OCS Study

BOEM 2020-002

NOAA NCCOS 270

US Department of the Interior

Bureau of Ocean Energy Management

Headquarters (Sterling, VA)

Regional Essential Fish Habitat 

Geospatial Assessment and Framework 

for Offshore Sand Features 

Volume 2: Shoal Identification and Classification of Sand 

Resources
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OCS Study

BOEM 2020-002

NCCOS 270

US Department of the Interior

Bureau of Ocean Energy Management

Headquarters (Sterling, VA)

Regional Essential Fish Habitat 

Geospatial Assessment and Framework 

for Offshore Sand Features 

Volume 2: Shoal Identification and Classification of Sand 

Resources

January 2020

Authors:

Bradley A. Pickens1,2

Mark Finkbeiner3

J. Christopher Taylor1

Prepared under Interagency Agreement: M17PG00028

By

1

NOAA, National Ocean Service, National Centers for Coastal Ocean Science, Biogeography Branch, 

Beaufort Laboratory, Beaufort, North Carolina, USA 28516

2

CSS-Inc., 10301 Democracy Lane, Suite 300, Fairfax, Virginia 22030, USA; Under NOAA/NCCOS 

Contract #GS-00F-217CA

3

NOAA, National Ocean Service, Office for Coastal Management, Charleston, South Carolina, USA
This page intentionally left blank
DISCLAIMER

This study was funded, in part, by the US Department of the Interior, Bureau of Ocean Energy 

Management (BOEM), Environmental Studies Program, Washington, DC, through Interagency 

Agreement Number IA M17PG00028 with the National Oceanic and Atmospheric Administration. This 

report has been technically reviewed by BOEM, and it has been approved for publication. The views and 

conclusions contained in this document are those of the authors and should not be interpreted as 

representing the opinions or policies of the US Government, nor does mention of trade names or 

commercial products constitute endorsement or recommendation for use.

REPORT AVAILABILITY

To download a PDF file of this report, go to the US Department of the Interior, Bureau of Ocean Energy 

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and 

search on 2019-2020. The report is also available at the National Technical Reports Library at 

https://ntrl.ntis.gov/NTRL/.

CITATION

Pickens BA, Finkbeiner M, Taylor JC. 2020. Volume 2: Shoal identification and a new classification 

system for sand resources. In: Pickens, BA, Taylor JC, editors. Regional Essential Fish Habitat geospatial 

assessment and framework for offshore sand features. Sterling (VA): US Department of the Interior, 

Bureau of Ocean Energy Management. OCS Study BOEM 2020-002 and NCCOS Technical 

Memorandum 270, https://doi.org/10.25923/akzd-8556 47 pp.

ACKNOWLEDGMENTS

We acknowledge the staff of the Marine Minerals Program for inputs throughout the development of the 

classification scheme. We engaged international subject matter experts from geological and biological 

fields participated in several in-person discussions, roundtables, workshops, and webinars. Outcomes 

from these discussions significantly clarified our collective understanding of coastal geologic processes 

and the importance of shoals in fish habitat associations. Work provided by CSS-Inc. was provided under 

NOAA/NCCOS Contract #GS-00F-217CA.
i

Table of Contents

List of Figures................................................................................................................................................ ii

List of Tables.................................................................................................................................................iii

List of Abbreviations and Acronyms............................................................................................................. iv

Abstract ......................................................................................................................................................... v

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

2 Identifying Shoals on the OCS.............................................................................................................. 2

2.1 Study Area..................................................................................................................................... 2

2.2 Source Data and Spatial Data Variables ...................................................................................... 3

2.3 Delineation of Geomorphic Features ............................................................................................ 4

3 Classifying OCS Shoal and Sediment Resources ................................................................................ 9

3.1 Goals and Design of the Shoal Classification Scheme................................................................. 9

3.2 Classification Scheme Structure .................................................................................................10

3.3 Applying the Classification Scheme............................................................................................18

3.4 Verifying the Shoal and Shoal Complex Dataset........................................................................23

4 Results and Discussion.......................................................................................................................28

5 Literature Cited......................................................................................Error! Bookmark not defined.

Appendix A: Shoal Classification Scheme Dictionary.................................................................................32
ii

List of Figures

Figure 2-1. Geographic boundaries for the study area (dashed line) in the US Gulf of Mexico and Atlantic 

Coasts, extending from the offshore state/Federal boundary to 50-m depths..............................................3

Figure 2-2. Examples of seafloor complexity metrics derived from bathymetry (depth) and used to 

delineate sand features and shoals. ............................................................................................................. 5

Figure 2-3. Delineated shoal features throughout the study area in the Gulf of Mexico and Atlantic OCS..8

Figure 3-1. Hierarchical diagram of CMECS classification scheme proposed for sand features. ..............11

Figure 3-2. Classified sand features throughout the study area in the Gulf of Mexico and Atlantic OCS. .19

Figure 3-3. Overview map of classified shoal distributions in the northern Gulf of Mexico region. ............20

Figure 3-4. Overview map of classified shoals along the southeast coast of the US.................................21

Figure 3-5. Overview map of classified shoal distributions in the US mid-Atlantic and New England region.

....................................................................................................................................................................22

Figure 3-6. Concordance of classified shoals with known and identified sand resources (tan polygons) 

south of Louisiana in the Gulf of Mexico region..........................................................................................25

Figure 3-7. Concordance of classified shoals with identified sand resources (tan polygons) off Cape 

Hatteras in the South Atlantic region. .........................................................................................................26

Figure 3-8. Concordance of classified shoals with verified sand resources (tan polygons) in the midAtlantic region. ............................................................................................................................................27
iii

List of Tables

Table 2-1. Variables used to delineate sand shoals via an unsupervised classification, descriptive 

statistics presented as mean ± (SD) for classified shoals, swales, and the entire dataset analyzed...........7

Table 3-1. CMECS settings and components.............................................................................................13

Table 3-2. Water Column Component. .......................................................................................................14

Table 3-3. Geoform Component. ................................................................................................................14

Table 3-4. Substrate Component................................................................................................................15

Table 3-5. Biotic Component. .....................................................................................................................16

Table 3-6. Modifiers. ...................................................................................................................................17

Table 3-7. Spatial alignment of detected shoal features with named places for "Marine Place Name" (Top) 

and "Undersea Feature Place Name" (Bottom) datasets. ..........................................................................24
iv

List of Abbreviations and Acronyms

BC Biotic Component

BGN Board on Geographic Names

BOEM Bureau of Ocean Energy Management

BPI bathymetric position index

CATAMI Collaborative and Automated Tools for Analysis of Marine Imagery 

CMECS Coastal Marine Ecological Classification Standard

CRM Coastal Relief Model

cy cubic yard(s)

DEM digital elevation model

DOC Department of Commerce

DOI Department of the Interior

EFH Essential Fish Habitat

GC Geoform Component

GMFMC Gulf of Mexico Marine Fisheries Council

GNS GEOnet Names Server

ha hectare

HAPC Habitat Areas of Particular Concern

km kilometer(s)

m meter(s)

mm millimeter(s)

MMIS Marine Minerals Information System

NGA National Geospatial-Intelligence Agency

NGDC National Geophysical Data Center

NCCOS National Centers for Coastal Ocean Science

nm nautical mile(s)

NOAA National Oceanic and Atmospheric Administration

OCS Outer Continental Shelf

SC Substrate Component

ShoalMATE Shoal Mapping Assessment Tool for EFH

SME subject matter expert

TNC The Nature Conservancy

US US Geological Survey

US EPA US Environmental Protection Agency 

USACE US Army Corps of Engineers

USGS US Geological Survey
v

Abstract

The demand is increasing in the United States for marine sand resources to mitigate risks of coastal 

erosion from modifications to the shoreline due to coastal development or impacts of waves and currents 

during major storms. As sources for sediment in the nearshore are being depleted, there is increased 

demand for sand resources from the Outer Continental Shelf (OCS). Extensive studies have helped 

elucidate the dynamics and geomorphological characteristics of some prominent shoals, but we lack a 

broader understanding of the extent and types of shoals that exist along the Gulf and Atlantic Coasts of 

the OCS. Furthermore, offshore sand features have typically been characterized in a geological 

framework, and we lack descriptors for sand features that contribute to Essential Fish Habitat (EFH)

descriptions. This volume comprises two components. First, we developed a seabed classification model

that identifies and delineates potential sand shoals using broadly available, unified digital elevation 

models for the seafloor along the Gulf of Mexico and Atlantic coastlines from 3 nautical miles (nm) from 

shore to the 50-m depth contour. Distance from shore as well as seafloor complexity and relief metrics, 

derived from the Coastal Relief Model, were used to classify offshore areas of relative higher relief and 

produce polygons showing geomorphological features consistent with sand shoals, ridges, and swales. 

Newly created maps depict bedforms, shoal complexes, and unclassified features along the Gulf and 

Atlantic OCS. Recognition of these features in the context of EFH and sand resource demand can aide in 

improved planning and conservation recommendations for sand dredging activities. Three workshops 

were hosted with biological and geological subject matter experts to define criteria for classifying shoals 

and other sand features. We then used this new classification scheme to attribute sand shoals according to 

characteristics such as geoforms and environmental criteria to develop a new scheme. This new 

classification scheme was formalized under the Coastal Marine Ecological Classification Standard 

(CMECS) and proposed for adoption as a new schema for classifying OCS shoals. 
1

1 Introduction

The demand for marine sand resources is increasing in the United States (Drucker et al. 2004) and 

worldwide (Charlier and Charlier 1992; de Jong et al. 2014; Kim et al. 2008; La Porta et al. 2009). The 

Netherlands alone uses an estimated 24 million m3 (31 million cy) of dredged sand annually, and the 

amount is expected to grow rapidly with rising sea level (de Jong et al. 2014). In the United States, coastal 

and offshore marine sands are commonly used for beach renourishment, barrier island restoration, and 

wetland restoration. As human populations and development expands along the coastline, erosion will 

continue to bring challenges to ensuring sustainability in the coastal zone. The restoration and 

maintenance of beaches, barrier islands, and other coastal infrastructure will require substantial sediment 

resources. Although prior sand mining activities have focused on nearshore sand sources (i.e., in state 

waters), the dredging of Outer Continental Shelf (OCS) sand resources is likely to increase in the near 

future as nearshore sand resources are depleted (Nairn et al. 2004). Throughout this report, we use the 

term "sand" to broadly characterize sediment resources, and we recognize that sediment dredging may 

include a variety of grain sizes depending on the application.

