NOAA Technical Memorandum NMFS-PIFSC-61

April 2017 

Acoustic Characterization of Mesophotic Coral Reef 

Ecosystems of West Hawaiʻi 

Rhonda Suka

John Rooney

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration 

U.S. Department of Commerce 

https://doi.org/10.7289/V5/TM-PIFSC-61
About this document

The mission of the National Oceanic and Atmospheric Administration (NOAA) is to 

understand and predict changes in the Earth’s environment and to conserve and manage 

coastal and oceanic marine resources and habitats to help meet our Nation’s economic, 

social, and environmental needs. As a branch of NOAA, the National Marine Fisheries 

Service (NMFS) conducts or sponsors research and monitoring programs to improve the 

scientific basis for conservation and management decisions. NMFS strives to make 

information about the purpose, methods, and results of its scientific studies widely available. 

NMFS’ Pacific Islands Fisheries Science Center (PIFSC) uses the NOAA Technical 

Memorandum NMFS series to achieve timely dissemination of scientific and technical 

information that is of high quality but inappropriate for publication in the formal peerreviewed literature. The contents are of broad scope, including technical workshop 

proceedings, large data compilations, status reports and reviews, lengthy scientific or 

statistical monographs, and more. NOAA Technical Memoranda published by the PIFSC, 

although informal, are subjected to extensive review and editing and reflect sound 

professional work. Accordingly, they may be referenced in the formal scientific and 

technical literature.

A NOAA Technical Memorandum NMFS issued by the PIFSC may be cited using the 

following format:

Suka, R., J. Rooney. 

2017. Acoustic Characterization of Mesophotic Coral Reef Ecosystems of 

West Hawaiʻi. U.S. Dep. Commer., NOAA Tech. Memo., NOAA-TMNMFS-PIFSC-61, 31 p. https://doi.org/10.7289/V5/TM-PIFSC-61. 

__________________________

For further information direct inquiries to

Chief, Scientific Information Services 

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

U.S. Department of Commerce 

1845 Wasp Blvd., #176 

Honolulu, Hawaiʻi 96818-5007 

Phone: 808-725-5386 

Fax: 808-725-5532 

___________________________________________________________ 

Cover: Photograph of mesophotic coral and associated biota at a depth of 111 m near

Kawaihae, Hawaiʻi, courtesy of Association for Marine Exploration. 
Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration 

U.S. Department of Commerce 

Acoustic Characterization of Mesophotic Coral Reef 

Ecosystems of West Hawaiʻi 

1

Rhonda R. Suka

2

John J. Rooney

1

Joint Institute for Marine and Atmospheric Research

University of Hawaiʻi

1000 Pope Road

Honolulu, Hawaiʻi 96822

2

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

1845 Wasp Boulevard, Building 176 

Honolulu, Hawaiʻi 96818

NOAA Technical Memorandum NMFS-PIFSC-61 

April 2017

https://doi.org/10.7289/V5/TM-PIFSC-61

iii

ABSTRACT 

Coral reef habitats in Hawaiʻi are common in shallow waters and extend into mesophotic depths 

(30-150 m). However, habitat monitoring efforts have been concentrated in depths constrained 

by safe dive limits of 0-30 m. Mesophotic coral ecosystems (MCEs), located in depths of 

30-150 m, are important components of the coral reef ecosystem, but only a small number of 

surveys have been conducted in these depths. Data for these deep-water habitats are essential for 

evaluating and monitoring their health and resilience. Classifying benthic habitats in mesophotic 

depths is challenging due to dive safety limits and water penetration capabilities of remote 

sensing options, such as satellite imagery and Light Detection and Ranging (LiDAR). The results 

of this effort show that acoustic data can be used to provide detailed substrate and biological 

cover maps that include mesophotic coral ecosystems. Here we employ a combination of

principal component analyses and unsupervised classification techniques to derive six substrate 

and five biological cover classes from multi-beam acoustic data, which are validated by optical 

seafloor imagery to create a complete benthic habitat map for the West Hawaiʻi Habitat Focus 

Area (WHHFA). Our results show that the overall accuracy of the benthic habitat maps is 59% 

for substrate classification and 61% for biological cover classification. Accuracy was higher for 

the following individual classes; 76% Complex Reef, 86% Sand, and 88% Coral. These habitat 

maps are the first within the WHHFA to incorporate mesophotic data and provide important 

information for evaluating and managing the coral reef ecosystem as a whole.
iv

ACKNOWLEDGMENTS

This technical memo was made possible with funding support from NOAA’s Coral Reef 

Conservation Program and NOAA’s office of Habitat Conservation in support of NOAA’s 

Habitat Blueprint Initiative. Numerous people helped at various stages of the project. Frank 

Parrish, Michael Parke, and Russell Watkins helped to get the initial funding. Data and feedback 

were provided by Tomoko Acoba, Tim Battista, Eric Conklin, Annette DesRochers, Jamison 

Gove, John Hansen, Brittany Huntington, Frances Lichowski, Robert O’Connor, Frank Parrish, 

Michael Parke, Jennifer Samson, Yuko Stender, Curt Storlazzi, Bernardo Vargas-Angel, Bill 

Walsh, Lani Watson, and Mariska Weijerman. Technical support and analysis were provided by 

Michael Akridge, Jade Austin, Gabriel Cohen, Bryan Costa, Allan Elegino, Carla Esquivel, 

Stuart Goldberg, and Liana Roberson. Field support was provided by Kelli Bliss, Carmen 

DeFazio, Hank Lynch, and Kristin Raja. 
v

LIST OF ABBREVIATIONS

BTM Benthic Terrain Modeler

CRAMP Coral Reef Assessment and Monitoring Program

CRCP Coral Reef Conservation Program

CREP Coral Reef Ecology Program

DAR Division of Aquatic Resources

EFH Essential Fish Habitat

GPS Geographic Positioning System

HIHWNM Hawaiian Islands Humpback Whale National Marine Sanctuary

HIMB Hawaiʻi Institute of Marine Biology

HMRG Hawaiʻi Mapping Research Group

LiDAR Light Detection and Ranging

MCES Mesophotic Coral Ecosystem

MMU Minimum Mapping Unit

MPA Marine Protected Area

NOAA National Oceanic and Atmospheric Administration

PCA Principal Component Analysis

TNC The Nature Conservancy

TOAD Tethered Optical Assessment Device

USACE US Army Corps of Engineers

USGS US Geological Survey

WHHFA West Hawaiʻi Habitat Focus Area
vi
vii

CONTENTS

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

MATERIALS AND METHODS.................................................................................................... 4

