flat image and is an intuitive method with which to view a LiDAR grid. More
advanced metrics that can be derived from a raster are roughness, rugosity, and
fractal dimension (Purkis and Kohler 2008; Zawada and Brock 2009). These are
all useful means of distilling complex topographic patterns into relatively simple
numerical explanations. The use of geomorphology will be explored in the following sections as a means of interrogating biotic and abiotic aspects of a coral
reef, as well as the surrounding marine and terrestrial environment.
5.3.2 Biotic Features
The aggradation of coral reefs creates bottom roughness, resulting in topographic
complexity ranging from centimetres to kilometres in spatial scale, which both
influences and reflects many ecological variables. LiDAR sensing of benthic
topographic complexity shows great promise as a proxy for more comprehensive
habitat complexity (Brock et al. 2004; Brock et al. 2008), a fundamental ecological
factor on coral reefs that is relevant to species diversity and richness, herbivore
shelter, predation, recruitment, metabolic processes, hydrodynamics, and nutrient
fluxes (McCormick 1994; Sale 1991; Sebens 1991; Szmant 1997; Purkis et al.
2008). On the basis of seabed roughness alone, for example, reef habitat can
reliably be separated from low relief sandy or rocky substrates. Though a simplistic split, this means of classifying reef tracts may be sufficient for the development of a management plan for a reef area. The high spatial resolution that will
be offered by the next generation LiDAR will allow more detailed partitions of
habitat to be made on the basis of the rich topographic complexity of the data.
Rugosity, a measure of topographic complexity traditionally assessed in the
field, is one of the important attributes to describe both biotic and abiotic seafloor
features, and is a parameter that can be readily extracted from LiDAR DEMs.
Several permutations on this calculation exist that range from an assessment of
slope variation within an area of interest (Greene et al. 2004), to ranges of elevation inside a kernel window (Dartnell 2000). A more sophisticated representation of surface roughness presented by Jenness (2002) calculates the ratio between
surface and planar (projective) area. This ratio will be equal to 1 for flat planes and
increases in value for more complex surfaces (Purkis et al. 2008; Purkis and
Kohler 2008).
With LiDAR soundings typically posted with spacings on the order of one
meter, the spatial resolution is insufficient to differentiate coral assemblages
strictly on the basis of their topographic variation. This renders LiDAR, for now at
least, as less capable than, for example, hyperspectral airborne imagery, for the
assessment of ecological communities. But note that the highest spatial density
now available for bathymetric LiDAR have already shown promise in the discrimination of large boulder coral colonies (Brock et al. 2006), and assemblagescale differences in LiDAR rugosity at scales of tens of metres can allow the
separation of coral-dominated habitat from other seabed types (Foster et al. 2009;
5 LiDAR Overview
131
advanced metrics that can be derived from a raster are roughness, rugosity, and
fractal dimension (Purkis and Kohler 2008; Zawada and Brock 2009). These are
all useful means of distilling complex topographic patterns into relatively simple
numerical explanations. The use of geomorphology will be explored in the following sections as a means of interrogating biotic and abiotic aspects of a coral
reef, as well as the surrounding marine and terrestrial environment.
5.3.2 Biotic Features
The aggradation of coral reefs creates bottom roughness, resulting in topographic
complexity ranging from centimetres to kilometres in spatial scale, which both
influences and reflects many ecological variables. LiDAR sensing of benthic
topographic complexity shows great promise as a proxy for more comprehensive
habitat complexity (Brock et al. 2004; Brock et al. 2008), a fundamental ecological
factor on coral reefs that is relevant to species diversity and richness, herbivore
shelter, predation, recruitment, metabolic processes, hydrodynamics, and nutrient
fluxes (McCormick 1994; Sale 1991; Sebens 1991; Szmant 1997; Purkis et al.
2008). On the basis of seabed roughness alone, for example, reef habitat can
reliably be separated from low relief sandy or rocky substrates. Though a simplistic split, this means of classifying reef tracts may be sufficient for the development of a management plan for a reef area. The high spatial resolution that will
be offered by the next generation LiDAR will allow more detailed partitions of
habitat to be made on the basis of the rich topographic complexity of the data.
Rugosity, a measure of topographic complexity traditionally assessed in the
field, is one of the important attributes to describe both biotic and abiotic seafloor
features, and is a parameter that can be readily extracted from LiDAR DEMs.
Several permutations on this calculation exist that range from an assessment of
slope variation within an area of interest (Greene et al. 2004), to ranges of elevation inside a kernel window (Dartnell 2000). A more sophisticated representation of surface roughness presented by Jenness (2002) calculates the ratio between
surface and planar (projective) area. This ratio will be equal to 1 for flat planes and
increases in value for more complex surfaces (Purkis et al. 2008; Purkis and
Kohler 2008).
With LiDAR soundings typically posted with spacings on the order of one
meter, the spatial resolution is insufficient to differentiate coral assemblages
strictly on the basis of their topographic variation. This renders LiDAR, for now at
least, as less capable than, for example, hyperspectral airborne imagery, for the
assessment of ecological communities. But note that the highest spatial density
now available for bathymetric LiDAR have already shown promise in the discrimination of large boulder coral colonies (Brock et al. 2006), and assemblagescale differences in LiDAR rugosity at scales of tens of metres can allow the
separation of coral-dominated habitat from other seabed types (Foster et al. 2009;
5 LiDAR Overview
131