Sand resource mapping in offshore waters has been ongoing for decades, and cooperative agreements 

have been established between Federal and state governments to synthesize existing and new information, 

including the mapping of sand shoals appropriate for sand mining (Drucker et al. 2004). Marine sand 

dredging occurs in relatively shallow waters (≤ 50 m), often within ridge and swale complexes where 

large volumes of sand can be extracted over relatively small areas. As of 2019, all 17 Atlantic and Gulf of 

Mexico coastal states have cooperative agreements with BOEM to identify available sand resources. As 

of 2017, there were no BOEM sand and gravel leases in New England and New York, but storms and 

erosion have led to an anticipation of offshore sand dredging in the region. For example, Maine, New 

Hampshire, Massachusetts, Rhode Island, and New York all signed cooperative agreements to evaluate 

sand resources in 2014 following Hurricane Sandy. The overall strong upward trend of OCS sand 

dredging necessitates a greater strategic vision for managing sand resources as a whole rather than the 

site-by-site approach that has been undertaken to this point. Current geophysical and geotechnical survey 

and other seabed mapping activities have only covered a small percentage of the OCS and vary in extent 

across geographic regions, leaving major gaps in knowledge of the location and extent of sand resources.

Sand shoals are also habitats for economically important fisheries. Along the United States (US) Atlantic 

and Gulf of Mexico Coasts, sand shoals are used by juvenile reef fish, shrimp, coastal pelagic fish species 

during seasonal spawning migrations, and as feeding grounds for demersal fishes and sharks (Rutecki et 

al. 2014). Particular shoals features have been designated as EFH, and conservation recommendations are 

provided for special considerations that minimize environmental impacts caused by commercial uses of 

the ocean and seafloor. The Bureau of Ocean Energy Management (BOEM), as part of the US

Department of the Interior, is responsible for the management and development of sand resources on the 

OCS. All proposed sand dredging projects require prior analysis of impacts to marine resources using the 

best available science to understand and mitigate environmental impacts when possible. The mapping of 

shoals will help assess potential impacts of sand dredging, assist efforts to understand species' habitat 

relationships with shoals, and set the stage for a broader classification of marine unconsolidated sediment 

ecosystems. Lastly, better information on the extent of possible sand resources will inform strategic 

decisions about mineral resource use and the availability to meet demand.

Shoals are common geologic features on the continental shelf and are defined as sand, or other 

unconsolidated material, that result in shallower water depths than surrounding areas (Rutecki et al. 

2014). Other terms noted for shoals include underwater “ridges,” “banks,” or “bars.” Characteristics of 

shoals include crests, troughs, areas with slope, and varying sediment substrates (Vasslides and Able 
2

2008). In the simplest terms, shoals are distinguished as areas of topographic relief compared to flat, lowrelief unconsolidated sediments. Bathymetric highs are often accompanied by troughs and swales. This 

report follows an intensive literature review by Rutecki et al. (2014), which provides extensive examples 

on the geology, geography, and general biological values of sand shoals. Although these definitions and 

case studies were sufficient to develop an understanding of the importance of shoals in coastal geology 

and as fish habitat, we still lack a method that delineates the distribution and extent of shoals on the OCS,

as well as a unified classification scheme for characterizing sand features in terms of geomorphology and

potential habitat value. The primary objective of this chapter was to develop spatially explicit models that 

classify, and whenever possible verify, the location and extent of potential shoals of the United States’ 

OCS spanning the northwest Atlantic Ocean and the Gulf of Mexico. The second objective was to

identify attributes that distinguish shoals from other marine geomorphic features and propose a 

classification scheme compatible with the Coastal and Marine Ecological Classification Standard 

(CMECS) (FGDC (Federal Geographic Data Committee) 2012). The classification scheme and shoal 

layers developed in this volume were incorporated into the interactive mapping and reporting tool 

ShoalMATE (Shoal Mapping Assessment Tool for EFH), which is described in more detail in Volume 4.

2 Identifying Shoals on the OCS

2.1 Study Area

The landward boundary of the study area was defined by the Outer Continental Shelf Lands Act, which 

distinguishes Federal and state managed waters. Federal waters are those ≥ 3 nm from the shoreline, with 

the exception of Texas and the Gulf Coast of Florida where Federal waters are defined as ≥ 9 nm from the 

shoreline. The oceanic boundary of the study area was defined by a 50 m contour line (Figure 2-1), which 

is the deepest extent of anticipated dredging activities, although 30 m is currently the deepest extent of 

existing dredge activities. The contour line was derived from the Coastal Relief Model of the National 

Oceanic and Atmospheric Administration (NOAA) (NOAA National Centers for Environmental 

Information 2010). To account for potential regional differences in seafloor geomorphology, separate 

analyses were conducted for three regions: Greater Atlantic, South Atlantic, and Gulf of Mexico. Because 

of concurrent fish research, the boundaries of the three regions generally coincided with Federal fishery 

management council jurisdictions and NOAA’s Essential Fish Habitat (EFH) designations. The Greater 

Atlantic region spanned from the northern extent of Maine to just south of Cape Hatteras, North Carolina. 

The South Atlantic region extended from Cape Hatteras, North Carolina, southward to the Florida Keys. 

The Gulf of Mexico region included Texas eastward through the Florida Keys.
3

Figure 2-1. Geographic boundaries for the study area (dashed line) in the US Gulf of Mexico and 

Atlantic Coasts, extending from the offshore state/Federal boundary to 50-m depths.

Map also shows the 30-m depth contour, which is the deepest extent of current dredging activities.

2.2 Source Data and Spatial Data Variables

Existing bathymetric digital elevation models (DEMs) provided the primary source data for this study.

Bathymetry for the central and western Gulf of Mexico was derived from NOAA’s Coastal Relief Model 

(NOAA National Centers for Environmental Information), which characterized waters offshore of Texas, 

Louisiana, Mississippi, and Alabama. However, several large errors were prominent offshore of Florida. 

Therefore, we used sounding data summarized and developed into a 50-m raster grid by the US 

Geological Survey (Robbins et al. 2007). To be consistent with the other datasets, these data were 

resampled to a 90-m resolution with bilinear resampling. For the South Atlantic, we used a bathymetry 

dataset derived as part of The Nature Conservancy’s South Atlantic Marine Bight Assessment (Conley et 

al. 2017). They synthesized 4.7 million depth sounding points obtained from the National Geophysical 
4

Data Center’s (NGDC’s) Coastal Relief Model (CRM) (NOAA National Centers for Environmental 

Information 2010). The initial hydrographic surveys compiled by NOAA were completed between 1851 

and 1965 and from survey data obtained digitally on National Ocean Service survey vessels since 1965 

(NOS (National Ocean Service) 2008). The Nature Conservancy (TNC) used only data after the 1950s, 

and kriging was used to interpolate among sounding points (Conley et al. 2017). Despite these data being 

the best available for the South Atlantic, a few notable errors persisted. We updated a 1,600-km2 and a 

400-km2 region offshore of northeastern North Carolina with sounding points acquired in 2016 (NOAA 

National Centers for Environmental Information 2016). To be consistent throughout the region, we used 

the same kriging methods conducted by TNC. Bathymetry data for the Greater Atlantic was simply 

obtained from the CRM (NOAA National Centers for Environmental Information 2010). Where the South 

Atlantic bathymetry overlapped with the Greater Atlantic study area (Virginia and northeast North 

Carolina), we used the South Atlantic bathymetry because it had already been improved with recent data.

All spatial data were converted to the North America Alberts Equal Area Conic map projection. All data 

were maintained at 90-m resolution, and analyses were conducted with ArcGIS 10.6 (ESRI, Redlands, 

CA).

Where sand resources are found in topographic mounds such as shoals or ridges, localized spatial models 

have typically used relatively coarse (> 10-m horizontal cell size) DEMs to detect anomalies in the 

seafloor using predictors, such as slope or rugosity, to locate seabed forms consistent with shoals (Knorr 

2017). In contrast, we used an unsupervised classification method for mapping shoals with data on depth, 

standard deviation of depth, slope, distance to shoreline, and the bathymetric position index (BPI) as key 

variables. In addition to the depth measures directly provided by bathymetry, we used the Benthic Terrain 

Modeler (Wright et al. 2012) to derive slope (3 x 3 cells) and the BPI (27 x 27 cells for Atlantic; 71 x 71 

cells for Gulf of Mexico) (Figure 2-2). We selected the BPI analysis scale by initially exploring 

possibilities for each region. The relatively narrow shoals of the Atlantic became distinguished at a 27 x 

27 cell analysis window, whereas shoals in the Gulf of Mexico were relatively wide and were 

distinguished by a 71 x 71 cell analysis window. The standard deviation of depth (SD depth) was 

computed with the Spatial Analyst, a focal statistics tool with a 9 x 9 cell analysis window. Distance to 

shoreline was calculated by back-transforming boundaries of the Submerged Lands Act (NOAA National 

Ocean Service, Office for Coastal Management 2016), which is set at a distance of 3–9 nm from the 

shoreline, depending on the state. We used the buffer tool to re-create the approximate shoreline 

boundaries, and then we calculated the Euclidean distance from the shoreline to the entirety of the study 

area.

2.3 Delineation of Geomorphic Features

After examining bathymetry-derived variables and visually inspecting the large-scale geomorphological 

patterns along the OCS, we restricted the analyses to waters ≤ 40 m of depth because deeper waters were 

often influenced by the increasing slope at the continental shelf break, which differed dramatically from 

the geomorphology on the shelf. Depths from 40 to 100 m are typically characterized by emergent rocky 

outcrops, ledges, and areas of high relief and steep slope. We excluded this area to constrain the 

bathymetry-derived variables to those more typical on the shelf. We standardized all predictor variables 

by subtracting the mean and dividing by the standard deviation of the raster layer. The subsequent data 

distribution had a mean of approximately zero and a standard deviation of one. We used the standardized 

predictor variables as inputs into the ArcGIS ISO Cluster Unsupervised Classification, which identifies 

clusters of cells with similar attributes. In this method, the user defines the number of groups to be 

classified. We ran the tool iteratively with the number of output classifications ranging consecutively 

from 2 to 10. Given the lack of defined boundaries of shoals, we had the goal of creating the fewest 

classification classes possible to simplify the identification of shoals and other geomorphic features. The 

minimum cells per classification were set at 1,000, and half the cells were used to train the classification.
5

Figure 2-2. Examples of seafloor complexity metrics derived from bathymetry (depth) and used to 

delineate sand features and shoals.

SD depth = standard deviation of depth, BPI = bathymetric position index. The arrow represents the process of 

deriving three surfaces from Depth to classify features as Shoals. See text for descriptions for statistical assumptions 

for each derived surface.

After the initial identification, we used an eight-neighbor majority filter to remove small, isolated 

features. Based on the locations of a few known sand shoals (e.g., Ship Shoal in Gulf of Mexico, capeassociated shoals in Atlantic) and preliminary descriptive statistics, we classified features into “shoal,”

“swale,” “high slope,” and “non-shoal.” Additionally, a minimum mapping unit of 5 hectare (ha) was 

used for shoal and swale classes in the Atlantic because 5 ha was the minimum size of shoal 

geomorphology identified in a recent review of sand shoals (Rutecki et al. 2014). For the Gulf of Mexico,

a 20-ha minimum was used (see details below). All features below these size thresholds were removed, 

which further removed isolated cells.