Study Area................................................................................................................................... 4

Data Collection............................................................................................................................ 4

Acoustic Data .......................................................................................................................... 4

Validation Data........................................................................................................................ 5

Data Processing and Analysis..................................................................................................... 8

Validation Image Classification .............................................................................................. 8

Seafloor Complexity Analysis............................................................................................... 10

Benthic Habitat Maps................................................................................................................ 12

RESULTS ..................................................................................................................................... 12

DISCUSSION AND CONCLUSION .......................................................................................... 14

REFERENCES ............................................................................................................................. 16
viii

LIST OF FIGURES

Figure 1.--Habitat mapping study area location (in blue) within the West Hawaiʻi Habitat Focus 

Area. Also shown are the boundaries for the NOAA CRCP priority site and other managed areas 

in West Hawaiʻi. . ........................................................................................................................... 2

Figure 2.--Location of optical data collected by CREP and contributed by partners swithin the 

WHHFA. ........................................................................................................................................ 5

Figure 3.--Towed Optical Assessment Device (TOAD) camera sled being deployed from a small 

research vessel. . ............................................................................................................................. 6

Figure 4.--Drop camera system designed and implemented during the 2015 field mission. ......... 7

Figure 5.--Bathymetry synthesis (depth) and eight surface complexity layers derived from 

bathymetry synthesis..................................................................................................................... 11

Figure 6.--Benthic habitat maps depicting dominant biological cover (left) and dominant 

substrate (right). ............................................................................................................................ 13
ix

LIST OF TABLES

Table 1.--Hybrid substrate classification scheme and definitions developed to standardize the 

analysis of seafloor imagery from a number of sources (USGS, NOAA Biogeography, NOAA 

CREP) using different collection methods. .................................................................................... 9

Table 2.--Definition for six classes of dominant biological cover................................................ 10

Table 3.--The percent of variance contributed by each of the complexity surfaces to each 

principal component (PC)............................................................................................................. 11

Table 4.--Confusion matrix showing map accuracy values for substrate classes......................... 14

Table 5.--Confusion matrix showing map accuracy values for Biological Cover classes............ 14
x
1

INTRODUCTION

The U.S. National Oceanic and Atmospheric Administration (NOAA) Coral Reef Conservation 

Program (CRCP) is tasked to protect, conserve, and restore the nation's coral reefs by 

maintaining healthy ecosystem function. Over the past 20 years, the condition of marine 

resources in Hawaiian waters around the populated southern islands has declined (Friedlander et 

al. 2008). The need for assessment and monitoring of coral reef ecosystems is important, as the 

effects of anthropogenic activities and climate change continue to threaten these communities, 

jeopardizing the ecosystem goods and services these systems provide. 

The CRCP recognizes three key priority threats to coral reef ecosystems; fishing, land-based 

sources of pollution, and climate change. To address these issues, the CRCP, in cooperation with 

the State of Hawaiʻi, selected priority sites in Hawaiʻi, for ridge-to-reef management 

implementation (Fig. 1). The NOAA Habitat Blueprint provides a framework for management of 

long-term habitat focus areas and includes the CRCP priority site within the West Hawaiʻi

Habitat Focus Area (WHHFA). The WHHFA compliments the CRCP priority site goals and 

objectives, and together they work to achieve aligned goals and objectives. The WHHFA was 

designated in an effort to decrease habitat loss and degradation and to encourage larger 

conservation activities through collaboration and efficient use of limited resources. 

Many coral reef mapping projects have taken place within the WHHFA with efforts concentrated 

in water depths of 0–30 m, using a combination of satellite imagery, sonar bathymetry, and 

optical validation images (i.e., ground truth images). These projects provided shallow-water 

benthic habitat maps, one of the primary tools used to assess resources and implement 

management strategies by providing essential characterization of the seascape (Cochran et al.,

2007; Coyne et al., 2003). These efforts have helped to identify threats within the WHHFA that 

are degrading the shallow coral reefs and include climate change, eutrophication, and 

sedimentation. 

Reducing the impact of land-based threats, from sedimentation and pollution, is critically 

important to the health and resilience of the coral reef ecosystem. When coral reefs are degraded,

macroalgae can become the dominant biological cover. Coral recovery is suppressed, as coral 

larvae require a hard substrate on which to settle (Birrell et al., 2005). Additionally, the low 

structural complexity of degraded coral reef habitat can sustain only a fraction of the biological 

productivity of a healthy reef decreasing fishery yields and biodiversity (Rogers et al., 2014).Yet, 

even if an acceptable substrate is available, recruitment of new corals is undermined as they are 

overgrown by the faster growing algae (Dixson et al., 2014). Suitable recruitment substrates may 

be found in deeper water, where reduced light limits algal growth and the increased distance 

from land reduces the harmful impacts of sedimentation and eutrophication. Under these 

favorable environmental conditions, certain corals normally found in shallow waters may recruit 

and successfully grow in waters as deep as 70 m creating a mesophotic coral ecosystem (MCE).
2

Figure 1.--Habitat mapping study area location (in blue) within the West Hawaiʻi Habitat Focus 

Area. Also shown are the boundaries for the NOAA CRCP priority site and other managed areas 

in west Hawaiʻi.

MCE are found at depths from 30 to150 m and are inhabited by many corals, fishes, macroalgae,

and sponges. The upper MCE (30-70 m) is an extension of the shallow-water coral reef 

ecosystem and is dominated by shallow-water species (Kahng et al., 2014). These ecosystems are 

naturally buffered from some of the threats that shallow corals face as they are in cooler, deeper 

waters and are typically further from shorelines where anthropogenic and land-based impacts are 

most prominent (Baker et al., 2016). MCEs may help us quantify the impacts shallow reef 

systems are experiencing and potentially serve as control sites for reef health and resilience if 

their community compositions are similar enough to shallow water reefs. 

