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Coral reef ecosystems exhibit biotic complexity and spatial heterogeneity in physical structure at multiple spatial scales. The recent application of technology to coral reef ecosystems has vastly improved the mapping and quantification of these physically complex ecological systems. Understanding the geomorphology of coral reefs, from a three-dimensional perspective, using LiDAR, offers great potential to advance our knowledge of the functional linkages between geomorphic structure and ecological processes in the marine environment. The recent application of LiDAR in coral reef ecosystems also demonstrates the depth and breadth of the potential for this technology to support research and mapping efforts in the coastal zone. This chapter builds upon the previous one, which covered the background and principles of LiDAR altimetry, by reviewing coral reef LiDAR applications and providing several case studies that highlight theutility of this technology. The application of LiDAR for navigational charting, engineering, benthic habitat mapping, ecological modeling, marine geology and environmental change detection are presented. The future directions of LiDAR applications are considered in the conclusion of this chapter, as well as the next steps for expanding the use of this remote sensing technology in coral reef environments.
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Chapter 6
LiDAR Applications
Simon J. Pittman, Bryan Costa and Lisa M. Wedding
Abstract Coral reef ecosystems exhibit biotic complexity and spatial heteroge-
neity in physical structure at multiple spatial scales. The recent application of
LiDAR technology to coral reef ecosystems has vastly improved the mapping and
quantification of these physically complex ecological systems. Understanding the
geomorphology of coral reefs, from a three-dimensional perspective, using
LiDAR, offers great potential to advance our knowledge of the functional linkages
between geomorphic structure and ecological processes in the marine environ-
ment. The recent application of LiDAR in coral reef ecosystems also demonstrates
the depth and breadth of the potential for this technology to support research and
mapping efforts in the coastal zone. This chapter builds upon the previous one,
which covered the background and principles of LiDAR altimetry, by reviewing
coral reef LiDAR applications and providing several case studies that highlight the
S. J. Pittman (&) B. Costa L. M. Wedding
NOAA/NOS/NCCOS/CCMA, Biogeography Branch,
1305 East West Highway, Silver Spring, Maryland, MD 20910, USA
B. Costa
L. M. Wedding
S. J. Pittman
Marine Science Center, University of the Virgin Islands, 2 John Brewers Bay, St. Thomas
VI, Virgin Islands 00802, USA
L. M. Wedding
Institute of Marine Science, University of California at Santa Cruz, 100 Shaffer Rd, Santa
Cruz, CA 95060, USA
L. M. Wedding
NOAA/SWFSC, Fisheries Ecology Division, 110 Shaffer Rd, Santa Cruz, CA 95060, USA
J. A. Goodman et al. (eds.), Coral Reef Remote Sensing,
DOI: 10.1007/978-90-481-9292-2_6,
Ó Springer Science+Business Media Dordrecht 2013
utility of this technology. The application of LiDAR for navigational charting,
engineering, benthic habitat mapping, ecological modeling, marine geology and
environmental change detection are presented. The future directions of LiDAR
applications are considered in the conclusion of this chapter, as well as the next
steps for expanding the use of this remote sensing technology in coral reef
6.1 Introduction
In tropical marine ecosystems, LiDAR systems have been used predominantly to
acquire bathymetric information about the seafloor in order to support navigational
charting (Irish and Lillycrop 1999; McKenzie et al. 2001; Wozencraft et al. 2008),
coastal engineering (Irish and White 1998; Wozencraft et al. 2000), benthic habitat
mapping (Brock et al. 2006; Wang and Philpot 2007; Wozencraft et al. 2008;
Walker et al. 2008; Walker 2009), ecological modeling (Wedding et al. 2008b;
Pittman et al. 2009, 2011a, b), shoreline extraction (Liu et al. 2007) and change
detection (Zhang et al. 2009). Airborne LiDAR has provided accurate seafloor data
for shallow coral reefs, as well as seamless, high resolution land-sea coastal terrain
models with sufficient vertical resolution for the forecasting of flood impacts from
tsunami and sea-level rise (Tang et al. 2009). In addition, vulnerability maps
produced from LiDAR data that depict regions prone to flooding have proven to be
essential to planners and managers responsible for mitigating the associated risks
and costs to both human communities and coral reef ecosystems (Brock and Purkis
2009; Gesch 2009).
6.2 Example LiDAR Applications
This chapter reviews coral reef LiDAR applications and highlights several case
studies to demonstrate the utility of this technology. Here we include examples of
applications of LiDAR related to: (1) navigational charting, (2) characterization
and ecological study of coral reef ecosystems, (3) examination of the geomor-
phology of coral reefs, (4) coastal engineering and modeling, and (5) under-
standing and monitoring environmental change. Wherever possible we provide
examples of direct applications of LiDAR to coral reef ecosystems. However, due
to the limited number of LiDAR surveys specifically addressing coral reefs, and
few published studies, some of our examples and applications are focused more
broadly in the coastal zone. We also include several applications that highlight the
potential for LiDAR to improve our knowledge of the broader scale patterns and
processes that influence the structure and function of coastal ecosystems, such as
monitoring coastal sedimentary processes across tropical seascapes.
146 S. J. Pittman et al.
6.2.1 Navigational Charting
LiDAR supports navigational charting by acquiring seafloor depths and identifying
possible hazards to navigation. This is particularly important in shallow waters
with hardbottom features, such as coral reefs, to avoid potentially hazardous
groundings and damage to sensitive and valuable coral reef communities.
According to International Hydrographic Organization (IHO) navigational chart-
ing standards, LiDAR surveys must not exceed predetermined levels of vertical
and horizontal uncertainties at the 95 % confidence level (IHO 2008). The max-
imum levels of vertical and horizontal uncertainty allowed depend primarily on the
depth of the surveyed area. In general, shallower areas (\40 m) are subject to more
rigorous standards, where under-keel clearance is critical. Deeper areas ([100 m)
are subject to less rigorous uncertainty standards, where a general description of
the seafloor is adequate. Given the depth dependent nature of these specifications,
bathymetric LiDAR surveys are most often conducted to meet the highest stan-
dards of uncertainty (i.e., IHO Special Order or Order 1), since most LiDAR
systems on average only penetrate 30 m into the water column (but in clear water
typical of many reef environments can penetrate as much as 60–70 m).
In 2006, a LiDAR survey of southwestern Puerto Rico was commissioned by
NOAA’s Office of Coast Survey (OCS) to map elevations between 50 m above sea
level downwards to 70 m below sea level. This survey was conducted using the
Laser Airborne Depth Sounder (LADS) Mk II Airborne System (Stephenson and
Sinclair 2006), which uses a 900 Hz Nd: YAG (neodymium-doped yttrium alumi-
num garnet) laser that is split by an optical coupler into infrared (1,064 nm) and
blue-green (532 nm) beams. The infrared beam measures the height of the plane
above the water surface at nadir, while the green beam oscillates beneath the sortie in
a rectilinear pattern to measure depths and elevations. The data were collected with
4 9 4 m sounding densities and 200 % seabed coverage, which thereby dictated the
swath width, line spacing and speed of the survey (Table 6.1; Baltsavias 1999). The
data collected for this project met IHO Order 1 uncertainty standards, and were used
by NOAA to update parts of the nautical charts for the west coast of Puerto Rico (i.e.,
Table 6.1 Scan pattern configuration of the LADS Mk II LiDAR system. Adapted from
Stephenson and Sinclair (2006)
density (m)
width (m)
Line spacing 200 %
coverage (m)
Line spacing 100 %
coverage (m)
Survey speed
6 9 6 288 125 250 210
5 9 5 240 100 200 175
4 9 4 192 80 160 140
4a 9 4a 150 60 120 175
3 9 3 100 40 80 150
2 9 2 50 20 40 140
Each pattern is available at all of the operational altitudes (e.g., 500–1,000 m in 100 m
6 LiDAR Applications 147
charts 25671, 25673 and 25675) (Fig. 6.1). Charts 25671 and 25675 had not been
updated since 2003, while chart 25673 had not been updated since 2006. New shoal
features and potential hazards to navigation were identified during the survey
(Fig. 6.2). These features were incorporated in the new versions of these charts,
which were released to the maritime community in 2010. Similar projects were
conducted using the LADS sensor in Miami, Florida and on the Alaskan Peninsula
(Fugro LADS 2010). In addition, several previously uncharted reefs were identified
by a LiDAR survey in the United States Virgin Islands in 2010, a region that was last
surveyed in 1924, and where boat groundings frequently occur.
