structure type (just as it was for the total mapped 

Chapter 4: Conclusion

Boundaries 

VICRNM 

St. John NOAA shallow-water habitat map (2009) 

Biological Cover 

Algae 10% - <50% 

Algae 50% - <90% 

Algae 90% - 100% 

Live Coral 50% - <90% 

Seagrass 50% - <90% 

Seagrass 90% - 100% 

No Cover 90% - 100% 0 1 2 3 km 

Figure 4.2. This fi gure depicts the major and detailed biological cover of the moderate-depth area that was mapped using acoustic imagery south of St. John. The black hatched polygon denotes the area mapped using optical imagery. The red polygons show the boundaries 

of the VICRNM. 

51 

St. John 

Boundaries 

VICRNM
 

NOAA shallow-water habitat map (2009)
 

Live Coral Cover 

0% - <10% 

10% - <50% 

50% - <90% 

Unknown 

0 1 2 3 km 

Figure 4.3. This fi gure depicts the amount of live coral cover present in the moderate-depth area that was mapped using acoustic imagery south of

St. John. The black hatched polygon denotes the area mapped using optical imagery. The red polygons show the boundaries of the VICRNM. 

page 
Chapter 4: Conclusion 

page 

52 

area) followed respectively by: (3) Aggregate Reef, (4) Pavement, (5) Aggregated Patch Reefs, (6) Rhodoliths 

with Scattered Coral and Rock, (7) Pavement with Sand Channels, (8) Individual Patch Reef, and (9) Sand with 

Scattered Coral and Rock. Although ecologically significant, Individual Patch Reefs and Aggregated Patch Reefs 

again only comprised approximately 3% of the moderate-depth habitat mapped outside the VICRNM boundaries. 

In looking at major biological cover, Algae colonized the majority of the mapped area outside the VICRNM boundaries (93.2%). Again, Algae dominated the mapped area outside the VICRNM boundaries because this category 

included any combination of numerous types of turf, fl eshy, filamentous or crustose coralline algal species. After 

Algae, No Cover was the second most dominant major cover type, followed respectively by Live Coral and Seagrass. When the percent cover modifier is added to the calculations, Algae 90% - 100% colonized the majority 

of the mapped area outside the park boundaries (49.5%), followed respectively by Algae 50% - <90%, No Cover 

90% - 100%, Algae 10% - <50%, Live Coral 50% - <90%, Seagrass 90% - 100% and Seagrass 50% - <90%. 

The area colonized by Live Coral 50% - <90% is the only polygon of its kind within the mapped area outside the 

VICRNM boundaries (and within the mapped area as a whole). Except for this one polygon, the majority (96.6%) 

of the mapped area outside the VICRNM boundaries was colonized by 0% - <10% live corals. Only 1.6 km2

 (or 

3.4%) of the mapped area outside the VICRNM was colonized by coral covers greater than or equal to 10%. 

Total Mapped Area Inside VICRNM Boundaries 

In looking at major structure, Coral Reef and Hardbottom also constituted the majority of the mapped area inside the VICRNM boundaries (91.1%), while Unconsolidated Sediment only comprised a small fraction (8.9%), 

because rhodoliths dominated the seafloor inside the park boundaries. If the Rhodoliths category is excluded, 

the Coral Reef and Hardbottom category only accounted for 15.3% of the mapped area within the VICRNM 

boundaries. After Rhodoliths, Sand was the second most dominant detailed structure type (just as it was for the 

total mapped area). This detailed structure type was followed respectively by: (3) Pavement, (4) Aggregated 

Patch Reefs, (5) Rhodoliths with Scattered Coral and Rock, (6) Aggregate Reef, (7) Individual Patch Reef, (8) 

Pavement with Sand Channels, and (9) Sand with Scattered Coral and Rock. Although ecologically significant, 

Individual Patch Reefs and Aggregated Patch Reefs again only comprised 5.4% of the moderate-depth habitat 

mapped inside the VICRNM boundaries. 