However, MCEs are difficult to study, as they are beyond the reach of recreational scuba (40 m) 

and expensive to survey with submersible vehicles. Despite these limitations, the study of MCEs

has increased over the past decade. In Hawaiʻi, the extent of these deep coral habitats has been

documented using a combination of submersibles, remotely operated vehicles, and mixed-gas 

scuba and rebreather diving. Mesophotic corals between depths of 50-90 m were found to cover 
3

tens of km2 (Pyle et al., 2016). These areas are the most expansive MCEs ever recorded. 

However, much is still unknown about these unique coral habitats. 

To evaluate and monitor the condition of Hawaiʻi’s coral reef ecosystems, the NOAA Pacific 

Islands Fisheries Science Center’s (PIFSC) Coral Reef Ecosystem Program (CREP) is tasked 

with producing comprehensive, seamless maps of all coral reef habitats in Hawaiʻi and 

throughout the Pacific. Using a combination of acoustic and optical mapping survey techniques, 

the CREP produces benthic habitat maps that characterize seafloor bathymetry, substrate, and 

coral cover.

The foundation of benthic substrate maps are high-resolution acoustic bathymetry data that 

provide information about the depth and topography of underwater terrain. In deep water,

benthic substrate maps are created from acoustic bathymetric survey. Using shipboard multibeam echo sounders, high-resolution bathymetry data, essential for substrate map production, are

collected. However, navigational hazards in shallow coastal waters inhibit acoustic data 

collection. In shallow water, bathymetry can be acquired using aerial bathymetric Light 

Detection and Range (LiDAR) systems (Irish et al., 2000). The combination of acoustic data and 

LiDAR can provide continuous bathymetric coverage from the shoreline to deep ocean depths

(Costa et al., 2009). Bathymetric LiDAR and multi-beam data exist for most of the main 

Hawaiian Islands and provide nearly continuous high-resolution bathymetry data.

To complement bathymetry, acoustic data are commonly used to derive geomorphological 

characteristics of the seafloor (Wright et al., 2012b; Lucieer et al., 2013; Pickrill and Todd,

2003). These characteristics provide a measure of the complexity of the seafloor morphology,

which is closely tied to fish abundance (Rogers et al., 2014) and species richness (Grigg, 1994; 

Walker et al., 2009). Geomorphological characteristics derived from acoustic data (e.g., slope, 

curvature and rugosity) offer additional spatial and complexity information that contribute to the 

production of benthic substrate maps.

Although acoustic data provide a wealth of information about the seafloor structure and 

complexity, biological cover cannot be directly identified. To produce a benthic habitat map, in 

situ validation data is necessary. Validation is achieved most commonly by a photograph or 

video of the seafloor collected by a diver, underwater vehicle, or sled that is spatially referenced 

to the bathymetric data. Satellite imagery has been used successfully to delineate seafloor 

characteristics (Watkins, 2015), but increased light absorption with depth limits the use of these 

images to areas shallower than 20-m deep. Regardless of depth, validation data are crucial to 

identify the biological cover of the seafloor. Validation images are analyzed to identify specific 

attributes of the seafloor structure and composition using a defined classification scheme. The 

classification scheme is developed based on knowledge of the substrate and biological cover 

within the area being mapped.

Here we apply a technique to derive benthic habitat from acoustic bathymetry and validation

imagery. This work extends efforts to map shallow water habitat by encompassing the entire

coral reef ecosystem, including the mesophotic zone within the WHHFA. 
4

MATERIALS AND METHODS

Study Area

The island of Hawai'i is the southernmost island of the Hawaiian archipelago. It is also the 

youngest and largest island with a land area of nearly 10,500 km2 and ongoing volcanic activity. 

Topographically the island is dominated by a series of shield volcanoes, with Mauna Kea and 

Mauna Loa reaching a maximum elevation over 4,000 m. 

Pacific trade winds from the northeast are prevalent from May through September, with ocean 

wind speeds of 10–20 m/s and periods of light and variable wind conditions from October 

through April that disrupt the trade wind flow. In west Hawaiʻi, Mauna Kea impedes the flow of 

trade winds creating the warmest and driest leeward weather conditions in the main Hawaiian 

Islands, with an average annual rainfall of less than 0.18 m compared to the windward side of the 

island that receives up to 7.5 m annually (WRCC, 1985). 

In the lee of Mauna Kea, west Hawaiʻi ocean conditions near shore are calm, and surface mixing 

is reduced creating a drastic surface temperature difference of up to 4° C in contrast to the rough 

seas of the Alenuihaha Channel that separates the island of Hawaiʻi from Maui. 

The warm, dry conditions of west Hawaiʻi are exacerbated by periodic El Niño conditions. In 

2015, a strong El Niño event brought record high temperatures to Hawaiʻi that caused elevated 

ocean surface temperatures for 18 consecutive weeks. The State of Hawaiʻi Division of Aquatic 

Resources (DAR) reported severe thermal stress in West Hawaiʻi causing 38–92% of all coral 

colonies to bleach, with the most severe bleaching occurring within the CRCP priority site where 

55–99% coral loss was recorded (DAR, 2016). This event, coupled with ongoing land-based 

sources of pollution from the west Hawaiʻi watershed, places increasing pressure on the coral 

reef ecosystem, highlighting the importance of conservation efforts in west Hawaiʻi where 

important cultural resources are located, and endemic, endangered, and threatened species 

inhabit the longest contiguous coral reef in the state.

The WHHFA extends from the slopes of Mauna Kea to the western shoreline and beyond, to 

three nautical miles offshore, encompassing an area of 1540 km2

, including 230 km2 of marine 

habitat (Fig. 1). The marine habitat within the WHHFA includes shallow and mesophotic coral 

reef ecosystems as well as the shallow portion of the Essential Fish Habitat (EFH) for Hawaiʻi

bottom fish (< 400-m depth). 