Fig. 6.1 Nautical charts (25671, 25673 and 25675) in western Puerto Rico that were updated
using LADS LiDAR data. The red polygon denotes the complete spatial extent of the LADS data
Fig. 6.2 In the U.S. Caribbean, nautical chart 25671 for the west coast of Puerto Rico was
updated using the LADS LiDAR system. New shoal features and hazards to navigation (located
within the red squares) were identified during the survey, and were used to update the 2003
edition of the chart (left). The new chart (right) was released in 2010. Soundings for both charts
are in fathoms (1 fathom = 1.83 m)
148 S. J. Pittman et al.
6.2.2 Benthic Habitat Mapping
An important goal of benthic habitat mapping is to help resource managers make
informed and ecologically relevant decisions, thereby supporting the process of
ecosystem-based management and marine spatial planning. Benthic habitat maps
have been used to: (1) understand and predict the spatial distribution of resources,
(2) detect environmental change, (3) design monitoring sampling strategies, and
(4) delineate zones and assess the efficacy of marine protected areas (Ward et al.
1999; Friedlander et al. 2007a, b; Pittman et al. 2011a, b). LiDAR supports benthic
habitat mapping by acquiring continuous information about the depth and struc-
tural properties of the seafloor in depths reaching 60–70 m under optimal condi-
tions (Stumpf et al. 2003). Seafloor habitats are differentiated from each other
based on their geomorphological structure (e.g., their physical composition) and
biological cover (i.e., the types and abundance of sessile organisms that colonize
those structures). The three-dimensional detail provided by LiDAR offers the
potential to develop highly accurate benthic habitat maps even in the absence of
other remote sensing data types. In locations with overlapping multispectral or
hyperspectral imagery and LiDAR data sets, combining LiDAR derived digital
elevation models (DEMs) with spectral data enhances the overall accuracy of the
derived benthic habitat maps (Chust et al. 2010; see Chap. 7). In Hawaii, Conger
et al. (2006) used LiDAR bathymetry from the USACE SHOALS system (U.S.
Army Corps of Engineers Scanning Hydrographic Operational Airborne LiDAR
Survey; Irish and Lillycrop 1999; Irish et al. 2000) in conjunction with multi-
spectral QuickBird imagery to develop a simple technique to decorrelate remote
sensing color band data from depth in areas of shallow water. The method pro-
duced pseudo-color bands that were suitable for direct knowledge-based inter-
pretation, as well as for calibration to absolute seafloor reflectance.
Seamless land topography and marine bathymetry digital elevation models are
now becoming available (see Chap. 5) and provide an opportunity for the devel-
opment of models that quantify land-sea interactions, such as runoff impacts to
nearshore coral reef ecosystems. Furthermore, combined bathymetric and topo-
graphic LiDAR systems can survey land and seafloor simultaneously, a useful
capability for mapping land adjacent to coral reef ecosystems or where emergent
features such as cays and intertidal flats exist. LiDAR provides a three-dimen-
sional representation of the seafloor, which has important utility in identifying and
mapping habitat types with differing geomorphological characteristics and varying
levels of topographic complexity. Three-dimensional surface features are also
important in predicting species distribution patterns across coral reef ecosystems
(Pittman et al. 2009; Pittman and Brown 2011; see Sect. 6.3.2).
The Experimental Advanced Airborne Research LiDAR (EAARL) (Wright and
Brock 2002) developed by the National Aeronautics and Space Administration
(NASA) and U.S. Geological Survey (USGS) was used to collect 1 9 1m
bathymetry for a broad swath of the northern Florida reef tract to map stony coral
reefs in Biscayne National Park (Brock et al. 2006). Rugosity, a measure of surface
6 LiDAR Applications 149
complexity, was calculated as the ratio of planar surface area to actual surface
area. Features exhibiting high rugosity were investigated further and correlated
with in situ observations using an underwater video camera (Fig. 6.3). This video
was manually classified into seven substratum classes having statistically different
rugosity values, with live coral having the highest mean rugosity out of the coral
colony classes. The EAARL system has also been used to map coral reefs at sub-
meter resolution for specific reefs, such as Johnson’s Reef in the U.S. Virgin
Islands, producing a topographic map with vertical and horizontal uncertainties of
10 and 40 cm, respectively. Given these results, the EAARL system has been
shown to have great potential for identifying and mapping stony coral colonies.
Other LiDAR systems, such as the SHOALS system (Wang and Philpot 2007;
Wozencraft et al. 2008) and LADS system (Walker 2009), have also been applied
to map geomorphology of coral reef ecosystems, albeit at broader spatial resolu-
tion of 1 acre minimum mapping unit (MMU).
An under-utilized data product, but currently evolving application area, of some
LiDAR systems is the intensity surface, which quantifies the amount of laser light
energy returned from the seafloor (e.g., seafloor pseudo reflectance or absolute
reflectance; see Chap. 7). For acoustic systems, intensity information is indicative
Fig. 6.3 LiDAR derived rugosity surface illustrating a patch reef in Biscayne Bay, Florida. The
green and blue points denote the location of underwater video that was taken of the seafloor
(adapted from Brock et al. 2006)
150 S. J. Pittman et al.
of sediment properties, including grain size, roughness and hardness (Hamilton
and Bachman 1982; Chaps. 810). These types of sediment properties, particularly
porosity, are important for benthic habitat mapping, as many tropical marine
organisms respond differently to hard bottom and soft bottom habitat types
(Friedlander and Parrish 1998; Pittman et al. 2007). Deriving intensity information
from LiDAR data is an active area of research. Most recently, intensity infor-
mation was processed for the Compact Hydrographic Airborne Rapid Total Survey
(CHARTS) system, and used to map benthic habitats and different submerged
aquatic vegetation types in Plymouth Harbor, MA (Reif et al. 2011). In the future,
more LiDAR systems may be capable of producing intensity surfaces similar to
acoustic multibeam sensors, as the technology advances and research refines signal
processing techniques and algorithms for classifying complex multivariate data
(Costa et al. 2009). Nonetheless, fundamental technical differences and data
characteristics exist between LiDAR and acoustic mapping systems, which are
indicative of different inherent capabilities between these systems.
6.2.3 Morphology and Topographic Complexity
Bathymetric mapping of three-dimensional habitat using remote sensing technol-
ogy is of great interest to ecologists because the structure and composition of
habitat greatly influences marine ecosystems. Coral reef ecosystems exist as
topographically complex surfaces varying across a wide range of morphological
characteristics that have ecological implications for the distribution of individuals,
species and spatial patterns in marine biodiversity (Pratchet et al. 2008; Pittman
et al. 2009; Zawada and Brock 2009). Topographic complexity also influences the
movement of water across coral reef seascapes (Monismith 2007; Nunes and
Pawlak 2008), and also enhances energy dissipation, which thus increases nutrient
uptake of benthic communities (Hearn et al. 2001). Very little is known about the
causal mechanisms that link bathymetric morphology to biological distributions
and ecosystem function, but it is emerging that patterns of topographic complexity
quantified across a range of spatial scales provide useful proxies or surrogate
variables for predicting spatial distributions of fishes and corals (Pittman et al.
2007; Purkis et al. 2008, 2009; Hearn et al. 2001). Understanding the ecological
relevance of structural complexity is increasingly important because human
activity in the coastal zone, combined with hurricanes, marine diseases, and
thermal stress, have resulted in broad-scale loss and degradation of biogenic
structure created by reef forming scleractinian corals, seagrasses and mangroves.
Over the past 20 years, for example, coral reefs of the Caribbean region have
experienced a significant decline in coral cover (Gardner et al. 2003) resulting in a
‘flattening’ of the topographic complexity (Alvarez-Filip et al. 2009).
LiDAR-derived bathymetry provides a primary surface from which many
morphological derivatives (e.g., slope, aspect, curvature), including topographic
6 LiDAR Applications 151
complexity, can be modeled and quantified using surface morphometrics from the
fields of digital terrain modeling and industrial surface metrology. In these fields,
morphometrics are used to quantify geomorphological surface features and
irregularities or roughness in engineered surfaces, such as for quality control or
examination of damage (Pike 2001a, b). Pittman et al. (2009) examined seven
surface morphometrics and found that topographic complexity, particularly the
slope-of-slope (a measure of the maximum rate of maximum slope change),
emerged as the most useful predictor of faunal diversity and abundance across
Caribbean coral reef seascapes. Although some co-linearity existed between
morphometrics, the differences between them, even if only subtle, appeared to
matter when predicting faunal distributions (Fig. 6.4). Subsequently, Pittman and
Brown (2011) examined the interaction between topographic complexity and
across-shelf location in SW Puerto Rico and found improved predictive perfor-
mance in mapped habitat suitability for several key fish species associated with
Caribbean coral reef seascapes. LiDAR derived topographic complexity, for
example, contributed most to the spatial model of habitat suitability for threespot
damselfish (Stegastes planifrons), an important indicator species of live coral
cover, producing a highly reliable prediction (Fig. 6.5). Studies by Wedding and
Friedlander (2008) in Hawaii, and Walker et al. (2009) in Florida, have also found
useful predictability between LiDAR topographic complexity and fish metrics.