For the Gulf of Mexico, the unsupervised classification resulted in nine classes, which included six 

labeled as “other seabed.” We removed classified shoals, swales, or high slopes that were parallel and 

continuous with a slope contour, which were particularly evident offshore of Texas, Louisiana, and the 

Florida Keys. These features were often adjacent to the boundary of bathymetry data and where 

bathymetry surfaces appeared relatively coarse. To further address this issue, we removed classifications 

with a perimeter to area ratio of > 10; this resulted in the smallest classification of shoal or swale being 

20 ha in size. Additionally, the two features identified as “high slope” were removed because they had 

such an extremely high slope that it was likely due to bathymetry errors rather than a true seabed feature. 

The two features removed included an area depicted as a large hole and a classified shoal/swale area 

> 120 km offshore of Florida (northwest of Tampa Bay) and an area in waters well known as a natural, 

rocky reef that had been repeatedly sampled as part of a reef fish surveys. The remaining features were 

aggregated into a single class called “shoals” or “swales”. Statistics derived from the seafloor bathymetry 

are shallower in depth, higher slope, higher SD of depth and higher BPI than the surrounding seafloor 

(Table 2-1). Shoals were mapped throughout the Gulf of Mexico (Figure 2-3). Later in this volume, we

further classify to shoal types using a new shoal classification scheme.

The South Atlantic shoals could be distinguished from regular bathymetric contours with a minimum of 

six classifications, and seven classifications distinguished both shoals and swales. With this methodology, 

an additional non-flat classification was created that depicted areas of extremely high slope and SD depth,

as well as a positive BPI. Many of these features were sloping areas on the edge of shoals; however, we 

removed polygons parallel to the shelf break in Florida because these areas were part of the broader shelf 
6

geology. A few of these sloping features were in, or surrounding, holes; these were also removed from the 

shoal classification. We removed a circle-shaped classified shoal near Cape Hatteras, North Carolina, 

which was shown to be erroneous data in the raw CRM. We also removed erroneously classified shoal 

and swale features from the Florida Keys region because this geography is dominated by coral reefs and 

hard bottom as is characterized by the designated Habitat Areas of Particular Concern (HAPCs). The 

misclassification of shoals is likely because of similar characteristics, including relatively high depth 

heterogeneity, slope, and a positive BPI. Shoals in the South Atlantic, like the Gulf of Mexico, were 

distinct from the surrounding seafloor (Table 2-1, Figure 2-3). For the Greater Atlantic, all shoals and 

swales were readily identifiable with four classes. Greater Atlantic shoals were similar distinct from the 

surrounding seafloor (Table 2-1, Figure 2-3).
7

Table 2-1. Variables used to delineate sand shoals via an unsupervised classification, descriptive statistics presented as mean ± (SD)

for classified shoals, swales, and the entire dataset analyzed.

Analyses were conducted independently for each region and then combined.

Greater Atlantic South Atlantic Gulf of Mexico

Variable

Scale of 

analysis 

(90-m cells)

Shoal Swale Entire 

seafloor 

Shoal Swale Entire 

seafloor 

Shoal Swale Entire 

seafloor 

Depth (m) 1 -20.87 

(6.65)

-28.71 

(8.26)

-28.11 

(7.95)

-20.35 

(6.65)

-26.45 

(8.17)

-24.62 

(8.42)

-19.93 

(8.20)

-31.52 

(7.17) -24.21 

(9.59)

SD of depth 9 x 9 1.32 

(0.54)

2.46 

(1.56)

0.67 

(0.72)

0.99 

(0.51)

1.72 

(1.15)

0.49 

(0.54)

0.53 

(0.29)

1.33 

(1.46)

0.23 

(0.40)

Slope 3 x 3 0.39 

(0.22)

0.79 

(0.63)

0.15 

(0.26)

0.29 

(0.24)

0.54 

(0.42)

0.11 

(0.20)

0.14 

(0.13)

0.41 

(060)

0.04 

(0.14)

Distance to 

shoreline (km) NA 25.46 

(13.78)

31.19 

(16.60)

29.61 

(17.34)

29.83 

(17.31)

37.49 

(20.92)

38.10 

(22.44)

31.10 

(15.02)

38.14 

(21.84)

48.85 

(27.49)

Bathymetric 

position index 

Gulf of 

Mexico: 

71 x 71; 

Atlantic: 

27 x 27

2.49 

(1.92)

-1.00 

(3.88)

0.36 

(1.62)

2.22 

(1.27)

-0.56 

(2.28)

0.32 

(1.10)

2.41 

(0.89)

-0.65 

(2.92)

0.21 

(1.15)
8

Figure 2-3. Delineated shoal features throughout the study area in the Gulf of Mexico and Atlantic 

OCS.

The dashed line indicates the study area of Federal waters to a maximum of 50-m depth. A 30-m depth contour also 

included as reference for deepest existing dredging project.
9

3 Classifying OCS Shoal and Sediment Resources

3.1 Goals and Design of the Shoal Classification Scheme

Having successfully identified shoals through semi-automated means, the next step was to group them 

into distinct classes that characterize their spatial location in the seascape, their geological origin and the 

environmental processes at work in their environment. This additional contextual information, conveyed 

through a classification system, is important to fully understanding the habitat value of these features. The 

classification system described in this section was based on physical and spatial variables relevant to 

managed fish species. This system applies to the OCS in Federal waters of the US Atlantic and Gulf of 

Mexico ≤ 50 m in depth.

The scheme is intended to meet the following criteria:

• It must be applicable over all US waters in the Atlantic and Gulf of Mexico.

• It must integrate with the CMECS. CMECS is a federally endorsed standard that also forms the 

framework for an associated BOEM product: the Marine Minerals Information System (MMIS). 

The Geoform and Substrate Components of CMECS are the most relevant ones in this case.

• It should be applicable at a variety of spatial scales.

• It should build on the best available science.

• Where possible, it should take into consideration the physical and chemical processes important 

to sand shoal formation, evolution, and temporal persistence.

• It should be conceptually open to updates as new science and data improve our knowledge.

A first step in the classification design process was a review of existing classification systems that might 

provide a framework or information relevant to this system. The following systems were reviewed:

• A Classification Scheme for Deep Seafloor Habitats (Greene et al. 1999)

• The CATAMI (Collaborative and Automated Tools for Analysis of Marine Imagery) 

Classification (Althaus et al. 2013)

• Seabed Geomorphology: A Two-Part Classification System (Dove et al. 2016)

• A Habitat Classification Scheme for the Long Island Sound Region (Auster et al. 2009)

• A New Classification Scheme of European Cold-Water Coral Habitats: Implications for 

Ecosystem-Based Management of the Deep Sea (Davies et al. 2017)

• A Geomorphic Classification of Estuaries and its Application to Coastal Resource Management –

A New Zealand Example (Hume and Herdendorf 1988)

• INFOMAR Seabed Survey Seabed Habitat Classification (Thorsnes et al. 2018)

• A Habitat Classification Scheme for Seamount Landscapes: Assessing the Functional Role of

Deep-Water Corals as Fish Habitat (Auster et al. 2005)

• Hierarchical Classifications of Sedimentary Architecture of Deep Marine Depositional Systems 

(Cullis et al. 2018)

• A Marine and Estuarine Habitat Classification System for Washington State (Dethier 1990)

Although some of these systems did reference geomorphological features, most included them as only as 

a descriptive part of a biological unit, or their focus was on a different geography than that of concern in 

this project. Three systems did address geomorphology and sand shoals directly (Auster et al. 2005; Dove 

et al. 2016; Greene et al. 1999), but only the following systems addressed the resources of interest in a 

systematic way, and they formed the source for most of the units:
10

• CMECS (FGDC (Federal Geographic Data Committee) 2012)

• Understanding the Habitat Value and Function of Shoals and Shoal Complexes to Fish and 

Fisheries on the Atlantic and Gulf of Mexico Outer Continental Shelf Literature Synthesis and 

Gap Analysis (Rutecki et al. 2014)

• Seafloor Geomorphology as Benthic Habitat (Harris and Baker 2012)

Given the breadth of the CMECS system, we decided to use it as a basis for the shoal classification 

scheme, and the other schemes were used in support of the method. We drew upon international subject 

matter experts (SMEs) to develop a framework for a classification scheme through consensus. Meetings 

with SMEs were scheduled to coincide with regional and international conferences, as well as facilitated 

webinars. In all cases, the participants were given a short presentation on the scope and objectives of the 

entire project and the need for a new classification scheme for sand features. The first meeting invited 

experts in the Southeast US region following a workshop on Improving Coordination in Seafloor 

Mapping in the Southeast US OCS in April 2018. Though regional in nature, this meeting drew upon 

geological models and knowledge for sand features off the Carolinas, Georgia, and Florida in the South 

Atlantic region. A meeting with international experts was scheduled around the GeoHab conference held 

in May 2018 in Santa Barbara, CA. For this meeting, our team focused on international standards for 

classifying sand features. Lastly, we hosted two facilitated webinars to gather additional comments on a 

draft scheme in June 2018 and concluded with a presentation and review among BOEM and US Army 

Corps of Engineers (USACE) staff in July 2018. From the totality of these meetings and calls, a structure 

for the classification scheme was developed.

3.2 Classification Scheme Structure

The sand shoal classification scheme draws from several elements of the CMECS and incorporates some 

new individual units from Rutecki et al.’s (2014) review. The CMECS classification system was used as 

the starting point in selecting appropriate variables and domains. CMECS was originally created through 

a collaboration between NatureServe, the NOAA, the US Environmental Protection Agency (US EPA), 

US Geological Survey (USGS), and the University of Rhode Island. This classification standard provides 

a method for categorizing the physical, biological, and chemical components of coastal and marine 

ecosystems. Figure 3-1 shows the hierarchical nature of CMECS with new levels and modifiers relevant 

to this study proposed for an update to CMECS. Table 3-1 presents the settings and components, 

including those suggested for inclusion in classifying shoal and related sand features. The following 

tables provide further details of the classification scheme components (Table 3-2 to Table 3-6). In the 

Appendix, we provide more detailed definitions for each class and modifier. Furthermore, the shoal 

classification scheme was applied using available data in the process described in Volume 4 of this report.
11

Figure 3-1. Hierarchical diagram of CMECS classification scheme proposed for sand features.
12

The following table illustrates the most basic structure of CMECS, which includes a series of settings, 

components, and modifiers. Indentation shows the hierarchical structure of items within each setting or 

component (Table 3-1). Items struck out in this classification scheme were not used in the ShoalMATE

tool. Some variables in the ShoalMATE tool contain only a subset of original CMECS domains, which 

will be explained in further detail in subsequent tables. For the ShoalMATE tool, no distinction was made 

between Level 1 and Level 2 Geoforms, although due to the spatial resolution of input data layers, it is 

likely that most of these are Level 1. No information from the CMECS Aquatic Setting category was used 

in the ShoalMATE tool.