In looking at major biological cover, Algae colonized the majority of the mapped area inside the VICRNM boundaries (92.2%). Again, Algae dominated the area inside the VICRNM boundaries because this category included 

any combination of numerous types of turf, fl eshy, filamentous or crustose coralline algal species. After Algae, No 

Cover was the second most dominant major cover type, followed respectively by Seagrass and Live Coral. When 

the percent cover modifier is added to the calculations, Algae 90% - 100% colonized the majority of the mapped 

area inside the park boundaries (60.4%), followed respectively by Algae 50% - <90%, No Cover 90% - 100%, 

Algae 10% - <50%, and Seagrass 50% - <90%. No polygons within the VICRNM boundaries were dominated 

by live coral. The majority (99.1%) of the mapped area inside the VICRNM boundaries was colonized by 0% -

<10% live scleractinian and/or soft corals. Only 0.4 km2

 (or 0.9%) of the mapped area inside the VICRNM was 

colonized by coral covers greater than or equal to 10%. 

Comparing Total Mapped Areas Inside and Outside VICRNM Boundaries 

The mapped areas inside and outside the VICRNM followed the same general trends for major and detailed 

structure types. In particular, the seafloor both outside and inside the park boundaries was dominated by Coral 

Reef and Hardbottom habitat because Rhodoliths were ubiquitous on the shelf south of St. John. When Rhodoliths were removed from the calculation, roughly 15% of both areas were covered by Coral Reef and Hardbottom. 

In terms of detailed structure, both areas had the same diversity of structure types represented, although the 

quantity of these structure types differed slightly. In particular, the mapped area outside the VICRNM boundaries 

had approximately: (1) 4.3% more Aggregate Reef, (2) 2.0% more Rhodoliths, and (3) 0.5% more Pavement with 

Sand Channels than did the mapped area inside the VICRNM boundaries. The mapped area inside the VICRNM 

boundaries, on the other hand, had approximately: (1) 2.8% more Pavement, (2) 2.3% more Aggregated Patch 

Reefs, (3) 1.1% more Sand, (4) 0.3% more Rhodoliths with Scattered Coral and Rock, (5) 0.2% more Individual 

Patch Reef, and (6) 0.2% more Sand with Scattered Coral and Rock than did the mapped area outside the VICRNM boundaries. 
The mapped areas inside and outside the VICRNM followed the same general trends for major and detailed 

biological cover types. In general, Algae colonized the majority of the mapped area both inside and outside the 

VICRNM boundary, with No Cover being the second most common cover category. Both areas were also dominated by continuous, high density algae (Algae 90% - 100%), followed respectively by Algae 50% - <90%, No 

Cover 90% - 100%, and Algae 10% - <50%. The mapped area outside the park boundary did, however, have 

11.7% more Algae 50% - <90% and 10.9% less Algae 90% - 100% than the mapped area inside the VICRNM 

boundary. 

In terms of coral cover, the majority (>96%) of both areas were colonized by 0% - <10% live scleractinian and/or 

soft corals. It is important to note, however, that the mapped area outside the VICRNM had one 0.25 km2 polygon 

dominated by live coral (i.e., Live Coral 50% - <90%), whereas the mapped area inside the VICRNM did not. In 

addition to this one polygon, the mapped area outside the park was found to have 1.9% more Live Coral 10% -

<50% habitat than the mapped area inside the VICRNM. This difference suggests that there is slightly more live 

coral outside (than inside) the current VICRNM boundaries south of St. John. 

4.2 INTEGRATION WITH SHALLOW-WATER NOAA HABITAT MAPS OF ST. JOHN 

In 2009, NOAA’s Biogeography Branch has successfully mapped the majority of the shallow-water (< 30 m) benthic habitats and a significant portion of the moderate-depth (30 – 60 m) benthic habitats around St. John in the 

U.S. Virgin Islands. The moderate-depth benthic habitat map begins at the optical limit of the shallow-water map, 

and continues to the edge of the acoustic imagery. Integration of the shallow-water and moderate-depth maps 

will provide NPS with one seamless benthic habitat map, extending from the shoreline of St. John southward to 

the 55 m isobath. 

The integration of these two maps is possible, given that the same general habitat classification schemes and 

MMUs were applied to both habitat maps. That being said, the differences between these two maps must be 

thoroughly understood, in order to recognize the limitations associated with using such an integrated map. Most 

notably, the important differences between the two maps include: (1) slightly different defi nitions of Pavement, 

(2) different defi nitions of Algae, and (3) different methods used to quantify percent biological cover. These classification differences and their potential ramifications are discussed in more detail in the following paragraph. 