Variance in depth (within a 75 m radius) demonstrated the strongest relationships
with fish abundance and species richness, while depth and slope were also found to
be useful spatial pattern metrics (Wedding and Friedlander 2008). Walker et al.
(2008) reported a depth dependent relationship between topographic complexity
and species richness, which was more pronounced in shallow coral reefs, as well as
a correlation between topographic complexity and fish abundance, which was
strongest in deeper offshore coral reefs. With increasing concern over the struc-
tural collapse of coral reefs, studies are now underway using LiDAR bathymetry to
forecast the impact of declining reef complexity on habitat suitability for fish
species and diversity to provide advance warning on the potential consequences
for fish and fisheries that depend on coral reef structure (Pittman et al. 2011b).
Variations in topographic complexity can also be used to characterize differ-
ences between benthic habitat classes. Pittman et al. (2009) showed that in SW
Puerto Rico aggregated patch reefs had the greatest proportion of high slope-of-
slope, followed by spur and groove; whereas the largest areal extent of high slope-
of-slope was quantified for the more common class of colonized pavement with
sand channels. These habitat classes were correspondingly found to support the
highest live coral cover and fish species richness values (Pittman et al. 2009). For
the Florida reef tract, Zawada and Brock (2009) quantified topographic complexity
using the fractal dimension (D) and found spatial patterns in D were positively
correlated with known reef zonation in the area, and consistent with physical
processes operating on the reef geomorphology, such as erosion and sea-level
dynamics. In similar studies using multibeam data from the Caribbean island of
152 S. J. Pittman et al.
Navassa, the highest fractal dimensions were quantified in areas characterized by
highest live coral cover (Zawada et al. 2010).
The high predictability of marine fauna across complex coral reef ecosystems
using LiDAR derivatives indicates that LiDAR is a useful tool for rapidly and cost-
effectively gathering broad scale data in support of conservation planning,
Fig. 6.4 Profiles for individual morphometrics at 1 m intervals along a 500 m transect across a
coral reef seascape in the La Parguera region of southwestern Puerto Rico. To examine scale
effects the seven morphometrics were calculated at multiple spatial scales using circular
neighborhoods of 4, 50 and 200 m radii (adapted from Pittman et al. 2009)
6 LiDAR Applications 153
designing targeted monitoring activities, and for improving our ecological
understanding of coral reef ecosystems. Nevertheless, a general consensus from
these studies is that finer-scale in situ measurements of topographic complexity
were more strongly correlated with fish variables than LiDAR-derived variables
(Wedding and Friedlander 2008; Pittman et al. 2009; Walker et al. 2009). This
suggests that finer resolution LiDAR may be required to boost the predictive
power of remotely sensed topographic complexity.
Fig. 6.5 Model of predicted habitat suitability for a potential indicator species of coral health,
the threespot damselfish (Stegastes planifrons), across the coral reef seascapes of southwestern
Puerto Rico. Maximum Entropy Distribution Modeling (MaxEnt) determined that LiDAR
derived slope-of-slope together with distance across the shelf were the most important spatial
predictors (adapted from Pittman and Brown 2011)
154 S. J. Pittman et al.
6.2.4 Marine Protected Area Planning
Effective implementation of coastal and marine spatial planning (CMSP) relies on a
comprehensive geospatial framework. For example, planning units are typically
discrete geographic locations or zones that may have particular characteristics of
interest and are considered to be ‘place-based’ (Norse et al. 2005; Olsen et al.
2010). In the marine environment, marine protected areas (MPAs) are among the
most widely implemented forms of place-based management (Lorenzen et al.
2010). One of the critical first steps in CMSP involves mapping and integrating
biological and physical datasets (Douvere 2008; Pittman et al. 2011a). This method
has been successful in marine planning and spatial conservation prioritization
efforts worldwide (Sala et al. 2002; Friedlander et al. 2003; Jordan et al. 2005).
Presented here is an example of marine spatial planning in Hawaii, where
LiDAR technology was applied to assist in the spatial characterization of complex
habitats to inform marine conservation planning and evaluation. In the Main
Hawaiian Islands, SHOALS data was utilized to spatially characterize habitat
complexity across a broad range of nearshore coral reef ecosystems. An initial
pilot study was first conducted in Hanauma Bay Marine Life Conservation District
(MLCD) to determine the utility of LiDAR data to quantify complexity in a
contiguous reef environment (Wedding et al. 2008). Digital maps of surface
rugosity were produced at 4 9 4 m resolution for the purpose of characterizing
fish habitat utilization patterns inside and outside of marine protected areas
(Wedding et al. 2008; Friedlander et al. 2007b, 2010; Fig. 6.6). Results indicated
that LiDAR-derived rugosity was significantly correlated with in situ chain-tape
rugosity, as measured by obtaining the ratio of the length of a chain laid across the
bottom along a transect line to the linear distance of the transect line (Wedding
et al. 2008). The initial study was also used to examine MPA configuration and
design in order to assess the range of habitat characteristics, such as water depth
Fig. 6.6 Hanauma Bay
Marine Life Conservation
District pilot study site for
evaluation of USACE
SHOALS LiDAR technology
for measuring coral reef
habitat complexity. Lidar-
derived rugosity was
calculated by obtaining the
ratio of seascape surface area
to the planimetric area in a
neighborhood analysis
6 LiDAR Applications 155
and habitat complexity, and mosaic of interconnected habitat types present in the
MPA. The application of LiDAR was then expanded in Hawaii to assist NOAA in
the evaluation of MPAs throughout the State (Friedlander et al. 2010). LiDAR data
was used to spatially characterize and quantify the three-dimensional seafloor
structure within each MPA (Friedlander et al. 2010). Here we highlight the results
from the MLCDs on the island of Oahu, where LiDAR-derived depth and slope-of-
slope were summarized to calculate the mean, standard deviation and range of
values within each MLCD boundary (Table 6.2; Fig. 6.7).
Waikiki MLCD. The Waikiki MLCD, located on the South Shore of Oahu, has a
very small depth range (0–5 m) and relatively low habitat complexity (Friedlander
et al. 2010), but Williams et al. (2006) reported fish biomass of target species in the
Waikiki MLCD was twice that of the adjacent area. Meyer and Holland (2005)
conducted a study of bluespine unicornfish (Naso unicornis) movements using
acoustic tracking and found the habitat utilization patterns were aligned with
topographically complex features on the fringing reef (e.g., the reef crest). So for a
large bodied surgeonfish, such as N. unicornis, this small (0.34 km
) MPA pro-
vides effective protection because their general home ranges are contained within
the MPA boundary (Meyer and Holland 2005). It also suggests that there is an
appropriate range of depth and habitat complexity within the MPA boundary for
protection of this species.
Hanauma Bay MLCD. In the Hanauma Bay MLCD, the depth range (0–28 m)
is much greater than in the Waikiki MLCD and the protected area shelters more
diverse benthic habitat types with a wide range of structural complexity (Fig. 6.7;
Friedlander et al. 2010). The fish assemblage within Hanauma Bay MLCD
boundary was found to harbor eight times the biomass, and shelter a greater
number of large-bodied fish species, compared to other adjacent open access areas
(Friedlander et al. 2006, 2007a, b). In Hanauma Bay, LiDAR-derived rugosity was
found to be a statistically significant predictor of fish biomass at multiple spatial
scales (4, 10, 15, 25 m) (Wedding et al. 2008). This MLCD offers physical pro-
tection to fishes in the form of structurally complex habitat in the absence of
fishing, which combines to support the high fish biomass.
Pupukea MLCD. Pupukea MCLD was originally established in 1983, and later
expanded in 2003 to include a significantly greater area of the seascape
([6 9 larger area), with a greater depth (e.g., 12–17 m) and habitat range (e.g.,
Table 6.2 Summary of LiDAR derived depth and habitat complexity for Marine Life Conser-
vation Districts (MLCDs) on Oahu, Hawaii based on bathymetric grids
MLCD Established Depth (m) Habitat complexity
Mean SD Range Mean SD Range
Pupukea 1983
8.1 4.2 0.0–16.9 29.9 21.8 0–84.7
Hanauma bay 1967 8.6 6.7 0.1–27.7 18.8 17.6 0–80.3
Waikiki 1988 2.1 1.2 0.0–5.0 7.5 8.6 0–64.6
Habitat complexity represented by slope-of-slope, and table values are percent
Pupukea MLCD was originally established in 1983 and the boundaries were modified in 2003.