Data Collection

Acoustic Data

These data can be derived in various ways from single-beam and multi-beam sonar, LiDAR, and 

satellite imagery. Within the WHHFA, high-resolution multi-beam and LiDAR coverage existed, 

but many bathymetric gaps remained. These gaps were covered during a multi-beam survey 
5

conducted by the CREP team in 2012. The new multi-beam bathymetry data were processed and 

integrated with existing sonar and LiDAR data sets maintained by the University of Hawaiʻi, 

Hawaiʻi Mapping Research Group (HMRG) to produce a high-resolution (5 m grid) bathymetry 

synthesis for the entire WHHFA seafloor.

Validation Data 

Optical validation of the seafloor structure and biological cover is a critical component in 

characterizing benthic habitat. Optical validation allows possible statistical correlations to be 

developed between benthic structure and benthic cover from bathymetric data. This is achieved 

by photographing the seafloor with a diver-operated camera or a tethered camera system

operated from a small boat. 

Within the WHHFA, numerous collaborations were leveraged to facilitate the integration and 

collection of validation data to provide accurate benthic habitat maps that can support 

ecosystem-based coral reef resource management and conservation efforts. Data providers 

included the NOAA National Centers for Coastal Ocean Science Biogeography Branch, The 

Nature Conservancy (TNC), US Army Corps of Engineers (USACE), State of Hawaii Division 

of Aquatic Resources (DAR) and the Hawaiʻi Institute of Marine Biology’s (HIMB) Coral Reef 

Assessment and Monitoring Program (CRAMP) (Fig. 2)

Figure 2.--Location of optical data collected by CREP and contributed by partners within the 

WHHFA.
6

In addition to this imagery, shallow-water habitat maps from the NOAA Biogeography Branch 

and the U.S. Geological Survey (USGS) were used as additional validation resources. In 2003, 

the NOAA Biogeography Branch produced a benthic habitat map to assess, inventory and 

monitor coral reef ecosystems along the West Hawaiʻi coastline to approximately 30-m depths 

(Coyne et al., 2003). In 2007, the USGS produced a benthic habitat map for the Puʻukoholā 

Heiau National Historic Site (Cochran et al., 2007) to evaluate the geologic resources offshore to 

approximately 30 m depths. Both maps were produced using a combination of satellite imagery 

and optical validation data. The USGS maps also incorporated bathymetry data. These maps 

provided a useful validation of seafloor characteristics in shallow-water areas where optical data 

were not available. In areas where the two maps overlapped, and two or more substrate or 

biological cover types were present, the dominant coverage type was used.

All existing validation data were concentrated in water depths of 30 m or less, covering just 13% 

of the WHHFA seafloor area. To validate seafloor characteristics in deeper water, additional 

seafloor imagery was collected in 2012 and 2015 by CREP. These surveys were conducted 

aboard the Pacific Island Fisheries Science Center (PIFSC) R/V AHI, the Hawaiian Islands 

Humpback Whale National Marine Sanctuary (HIHWNM) R/V Koholā, and the Nature 

Conservancy’s (TNC) R/V Kākū. The team used a Towed Optical Assessment Device (TOAD) 

camera sled (Fig. 3) and a drop camera system (Fig. 4) to collect data from 30 m to 150 m 

depths. Survey sites were selected using a stratified random survey design. 

Figure 3.--Towed Optical Assessment Device (TOAD) camera sled being deployed from a small 

research vessel.
7

Figure 4.--Drop camera system designed and implemented during the 2015 field mission.

The TOAD camera sled is equipped with a forward-angled low-light color video camera and a 

downward-facing digital still camera. A pair of parallel lasers provided a scale reference in still 

images. The sled is also outfitted with an altimeter, a pressure (depth) sensor, and a fluxgate 

compass. The TOAD was deployed on a cable using a pot-hauler. A live-feed video monitor 

displayed the position of the sled relative to the seafloor which allowed the operator to raise and 

lower the sled to avoid obstacles. Still imagery was collected at 10-second intervals. A serial 

cable was connected to a Garmin Global Positioning system (GPS) to provide position of the 

vessel, and Hypack 2014 hydrographic software was used to record position data (from the GPS) 

and time to calculate the layback position of the sled. 

Due to challenges faced in field operations, a drop camera system was constructed and 

implemented for the final five days of data collection in 2015. The drop camera system consisted 

of a forward-angled low-light video camera and a downward-facing GoPro Hero 3+ still camera. 

A pair of parallel lasers provided a scale reference in still images. A live-feed video display was 

used to monitor the position of the sled relative to the seafloor, and video data were recorded to 

digital files using a digital video recorder. Still imagery was collected at 10-second intervals. A 

hand-held Garmin 76Cx GPS unit was used to record the position of the vessel. Vessel 

movement was kept to a minimum (1 knot) to maintain a vertical angle on the umbilical cable to 

reduce position error as layback corrections were not applied to these data.
8

Data Processing and Analysis

Validation Image Classification

A total of 9,139 optical validation images were collected in the field and contributed by partners. 

All images were processed and classified at CREP by trained seafloor image analysts. As 

seafloor images were obtained from several sources, collection methods varied among data sets. 

Due to these differences, the seafloor coverage captured in photographs varied as well. For 

example, in some images, large structures such as individual patch reefs were visible, while in 

others only a small area of the seafloor was captured. Due to this discrepancy between image 

data sets, the standard classification scheme used by CREP, USGS, or the NOAA Biogeography 

Branch was not feasible. To resolve this, a hybrid classification scheme was developed to 

identify eight dominant substrate types based on minimum observable features, and all imagery 

was classified using this scheme (Table 1). 

In addition to substrate, six biological cover classifications were defined based on the CREP 

classification scheme developed in 2011 (Table 2). These classifications are closely related to the 

habitat definitions used by the NOAA Biogeography Branch (Cochran et al., 2007). The USGS 

scheme designates major structures only.

To determine the dominant substrate and biological cover classification, each image was 

assigned 10 random points across the image using Coral Point Count with Excel extensions 

(Kohler and Gill, 2006) software. An analyst classified each point using the schemes outlined 

above for substrate type and biological cover. The percent cover for each substrate type and 

biological cover identified within the classified image was calculated. The highest percent cover 

dictated the dominant substrate and cover for each image. For example, if an image was 70% 

coral cover and 30% macroalgae cover, the image was considered 50–90% Coral. These image 

classifications are used to validate and correct the ArcGIS automated delineation of habitat 

classes.
9

Table 1.--Hybrid substrate classification scheme and definitions developed to standardize the 

analysis of seafloor imagery from several sources (USGS, NOAA Biogeography, NOAA CREP) 

using different collection methods.

