of sediment properties, including grain size, roughness and hardness (Hamilton
and Bachman 1982; Chaps. 8–10). 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 information 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 technology 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
and Bachman 1982; Chaps. 8–10). 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 information 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 technology 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