Data in the above table were calculated based on the 2003 boundary
156 S. J. Pittman et al.
inclusion of deeper coral rich habitat and sand channels) (Friedlander et al. 2010;
Fig. 6.7). NOAA Biogeography Branch benthic habitat maps were utilized to
compare the change in biological cover within the expanded Pupukea MLCD
boundary following the MLCD expansion (Friedlander et al. 2010). By coupling
these habitat maps with the LiDAR data it was evident that the 1983 MLCD
protected a very small depth range that was dominated by macroalgae. After the
boundary expansion in 2003, the LiDAR data characterized a greater depth range,
and the NOAA benthic habitat maps demonstrated the MLCD now protected
deeper coral-rich habitat and large sand channels. With the inclusion of deeper
coral habitats in Pupukea MLCD, a NOAA fish-habitat utilization study found that
there was a greater diversity and biomass of fishes protected within this new
reserve boundary (Friedlander et al. 2010).
These studies indicate that LiDAR data can prove useful towards identifying
depth range, habitat complexity, and identify natural borders or corridors for fish
movement in order to reduce the possibility of fish home ranges extending outside
MPA boundaries. It also reveals that remotely sensed LiDAR data can be effec-
tively combined with acoustic fish tracking (see Chap. 8), and other fish-habitat
utilization information, as well as benthic habitat maps, to design boundary
alternatives that support the optimal placement of marine protected areas.
Fig. 6.7 Map of LiDAR derived depth for marine life conservation districts (MLCDs) on Oahu,
Hawaii: a Pupukea MLCD, b Waikiki MLCD, c Hanauma bay MLCD
6 LiDAR Applications 157
6.2.5 Marine Geology
There are extensive knowledge gaps related to marine geomorphology since only
approximately 10 % of the world’s seafloor has been mapped from air and ship-
borne sensors (Sandwell et al. 2003). Airborne laser altimetry has recently been
applied to map marine geomorphology and enhance the understanding of coastal
geomorphic processes (Sallenger et al. 2003; Brock et al. 2004; Brock and Purkis
2009; Chust et al. 2010). Coral reef geomorphology is a result of the unique
oceanographic and geological conditions distinct to each geographic location, and
the complex morphology of coral reefs can be mapped at high resolution across a
broad spatial extent using LiDAR. A number of studies have demonstrated the
utility of LiDAR technology for collecting quantitative data sets on coastal geo-
morphological systems (Sallenger et al. 2003; Liu et al. 2007) and in mapping
geomorphic structure in shallow coral reef environments (Storlazzi et al. 2003;
Finkl et al. 2005, 2008; Banks et al. 2007; Purkis and Kohler 2008). In this section,
we present a case study of the application of LiDAR technology to understand the
processes that shaped a large fringing reef tract in South Molokai (Fig. 6.8).
LiDAR technology provided three dimensional data sets in the form of DEMs to
allow for the enhanced interpretation of geological processes that shaped coral reef
morphological development (Field et al. 2008; Storlazzi et al. 2008).
Fig. 6.8 a Shaded relief map of SHOALS LiDAR bathymetry overlaid with 2 m contours.
b Example of shore-parallel bathymetric profile along the 10 m isobath (bold white line in a)
(adapted from Storlazzi et al. 2003, courtesy USGS)
158 S. J. Pittman et al.
Field et al. (2008) utilized SHOALS LiDAR data combined with NOAA aerial
photographs from the island of Molokai to study shallow-water coral reef devel-
opment and response to sedimentation. The study area included a 40 km fringing
coral reef located on the southern coast of the island of Molokai in the main
Hawaiian Islands. Molokai’s south shore is well protected from storm surge and
wave energy and this has allowed for the development of one of the largest
continuous fringing reefs in Hawaii. In addition, the steep terrestrial slopes and
extensive runoff of upland soils has impacted coral reefs along the south shore.
The fusion of aerial photography (2D) and bathymetric LiDAR (3D) were sup-
plemented with in situ observations to infer linkages between the morphological
patterns in reef structure and the coastal processes that shaped this reef tract. For
instance, the LiDAR data highlighted a pronounced channel in the fringing reef off
the coast that was formed from stream erosion during a period of lower sea-level
(Fig. 6.9; Field et al. 2008; Storlazzi et al. 2008).
Fig. 6.9 The coastal area at Palaau is characterized by an extensive mud and salt flat (1) that
formed from heavy flooding and run-off in the early 1900s and an extensive mangrove forest (2)
that was started in 1903 to curb the heavy sediment run-off. The elongated structure (3) east of the
mangroves is a shrimp farm. The reef at Palaau is dissected by a meandering channel (4) that
resulted from erosion during a period of lower sea-level ([12,000 years ago). Note that the reef is
not breached at the end of the channel (5), possibly because the water flowed through the porous
reef rather than over it. East of the channel the reef flat is a broad, barren surface (6) covered by
thin deposits of muddy sand. The middle part of the reef is characterized by large pits (7), which
likely result from extensive, long-term karstic dissolution by fresh water flowing through the reef
(adapted from Field et al. 2008, courtesy USGS)
6 LiDAR Applications 159
The fusion of LiDAR and aerial photography also highlighted an extensive,
shallow reef flat (\2 m) with scattered deep, sediment filled pits (i.e., blue holes,
\25 m in depth; Fig. 6.10). Many of the blue holes were found to be correlated
with onshore drainage and it is hypothesized that these patterns may have been
produced during sea-level low-stands from either freshwater-induced (karst) dis-
solution, or stream incision. The morphology of spur-and-groove structures on the
fringing reef was defined from the LiDAR DEMs and a series of depth profiles
taken along transects running perpendicular to shore and used to quantify the
broader scale (1–10 km) morphology of the reef structure. The LiDAR depth
profiles identified extensive reef flats (extending[1,200 m offshore) along the well
protected, central portion of the fringing reef complex, but along the eastern and
western ends of the south shore no shallow reef flat was identified (Storlazzi et al.
2008, 2003).
Beyond the Molokai case study, the application of LiDAR technology has
supported the identification and mapping of coral reef geomorphology in a number
of other locations (Brock et al. 2006, 2008; Banks et al. 2007; Finkl et al. 2005,
2008). EAARL LiDAR in the Florida Keys, for example, was utilized to quantify
morphologic differences in patch reef systems and to interpret fluctuating sea-level
conditions in the Holocene based on two stages of reef accretion (Brock et al.
2008). The LiDAR-derived DEMs assisted in identifying two morphologically
Fig. 6.10 Example of ‘blue holes’ on the reef flat in Molokai: a air photo shows the dark blue
color of the water in a blue hole off Kakahaia, b SHOALS LiDAR bathymetry of the same area
(adapted from Storlazzi et al. 2008, courtesy USGS)
160 S. J. Pittman et al.
different patch-reef populations, and infer differences from changing sea-level
regimes during the Early versus Late Holocene (Brock et al. 2008).
Other types of active remote sensors (i.e., acoustic systems; Chaps. 810) are
available to map coral reef geomorphology, and may be the only viable option for
mapping the seafloor in turbid and/or deep ([30 m) water. However, in other
situations (e.g., in clear, shallow waters), LiDAR can be more time and cost
efficient at certain spatial resolutions (C4 9 4 m), allowing for large areas of
shallow and emergent seafloor to be rapidly mapped (Costa et al. 2009). With the
increasing construction of LiDAR sensors and the lowering cost of data acquisition
combined with opportunities for data fusion (e.g., hyperspectral; Chap. 7), LiDAR
is becoming a viable technology for a wide range of geomorphological studies. For
instance, in the Molokai case study, the fusion of LiDAR and aerial imagery
provided enhanced information about marine geomorphology in the coastal
environment (Field et al. 2008; Storlazzi et al. 2008, 2003). Walker et al. (2008)
similarly combined aerial photography and laser bathymetry to map coral reefs,
but also integrated acoustic ground discrimination and sub-bottom profiling into a
GIS environment to support mapping efforts. In addition, bathymetric DEMs
produced using the Hawk Eye LiDAR system in Spain were combined with
multispectral imagery to enhance coastal habitat classification and mapping efforts
(Chust et al. 2010). Understanding the geomorphology of coral reefs from a three-
dimensional perspective, and across a range of spatial scales, offers great potential
to advance our knowledge of the functional linkages between geomorphic struc-
ture and ecological processes.