Hybrid 

Substrate 

Classification 

Scheme

Definition USGS Scheme NOAA 

Biogeography 

Scheme

NOAA CREP 

2011 Scheme

COMPLEX 

REEF:

50% or more Hard 

Bottom or Rock and 

NOT Rubble, Boulder 

or Pavement category

Aggregate Reef, 

Aggregated 

Patch Reef, 

Individual Patch 

Reef, Spur and 

Groove

Aggregate Reef, 

Aggregated Patch 

Reef, Individual 

Patch Reef, Spur 

and Groove

Hard bottom or 

Rock

PAVEMENT: 50% or more Hard 

Bottom or Rock and 

dominant biological 

cover consisting of 

Coralline Algae or 

Turf Algae

Volcanic 

Pavement with 

Sand Channels, 

Volcanic 

Pavement with 

10–50% 

Rocks/Boulders, 

Volcanic 

Pavement

Pavement, 

Pavement w/Sand 

Channels, 

Scattered 

Coral/Rock

Hard bottom or 

Rock with 50% 

or more 

Coralline Algae 

or Turf Algae

MIXED 

SUBSTRATE:

Less than 50% of any 

combination of 

substrates 

None None None

RUBBLE (> 2

mm and < 25

cm):

50% or more Rubble Reef Rubble Rubble Rubble

BOULDER (>

25 cm):

50% or more Boulder Volcanic 

Pavement w/

> 50% 

Rocks/Boulders

Rock/Boulder Boulder

SAND (≥ 1/16 

mm and < 2

mm):

50% or more Sand Sand Sand Sand

MUD < 16 mm: 50% or more Mud Mud Mud Mud

MAN-MADE: 50% or more Manmade

Artificial Artificial Man-Made
10

Table 2.--Definition for six classes of dominant biological cover.

Biological Cover 

Classification 

Scheme

CREP Definition NOAA Biogeography Definition

CORAL Hard coral that contribute to persistent, 

3D structure of coral reef.

Hardened substrate of unspecified relief 

formed by the deposition of calcium 

carbonate by reef building corals.

CORALLINE 

ALGAE

Crustose coralline algae includes any 

calcified, hard, non-segmented, 

encrusting or branched, red (includes 

shades of pink, purple etc.) alga.

Any combination of numerous species of 

encrusting or coralline algae.

TURF ALGAE Turf algae include many species of red, 

green, and brown algae and 

cyanobacteria. These occur in mixed 

algal assemblages that are typically 

short (<2 cm height) and often contain 

filamentous rather than fleshy algae.

Not designated as a distinct classification.

MACROALGAE Macroalgae with a structure that can be 

seen in photos and video (unlike turf 

algae that may be invisible or appear as 

fine fuzz). This group includes 

numerous morphologies.

Any combination of numerous species of 

red, green, or brown macroalgae.

UNCOLONIZED No living cover on the substrate. This 

only occurs when the underlying 

substrate is mud or sand or 

unconsolidated. Any hard substrate 

will be rapidly colonized, even if it is 

not possible to see the colonizer, as in 

the case of invisible turf algae.

Flat, low relief, solid carbonate rock that is 

often covered by a thin sand veneer. 

Surface often has sparse coverage of 

macroalgae, hard coral, zoanthids and other 

sessile invertebrates that does not obscure 

the underlying surface.

UNCLASSIFIED Low image quality, shadow or other 

obstruction that inhibits proper 

classification

Bottom type uninterpretable due to 

turbidity, cloud cover, water depth or other 

interference.

Seafloor Complexity Analysis

Surface complexity layers are important components of a benthic structure data set. Bathymetric 

data can be used to derive seafloor characteristics that define structure for benthic organisms. To 

characterize the complexity of the seafloor, the Benthic Terrain Modeler (BTM) (Wright et al.,

2012a) in ArcGIS 10.3 software was used to derive a series of surface complexity layers from a 5 

m grid bathymetry synthesis. Eight surface complexity layers (mean depth, standard deviation of 

depth, curvature, plan curvature, profile curvature, rugosity, slope, and slope of slope) were 

produced (Fig. 5).
11

Figure 5.--Bathymetry synthesis (depth) and eight surface complexity layers (mean depth, 

standard deviation of depth, curvature, plan curvature, profile curvature, rugosity, slope and 

slope of slope) derived from the bathymetry synthesis. 

The three-dimensional complexity of the seafloor is a vital driver of the distribution and 

abundance of marine organisms (Pittman and Brown 2011). Surface complexity layers can be 

analyzed together with bathymetry data to emphasize variation and identify strong patterns 

within the data set (Costa 2013). One method used to achieve this is Principal Component 

Analysis (PCA). 

A PCA, using ENVI 5.3 software, was performed on the bathymetry synthesis and eight 

complexity surface layers to remove highly correlated data. Bathymetry and complexity surfaces 

were used in this analysis because previous studies have demonstrated useful application of these 

data for characterizing coral reef ecosystems in the US Caribbean (Pittman et al, 2007 Costa 

2013). The PCA is also useful in determining which complexity layers are contributing the most 

variance. The PCA performed transformed the 9-band bathymetry and complexity surface image 

into nine principal components. In this analysis, the first three components described 99% of the 

data variability. The complexity surfaces that contributed the most variance were bathymetry 

and mean depth. Using the bathymetry alone would have provided only 50% of the variance. By 

including the complexity surfaces, we can determine where the variance lies within the data and 

retain those components. The rugosity surface contributed minimally to the overall variance but 

was maintained to capture the finer variance among biological cover classes (Table 3). 

Contributions from the remaining complexity surfaces were nearer or equal to zero. The 

bathymetry, mean depth, and rugosity principal components are thus considered the most 

important for classifying benthic habitat and were retained for use in the benthic classification 

process. 

