Zawada and Brock 2009). By contrast, seagrass and algal meadows, classes that do
have ecological relevance, cannot be identified by topographic roughness alone
and their identification demands the use of spectral or photographic data in unison
with LiDAR (Chap. 7). Both the EAARL and the CZMIL acquire digital imagery
in tandem to laser soundings. Calibrated lasers (see Sect. 5.2.1) may also offer
some differentiation capacity for habitats such as seagrass that lack a topographic
signature (Tuell et al. 2005).
An ecosystem parameter that is now commonly sought in both field and remote
sensing surveys is ‘ecological resilience’. This describes the capacity of an ecosystem to cope with disturbances, such as storms, pollution, and global change,
without shifting into a qualitatively different state. For coral reefs, this shift is
typically from a vital coral-dominated state, to one with low coral cover and an
abundance of fleshy macro-algae. When assessed by a diver, ecosystem status will
be measured in terms of the prevalence of key assemblages such as: (1) coral cover
in percentage, (2) algal cover in percentage, and (3) soft coral, sponges and corallimorphs, quantified as percentages. Further measurements of the community
will also be required to assess resilience, and likely focus on parameters such as
the number of small corals in the assemblage (a proxy for recruitment), the sizefrequency distribution of colonies, a calculation of dominance/evenness/overall
diversity of the assemblage, as well as physiological parameters such as bleaching
susceptibility. None of these parameters can be derived from LiDAR remote
sensing. However, despite these limitations, some biotic parameters relevant to
reef resilience can be accessed using LiDAR. Laser surveys do deliver information
that can be used to calculate resilience ‘indicators’, such as the propensity of a
water body to resist warming beyond thresholds tolerable to reef growth. This can
be summarised as ranked assessments of water movement, distance to deep water,
depth of reef base, and exposure, all readily calculable from a DEM.
5.3.3 Abiotic Features
Besides living coral cover, reefs are also classified according to geomorphology.
For instance, the evolutionary reef classification scheme of Hopley (1982) and
Hopley et al. (2007) adopts a threefold separation into juvenile, mature and senile
reefs, on the basis of the relative depth of the lagoon and surrounding rim. These
‘architectural’ properties of the reef do not pertain to the coverage or diversity of
live corals and cannot be appraised without detailed information on the water
depth across the system. In contrast to satellite imagery, LiDAR is well poised to
provide this overview and can cover the scales of entire reef tracts. As demonstrated by various papers that analyse morphometric attributes of reefs from
topographic data (e.g., size, shape, orientation and complexity), reefs can be
partitioned mathematically according to their morphology (Purkis et al. 2007;
Brock et al. 2008: Purkis and Kohler 2008; Zieger et al. 2009; Purkis et al. 2010;
Harris et al. 2011). Though limited to shallow waters, bathymetric LiDAR
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have ecological relevance, cannot be identified by topographic roughness alone
and their identification demands the use of spectral or photographic data in unison
with LiDAR (Chap. 7). Both the EAARL and the CZMIL acquire digital imagery
in tandem to laser soundings. Calibrated lasers (see Sect. 5.2.1) may also offer
some differentiation capacity for habitats such as seagrass that lack a topographic
signature (Tuell et al. 2005).
An ecosystem parameter that is now commonly sought in both field and remote
sensing surveys is ‘ecological resilience’. This describes the capacity of an ecosystem to cope with disturbances, such as storms, pollution, and global change,
without shifting into a qualitatively different state. For coral reefs, this shift is
typically from a vital coral-dominated state, to one with low coral cover and an
abundance of fleshy macro-algae. When assessed by a diver, ecosystem status will
be measured in terms of the prevalence of key assemblages such as: (1) coral cover
in percentage, (2) algal cover in percentage, and (3) soft coral, sponges and corallimorphs, quantified as percentages. Further measurements of the community
will also be required to assess resilience, and likely focus on parameters such as
the number of small corals in the assemblage (a proxy for recruitment), the sizefrequency distribution of colonies, a calculation of dominance/evenness/overall
diversity of the assemblage, as well as physiological parameters such as bleaching
susceptibility. None of these parameters can be derived from LiDAR remote
sensing. However, despite these limitations, some biotic parameters relevant to
reef resilience can be accessed using LiDAR. Laser surveys do deliver information
that can be used to calculate resilience ‘indicators’, such as the propensity of a
water body to resist warming beyond thresholds tolerable to reef growth. This can
be summarised as ranked assessments of water movement, distance to deep water,
depth of reef base, and exposure, all readily calculable from a DEM.
5.3.3 Abiotic Features
Besides living coral cover, reefs are also classified according to geomorphology.
For instance, the evolutionary reef classification scheme of Hopley (1982) and
Hopley et al. (2007) adopts a threefold separation into juvenile, mature and senile
reefs, on the basis of the relative depth of the lagoon and surrounding rim. These
‘architectural’ properties of the reef do not pertain to the coverage or diversity of
live corals and cannot be appraised without detailed information on the water
depth across the system. In contrast to satellite imagery, LiDAR is well poised to
provide this overview and can cover the scales of entire reef tracts. As demonstrated by various papers that analyse morphometric attributes of reefs from
topographic data (e.g., size, shape, orientation and complexity), reefs can be
partitioned mathematically according to their morphology (Purkis et al. 2007;
Brock et al. 2008: Purkis and Kohler 2008; Zieger et al. 2009; Purkis et al. 2010;
Harris et al. 2011). Though limited to shallow waters, bathymetric LiDAR
132
S. J. Purkis and J. C. Brock