In Table 3-2, a subset of the available values from the CMECS ecoregion variable were used in the 

ShoalMATE tool. The Gulf of Mexico Fisheries Management Council (GMFMC) subdivided the Gulf of 

Mexico into a set of ecoregions that do not coincide with those from CMECS. These GMFMC-derived 

ecoregions were included in the ShoalMATE tool in a separate attribute for added spatial resolution and 

to match fish distribution descriptions in the GMFMC Fisheries Management Plan. For EcoregionFMC, 

entries may also be plural to include multiple ecoregions, such as “GMFMC Ecoregions 2-5.” 

Table 3-2), the Salinity and Temperature Subcomponents are broken down into qualitative variables 

based on quantitative range descriptions. The ShoalMATE tool, however, includes quantitative range 

variables (min/max) as well as a variable for average temperature. This method ensures the highest level 

of precision for these values can be taken directly from source literature.

In Table 3-3, CMECS divides Geoform Components into Level 1 and Level 2. Level 1 Geoform 

components are generally larger than one square kilometer, while Level 2 are generally less than one 

square kilometer (see Federal Geographic Data Committee (FGDC) 2012, Section 6 for more details). 

However, the ShoalMATE tool does not make this distinction. A subset of the possible values were used 

for the CMECS variables Origin, Geoform, and GeoformType.

Table 3-4 shows the subset of CMECS Substrate Component used in the ShoalMATE tool in the native 

CMECS hierarchical structure.

In Table 3-5, the CMECS Biotic Setting and Biotic Class were selected for use in the ShoalMATE tool. 

Based on the level of detail in source documentation, it was determined that inclusion of the CMECS 

Biotic Subclass, Biotic Group, and Biotic Community were not necessary.

Lastly, in the variables are based on CMECS modifiers. In these cases, the CMECS modifiers were 

qualitative values based often on quantitative ranges, while the final variables selected for the 

ShoalMATE tool remained quantitative and captured varying ranges.
13

Table 3-1. CMECS settings and components. 

Also see Table 2.1 in Federal Geographic Data Subcommittee (2012). Modifiers may be applied to one or more components. See Federal Geographic Data 

Subcommittee 2012 for further details. 

Biogeographic 

Setting (BS)

Aquatic 

Setting (AS)

Water Column 

Component (WC)

Geoform Component 

(GC)

Substrate 

Component (SC)

Biotic Component 

(BC)

Realm

Province

Ecoregion

System

Subsystem

Tidal Zone

Layer

Subcomponent

Tectonic Setting

Subcomponent

Substrate Origin

Substrate Class

Substrate Subclass

Substrate Group

Substrate Subgroup

Biotic Setting

Biotic Class

Biotic Subclass

Biotic Group

Biotic Community

Salinity

Subcomponent

Physiographic Setting

Subcomponent

Temperature

Subcomponent

Level 1 Geoform

Subcomponent

Geoform Origin

Level 1 Geoform

Level 1 Geoform Type

Hydroform

Subcomponent

Hydroform Class

Hydroform

Hydroform Type

Level 2 Geoform

Subcomponent

Geoform Origin

Level 2 Geoform

Level 2 Geoform Type

Beiogeochemical Feature

Subcomponent
14

Table 3-2. Biogeographic Setting.

Ecoregion EcoregionFMC*

Scotian Shelf GMFMC Ecoregion 1

Gulf of Maine/Bay of Fundy GMFMC Ecoregion 2

Virginian GMFMC Ecoregion 3

Carolinian GMFMC Ecoregion 4

Northern Gulf of Mexico GMFMC Ecoregion 5

Floridian

Table 3-2. Water Column Component.

ShoalMATE tool Water Column Component Variables:

TempMin

TempMax

TempAvg

SalinityMin

SalinityMax

Table 3-3. Geoform Component.

Geoform Component Origin (GC 

Origin)

Geoform Component 

Geoform (GC Geoform)

Geoform Component Type 

(GC Type)

Geologic Shoal Moraine Shoal

Cape-Associated Shoal 

(CMECS provisional unit)

Bedform Shoal (CMECS 

provisional unit)

Isolated Shelf Shoals (CMECS 

provisional unit)

Dredge Deposit Dredge Deposit Shoal

Swale -

Sediment Wave Field -

Sediment Sheet -

Flat Tidal Flat
15

Table 3-4. Substrate Component.

Substrate Component 

Origin (SC Origin)

Substrate Component 

Class (SC Class)

Substrate Component 

Subclass (SC Subclass)

Substrate Component 

Group (SC Group)

Substrate Component 

SubGroup (SC Subgroup)

Geologic Substrate Unconsolidated Mineral 

Substrate

Coarse Unconsolidated 

Substrate

Gravel

Boulder

Cobble

Pebble

Granule

Gravel Mixes

Sandy Gravel

Muddy Sandy Gravel

Muddy Gravel

Gravelly

Gravelly Sand

Gravelly Muddy Sand

Gravelly Mud

Fine Unconsolidated 

Substrate

Slightly Gravelly

Slightly Gravelly Sand

Slightly Gravelly Muddy Sand

Slightly Gravelly Sandy Mud

Slightly Gravelly Mud

Sand

Very Coarse Sand

Coarse Sand

Medium Sand

Fine Sand

Very Fine Sand

Muddy Sand

Silty Sand

Silty-Clayey Sand

Clayey Sand

Sandy Mud

Sandy Silt

Sandy Silt-Clay

Sandy Clay

Mud

Silt

Silt-Clay

Clay

Biogenic Substrate Shell Substrate Shell Hash

Clam Hash Coquina Hash

Crepidula Hash -

Mussel Hash -

Oyster Hash -

Shell Sand Clam Sand Coquina Sand
16

Table 3-5. Biotic Component.

Biotic Component Setting (BCSetting) Biotic Component Class (BCClass)

Planktonic Zooplankton

Floating/Suspended Plants and Macroalgae

Phytoplankton

Floating/Suspended Microbes

Benthic/Attached Reef Biota

Faunal Bed

Microbial Communities

Aquatic Vegetation Bed

Emergent Wetland

Scrub-Shrub Wetland
17

Table 3-6. Modifiers.

ShoalMATE tool 

Variable Name

ShoalMATE tool Variable 

Description

Original CMECS 

Variable Name

Original CMECS 

Variable Description

Grain Size (Phi) Sediment grain size in Phi (numerical) Seafloor Rugosity

Qualitative descriptors 

based on ranges of grain 

size

DOmin

Minimum dissolved oxygen (mg/L) at 

which a fish species or life stage can 

survive, or minimum dissolved oxygen 

measured over a sand resource Oxygen Qualitative descriptors 

based on ranges of 

dissolved oxygen values

DOmax

Maximum dissolved oxygen (mg/L) at 

which a fish species or life stage can 

survive, or maximum dissolved oxygen 

measured over a sand resource

DepthMin_m

Minimum depth (m) at which a fish 

species or life stage can be found, or 

the minimum measured depth of a sand 

resource (not calculated during storm 

events) Benthic Depth 

Zones

Qualitative descriptors 

based on depth ranges 

(m)

DepthMax_m

Maximum depth (m) at which a fish 

species or life stage can be found, or 

the maximum measured depth of a sand 

resource (not calculated during storm 

events)

ChlaMin

Minimum chlorophyll a (μg/L) at which a 

fish species or life stage can survive, or 

the minimum measured chlorophyll a 

(μg/L) measured over a sand resource Phytoplankton 

Productivity

Qualitative descriptors 

based on a range of 

chlorophyll a (μg/L)

ChlaMax

Maximum chlorophyll a (μg/L) at which a 

fish species or life stage can survive, or 

the maximum measured chlorophyll a 

(μg/L) measured over a sand resource

TurbMin

Minimum turbidity at which a fish 

species or life stage can survive, or the 

minimum turbidity measured on a sand 

resource Turbidity

Qualitative descriptors 

based on a range of 

turbidity values measured 

in Secchi depths TurbMax

Maximum turbidity at which a fish 

species or life stage can survive, or the 

maximum turbidity measured on a sand 

resource

SubstrateDesc

A subset of the associated CMECS 

variable (see right). Allowed values:

Carbonate, Compacted,

Mobile, Non-Mobile,

Patchy, Siliciclastic,

Sulfidic, Volcaniclastic,

Volcanic Ash, Well-Mixed,

Heterogenous (CMECS provisional unit)

Substrate 

Descriptors

Qualitative descriptors of 

seafloor substrate
18

3.3 Applying the Classification Scheme

The process of classifying the delineated shoals into scheme units builds on the characteristics described 

in Section 2.3, which covers the identification of the shoal. Rutecki et al. (2014) includes discussion of 

the environmental drivers that influence sand distribution and movement on the OCS. They also convey a 

number of shoal geoform types that have been integrated into this classification system as provisional 

CMECS units. Beyond the identification of shoal geoform types and their definitions, Rutecki et al. 

(2014) also provides specific examples of each geoform type in the Atlantic and Gulf of Mexico. In this 

section, we focus on classifying the shoal features into geoforms defined above.

Individual shoals were often grouped in space, especially in proximity to offshore capes. Groups of shoals 

are refered to as “complexes,” which contain two or more shoals with intermingling troughs or swales.

The shoal complex are connected by past or present sedimentary and hydrodynamic processes (Rutecki et 

al. 2014). To assign individual shoals and swales into “shoal complexes,” we applied Tobler’s first law of 

geography (Tobler 1970), “everything is related to everything else, but near things are more related than 

distant things.” Specifically, we grouped all shoals/swales that were within 2.5 km of each other. Initially, 

we tested the aggregation of shoals/swales with distances ranging from 0.5–5 km. We selected a distance 

of 2.5 km because shorter distances led to multiple shoal complexes identified within singular, capeassociated shoal areas with the same geological origins. Meanwhile, distances ≥ 3 km led to capeassociated shoals clumped together with sand shoals with different geological origins (e.g., capeassociated and bedform shoals).