6.2.6 Coastal Sediment Management
LiDAR supports engineering projects by acquiring seamless topographic eleva-
tions and seafloor depths, which can be used to calculate relative sediment area for
regional sediment management. The goals of coastal sediment management are to
increase efficiency of dredging operations through an understanding of coastal
processes, and to provide a regional context for coastal projects so that they can be
managed as a system of projects, rather than individual projects (Wozencraft and
Millar 2005). The Regional Sediment Management Demonstration Program
(RSMDP) has provided opportunities to show how broad scale, high resolution,
bathymetric and topographic data can be used to identify sediment transport
pathways and to reliably calculate spatial distribution of relative sediment volumes
for regional sediment budgets (Wozencraft and Irish 2000). The RSMDP
encompasses 360 km of shoreline in the Gulf of Mexico, stretching from Dauphin
Island, Alabama east to Apalachicola Bay, Florida. In this area, approximately five
million topographic and bathymetric LiDAR soundings were collected using the
SHOALS system from 1995 to 2000. The SHOALS system was developed by
USACE in the early 1990s as a tool for monitoring near-shore marine environ-
ments and later for coastal terrestrial environments. The SHOALS system is made
6 LiDAR Applications 161
up of two parts: the airborne system and the ground-processing system. The air-
borne system uses a 400 Hz Nd: YAG infrared (1,064 nm) and a blue-green
(532 nm) laser transmitter with five receiver channels. The infrared frequency
measures the sea surface distance at nadir, while the blue-green frequency scans
below the sortie to measure marine depths and/or terrestrial elevations. SHOALS
can be mounted on a variety of aircraft, and is usually operated at an altitude of
200–400 m and speed of 117–140 knots. This configuration allows for data col-
lection with a horizontal spot spacing of 4 m in a 100–300 m swath below the
In support of the RSMDP, several SHOALS surveys near Destin, Okaloosa
County, Florida were analyzed (Wozencraft and Irish 2000). In Destin, a navigable
depth of 4.3 m is authorized by the federal government for the tidal inlet of East
Pass, which connects Choctawhatchee Bay and the Gulf of Mexico. The first
surveys followed Hurricane Opal in 1995, which caused significant sediment in-
filling throughout the entire inlet system. The LiDAR survey detected this infilling,
and illustrated the need to dredge sand from the navigation channel, nourish
eroded adjacent beaches, and use it to repair breaches of Norriego Point. The
subsequent surveys occurred in 1996 and later, in 1997, to document the repair of
jetties along the mouth of the inlet. Additional rock was used to rebuild these
jetties, which were washed away by the storm surges of Hurricane Opal. This
survey also detected additional breaches of Norriego Point, despite previous efforts
to restore it using dredged material. By comparing the different depth surfaces
through time, the USACE was able to understand the morphological changes that
were taking place in this dynamic environment (Fig. 6.11). These depth surfaces
were also used to compute sediment volumes that were lost and gained during this
two year time period, allowing engineers to quantify the sediment budget of the
inlet and begin to explain the transport mechanisms (e.g., waves, tides, currents,
wind, etc.) driving this exchange of material.
The USACE has invested in data collection to support regional sediment
management by establishing the National Coastal Mapping Program (Wozencraft
and Lillycrop 2006). Using the NAVOCEANO CHARTS system, topographic
lidar, bathymetric lidar, aerial photography, and hyperspectral imagery are col-
lected around the coast of the U.S. on a re-occuring schedule to provide the repeat,
high-resolution, high-accuracy data needed to perform these analyses for all US-
ACE coastal projects (Reif et al. 2012).
6.2.7 Risk Assessment and Environmental Change
Climate change threatens coral reef ecosystems in several ways. Rising ocean
temperatures and increasing ocean acidification levels, in particular, may lead to
mass coral bleaching events and disease epidemics (Hoegh-Guldberg 2007). Cli-
mate change also threatens the livelihoods of communities that depend on coral
reef ecosystems, by altering the capacity to provide ecosystem goods and services,
162 S. J. Pittman et al.
as well as by threatening to inundate low lying areas as sea levels rise and storm
events intensify. Technologies such as LiDAR can help assess the risks of flooding
in the coastal zones by allowing governments to design, plan, implement and
evaluate climate change mitigation and adaptation strategies.
One such LiDAR project is the Future Coasts Program in Australia conducted
by the Victoria State Government Department of Sustainability and Environment
(VicDSE) ( to prepare Australia’s
coasts for the effects of climate change as well as manage and mitigate the long
term risks to coastal communities and natural environments (Sinclair and Quadros
2010). High resolution topographic and bathymetric information was needed to
assess the effects of rising sea levels which could lead to significant changes to the
coastline of Australia. This topographic and bathymetric information was collected
at 2.5–5 m horizontal resolution using two LiDAR sensors (LADS Mk II and
Hawk Eye II). The LADS Mk II system mapped the entire coastline 100 m inland
from the vegetation line offshore to the 20 m isobath. The Hawk Eye II system
mapped certain small bays and inlets to about 10 m in depth. The datasets from the
two systems were later integrated to create a seamless topographic/bathymetric
surface for the entire Victorian coastline. This seamless surface is currently being
Fig. 6.11 LiDAR collected in Destin, Okaloosa County, Florida by the U.S. Army Corps of
Engineers. This dataset was used to describe the sediment budget (i.e., erosion and accretion of
sand) to inform dredging operations in the East Pass navigable waterway
6 LiDAR Applications 163
used by the VicDSE to model coastal flooding from storm surge events, assess the
areas that are at risk, manage future development along the coasts and determine
effective prevention measures.
In addition to sea-level rise, LiDAR products can also be used to assess the
effects of tsunamis and storm surges (Brock and Purkis 2009b; Gesch 2009).
LiDAR systems provide the accurate, high-resolution data sets that are necessary
to evaluate the vulnerability of coastal areas to inundation (Stockdon et al. 2009).
For example, dune elevations have been extracted from LiDAR data to evaluate
the vulnerability of barrier island beaches to hurricanes (Stockdon et al. 2009).
Recurrent LiDAR surveys support volumetric change analysis (White and Wang
2003) and repeat coastal surveys after major storm events can be used to monitor
the magnitude of coastal change and evolution (Liu et al. 2010). LiDAR is also
applied to subtidal regions to quantify change in habitat type and calculate
transport of sediment or sand. Conger et al. (2009a) utilized QuickBird imagery
and SHOALS LiDAR data to identify and characterize sand deposit distribution on
a fringing reef in Oahu (Fig. 6.12). Sand is an important component of coral reef
ecosystems and is a highly dynamic substrate type (Conger et al. 2009a) especially
considering accretion rates of reef building corals (e.g., 0–2 mm/year in Hawaii;
Grigg 1982, 1998). This study found that sand deposits in the fringing reef
environment were strongly controlled by morphology and to a lesser degree by
wave action and hydrodynamic energy (Conger et al. 2009b). Finkl et al. (2005)
Fig. 6.12 LiDAR map of sand distribution on the South shore of Oahu, Hawaii. Sand bodies are
denoted by red polygons
164 S. J. Pittman et al.
has similarly inferred linkages between coastal processes (e.g., wave transforma-
tion patterns and beach morphodynamics) and geomorphic pattern in the seabed
morphology in southeast Florida. Identifying this relationship between coastal
processes and geomorphic patterns using high resolution LiDAR data is an
important step in the field of marine geology.
Tsunami modeling predicts which coastal areas will be inundated in the event
of a tsunami. LiDAR data provides high resolution continuous seafloor depths and
topographic elevations, which can be used to simulate tsunami propagation and
inundation along the coastline. These high resolution surfaces are needed in order
to realistically model the non-linear wave dynamics of coastal inundation (Gon-
zález et al. 2005; Venturato 2005), because even small variations in nearshore
depths, coastlines and topography can affect the behavior of a tsunami (Tang et al.
2006). In the United States, tsunami inundation predictions and evacuation plan-
ning fall under the responsibility of NOAA’s two Tsunami Warning Centers. The
West Coast and Alaska Tsunami Warning Center (WC/ATWC) is located in
Palmer, Alaska and is responsible for issuing tsunami warnings for the west and
east coasts of North America. The Pacific Tsunami Warning Center (PTWC) is
located in Honolulu, Hawaii and is responsible for issuing warnings for most of the
countries bordering the Pacific Ocean (under the auspices of the UNESCO/IOC
International Coordination Group for the Pacific Tsunami Warning System). In
2006, a new site was proposed for the PTWC on Ford Island in Pearl Harbor.