Table 3.--The percent of variance contributed by each of the complexity surfaces to each 

principal component (PC).
12

Principal 

Component Bathymetry Mean Depth

Standard 

Deviation 

Depth Curvature Plan Curve Profile Curve Rugosity Slope Slope of Slope

Accumulated 

% of Variance

1 50.04 49.90 0.02 0.04 0.00 0.00 0.00 0.00 0.00 99.8965

2 49.94 50.03 0.00 0.03 0.00 0.00 0.00 0.00 0.00 99.946

3 0.00 0.00 0.15 0.01 1.10 0.05 92.36 6.32 0.00 99.9736

Benthic Habitat Maps

Manual delineation of benthic habitats over large areas like the WHHFA can be very time 

consuming and is particularly difficult in deep water due to the sparsity of validation data. 

Fortunately, ArcGIS provides a tool to automate this process to reduce time and user bias 

introduced during manual delineation of habitat areas. The Iso Cluster Unsupervised 

Classification tool was used to classify the three-band PCA image into distinct classes or 

clusters. This tool computes the minimum Euclidean distance when assigning each cell to a 

cluster. Initially, an arbitrary mean is assigned by the software for each cluster defined by the 

user. Each cell is then assigned to the closest mean. New means are recalculated for a cluster 

based on the attribute distance of cells in the cluster after the first iteration. This process is 

repeated until all unknown data are grouped into a cluster or the maximum iteration is reached. 

Once delineated, this image was converted to a polygon shapefile. Polygons smaller than the 

minimum mapping unit (MMU) of 100 m2 were merged with neighboring polygons that shared 

the longest common border. This MMU was chosen to be consistent with smallest MMU used in 

the adjacent shallow-water habitat maps. This process produced a delineated image of cell 

clusters with similar characteristics.

To verify the accuracy of the automated delineation, the classified optical validation points were 

overlaid onto the delineated image to confirm or correct the dominant substrate and biological 

cover composition of the seafloor at a given location. The number of images used to validate a 

given polygon varied due to data availability and size of the polygon. In cases where multiple 

validation data were available but conflicted within a polygon, the majority ruled. A minimum of 

5 images were used to determine the classification for each polygon. The delineated polygon 

classifications were manually corrected or modified if they disagreed with the optical validation 

points. When optical validation points were absent, the USGS and NOAA Biogeography Branch 

benthic habitat maps were used as validation. For areas where no validation data existed, the 

polygon was considered unclassified. This process created verified substrate and biological cover 

maps for the WHHFA.

RESULTS

The substrate and biological cover maps created through this project are thematic in nature (Fig.

6). They depict the distribution of seafloor characteristics across geographic space. The accuracy 

of the maps is highly dependent upon the validation imagery. Uncertainties in geographic 

position, image classification, and changes in habitat over the timespan of data collection may 

have introduced conflicts in the data. 
13

Figure 6.--Benthic habitat maps depicting dominant biological cover (left) and dominant 

substrate (right).

To assess the thematic accuracy, a subset of optical validation points was set aside after the 

image classification process was complete. This subset of points was selected using a stratified 

random process to ensure all seafloor classes were included and that validation points for each 

class were well distributed across geographic space. Validation points from the classified 

imagery were selected for substrate classes (n = 219) and weighted for the larger area covered by 

sand substrate. A separate set of validation points were selected for the biological cover classes

(n = 187) and weighted for the larger area covered by uncolonized cover. 

Each subset of validation points was overlaid onto the final habitat map. Values from the map 

were extracted to the validation point spreadsheet, and an error matrix (Story and Congalton, 

1986) was generated to determine overall accuracy, Producer’s accuracy, and User’s accuracy 

for substrate (Table 4) and biological cover (Table 5). 

Overall accuracy, which is the probability for a randomly selected location in the map to be 

correctly classified, was slightly better for biological cover at 61% versus structure at 59%. 

User’s accuracy, a measure of reliability, which is the conditional probability that an area 

specifically classified in the map is also classified as the same category in the validation data. 

Low values signify that the area for the classification is overestimated. User’s accuracy values 

were high for mixed and sand substrate classifications (100% and 83%, respectively) and for 

macroalgae cover (80%). For the remaining substrate and cover classes, the user’s accuracy was 

low (44–73% and 29–59%, respectively), suggesting that polygons were assigned to the wrong 

classes, and that this effect was less pronounced for biological cover classes or that errors were 

introduced in scale selection or position accuracy. Producer’s accuracy measures how well the 

classification aligned with the test pixels, where low values indicate classes that are 
14

underestimated in the map. Producer’s accuracy was high for sand substrate (86%) and coral 

(88%) and uncolonized (81%) cover. Mixed and boulder substrates had high omission, which is 

expected due to the relatively low occurrence of these substrates.

Table 4.--Confusion matrix showing map accuracy values for substrate classes

Ground Truth

Complex Pavement Mixed Rubble Sand Boulder Total

User's 

Accuracy

Complex 19 11 18 2 3 13 66 28.78%

Pavement 4 8 3 0 2 5 22 36.36%

As Mixed 0 0 1 0 0 0 1 100%

Mapped Rubble 0 0 0 16 9 2 27 59.25%

Sand 2 5 2 7 86 1 103 83.49%

Boulder 0 0 0 0 0 0 0 0%

Total 25 24 24 25 100 21 219

Producer's 

Accuarcy 76% 33% 4% 64% 86% 0%

Diagonal Sum: 130

Overall Accuracy: 59%

Kappa Coefficient: 0.44

Table 5.--Confusion matrix showing map accuracy values for Biological Cover classes