The labelling of shoals and complexes by geoform type is inevitably an interpretive process relying on a 

mix of localized studies, geographic position, and context, as well as other quantitative and qualitative 

criteria. These criteria can be referred to by the term “classifiers,” which are characteristics of individual 

shoals that are necessary to assign them to specific categories. For example, dominant tree height would 

be a classifier that allows one to distinguish between forest and shrub land. The geoform type definitions

and specific geographic examples of each shoal type from Rutecki et al. (2014) formed a basis for the 

qualitative classification of the mapped shoals into the various categories. Specific classifiers applied in 

this process include:

• Proximity to coastal landforms such as capes, inlets, deltas, and historical deltas

• Shape and orientation

• Proximity and spatial relationship to nearby similar shoals

• Proximity to shoals that were classified into geoform types in Rutecki et al. (2014) and associated 

studies

• Position on the OCS (coastal or offshore)

• Spatial continuity with terrestrial ridge-like features

Evaluation of these factors, consideration of the quantitative bathymetric parameters, and prior 

characterization by other researchers was used to arrive at the final classified shoal data layer employed in 

ShoalMATE and presented in the following maps (Figure 3-2 to Figure 3-5). Cape-associated shoals are 

distributed as their name indicates with prominent landward capes along the Atlantic and to lesser extent 

Gulf of Mexico (Figure 3-2). Isolated shelf shoals are the most dominant in the Gulf of Mexico (Figure 

3-3), whereas bedform shoal features dominate the features on the Atlantic Coast (Figure 3-4 and Figure 

3-5).
19

Figure 3-2. Classified sand features throughout the study area in the Gulf of Mexico and Atlantic 

OCS.
20

Figure 3-3. Overview map of classified shoal distributions in the northern Gulf of Mexico region.
21

Figure 3-4. Overview map of classified shoals along the southeast coast of the US.
22

Figure 3-5. Overview map of classified shoal distributions in the US mid-Atlantic and New England 

region.
23

3.4 Verifying the Shoal and Shoal Complex Dataset

Before integrating the classified shoals dataset into the ShoalMATE tool, we assessed the accuracy of the 

results. We did not have the capability to collect new high-resolution geophysical or geotechnical data to 

quantitatively validate our modeled distribution of sand shoals. Instead, we conducted a qualitative 

assessment by comparing classified shoals to shoal features commonly known and mapped as “shoals” or 

similar features. More specifically, our classifications were compared to two reference place-name 

datasets to see how well the product characterized named shoals and shoal-like features.

1. Marine Place Names - 2016. These data are derived from features on the NOAA Electronic 

Navigational Charts and contain names for features in the US territorial waters and the OCS. 

Because different place names are displayed depending on the scale displayed, we used a 

1:80,000 scale. The dataset had a total of 55 categories of features, and we used the following 

categories for spatial comparison with our results: a) bank, b) bar, c) ridge, d) shoal

2. Undersea Feature Names - 2017. The GEOnet Names Server (GNS) provides access to the 

National Geospatial Intelligence Agency's (NGA) and the US Board on Geographic Names' 

(BGN) database of geographic feature names. The database is the official repository of foreign 

place-name decisions approved by the BGN. Geographic coordinates are approximate and are 

intended for general location. Place-name information is based on the Geographic Names 

Database, which contains official standard names approved by the United States Board on 

Geographic Names and maintained by the National Geospatial-Intelligence Agency. A total of 

55 types of features were present in this dataset. The following were extracted for the 

comparison: a) undersea bank and banks, b) undersea ridge and ridges, c) undersea shoal and 

shoals.

The source datasets have point topology and consist of the centroids of what become textual labels on 

Electronic Navigation Charts or other cartographic products. These labels are intended to loosely 

characterize the extent and orientation of the features on a map. To avoid false negatives where the textual 

label may have intersected the shoal dataset but the centroid did not, the centroid points were buffered to 

a 3-km radius. Both datasets include features in both state and Federal waters. Buffered features that fell 

outside that zone were removed because the area of interest pertains to Federal waters less than 50 m in 

depth and because we restricted our classification shoreward of the continental shelf break beginning at 

40 m.

Overall the shoals dataset strongly aligns with named shoals and shoal-like features as compared with two 

nationally scoped datasets. Named shoals in these source data tended to be located in nearshore waters. 

This is probably a function of their role as a navigation hazard and ease of observation. Many of the 

named shoals in Federal waters were located near Nantucket, RI, so there is some geographical bias 

inherent in these results. Nonetheless, the results suggest that the derived shoal maps are accurately 

capturing these features and should be suitable for its purpose as a screening tool to identify other 

possible sand resources. Many of the largest sand features delineated corresponded well to verified sand 

resources. It is important to note that these classified features are predictions; those features not already 

named or verified as sand resources by BOEM and other agencies need further validation and highresolution seafloor surveys.

The Marine Place Name database included the feature type “bars” that exclusively fell within state waters 

shoreward of the boundary for our study area, and ridges occurred at depths beyond 50 m or outside the 

Atlantic and Gulf of Mexico. The vast majority of named shoals also were identified in state waters, 

outside our area of study. The results showed a user’s accuracy (percent of named shoals present in the 
24

dataset) of 95% for shoal-like features (Table 3-7). An assessment of the producer’s accuracy (how many 

of the mapped shoals have been mapped correctly) is not possible without a separate validation process.

The Undersea Feature Place Names database included a much smaller number of shoal-like features in the 

source data. This is likely a function of the offshore focus of this database. Fewer banks were present 

overall, but a strong percentage were located in the study area. Few ridges occurred in the database and in 

the study area as well. Those shoals that were present were accurately identified in the derived shoal maps 

(Table 3-7). The results showed a user’s accuracy (percent of named shoals present in the dataset) of 88% 

for shoals and an aggregate accuracy of 57% for shoal-like features. As with the Marine Place Names 

data, a producer’s accuracy is not possible at this time.

Table 3-7. Spatial alignment of detected shoal features with named places for "Marine Place 

Name" (Top) and "Undersea Feature Place Name" (Bottom) datasets.

Marine Place 

Name Feature 

Type

Number of Features 

in Source Data

Number of Features 

within Study Area

Number of Features 

Intersecting with 

Shoal Dataset

Bars 63 0 0

Ridges 30 0 0

Shoals 761 46 44

Undersea 

Feature Type

Number of Features 

in Source Data

Number of Features 

within Study Area 

Number of Features 

Intersecting with 

Shoal Dataset

Banks 21 6 3

Ridges 7 1 1

Shoals 15 9 8

Our analysis of a large-scale coastal relief digital elevation model was limited by the quality and coverage 

in the dataset. The CRM is a compilation of soundings from current and modern hydrographic sonar 

survey, as well as historical and sometimes sparse soundings, smoothed to a consistent 90-m resolution 

through interpolation or modeling. The aggregation of data across time does ignore the temporal 

dynamics and movement of sediment along the Gulf and Atlantic Coasts and may only represent larger 

features or shoal complexes that are relatively persistent as features, even if their exact position may 

change over time with sediment transport (Pendleton et al. 2017). The classified collection of shoal 

complexes should be applied as an initial screening tool for planning modern surveys that would be

required to locate the amount and shape of sand resources available. Our analysis restricted feature sizes 

to ≥ 5 ha in the Atlantic based upon prior reviews of significant sand resources (Rutecki et al. 2014), and 

we were further limited to identifying features of > 20 ha in the Gulf of Mexico. One of the major

challenges in our analysis was detecting and delineating relatively low-relief and small bedform sand 

features, as are present off the coast of North Carolina, South Carolina, Georgia, and the western Gulf 

Coast of Florida. The relative importance of these features as sand resources and fish habitat need to be 

further explored.

A second step in evaluating the quality of the shoal dataset was a visual comparison to known sand 

resources. Classified shoals aligned well with verified sand resources identified by BOEM and its 

partners. In the Gulf of Mexico (Figure 3-6), Ship Shoal was identified as an isolated shelf shoal, though 

the shape of the polygon from the classification model deviates from the boundary of the sand resource. 

This is likely because sand resource mapping included both shoal and non-shoal sediment resources. 

Similarly, verified sand resources aligned well with the bedform shoals delineated off the northern Outer 

Banks of North Carolina (Figure 3-7), though the model classifies additional similarly shaped shore 

parallel sand features in the region. Differences in the shapes and extent between physically mapped sand 
25

resources and the classified layer could be due to the knowledge of the subsurface sand depth, resolution 

of the source data, or an artifact of temporal dynamics of the shoals. Verified sand resources have been 

surveyed using modern hydrographic techniques and higher resolution (meters to tens of meters) surfaces. 

The delineation of our classified layer was made from a composite CRM, which may include another 

temporal image of the shoal that may have shifted or changed in morphology due to sediment transport 

and coastal circulation dynamics. Similarly, bedform shoals were delineated and conform to identified 

sand resources off Virginia and Delaware, though once again, there are numerous sand features classified 

by the model, with many farther offshore that have not yet been verified as sand resources by BOEM and 

others (Figure 3-8).

Figure 3-6. Concordance of classified shoals with known and identified sand resources (tan 

polygons) south of Louisiana in the Gulf of Mexico region.
26

Figure 3-7. Concordance of classified shoals with identified sand resources (tan polygons) off 

Cape Hatteras in the South Atlantic region.

Note identified sand resource area also shown shoreward of the state boundary outside the study area.
27

Figure 3-8. Concordance of classified shoals with verified sand resources (tan polygons) in the 

mid-Atlantic region.

Note identified sand resource area also shown shoreward of the state boundary outside the study area.

Improvements to this classification model and derived layers could come in several forms. First, our 

exercise focused exclusively on a study area bounded by Federal management jurisdictions and excluded 

waters under state management jurisdiction. Although the overall framework and modeling approach 

could be extended to include state waters, several important factors may complicate the modeling process,
28

including increased variation and noise in nearshore BPI and slope values, as well as the reduced 

usefulness of factors like distance to shore and depth. A separate model derivation would be required to 

extend classifications to these areas. It is not likely that dredging efforts would extend beyond the 50-m 

depth contour, so extensions offshore may not be necessary. Secondly, we did not include sediment type 

into the shoal and sand feature classifications. Sediment and bedform maps exist for both the Gulf and 

Atlantic Coasts elsewhere, developed through separate initiatives (e.g., TNC's South Atlantic Bight 

Marine Assessment (Conley et al. 2017), Chris Jenkins, University of Colorado, unpublished data). These

maps were modeled using various spatial interpolation techniques from compilation of historical to 

modern bottom samples. In all cases, these classifications should be taken as screening tools and will 

require modern geological surveys and validation.

4 Results and Discussion

This study builds upon previous syntheses of the dynamics and distribution of offshore sand features in 

the Gulf of Mexico and US Atlantic continental shelf (Rutecki et al. 2014). Prior shoal classification 

studies have focused on discrete areas, such as cape-associated shoals along coastal North Carolina 

(Thieler et al. 2014) or shoal complexes near southwest Florida in the Gulf of Mexico (Finkl et al. 2007). 