Pavement, first of all, is defined as an area of “flat, low-relief solid carbonate rock with little or no fi ne-scale rugosity” in the shallow-water scheme. In the moderate-depth scheme, Pavement is defined as an area of “flat, 

low-relief or sloping solid carbonate rock with little or no fine-scale rugosity.” Thus, in the shallow-water scheme, 

Pavement does not have relief, whereas in the moderate-depth classifi cation scheme, Pavement may be sloping. These differing degrees of slope may influence the physical oceanography of an area, which in turn may 

affect the zonation and distribution patterns of corals (Geister, 1977; Sheppard, 1982; Done, 1983). 

Algae, secondly, is defined as an area dominated by “any combination of numerous species of red, green, or 

brown algae that may be turf, fl eshy or filamentous species” in the shallow-water scheme. In the moderate-depth 

scheme, Algae is defined as an area of “any combination of numerous species of red, green, or brown algae 

that may be turf, fl eshy, filamentous or crustose coralline species.” Thus, in the shallow-water scheme, the Algae 

class does not include crustose coralline species, whereas in the moderate-depth classification scheme, the Algae class does include these algal types. As a result, the Algae and Coralline Algae classes should be merged 

together in the shallow-water habitat map, if it is to be integrated with the moderate-depth habitat map. 

Lastly, in the shallow-water scheme, the percent biological cover modifier represents a measure of the level of 

patchiness of the biological cover at the scale of the ≥ 1,000 m2

 habitat feature. It does not represent the density of biological cover observed by divers in the water. The moderate-depth classification scheme, on the other 

hand, quantified percent biological cover by accounting for both the density of biological cover at the scale of 

the 4 m2

 pixel and the patchiness of biological cover at the scale of the ≥ 1,000 m2 habitat feature. This methodological difference caused some polygons in the shallow-water map to have higher percent covers than spatially 

coincident polygons in the moderate-depth map. Thus, the percent biological cover modifiers are not directly 

comparable in both maps, and should be viewed independently. 

Chapter 4: Conclusion 

page 

53 
Table 4.4. Estimated time required to map St. John’s shallow-water habitats using the heads-up digitizing and attribution method, and St. John’s 

moderate-depth habitats using the semi-automated delineation and classification technique. These numbers were used to estimate of the number 

of square kilometers that were mapped per hour using each technique. 

Chapter 4: Conclusion 

page 

54 

4.3 THE NEXT GENERATION OF MAPPING 

The majority of shallow-water coral reef habitats have been successfully characterized at high thematic resolutions (≤ 32 classes) by conducting heads-up digitizing and interpretation of high resolution (≤ 4 x 4 m) imagery 

(Kendall et al., 2001; Battista et al., 2007a

; Battista et al., 2007b). These resulting maps, however, are time-intensive to produce, limited by the size of the minimum mapping unit, and ultimately subjective and irreproducible 

because they depend on the accuracy and interpretation of the person that is digitizing. 

Other studies have also successfully mapped marine habitats using pixel-based semi-automated classification 

techniques (Maeder et al., 2002; Mishra et al., 2006; Purkis et al., 2006). While these methods are more objective and reproducible than heads-up digitizing, they are not synoptic and easily scalable to coral reef areas of different sizes. Changing environmental conditions also inhibit these methods from producing maps with thematic 

accuracies and resolutions high enough to meet the needs of most marine resource managers. 

In order to address these difficulties and increase 

the repeatability and efficiency with which maps 

are produced, the Biogeography Branch has 

been experimenting with alternative techniques 

to automate the process of delineating and attributing features on the seafloor. This moderate-depth map represents the Biogeography 

Branch’s first attempt to create a benthic habitat 

map from acoustic imagery using this new semiautomated methodology. Results from this moderate-depth mapping effort suggest that this new 

mapping approach is 7x more time effi cient (and 

just as thematically accurate) as heads-up habitat delineation and attribution (Table 4.4). Additionally, less than 40% of the polygons created 

using the semi-automated method were manually edited (Tables 2.7 and 2.8 (pg. 36)), suggesting that the majority of the moderate-depth 

map produced using this method is completely 

reproducible. 