Coral Coralline Algae Turf Algae Macroalgae Uncolonized Total

User's 

Accuracy

Coral 23 7 11 0 10 51 45.09%

Coralline Algae 0 5 1 0 1 7 71.42%

As Turf Algae 2 4 11 3 5 25 44.00%

Mapped Macroalgae 0 0 0 4 1 5 80.00%

Uncolonized 1 5 2 19 72 99 72.72%

Total 26 21 25 26 89 187

Producer's 

Accuracy 88.46% 24.81% 44.00% 15.38% 81.90%

Diaganol Sum: 115

Overall Accuracy: 61%

Kappa Coefficient: 0.44

Ground Truth

DISCUSSION AND CONCLUSION

Acoustic-derived benthic substrate maps provide crucial information about the extent and 

composition of coral reef ecosystems, especially in depths beyond the limits of recreational 

scuba and aerial remote sensing techniques. The WHHFA contains a large seafloor area with 

most data collection efforts concentrated in shallow depths (< 30 m), which make up less than 

one quarter of the marine environment in this area. This study filled bathymetry and validation

data gaps to produce a continuous benthic habitat map for the entire WHHFA including deepwater MCES’s, which are an important component of the coral reef ecosystem. 
15

Although every effort was made by the CREP to collect as much data as possible, the deep-water 

imagery makes up less than 50% of the total data collected. The sparsity of deep-water data 

makes it difficult to bridge spatial gaps between data sets. In addition, the shallow-water data 

collection spans a longer time period, from 2005 to 2015, which may introduce temporal or 

seasonal differences between data sets. This combination of factors may have impacted the 

thematic accuracy of the final benthic habitat maps.

However, it is promising that this approach, using only high-resolution bathymetry and optical 

validation data, has potential to improve characterization of deep-water habitats where data 

options and availability are limited. At depths greater than ~30 m, the water column obscures 

remote sensing capabilities, and in situ observations become more expensive and timeconsuming with greater depth. One option—although not currently available within the 

WHHFA—is backscatter, a derivative of multi-beam sonar data. Backscatter can be processed to 

predict seafloor impedance, roughness, and sediment grain size. Used in conjunction with highresolution bathymetry and optical validation data, substrate classification of deeper water benthic 

habitats can be improved (Che Hasan et al., 2014)

With the continued advances in airborne image collection (LiDAR, Satellite, Drone, etc.) and inwater photography, the accuracy and detail of benthic habitat maps for shallow waters will 

continue to improve. However, with the severity of impacts shallow-water corals are facing 

across the Pacific, extending survey capabilities into deeper waters will be increasingly 

important to identify and monitor areas where shallow-water species extend into mesophotic 

coral ecosystems (Bongaerts et al., 2017; Pyle et al., 2016; Rooney et al., 2010).

Within the WHHFA, coral covers 17.10 km2 (7.4%) of the seafloor, and 0.97 km2 (5.7%) of that 

coral rich area is deeper than 30 m. This distribution highlights the importance of protecting and 

properly managing coral in deeper waters because mounting stress from climate change and 

human activities may further shift the distribution of coral cover to mesophotic depths where 

these impacts are dampened (Bongaerts et al., 2010). Current research is beginning to illustrate 

that the extent and diversity of mesophotic reefs is much greater than previously thought (Pyle et 

al., 2016) and that the shallow portion of mesophotic coral reefs (30–60 m depths) offer habitat 

for shallow-water marine organisms (Kahng et al., 2014). Coral coverage within this area of the 

mesophotic is dominated by shallow-water coral including; Pocillopora meandrina, Pocillopora

damicornis, Montipora capitata, and Porites lobata. One species, Montipora capitata, dominates 

coral cover down to 75 m depth (Rooney et al., 2010). The upper MCE may provide a critical 

opportunity for refuge when shallow reef ecosystems are heavily impacted or destroyed. 

Without details of the seafloor habitat, resource managers, policy makers, and stakeholders are 

poorly equipped to protect and manage marine habitats. By providing a baseline characterization 

of the benthic seascape and habitats throughout the WHHFA, the data and maps created have the 

potential to facilitate development of actionable policies, plans, and management practices, 

ensuring healthy fish and coral populations and improved fisheries.
16

REFERENCES

Baker, E. K., K. A. Puglise, and P. T. Harris.

2016. Mesophotic coral ecosystms_--A lifeboat for coral reefs? The United Nations Environment 

Programme and GRID-Arendal, Nairobi and Arendal, 98 p.

Birrell, C. L., L. J. McCook, and B. L. Willis.

2005. Effects of algal turfs and sediment on coral settlement. Marine Pollution Bulletin. 51:408-414.

Bongaerts, P., T. Ridgway, E. M. Sampayo, and O. Hoegh-Guldberg.

2010. Assessing the ‘deep reef refugia’ hypothesis: focus on Caribbean reefs. Coral Reefs. 29:309-

327.

Bongaerts, P., C. Riginos, R. Brunner, N. Englebert, S. R. Smith, and O Hoegh-Guldberg.

2017. Deep reefs are not universal refuges: Reseeding potential varies among coral species. Science 

Advances. 3:2: e1602373.

Che Hasan, R., D. Lerodiaconou, L. Laurenson, and A. Schimel.

2014. Integrating Multi-beam Backscatter Angular Response, Mosaic and Bathymetry Data for 

Benthic Habitat Mapping. PLoS ONe. 9:e97339.

Cochran, S. A., A.E. Gibbs, and J.B. Logan. 

2007. Geologic resource evaluation of Puukohola Heiau National Historic Site, Hawaiʻi: Part II, 

Benthic habitat mapping. 20. U.S. Geological Survey.

Costa, B. M., T. A. Battista, and S. J. Pittman 

2009. Comparative evaluation of airborne LiDAR and ship-based multi-beam SoNAR bathymetry 

and intensity for mapping coral reef ecosystems. Remote Sensing of Environment. 113:1082-

1100.

Costa, B. M. and T.A. Battista.

2013. The semi-automated classification of acoustic imagery for characterizing coral reef ecosystems. 

International Journal of Remote Sensing. 34.

Coyne, M. S., T.A. Battista, M. Anderson, J. Waddell, W. Smith, P. Jokiel, M.S. Kendall, andM.E. 

Monaco. 

2003. Benthic Habitats of the Main Hawaiʻian Islands. National Oceanic and Atmospheric 

Administration, National Centers for Coastal Ocean Science, Center for Coastal Monitoring and 

Assessment.

Division of Aquatic Resources

2016. 2015 Coral Bleaching Surveys: South Kohala, North Kona. 2.

Dixson, D. L., D. Abrego, and M. E. Hay.