The classification conducted here extends the concept that seafloor geomorphology and complexity 

metrics derived from broad-scale coastal elevation models can be used as an initial survey of broad areas 

of the continental shelf to delineate shoals and shoal complexes (Knorr 2017). Previously, a classification 

model of the east coast of Florida used 10-m resolution seafloor imagery to detect shoals and similar sand 

features (Knorr 2017) and found that simple thresholds of complexity metrics like rugosity can be used to 

delineate sand features. In contrast to Knorr (2017), we used a broader extent and a coarser 90-m data 

resolution to analyze seafloor geomorphology and complexity metrics to delineate features and classify 

shoals. Our results showed seafloor complexity metrics were still distinct from the surrounding seafloor at 

this 90-m resolution. Seafloor metrics, such as slope and the BPI, were substantially higher than the 

surrounding seafloor across all three geographies analyzed here (Table 2-1). Our study also differed from 

Knorr (2017) in that we used the BPI and distance from shore as predictors. Large shoals and shoal 

complexes were readily visible in the bathymetry surfaces, and these were delineated using a range of 

spatial scales of the BPI, depending upon the geographic area. For the Gulf of Mexico in particular, the 

larger scale of BPI (71 x 71 cells) was helpful to delineate wide (> 3 km) shoals that were partially 

characterized by low slope (i.e., flat cells) within 90-m resolution cells. Distance to shoreline was helpful 

in the classification because shoals are distinguished as shallow areas that are farther offshore than other 

waters of similar depths. The unsupervised classification accounts for such predictor combinations, 

although such interactions are difficult to quantify in descriptive statistics.

In the Gulf of Mexico, large cape-associated and isolated shelf shoals were the most prominent sand 

features detected and classified (Figure 3-3), specifically Ship Shoal, Trinity Shoal, Sabine Bank, and St. 

Bernard Shoals near Louisiana and the cape shoals near Apalachicola, FL. There were other classified 

shoals scattered along Texas, Louisiana, and Alabama that were left as uncharacterized sand feature 

classes. Similarly, the majority of the west Florida shelf was populated by smaller features that have been 

previously identified as valuable sand sources for beach nourishment in the region (Finkl et al. 2007). 

Cape-associated shoals are readily visible in the base bathymetry maps and easily delineated in our 

models in the US South Atlantic (Figure 3-4). These shoals are associated with dynamic seabed areas off 

Cape Canaveral, FL, and along the Carolinas and Virginia Coasts. However, the majority of areas of 

potential sand resources appear to be captured in the scattered and smaller bedform features off South 

Carolina and Georgia (Figure 3-4). The small size and relatively low relief of these features may result in 

uncertainty in size, number, and extent in this region. North of Cape Hatteras into the mid-Atlantic and 
29

New England, smaller bedforms dominate except around Cape Cod where cape-associated shoals are 

present (Figure 3-5).

We labeled shoal complexes by their geoform whenever possible with a basis from Rutecki et al.’s (2014)

classification based on geological origin. More specifically, we classified geoform types into: 1) capeassociated shoals, 2) bedform shoals, 3) isolated shelf shoals, 4) dredge disposal sites, and 5) 

uncharacterized shoals. To name shoals, we examined labels from the ESRI oceans basemap, literature 

sources, and examples from the Rutecki et al. (2014) review of sand shoals. Dredge disposal sites were 

identified from disposal locations that were categorized as dredged material disposal or spoil grounds 

(U.S. Environmental Protection Agency n.d.).

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Appendix A: Shoal Classification Scheme Dictionary

In the following text, superscript next to the unit name indicates the primary source of the definition. 

10 Indicates units or definitions drawn from CMECS text (FGDC 2012). 24 Indicates definitions drawn from the Rutecki et al. (2015) report. In some cases, the definition is a 

hybrid of both.

A.1 Shoal Classification Scheme Component Definitions

Biogeographic Setting (EC)

EC units are included to add contextual information to the sediment resources data layer such as 

prevailing oceanographic conditions. The CMECS Biogeographic Setting is a fully hierarchical 

component. For the purposes of this project, we intend to apply Ecoregion units and aggregate upward in 

the hierarchy if necessary.

Ecoregions:10 Areas of relatively homogeneous species composition, clearly distinct from adjacent 

systems. The species composition is likely to be determined by the predominance of a small number of 

ecosystems and/or a distinct suite of oceanographic or topographic features. The dominant biogeographic 

forcing agents defining the ecoregions vary from location to location but may include isolation, 

upwelling, nutrient inputs, freshwater influx, temperature regimes, ice regimes, exposure, sediments, 

currents, and bathymetric or coastal complexity.”

CMECS ecoregions are drawn from the Marine Ecosystems of the World (Spalding et al. 2007) Units 

relevant to this report consist of the following:

• Scotian

• Gulf of Maine / Bay of Fundy

• Virginian

• Carolinian

• Northern Gulf of Mexico, and

• Floridian

Geoform (GC)

The geomorphology of the seafloor is one of the two primary characteristics of interest in this project. The 

Geoform Component is a semi-hierarchical framework. This project will focus on the physiographic 

setting, Geoform and Goform Types, which are hierarchical. It should be noted that like many geomorphic 

classifications, the definitions are somewhat subjective and there is conceptual overlap between units. 

Some banks can considered bars, some ridges can be considered shoals, and so on. Nevertheless, these 

terms are helpful and provide a mental picture of the feature.

Physiographic Setting:10

With the geographic scope of this project being the Federal waters of the Atlantic and Gulf of Mexico 

Exclusive Economic Zone, the only relevant physiographic setting needed was Continental Shelf. As the 

geography of the tool expands, it is likely that additional CMECS physiographic units may be come 

relevant and need to be included.

Continental/Island Shelf:10 That part of the continental margin that is between the shoreline and 

the continental slope (or a depth or 200 m when there is no noticeable continental slope); it is 
33

characterized by its very gentle slope of 0.1°. Island shelves are analogous to the continental 

shelves, but surround islands.

For the purposes of this project, where only coarse resolution data may be available, this system proposes 

adding a provisional Complex level to the CMECS Geoform. The proposed units are as follows:

Geoform Complex:

10 This is a new provisional CMECS level. Geoform complexes consist of many small 

geoforms within an area or a repeatable assemblage of associated geoforms that function as a system. 

Examples include salt marshes that contain tidal creeks, marsh platforms, banks and pannes. The Complex

level should be used when data resolutions are not high enough to distinguish the boundaries of individual 

geoforms or where the minimum mapping unit consists of multiple geoform units.

Shoal Complex: 24 Two or more shoals (and includes adjacent morphologies, such as troughs 

separating shoals) that are interconnected by past and or present sedimentary and hydrodynamic 

processes. An area consisting of several shoals too small to be distinguished individually due to 

data resolution or mapping constraints.

Geoforms10 are physical, coastal and seafloor structures that are generally no larger than hundreds of 

square kilometers in size. This size determination may be an areal extent or a linear distance. Larger 

geoforms (Level 1) are generally larger than 1 km2

, and correspond to Megahabitats in the Greene et al. 

(2007) classification system. These features can be defined using geologic or geomorphic maps and 

bathymetric images of the seafloor at map scales of 1:250,000 or less. Smaller geoforms (Level 2) are 

generally less than 1 km2 in size (or less than 1 km in distance); and correspond to Meso- and Macrohabitats in the Greene et al. (2007) system. Level 2 geoforms (such as individual coral reefs, tide pools, 

and sand wave fields) can be identified through in situ observational methods (such as underwater 

videography) or through low-altitude, high-resolution optical or acoustic remote sensing.

Shoal:10,24 A natural, underwater ridge, bank, or bar consisting of, or covered by, sand or other 

unconsolidated material, resulting in shallower water depths (> 1 m) than surrounding areas. 

Morphologically and spatially dynamic, they are primarily shaped by waves and currents and can 

be driven across the seafloor during tropical storms and hurricanes as well as less intense (but 

more frequent northern meteorological fronts and other lower intensity events. In some cases, 

shoals may be exposed during low tides.

Moraine Shoal:10 The submerged portion of a glacial moraine that reaches close to the 

surface. These often occur where sea-level rise has drowned former terrestrial glacial 

features.

Cape-Associated Shoals:

24 Active sedimentary systems that extend from cuspate 

foreland promontories formed by two barrier islands (Rutecki et al. 2014 figures 2-3 and 

2-5) or mainland beach ridges joined at approximately right angles (McNinch and 

Luettich Jr 2000). Cape-associated shoals form due to the convergence of two longshore 

drift cells, and as a result of self-organization of the coast in response to a high-anglewave instability in shoreline shape. Cape-associated shoals can also be influenced by the 

preexisting geological framework (Rutecki et al. 2014 figure 2-4; Thieler and Ashton 

2011)

Bedform Shoals:

10,24 A continuum of morpho-sedimentary bedforms exists along the 

inner- and mid-continental shelf of siliciclastic passive continental margins. The 

continuum ranges from sorted bedforms occupying the sediment-starved end of the 

continuum and linear shoals and shore-attached ridges on the sediment abundant end of 

the continuum.
34

Isolated Shelf Shoals:

24 Shoals formed by relict coastal sedimentary processes exposed 

by ravinement. These are discrete features associated with a single landform or shoreline 

position.

Geoforms Subtypes are further refined types of geoforms and are fully nested within the Geoform level. 

Geoform Subtype is a new proposed level for CMECS based on the source data and needs of this project. 

The two units below are currently the only Geoform Subtypes proposed.

Linear Shelf Sand Ridges:

24 Along the mid-Atlantic coast, linear shore-normal shelf sand shoal 

complexes are most prominent along the Delaware-Maryland-Virginia inner shelf, where they are 

the dominant features (Hayes and Nairn 2004; Swift and Field 1981, Figures 2-8 and 2-9). 

According to Swift and Field (1981), there are three basic types of linear shore-normal shelf sand 

shoal (called ridge and swale by the authors) morphologies found within the Delaware-Maryland 

system; they include shore-attached ridges (0- to 3-m isobaths), nearshore ridges (6- to 18-m 

isobaths and within 10 km off shore), and offshore ridges (greater than 10 km offshore). Each 

ridge is roughly 3–4 km long and 0.5–1 km wide with ridges spaced 1–4 km apart.

Rippled Scour Depressions:24 Are bathymetrically subtle, large-scale bed features that are 

characterized by alternating bands of coarse- and fine-grained sediment with wavelengths of 

hundreds of meters (Van Oyen et al. 2011), and negative relief of ~ 1 m that trend obliquely to 

the coast (Guitierrez et al. 2005, Figure 2-6). Where there is a dominant direction of suspended 

sediment transport, these features tend to be asymmetrical, with coarser flanks facing updrift, into 

the direction of dominant sediment transport. Where there is no dominant current direction, they 

tend to be symmetric (e.g., Goff et al. 2005; Diesing et al. 2006). The coarse material is in the 

troughs (or swales), and the ridges are finer grained. Self-organizing features due to the 

interaction of frictional sediment transport, bottom composition, and turbulence, with bottom 

roughness over the troughs causing turbulence that inhibits the settling of fines within the 

troughs.