Before the center’s relocation, the vulnerability of the site to inundation by a
tsunami was assessed using a seamless topographic/bathymetric digital elevation
model (Tang et al. 2006). Several datasets were used to create this DEM, including
two LiDAR datasets. One LiDAR dataset was collected by the Joint Airborne
LiDAR Bathymetry Technical Center of Expertise (JALBTCX) at 1–5 m hori-
zontal resolution using the SHOALS system. The other LiDAR dataset was col-
lected by NOAA’s Coastal Services Center (CSC) at a 3 m horizontal resolution
using the Leica ALS-40 Aerial LiDAR system. Together, these surfaces (and
several acoustic datasets) were combined to create a 10 m resolution digital ele-
vation model for Pearl Harbor in Honolulu. Tsunami waveforms were modeled at
16 distinct points (Fig. 6.13) in order to evaluate the potential impacts on Pearl
Harbor. Tang et al. (2006) concluded that none of the 18 modeled tsunami sce-
narios, or the past recorded tsunami events, have caused inundation at the proposed
NOAA site on Ford Island, Oahu. The NOAA building site on Ford Island is
situated at 3.0 m above mean high water level (MHW) and all of the modeled
tsunami scenarios were less than 1.5 m above MHW.
Airborne LiDAR systems have also been widely applied to map shorelines,
understand coastal geomorphology, and support change detection (Brock and
Purkis 2009). Shoreline information is critical for coastal geomorphologists to
quantify coastal erosion, accretion and estimate sediment transport budgets (Liu
et al. 2007). Traditionally, shoreline extraction for accurate maps was done using
in situ surveys and aerial photography interpretation (Morton et al. 2005). The
LiDAR-derived shorelines, however, can be explicitly referenced to the tidal
datum surface and therefore represent a great improvement from using the beach
6 LiDAR Applications 165
line on aerial photographs as the shoreline proxy (Liu et al. 2007). Beyond
shoreline extraction, DEMs support the three dimensional visualization of coast
habitat and volumetric change analysis in these systems (Zhang et al. 2009). For
instance, DEMs produced from LiDAR data have been used to study geomor-
phological change in coastlines and barrier islands (White and Wang 2003).
Further, LiDAR-derived metrics have been applied to establish a relationship
between coastal erosion and accretion with beach morphology. Saye et al. (2005),
for example, found that LiDAR characterized eroding dunes commonly located in
association with steep-sloping, narrow beaches and that accreting dunes were
found adjacent to low-sloping, wide beaches.
6.3 Future Directions in LiDAR
6.3.1 Integration with Other Sensors
In the last decade, research in data fusion and integration techniques has grown
with access to multi-resolution, multi-temporal and multi-frequency datasets (Pohl
Fig. 6.13 Map denoting the 16 tsunami inundation modeling locations overlaid on a digital
elevation model generated partly from LiDAR depths and elevations (adapted from Tang et al. 2006)
166 S. J. Pittman et al.
and Van Genderen 1998). Remotely sensed imagery collected using different
sensors can be fused into integrated analysis approaches to glean additional
information than otherwise could be extracted from the individual images on their
own. LiDAR data has been integrated with a variety of sensors, including multi-
spectral (Cochran-Marquez 2005; Chust et al. 2008; Walker 2009) and hyper-
spectral sensors (Lee 2003; Chap. 7), in order to improve the classification of
nearshore coral reefs and improve hydrographic surveying (Smith et al. 2000). In
addition to multispectral and hyperspectral sensors, LiDAR data has also been
integrated with imagery from acoustic sensors (Tang et al. 2009; Walker et al.
2008). In particular, in the Walker et al. (2008) study, shallow-water (\35 m)
benthic habitat maps were developed for areas offshore of Broward County,
Florida by integrating LiDAR with aerial photography and two types of acoustic
information: acoustic ground discrimination systems (AGDS) and sub-bottom
profilers. Habitats were defined by their geographic location, geomorphologic
characteristics and biological communities. The LiDAR data, collected using the
LADS system, was used primarily to map the location and geomorphology of
seafloor features. The final habitat map had an overall thematic accuracy of
89.6 %. Given the importance of habitat maps, it is essential to extract as much
information about the seafloor as possible from the imagery. The fusion and
integration of LiDAR with different sensors offers new ways for extracting this
information, and ultimately, to better understand the benthic marine environment.
6.3.2 Deployment on Different Platforms
In addition to being mounted on piloted airplanes, LiDAR systems can also be
mounted on ground vehicles, unmanned aerial vehicles (UAVs) or integrated with
satellites. For example, the Ice, Cloud, and Land Elevation Satellite (ICESat)
collected laser altimetry data that was used primarily to describe ice sheet mass
balance until it went out of operation in 2009. It is scheduled to be replaced in
2016 by ICESat-2. Such LiDAR systems are also used to measure chemical
concentrations (e.g., ozone, water vapor and other pollutants; Fig. 6.14; Engel-Cox
et al. 2006) as well as wind speeds at different altitudes in the atmosphere
(Gentry et al. 2000) based on the backscattered return and the Doppler shift effect
(Baker et al. 1995). For instance, the Cloud-Aerosol LiDAR Infrared Pathfinder
Satellite Observations (CALIPSO) is providing new opportunities to study clouds
and aerosols, which are important because they have direct effects on the radiation
balance of the Earth (Ramanathan et al. 2001), making them relevant to coral
bleaching studies and the future of coral reef ecosystems. If cloud cover were to
decrease during the summer months, shallow-water corals would be at higher risk
for bleaching, as was the case with the 1983 bleaching event in Indonesia, which
followed windless and cloudless conditions (Brown and Suharzono 1990).
Consequently, space-based LiDAR systems may prove to be a valuable tool in a
6 LiDAR Applications 167
resource manager’s toolbox for predicting and responding to bleaching events that
will affect the health of the coral reef ecosystems.
6.4 Conclusion
This chapter highlighted LiDAR applications that have successfully integrated this
remote sensing technology for navigational charting, engineering, benthic habitat
mapping, ecological modeling, marine geology and environmental change detec-
tion in coral reef ecosystems. These LiDAR applications demonstrated the depth
and breadth of applications to support research and mapping efforts on coral reefs
and surrounding ecosystems. Several case studies were described in greater detail
to demonstrate the utility of LiDAR technology to address specific research goals
and to illustrate the potential for wider applications. Understanding the geomor-
phology of coral reefs from a three-dimensional perspective using LiDAR offers
great potential to advance our knowledge of the functional linkages between
geomorphic structure and ecological processes in the marine environment. Further,
seamless land topography and marine bathymetry DEMs are now becoming
available, providing a valuable opportunity for the development of models that
quantify land-sea interactions. The future directions of LiDAR applications
involve mounting LiDAR sensors on alternative platforms, fusing LiDAR with
Fig. 6.14 LiDAR image depicting high-level (*4 km) smoke in the atmosphere (adapted from
Engel-Cox et al. 2006)
168 S. J. Pittman et al.
other high resolution imagery to further enhance the information on coral reef
structure, and exploiting the information that can be derived from LiDAR-derived
seafloor intensity surfaces. In the future, as the technology advances, and research
efforts continue to refine signal processing techniques and algorithms, the capa-
bilities and products that can be derived from LiDAR will similarly improve and
Acknowledgments This chapter was made possible with contributions from Tim Battista
(NOAA Biogeography Branch), Alan M. Friedlander (University of Hawaii/USGS), Curt D.
Storlazzi (USGS), Michael E. Field and (USGS) and Christopher L. Conger. Support for the
authors was provided by NOAA’s Coral Reef Conservation Program.
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... Airborne bathymetric LiDAR, which can achieve spatial resolutions of ≤1 m and vertical resolutions of ≤15 cm (Brock et al., 2004;Zawada & Brock, 2009), is emerging as the premier technology for creating high-resolution digital terrain models for coral reefs and other shallow-water environments (Brock et al., 2004;Costa et al., 2009;Pittman et al., 2013;Purkis & Kohler, 2008;Walker et al., 2008;Zawada & Brock, 2009). However, the cost of LiDAR is still prohibitive for large-scale surveys, although ICESat-2 might eventually make LiDAR bathymetry more accessible (Forfinski-Sarkozi & Parrish, 2016). ...
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Satellite imagery offers an efficient and cost-effective means of estimating water depth in shallow environments. However, traditional empirical algorithms for calculating water depth often are unable to account for varying bottom reflectance, and therefore yield biased estimates for certain benthic environments. We present a simple method that is grounded in the physics of radiative transfer in seawater, but made more robust through the calibration of individual color-to-depth relationships for separate spectral classes. Our cluster-based regression (CBR) algorithm, applied to a portion of the Great Bahama Bank, drastically reduces the geographic structure in the residual and has a mean absolute error of 0.19 m with quantified uncertainties. Our CBR bathymetry is 3–5 times more accurate than existing models and outperforms machine learning protocols at extrapolating beyond the calibration data. Finally, we demonstrate how comparison of CBR with traditional models sensitive to bottom type reveals the characteristic length scales of biosedimentary facies belts.