2014. Chemically mediated behavior of recruiting corals and fishes: A tipping point that may limit 

reef recovery. Science. 345:892-897.
17

Friedlander, A., G. Aeby, R. Brainard, E. Brown, K. Chaston, A. Clark, P. McGowan, T. Montgomery, 

W. Walsh, and I. Williams.

2008. The state of coral reef ecosystems of the main Hawaiʻian Islands. The state of coral reef 

ecosystems of the United States and Pacific freely associated states. 222-269.

Grigg, R.

1994. Effects of sewage discharge, fishing pressure and habitat complexity on coral ecosystems and 

reef fishes in Hawaii. Marine Ecology Progress Series. 103:25-34.

Irish, J., J. McClung, and W. Lillycrop.

2000. Airborne Lidar Bathymetry-The Shoals System. Bulletin-International Navigation Association.

43-54.

Kahng, S. E., J. M. Copus, and D. Wagner.

2014. Recent advances in the ecology of mesophotic coral ecosystems (MCEs). Current Opinion in 

Environmental Sustainability. 7:72-81.

Kohler, K. E. and S. M. Gill.

2006. Coral Point Count with Excel Extenstions (CPCe): A Visual Basic program for the 

determination of coarl and substrate coverage using random point count methodology. Computers 

and Geosciences. 32:1259-1269.

Lucieer, V., N. A. Hill, N. S. Barrett, and S. Nichol.

2013. Do marine substrates ‘look’ and ‘sound’ the same? Supervised classification of multi-beam

acoustic data using autonomous underwater vehicle images. Estuarine, Coastal and Shelf Science.

117:94-106.

Pickrill, R. A. and B. J. Todd.

2003. The multiple roles of acoustic mapping in integrated ocean management, Canadian Atlantic 

continental margin. Ocean & Coastal Management. 46:601-614.

Pittman, S.J., C. Caldow, S.D. Hile, and M.E. Monaco.

2007. Using seascape types to explain the spatial patterns of fish in the mangroves of SW Puerto 

Rico. Marine Ecology Press Series 348:273-284.

Pittman, S. J. and K. A. Brown.

2011. Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes. 

PLoS ONE. 6:e20583.

Pyle, R. L., R. Boland, H. Bolick, B. W. Bowen, C. J. Bradley, C. Kane, R. K. Kosaki, R. Langston, K. 

Longenecker, A. Montgomery, F. A. Parrish, B. N. Popp, J. Rooney, C. M. Smith, D. Wagner, and H. L. 

Spalding.

2016. A comprehensive investigation of mesophotic coral ecosystems in the Hawaiʻian Archipelago. 

PeerJ. 4:e2475.

Rogers, A., J.L. Blanchard, and P.J. Mumby.

 2014. Vulnerability of Coral Reef Fisheries to a Loss of Structural Complexity. Current Biology, 24,

1000-1005.
18

Rooney, J., E. Donham, A. Montgomery, H. Spalding, F. Parrish, R. Boland, D. Fenner, J. Gove, andO. 

Vetter.

2010. Mesophotic coral ecosystems in the Hawaiʻian Archipelago. Coral Reefs. 29:361-367.

Story, M., and R. Congalton.

1986. Accuracy assessment: a user's perspective. Photogramm. Eng. Remote Sens. 52(3):397-399.

Walker, B. K., L. K. B. Jordan, and R. E. Spieler.

2009. Relationship of Reef Fish Assemblages and Topographic Complexity on Southeastern Florida 

Coral Reef Habitats. Journal of Coastal Research. 39-48.

Watkins, R. 

2015. A methodology for classification of benthic features using WorldView-2 Imagery. Ecospatial 

Information Team, Coral Reef Ecosystem Division, Pacific Islands Fisheries Science Center. 29.

Wright, D. J., M. Pendleton, J. Boulware, S. Walbridge, B. Gerlt, D. Eslinger, D. Sampson, and E. 

Huntley.

2012a. ArcGIS Benthic Terrain Modeler (BTM), v. 3.0. Environmental Systems Research Institute.

Wright, D. J., J. T. Roberts, D. Fenner, J. R. Smith, A. A. P. Koppers, D. F. Naar, E. R. Hirsch, L. W. 

Clift, and K. R. Hogrefe. 

2012b. Seamounts, Ridges, and Reef Habitats of American Samoa. In: E. K. Baker (ed.) Seafloor 

Geomorphology as Benthic Habitat. p. 791-806. Elsevier, London.

WRCC.

1985. National Oceanic and Atmospheric Administration, Western Regional Climate Center, Climate 

Narratives of Hawaiʻi, Third Edition. Gale Research Company. 

http://www.wrcc.dri.edu/narratives/Hawaiʻi. Accessed December 8, 2016.
19

Availability of NOAA Technical Memorandum NMFS

Copies of this and other documents in the NOAA Technical Memorandum NMFS series issued 

by the Pacific Islands Fisheries Science Center are available online at the PIFSC Web site 

http://www.pifsc.noaa.gov in PDF format. In addition, this series and a wide range of other 

NOAA documents are available in various formats from the National Technical Information 

Service, 5285 Port Royal Road, Springfield, VA 22161, U.S.A. [Tel: (703)-605-6000]; URL: 

http://www.ntis.gov. A fee may be charged.

Recent issues of NOAA Technical Memorandum NMFS–PIFSC are listed below:

NOAA-TM-NMFS-PIFSC-49 Development and testing of two towed volumetric hydrophone 

array prototypes to improve localization accuracy during 

shipboard line-transect cetacean surveys.

Y. BARKLEY, J. BARLOW, S. RANKIN, G. D’SPAIN, and E. 

OLESON

(March 2016)

50 Indury dterminations for marine mammals observed interacting 

with Hawaii and American Samoa longline fisheries during 

2009-2013. 

A. L. BRADFORD and K. A. FORNEY

(March 2016)

51 Stock assessment updates of the bottomfish management unit 

species of American Samoa, the Commonwealth of the Northern 

Mariana Islands, and Guam in 2015 Using Data through 2013.

A. YAU, M. NADON, B. RICHARDS, J. BRODZIAK, and E. 

FLETCHER.

(March 2016)