Sediment Wave Field:10 An area of wave-like bedforms in sand or other unconsolidated 

material, which are formed by the action of tides, currents, or waves. These bedforms range from 

centimeters to meters in size and may be superimposed on larger features. Sand waves lack the 

deep scour associated with dunes or megaripples (Bates and Jackson 1984). For this project, these 

features can be distinguished from other shoals due to their lack of physical relief relative to the 

surrounding sea floor. They are distinguished from Sediment Sheets by the presence of bedforms 

(ripples) indicating higher energy and potentially coarser substrates.

Sediment Sheet:10 A thin, widespread, sedimentary deposit formed by a transgressive sea 

advancing for a considerable distance over a stable shelf area; may also be called a blanket 

deposit (Bates and Jackson 1984). For this project, the term will be used to describe 

unconsolidated substrates lacking bedforms or rippling and without physical relief relative to the 

surrounding seafloor.

Dredge Deposit:10 An accumulation on the seafloor (or land surface) where spoil materials from 

a dredging operation are placed. They often exhibit some topographical expression and can 

support biological communities that are different than the surrounding area. These deposits are 

often unconsolidated in character, but they can also be relatively stable.

Dredge Deposit Shoal:10 A subaqueous area that is substantially shallower than the 

surrounding area, which resulted from the deposition of materials from dredging and 

dumping.
35

Substrate Component (SC)

Classification units for describing the surficial substrate will draw directly from the CMECS SC. This is a 

fully hierarchical framework organized by substrate origin (geologic, biogenic, and anthropogenic). Most 

units to be applied in this project are from the Unconsolidated Sediments Class, which has sub-units 

based on Wentworth grain size fractions. The Biogenic Shell Substrate class is included because source 

literature indicates that the presence and amount of shell hash is important to fish habitat value.

It is unlikely that actual grain size information necessary to classify substrate to the group or subgroup

level will be available throughout the project geography; therefore, the definitions below are at the 

CMECS subclass level. Definitions for Substrate Group and Substrate Subgroup levels can be found at 

https://iocm.noaa.gov/cmecs.

Substrate Subclasses:10 are determined by the composition and particle size of the dominant substrate 

origin in the surface sediments. Class and subclass definitions represent a merging of approaches from 

Wentworth (1922), Folk (1954), and the FGDC-STD-004.

Coarse Unconsolidated Substrate:10 Geologic Substrate surface layer contains > 5% Gravel 

(particles 2 mm to < 4,096 mm). These sediments are classified using the upper three rows of the 

Folk (1954) Gravel-Sand-Mud diagram.

Fine Unconsolidated Substrate:10 Geologic Substrate surface layer contains less than 5% gravel 

(particles 2 mm to < 4,096 mm in diameter). These sediments are classified using the bottom two 

rows of the Folk (1954) Gravel-Sand-Mud diagram and the entire Folk (1954) Sand-Silt-Clay 

diagram.

Shell Hash:10 Surface substrate layers are dominated by loose shell accumulations with a median 

particle size of 2 mm to < 64 mm (Granules and Pebbles). Shells may be broken or whole. The 

presence of Shell Hash is noted in this subclass (and in the following groups).

Shell Sand:10 Biogenic Substrate layers that are dominated by Sand that is primarily composed of 

shell particles with a median particle size of 0.0625 mm to < 2 mm (Sand). Shells or remains are 

generally broken and difficult to identify. For this reason, only substrate-forming taxa that 

produce distinctive Sand types are listed as substrate groups. When the composition and origin of 

Sand is unclear, it is assumed to be mineral Sand and is classified as a Geologic Origin substrate.

Biotic Component (BC)

The CMECS Biotic Component focuses on living organisms of the water column and seabed at a variety 

of scales. The BC is organized into a branched hierarchy of five nested levels: Biotic Setting, Biotic 

Class, Biotic Subclass, Biotic Group, and Biotic Community. The biotic setting indicates whether the 

biota are attached or closely associated with the benthos or are suspended or floating in the water column. 

Biotic classes and biotic subclasses describe major biological characteristics at a fairly coarse level. 

Unless otherwise noted, biotic classification units are defined by the dominance of life forms, taxa, or 

other classifiers in an observation. For collected observations (such as grab samples or cores), 

dominance is measured in terms of biomass or numbers of individuals, as specified by the user. In the 

case of images and visual estimates, dominance is assigned to the taxa with the greatest spatial 

percent cover in the observational footprint (image footprint or field of view).

Based on the source data available for the project, it is expected that only the Biotic Setting and 

Biotic Class units would be useful. Of these only a subset would be expected to occur in the project 

study area. These are defined below.
36

Planktonic Biota10: This setting includes biota that drift, float, or remain suspended in the water 

column in aggregations that are big enough to be (a) detected by the human eye (or with mild 

magnification) or (b) sampled with a fine-plankton net. Planktonic biota are not regularly associated 

with the seafloor.

Zooplankton:10 Zooplankton are heterotrophic biota of the water column; zooplankton drift with 

the currents, but may (or may not) be able to move through the water under their own power. 

CMECS classifies zooplankton that may range in size from gigantic salp chains (strings of 

gelatinous filter feeding tunicates that attain a length of 30 m or more), to radiolarians (minute, 

shelled amoebas). CMECS was not designed to be used for the smallest planktonic forms 

(nanoplankton or picoplankton). CMECS Class Zooplankton includes both Holoplankton (that 

live out their entire life histories in the plankton) and Meroplankton (that are transient in the 

plankton).

Floating/Suspended Plants and Macroalgae:10 This class includes areas dominated by vascular 

plants, detached plant parts, or macroalgae that are floating on the surface or are suspended in the 

water column—that is, plants and macroalgae that are not rooted or attached to the bottom.

Phytoplankton:10 This class includes areas of floating or suspended microscopic algae that are 

capable of photosynthesis. Although some species are motile, they are generally passively 

transported by water movements. Under certain conditions, they can form aggregations, large 

blooms, or colonies.

Floating/Suspended Microbes:10 Aggregations of microbes that are floating or suspended in the 

water column and not attached to the bottom or to any benthic substrate.

Benthic Biota10: This biotic setting describes areas where biota lives on, in, or in close association with 

the seafloor or other substrates (e.g., pilings, buoys), extending down to include the layers of sediment 

that contain multi-cellular life.

Reef Biota:10 Areas dominated by reef-building fauna, including living corals, mollusks, 

polychaetes, or glass sponges. In order to be classified as Reef Biota, colonizing organisms must 

be judged to be sufficiently abundant to construct identifiable biogenic substrates. When not 

present in densities sufficient to construct reef substrate, the biota is classified in the Aquatic 

Vegetation Bed or Faunal Bed classes.

The Reef Biota Class refers to only the living component of reef structures. If referring to the reef 

structure, users should use the reef units in the Geoform Component. If referring to the 

composition of the reef substrate independent of the living cover, users should employ the Coral 

Substrate, Shell Substrate, or Worm Substrate Classes in the SC.

Faunal Bed:10 In this class the seabed is dominated or characterized by a cover of animals that 

are closely associated with the bottom, including attached, clinging, sessile, infaunal, burrowing, 

laying, interstitial, and slow moving animals, but not animals that have created substrate (Reef 

Biota). Unlike Reef Biota, Faunal Bed biota cannot (or are not sufficiently abundant to) construct 

identifiable substrate. “Slow moving” animals included in the Faunal Bed class are defined as 

being incapable of moving outside the boundaries of the classification unit within one day. Faunal 

Bed organisms are aquatic, but they may be able to withstand periods of exposure to air.

Faunal Bed food webs may receive basic trophic inputs from benthic photosynthesis or 

chemosynthesis, plankton, allochthonous detritus and debris, or other sources. In nature, Faunal 

Bed habitats are often composed of complex mixes and associations of animals of different phyla, 
37

sizes, feeding strategies, and habits, and these areas can be difficult to classify. Faunal Bed 

classifications are determined in CMECS by greatest percent cover of fauna or faunal structures, 

or (particularly for infauna) by estimates of greatest biomass.

Microbial Communities:10 These are areas dominated by colonies of microscopic or singlecelled organisms that form a hard structure, visible film, layer, or mat on or near the surface of 

the substrate. Colonies may be composed of benthic microalgae (e.g., diatoms), photosynthetic 

bacteria (e.g., cyanobacteria), archaea, saprotrophic bacteria (e.g., decomposers or decay 

organisms), chemoautotrophic bacteria, or other microbial groups. These features may exist on or 

near the surface of the sediment either subtidally or subaerially, or they may exist as extensive 

areas of decay associated with dead organisms that have fallen to the seafloor.

The additional remaining CMECS Classes of Aquatic Vegetation Bed, Emergent Wetland, and ScrubShrub Wetland are not expected to occur in the project study area and are not included in this data 

dictionary.

Modifiers (M)

Modifiers further describe classification units and can be applied as needed and where source data 

supports their use. In some cases, modifiers may themselves be mapping units (e.g., rugosity grids). 

Modifers for use in this project are grouped as follows:

Anthropogenic Modifiers

Dredged:10 Landscape that is mechanically altered by the removal of sediments or other materials 

(e.g., shell) in order to deepen or widen channels (e.g., for navigation or alteration to hydrology).

Filled Deposition Site:10 Areas where materials (such as sand or shell) have been placed on (or 

in) an area of coast or a water body.

Physical Modifiers

Energy Direction:10 Energy can be classified according to its principal direction of travel or 

influence. In the case of tidal energy, this is generally an oscillation between onshore and 

offshore motions. In the case of currents and waves, the energy is usually directional.

Baroclinic Motion along lines of equal pressure within the water column

Circular Motion in a closed, circular form

Downward Descending and perpendicular to the sea surface or bottom

Horizontal Parallel to the sea surface or bottom

Mixed Combination of more than one of above directions

Seaward On land, water currents following a topographic gradient toward the sea

Upward Ascending and perpendicular to the sea surface or bottom

Energy Intensity:10 Energy Intensity is classified into four categories as shown. Additional terms 

may be applied in this system as necessary to better reflect conditions at the sediment/water 

interface.
38

Very Low Current Energy Area experiences little current motion under most 

conditions

Low Current Energy Area typically experiences very weak currents (0–1 

knots)

Moderate Current Energy Area regularly experiences moderate tidal currents (> 1–

3 knots)

High Current Energy Area regularly experiences strong currents (> 3 knots)

Energy Type:10 The Energy Type Modifier is adapted from Dethier (1990) and Zacharias et al. 