... Water depth and the topographical complexity of the sea floor were quantified at multiple spatial scales from a high resolution (3 × 3 m) bathymetric terrain model derived from airborne hydrographic Light Detection and Ranging (LiDAR). The LiDAR sensor measures the difference in the time of reflectance for pulses of high-energy laser to return to the aircraft from the sea floor and the water surface to estimate water depth (Pittman, Costa, & Wedding, 2013). Topographical complexity was measured by applying a slope-of-the-slope morphometric to the digital terrain using a geographical information system (GIS). ...
Geographical patterning of fish diversity across coral reef seascapes is driven by many interacting environmental variables operating at multiple spatial scales. Identifying suites of variables that explain spatial patterns of fish diversity is central to ecology and informs prioritization in marine conservation, particularly where protection of the highest biodiversity coral reefs is a primary goal. However, the relative importance of conventional within-patch variables versus the spatial patterning of the surrounding seascape is still unclear in the ecology of fishes on coral reefs. A multi-scale seascape approach derived from landscape ecology was applied to quantify and examine the explanatory roles of a wide range of variables at different spatial scales including: (i) within-patch structural attributes from field data (5 × 1 m2 sample unit area); (ii) geometry of the seascape from sea-floor maps (10–50 m radius seascape units); and wave exposure from a hydrodynamic model (240 m resolution) for 251 coral reef survey sites in the US Virgin Islands. Non-parametric statistical learning techniques using single classification and regression trees (CART) and ensembles of boosted regression trees (TreeNet) were used to: (i) model interactions; and (ii) identify the most influential environmental predictors from multiple data types (diver surveys, terrain models, habitat maps) across multiple spatial scales (1–196,350 m2). Classifying the continuous response variables into a binary category and instead predicting the presence and absence of fish species richness hotspots (top 10% richness) increased the predictive performance of the models. The best CART model predicted fish richness hotspots with 80% accuracy. The statistical interaction between abundance of living scleractinian corals measured by SCUBA divers within 1 m2 quadrats and the topographical complexity of the surrounding sea-floor terrain (150 m radius seascape unit) measured from a high-resolution terrain model best explained geographical patterns in fish richness hotspots. The comparatively poor performance of models predicting continuous variability in fish diversity across the seascape could be a result of a decoupling of the diversity-environment relationship owing to structural degradation leading to a widespread homogenization of coral reef structure.
... Recent progress in developing remote-sensing systems such as light detection and ranging (LiDAR) for shallow-water mapping has allowed collection of high-quality bathymetric data in often difficult to survey areas (Smith & Sandwell 1997;Wang & Philpot 2007). Seafloor mapping data have applications that often extend beyond the initial scope of physiographic surveys, thereby maximising the value of mapping programmes worldwide (Ierodiaconou et al. 2011;Brown et al. 2012;Pittman et al. 2013). This information can be particularly useful for the assessment of benthic species that show affinity towards specific terrain characteristics and may provide a valuable opportunity to understand better population distribution patterns, ecological resilience and seascape factors that drive productivity rates and stock availability in commercial shell fisheries. ...
Infectious pathogens figure prominently among those factors threatening marine wildlife. Mass mortality events caused by pathogens can fundamentally alter the structure of wild fish stocks and depress recruitment rates and yield. In the most severe instances, this can precipitate stock collapses resulting in dramatic economic losses to once valuable commercial fisheries. An outbreak of a herpes-like virus among commercially fished abalone populations in the south-west fishery of Victoria, Australia, during 2006-2007, has been associated with high mortality rates among all cohorts. Long-term records from fishery-independent surveys of blacklip abalone Haliotis rubra (Leach) enabled abundance from pre- and post-viral periods to be analysed to estimate stock density and biomass. The spatial distribution of abundance in relation to physical habitat variables derived from high-resolution bathymetric LiDAR data was investigated. Significant differences were observed in both measures between pre- and post-viral periods. Although there was some limited evidence of gradual stock improvement in recent years, disease-affected reefs have remained below productivity rates prior to the disease outbreak suggesting a reduction in larval availability or settlement success. This was corroborated by trends in sublegal sized blacklip abalone abundance that has yet to show substantial recovery post-disease. Abundance data were modelled as a function of habitat variables using a generalised additive model (GAM) and indicated that high abundance was associated with complex reef structures of coastal waters (<15 m). This study highlights the importance of long-term surveys to understand abalone recovery following mass mortality and the links between stock abundance and seafloor variability.
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Remote sensing is playing an increasingly important role in the monitoring and management of coastal regions, coral reefs, inland lakes, waterways, and other shallow aquatic environments. Ongoing advances in algorithm development, sensor technology, computing capabilities, and data availability are continuing to improve our ability to accurately derive information on water properties, water depth, benthic habitat composition, and ecosystem health. However, given the physical complexity and inherent variability of the aquatic environment, most of the remote sensing models used to address these challenges require localized input parameters to be effective and are thereby limited in geographic scope. Additionally, since the parameters in these models are interconnected, particularly with respect to bathymetry, errors in deriving one parameter can significantly impact the accuracy of other derived parameters and products. This study utilizes hyperspectral data acquired in Hawaii in 2000–2001 and 2017–2018 using NASA’s Classic Airborne Visible/Infrared Imaging Spectrometer to evaluate performance and sensitivity of a well-established semi-analytical inversion model used in the assessment of coral reefs. Analysis is performed at several modeled spatial resolutions to emulate characteristics of different feasible moderate resolution hyperspectral satellites, and data processing is approached with the objective of developing a generalized, scalable, automated workflow. Accuracy of derived water depth is evaluated using bathymetric lidar data, which serves to both validate model performance and underscore the importance of image quality on achieving optimal model output. Data are then used to perform a sensitivity analysis and develop confidence levels for model validity and accuracy. Analysis indicates that derived benthic reflectance is most sensitive to errors in bathymetry at shallower depths, yet remains significant at all depths. The confidence levels provide a first-order method for internal quality assessment to determine the physical extent of where and to what degree model output is considered valid. Consistent results were found across different study sites and different spatial resolutions, confirming the suitability of the model for deriving water depth in complex coral reef environments, and expanding our ability to achieve automated widespread mapping and monitoring of global coral reefs.
A key requirement for managing commercial fisheries is understanding the geographic footprint of the resource, the level of exploitation and the potential impacts of changing climate or habitat conditions. The development of spatially explicit predictive models of species distributions combined with predictions of changing oceanographic conditions provides the opportunity to obtain new insights of species‐habitat associations. Here, generalized linear models (GLMs) were used to model the abundance of two commercially important marine macro‐invertebrates, blacklip abalone Haliotis rubra and long‐spined sea urchin Centrostephanus rodgersii, along the coast of Victoria, Australia. We combined abundance data from fisheries independent diver surveys with environmental variables derived from bathymetric light detection and ranging (LiDAR) and oceanographic parameters derived from satellite imagery. The GLM was used to predict species responses to environmental gradients where reef complexity, sea surface temperature (SST) and depth were strongly associated with species distributions. The abundance of H. rubra declined with increasing summer SST. In comparison, the abundance of C. rodgersii increased with increasing winter SST. The GLM showed that the projected increase in ocean temperatures will likely lead to a decline in abundance across the H. rubra fishery. Conversely, a range expansion of C. rodgersii is likely due to the strengthening of the East Australian Current. For species that exhibit a high affinity to specific seascape features, this research demonstrated how recent advances in seabed mapping can allow the identification of areas with high conservation or fisheries value at a fine‐scale relevant to resource exploitation across large geographic regions.
The distribution of seabed rock in the coastal area is relevant to navigation safety and development of ocean resources where it is an essential hydrographic measurement. Currently, the distribution of seabed rock relies on interpretations of water depth data or point based bottom materials survey methods, which have low efficiency. This study uses the airborne bathymetric Lidar data and the hyperspectral image to detect seabed rock in the coastal area of the East Sea. Airborne bathymetric Lidar data detected seabed rocks with texture information that provided 88% accuracy and 24% commission error. Using the airborne hyperspectral image, a classification result of rock and sand gave 79% accuracy, 11% commission error and 7% omission error. The texture data and hyperspectral image were fused to overcome the limitations of individual data. The classification result using fused data showed an improved result with 96% accuracy, 6% commission error and 1% omission error.