These initial results indicate that this new semi-automated approach has the potential to increase the repeatability and efficiency with which maps are produced. The ability to quickly, accurately and objectively create benthic 

habitat maps would transform the process of mapping from a static, resource inventory tool to a dynamic, resource monitoring tool. By doing so, resource managers would be able to more frequently assess the changing 

distribution (and ultimately, health) of the coral reef systems that they manage. Improving our understanding of 

these ecosystems is the key to identifying and mitigating the heterogeneous threats that face these important 

and precious resources. 

Estimated Area Area Mapped Estimated Mapping Method Mapped (km2

) (km2

) Time (hours)* per Hour 

Manual Heads-up 

Digitizing and 53.4 571 0.09 

Attribution 

Semi-automated 

Delineation and 90.2 120 0.75 

Attribution 

Total Difference -36.8 451 -0.66 

*This number only includes the time needed to delineate and attribute habitat features in the imagery. It 

does not included the time required for image processing, field work, data processing, expert reviews as 

well as report and product generation. 
LITERATURE CITED 

Battista, T. A., B.M. Costa and S.M. Anderson. 2007a

. Shallow-water benthic habitats of the main eight Hawaiian 

islands. NOAA Technical Memorandum NOS NCCOS 61(Online). 

Battista, T.A., B.M. Costa and S.M. Anderson. 2007b

. Shallow-Water Benthic Habitats of the Republic of Palau. 

NOAA Technical Memorandum NOS NCCOS 59 (Online). 

Done, T. J. 1983. Coral zonation: Its nature and significance. In D. J. Barnes (ed.), Perspectives on coral reefs. 

Australian Institute of Marine Science and Brian Clooston, Manuka, ACT, Australia. pp. 107-147. 

Geister, J. 1977. The influence of wave exposure on the ecological zonation of Caribbean coral reefs. Procedures of the 3rd International Symposium on Coral Reefs, 1, 23-29. 

Kendall, M.S., M.E. Monaco, K.R. Buja, J.D. Christensen, C.R. Kruer, M. Finkbeiner and R.A. Warner. 2001. 

Methods used to map the benthic habitats of Puerto Rico and the U.S. Virgin Islands. NOAA Technical Report 

(Online). 

Maeder, J., S. Narumainai, D.C. Rundquist, R.L. Perk, J. Schalles, K. Hutchins and J. Keck. 2002. Classifying 

and Mapping General Coral-Reef Structure Using IKONOS Data. Photogrammetric Engineering and Remote 

Sensing 1297-1305. 

Mishra, D., S. Narumaiani, D. Rundquist and M. Lawson .2006. Benthic Habitat Mapping in Tropical Marine Environments Using QuickBird Multispectral Data. Photogrammetric Engineering and Remote Sensing, 1037-1048. 

Purkis, S.J., S.W. Myint and B.M. Riegl. 2006. Enhanced detection of the coral Acropora cervicornis from satellite 

imagery using a textural operator. Remote Sensing of Environment, 101: 82-94. 

Sheppard, C. R. C. 1982. Coral populations on reef slopes and their major controls. Marine Ecology Progress 

Series, 7, 83-115. 

Chapter 4: Conclusion 

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55 
Chapter 4: Conclusion 

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56 

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ACKNOWLEDGEMENTS 

Funding for this study was provided by NOAA’s Coral Reef Conservation Program. This report would not have 

been possible without the numerous people who shared their data, information and time throughout this process. We appreciate the support of U.S. National Park Service staff including Rafe Boulon, Jeff Miller and Peter 

Laurencin. We also appreciate the support of the crew and officers onboard the NOAA ship Nancy Foster, as 

well as many scientists at NOAA’s National Undersea Research Program (NURP) and National Marine Fisheries 

Service (NMFS). Also, many thanks to Randy Clark for helping to collect ground validation and accuracy assessment data in the field, and to Jamie Higgins for organizing the content of this report. 

Acknowledgements 

page 

57 


United States Department of Commerce 

Gary Locke 

Secretary 

National Oceanic and Atmospheric Administration 

Jane Lubchenco 

Administrator 

National Ocean Service 

John H. Dunnigan 

Assistant Administrator 

U.S. DEPARTMENT OF COMMERCE

NAT OI NAL OCEANIC AND ATMOSPHERIC ADMINISTRAT OI N