(1998) with type categories as follows:

Current Coherent directional motion of the water

Internal Wave Vertical and transverse oscillating water motion, below 

the surface, due to seismic energy or a pressure 

differential

Surface Wave Vertical and transverse oscillating surface water motion 

due to wind or seismic energy

Tide Periodic, horizontally oscillating water motion

Wind Coherent directional motion of the atmosphere

Seafloor Rugosity:10 Seafloor rugosity, a measure of surface "roughness," is applicable at several 

scales using different measures (e.g., bathymetric x-y-z data, measured transect data, video data). 

Rugosity is derived as the ratio of surface area to planar (flat) area for a grid cell, or as the ratio of 

surface area to linear area along transects, and is calculated as follows:

fr = Ar / Ag

where Ar is the real (true, actual) surface area and Ag is the geometric surface area (IUPAC 

1997).

Values for Seafloor Rugosity are taken from Greene et al. 2007. The five rugosity types and their 

associated numeric values are shown below

Very Low 1.0 to < 1.25

Low 1.25 to < 1.50

Moderate 1.50 to < 1.75

High 1.75 to < 2.00

Very High ≥ 2.00

Rugosity Value: Recognizing that small differences in rugosity may be important to the habitat 

value of certain biota, this field will be populated by the actual rugosity value and not assigned to 

one of the more general CMECS categories.
39

Slope:10 The Slope modifier refers to the angle of the substrate at a scale appropriate for the 

feature being described; Greene et al.’s (2007) geological classification is followed here to 

characterize slope.

Substrate Descriptors:10 Although the CMECS SC describes substrate size and composition,

following Wentworth (1922) and Folk (1954) to describe particle sizes and mixes, it generally 

does not consider geologic composition or several other important attributes. The following 

substrate descriptors provide consistent terminology to meet the needs of this project.

Carbonate Geologic Origin particles or substrates composed mainly of carbonate 

minerals (e.g., limestone, dolostone).

Compacted Unconsolidated sediments with very little water content and a hard, 

packed form that resists penetration and resuspension. This is one of 

several terms that are used in CMECS to describe the fluid consistency 

of substrates.

Mobile Bedded sediments which regularly re-suspend and/or move with local 

hydrodynamics due to the density, grain size, shape, and/or high water 

content of the sediment, or due to the higher hydrodynamic energy 

experienced in the local area. This term and the corresponding term NonMobile are used in CMECS to describe or predict behavior of substrates.

Non-mobile Bedded sediments that do not regularly re-suspend and/or move with 

local hydrodynamics due to the density, grain size, shape, and/or 

compaction (low water content) of the sediment particles, or due to the 

lower hydrodynamic energy experienced in the local area.

Patchy Different elements within a sample, observational unit, or reporting unit 

are grouped into clusters or patches at the scale of the sample or unit. 

“Patchy” implies that clusters of elements or particles are arranged in a 

haphazard manner, as clusters of pebbles scattered on sand. This is one 

of several terms used in CMECS to describe unit variability.

Siliciclastic Geologic Substrate Origin particles or substrates composed primarily of 

silicate minerals, e.g., quartz, sandstone, siltstone.

Sulfidic Substrate in which bacterial sulfate reduction is an important 

biogeochemical process; this generally occurs in anaerobic 

environments, is often identifiable by a very low reflectance black or 

blue color, and is a characteristic “rotten egg” odor when sediments are 

examined in air.

Volcaniclastic Geologic Origin particles or substrates composed primarily of volcanic 

rock, crystals, glassy pumice, ash, or other volcanic products.

Volcanic Ash A substrate or substrate layer composed primarily of volcanic dust and 

volcanic ash, often with various aeolian or marine-generated particles 

mixed in. In areas of the deep sea, where terrigenous input and 

bioturbation are limited, Volcanic Ash may be present in distinct layers 

at depth in the substrate matrix (see the “Layers” modifier).
40

Well-mixed Different elements within a sample, observational unit, or reporting unit 

are well-mixed or poorly sorted at the scale of the sample or unit. Wellmixed implies that elements or particles are completely and relatively 

evenly intermingled, e.g., Granule/Sand/Mud particles in an area with 

high bioturbation. This is one of several terms used in CMECS to 

describe unit variability. Note that CMECS does not use the equivalent 

geological term “Poorly Sorted,” because the descriptor may be used to 

describe distributions of non-geological features (such as biological 

communities or Geoform Component structures).

Well-sorted Different elements within a sample, observational unit, or reporting unit 

are separated into different areas at the scale of the sample or unit. Wellsorted implies that elements or particles are (or have been) separated and 

arranged in a non-haphazard manner, as an area of Coarse Sand adjacent 

to an area of Clay. This is one of several terms used in CMECS to 

describe unit variability.

Heterogenous A diverse complex substrate pattern that contains multiple pattern types 

or may be applied in situations where there is evidence of several 

patterns, but resolution of the data does not allow discrimination of the 

individual types.

Surface Pattern:10 These roughness patterns may have physical origins (e.g., caused by wave or 

current action) or biological origins (due to activities of life forms, e.g., mounds or tunnels).

Biological Roughness appears due to bioturbation, fecal mounds, tunneling, feeding 

or locomotory activities of megafauna, or other faunal activities. Further 

characterization of biological features is described in the Biotic 

Component.

Irregular Sediment surface has a perceptible roughness or texture that is nonregular in either frequency, direction, or amplitude.

Physical Roughness appears due to water motion, but the nature of the roughness 

is other than Rippled.

Rippled Closely spaced, regular, repeating, vertical variations in the height of a 

sandy or muddy bottom, with a very short wavelength on the order of 

centimeters. A rippled substrate is generally caused by the physical 

processes of water motion.

Scarred Roughness appears due to localized sediment disturbance resulting either 

from natural causes (e.g., slumps) or anthropogenic causes (e.g., anchor 

scars, propeller scars, trawl scars, or other fishing gear scars), but not as 

an artifact of camera or sampling gear deployment.

Smooth There is no perceptible roughness or texture to sediment surface at scales 

of less than 1 m.

Heterogenous The surface has a complex mix of patterns or the pattern appears mixed 

but cannot be further described due to insufficient resolution of the data.
41

Physiochemical Modifiers

Oxygen Regime:10 For the purposes of this project actual dissolved oxygen minimum values will 

be reported rather than the range categories in CMECS. These values will be reported in mg/liter.

Spatial Modifiers

Benthic Depth Zone:10 These are generally based on the zones in which surf or ocean swell

influences bottom communities, lower limits of vegetation (such as kelp), overall photic 

availability, and temperature. The zones within this category are drawn or adapted from Greene et 

al. (2007) and Connor (1997). The following definitions are intended as guidance for adaptation 

of depth ranges to regional environmental conditions:

Shallow Infralittoral 0 to < 5 m deep

Deep Infralittoral 5 to < 30 m deep

Circalittoral 30 to < 200 m deep

Temporal Modifiers

Temporal Persistence:10 The Temporal Persistence Modifier describes the permanency or 

variability of a hydromorphic, geomorphic, or biological feature. Though qualitative and relative, 

it is useful is distinguishing between features that are similar in morphology—but are temporally 

diverse in terms of stability. For this project this will refer to the physical integrity of a feature 

(shoal, ridge, etc.) and over what time period it maintains its shape. The following CMECS 

temporal persistence units are included in this project:

Months

Years

Inter-Annual

Decades

Centuries

Additional Modifiers (M)

A series of additional modifiers or descriptive units have been considered potentially valuable to 

assessing the character of sand resources/shoals. These are intended to characterize the spatial relationship 

of individual features to the surrounding landscape and understand their longevity and integrity, which 

may be important to their long-term function as fish habitat. It is understood that further research, spatial 

analysis, and time-series information will be needed to apply these modifiers with confidence. As data 

becomes available to conduct these types of analysis, it is expected that they will become part of the 

shoals descriptive database.

Benthic Depth Zone:10 These are generally based on the zones in which surf or ocean swell 

influences bottom communities, lower limits of vegetation (such as kelp), overall photic 

availability, and temperature. The zones within this category are drawn or adapted from Greene et 

al. (2007) and Connor (1997). The following definitions are intended as guidance for adaptation 

of depth ranges to regional environmental conditions:
42

Shallow Infralittoral 0 to < 5 m deep

Deep Infralittoral 5 to < 30 m deep

Circalittoral 30 to < 200 m deep

Feature Orientation: Feature orientation is a reflection of the cardinal direction of the longest 

axis of a shoal, ridge, or other bathymetric feature. This will be expressed as a numeric value in 

degrees based on a geographic compass rose.

Positional Stability: This expresses the likelihood of an ephemeral feature changing geographic 

location while still generally maintaining its structure and size. Positional stability is most 

relevant in capturing the movement across the seascape of sand waves, dunes, etc. Units for this 

modifier are still in development and will be informed by time-series data.

Accretion Status: This is a measure of whether a feature (shoal, bank, ridge, dune, or bar) is 

growing in volume and extent due to sediment accretion or shrinking due to erosion. In cases 

where neither process is underway or where both processes are cancelling each other out, then the 

status would be neutral. Accretion status can only be assessed in the context of a timeframe. A 

window of at least 3 years is needed for BOEM borrow areas.

Shelf Position: This is a relative description of the spatial location of a feature on the continental 

shelf. Units for this modifier are still in development and will be determined by the source data.

Bathymetric Position Index (BPI): Output slope values (raster grids) are derived for each cell as 

the maximum rate of change from the cell to its neighbor. BPI is a second-order derivative of 

bathymetry modified from topographic position index as defined in Weiss (2001) and Iampietro 

and Kvitek (2002).

Standard Deviation of Depth: This metric will be applied to characterize the frequency 

(distance between ripple crests) and amplitude (height of crest above trough) of ripples and 

systematically repeating bedforms.

Disturbance Regime: This is measure of how often the surface of the substrate is disturbed or reworked by storm events or strong ocean currents. Disturbance regimes of months to years are 

appropriate for sediment deposits in Federal waters less than 50 m deep.
43

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Department of the Interior (DOI)

The Department of the Interior protects and manages the Nation's natural 

resources and cultural heritage; provides scientific and other information about 

those resources; and honors the Nation’s trust responsibilities or special 

commitments to American Indians, Alaska Natives, and affiliated island 

communities.

Bureau of Ocean Energy Management (BOEM)

The mission of the Bureau of Ocean Energy Management is to manage 

development of U.S. Outer Continental Shelf energy and mineral resources in 

an environmentally and economically responsible way.

BOEM Environmental Studies Program

The mission of the Environmental Studies Program is to provide the 

information needed to predict, assess, and manage impacts from offshore 

energy and marine mineral exploration, development, and production activities 

on human, marine, and coastal environments. The proposal, selection, research, 

review, collaboration, production, and dissemination of each of BOEM’s 

Environmental Studies follows the DOI Code of Scientific and Scholarly 

Conduct, in support of a culture of scientific and professional integrity, as set 

out in the DOI Departmental Manual (305 DM 3).