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Coral reef habitat maps describe the spatial distribution and abundance of tropical marine resources, making them essential for ecosystem-based approaches to planning and management. Typically, these habitat maps have been created from optical and acoustic remotely sensed imagery using manual, pixel- and object-based classification methods. However, past studies have shown that none of these classification methods alone are optimal for characterizing coral reef habitats for multiple management applications because the maps they produce (1) are not synoptic, (2) are time consuming to develop, (3) have low thematic resolutions (i.e. number of classes), or (4) have low overall thematic accuracies. To address these deficiencies, a novel, semi-automated objectand pixel-based technique was applied to multibeam echo sounder imagery to determine its utility for characterizing coral reef ecosystems. This study is not a direct comparison of these different methods but rather, a first attempt at applying a new classification technique to acoustic imagery. This technique used a combination of principal components analysis, edge-based segmentation, and Quick, Unbiased, and Efficient Statistical Trees (QUEST) to successfully partition the acoustic imagery into 35 distinct combinations of (1) major and (2) detailed geomorphological structure, (3) major and (4) detailed biological cover, and (5) live coral cover types. Thematic accuracies for these classes (corrected for proportional bias) were as follows: (1) 95.7%, (2) 88.7%, (3) 95.0%, (4) 74.0%, and (5) 88.3%, respectively. Approximately half of the habitat polygons were manually edited (hence the name ‘semi-automated’) due to a combination of mis-classifications by QUEST and noise in the acoustic data. While this method did not generate a map that was entirely reproducible, it does show promise for increasing the amount of automation with which thematically accurate benthic habitat maps can be generated from acoustic imagery.
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Seascape ecology studies indicate that the spatial arrangement of habitat types and the topographic complexity of the seascape are major environmental drivers of fish distributions and diversity across coral reef ecosystems. Impairment of one component of an ecologically functional habitat mosaic and reduction in the architectural complexity of coral reefs is likely to lower the quality of habitat for many fish including important fished species. Documented declines in coral cover and topographic complexity are reported from a decade of long-term coral reef ecosystem monitoring in SW Puerto Rico. To examine broader scale impacts we use “reef flattening scenarios” and spatial predictive modeling to demonstrate how declining seascape complexity will lead to contractions and fragmentation in the local spatial distribution of fish. This change may result in impaired connectivity, cascading impacts to ecological functioning and reduced resilience to environmental stressors. We propose that a shift in perspective is needed towards a more holistic and spatially-explicit seascape approach to ecosystem-based management that can help monitor structural change, predict ecological consequences, guide targeted restoration efforts and inform spatial prioritization in marine spatial planning.
A scanning airborne topographic lidar was evaluated for its ability to quantify beach topography and changes during the Sandy Duck experiment in 1997 along the North Carolina coast. Elevation estimates, acquired with NASA's Airborne Topographic Mapper (ATM), were compared to elevations measured with three types of ground-based measurements - 1) differential GPS equipped all-terrain vehicle (ATV) that surveyed a 3-km reach of beach from the shoreline to the dune, 2) GPS antenna mounted on a stadia rod used to intensely survey a different 100 m reach of beach, and 3) a second GPS-equipped ATV that surveyed a 70-km-long transect along the coast. Over 40,000 individual intercomparisons between ATM and ground surveys were calculated. RMS vertical differences associated with the ATM when compared to ground measurements ranged from 13 to 19 cm. Considering all of the intercomparisons together, RMS ≃ 15 cm. This RMS error represents a total error for individual elevation estimates including uncertainties associated with random and mean errors. The latter was the largest source of error and was attributed to drift in differential GPS. The ≃ 15 cm vertical accuracy of the ATM is adequate to resolve beach-change signals typical of the impact of storms. For example, ATM surveys of Assateague Island (spanning the border of MD and VA) prior to and immediately following a severe northeaster showed vertical beach changes in places greater than 2 m, much greater than expected errors associated with the ATM. A major asset of airborne lidar is the high spatial data density. Measurements of elevation are acquired every few m2 over regional scales of hundreds of kilometers. Hence, many scales of beach morphology and change can be resolved, from beach cusps tens of meters in wavelength to entire coastal cells comprising tens to hundreds of kilometers of coast. Topographic lidars similar to the ATM are becoming increasingly available from commercial vendors and should, in the future, be widely used in beach surveying.
The Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) provides spatial data to support the U.S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP) and hurricane damage evaluation and response. The NCMP was designed to provide topographic and bathymetric elevation data with accompanying digital, geo-referenced imagery to USACE District engineers and scientists. The data support monitoring and maintenance of federal navigation and shore protection projects, and regional sediment management. The main source of these data is the CHARTS system, which is owned by the U.S. Naval Oceanographic Office and operated through the JALBTCX. The NCMP leverages other federal entities' funding, equipment, and mapping programs to efficiently provide these data to the USAGE, and avoid duplication of coastal mapping initiatives.
Conference Paper
JALBTCX has collected valuable datasets in the state of Hawaii in 1999, 2000, and 2007. Applications for the data include coral reef mapping, tsunami modeling, and nautical charting. In 1999 and 2000, SHOALS measured bathymetry on a 4- by 4-m grid for all of Maui, Oahu, and Kauai, the western shore of Hawaii, and the southern shore of Molokai. The data were delivered in XYZ ASCII format and paper map sheets. In 2007, CHARTS measured topography on a 1 - by 1.5-m grid on the northern shores of Maui, Oahu, Kauai, Hawaii, and Molokai. The data were delivered in LAS format with several derived digital information products including first and last return DEMs, bare earth models, coverage maps, RGB orthornosaics and hyperspectral image mosaics. Advanced digital data products like the basic land cover classification and color-balanced mosaic were generated using a combination of the lidar data and hyperspectral imagery.
In March 1994 the US Army Corps of Engineers completed development and field testing of the SHOALS which is now in its fifth year of operation. During these five years. SHOALS has proven airborne lidar bathymetry's benefits to the navigation and coastal community. Namely. SHOALS demonstrates the ability to achieve order-of-magnitude increases in survey speed for collection of accurate, densely spaced bathymetric and topographic measurements while remaining cost-competitive with conventional survey methods. Surveying 16 km2 per hour and collecting soundings every 4 m. SHOALS remotely measures water depths using state-of-the-art laser technology. With vertical measurements ranging from adjacent beach and structure topography through depths of 40 m, this unique capability allows rapid, accurate mapping of coastal projects. SHOALS missions have had a variety of purposes. Among these are navigation. shore protection. coastal structure evaluation. nautical charting. and emergency response. This paper presents lidar bathymetry technology by describing the SHOALS system and discussing several projects surveyed to date.
Ecologists have paid increasing attention to the design of marine protected areas (MPAs), and their design advice consistently recommends representing all habitat types within MPAs or MPA networks as a means to provide protection to all parts of the natural ocean system. Recent developments of new habitat-mapping techniques make this advice more achievable, but the success of such an approach depends largely on our ability to define habitat types in a way that is ecologically relevant. We devised and tested the ecological relevance of a set of habitat-type definitions through our participation in a stakeholder-driven process to design a network of MPAs, focusing on no-take marine reserves in the Seaflower Biosphere Reserve, San Andrés Archipelago, Colombia. A priori definitions of habitat types were ecologically relevant, in that our habitat-type definitions corresponded to identifiable and unique characteristics in the ecological communities found there. The identification of ecological pathways and connectivity among habitats also helped in designing ecologically relevant reserve boundaries. Our findings contributed to the overall design process, along with our summary of other general principles of marine reserve design. Extensive stakeholder input provided information concerning the resources and their patterns of use. These inputs also contributed to the reserve design process. We anticipate success for the Seaflower Biosphere Reserve at achieving conservation and social goals because its zoning process includes detailed yet flexible scientific advice and the participation of stakeholders at every step.
As part of a pilot study to modernize Flood Insurance Rate Maps for the Federal Emergency Management Agency (FEMA), a digital elevation model (DEM) was developed for the purpose of modeling tsunami inundation for Seaside, Oregon. The DEM consists of elevation data values with a horizontal grid spacing of 1/3 arc seconds, or approximately 10 meters. The DEM was generated from several topographic and bathymetric data sources, requiring significant processing challenges. These challenges included conversion to a single specified projection, units, horizontal datum, and vertical datum; analysis and removal of errant data from hydrographic, topographic, and LIDAR surveys; and a point-by-point analysis of overlapping data sources. Data were collected from the National Oceanic and Atmospheric Administration National Ocean Service and National Geophysical Data Center, the U.S. Geological Survey, the Oregon Geospatial Data Center, the University of Oregon, and the Oregon Department of Geology and Mineral Industries. Data were converted into formats compatible with ESRI ArcGIS 3.3 software. ArcGIS was used for spatial analysis, error correction, and surface grid development using triangular irregular networking. Post-processing involved a consistency analysis and comparison with original data and control data sources. The final DEM was compared with a previous DEM developed for tsunami inundation modeling in 1997. Significant shoreline differences were found between the DEMs, resulting in an analysis of the shoreline changes around the mouth of the Necanicum River. The shoreline analysis includes a spatial analysis of digital orthophotos over the recent past and a review of historical accretion and erosion rates along the Columbia River littoral cell.