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 multispectral (Cochran-Marquez 2005; Chust et al. 2008; Walker 2009) and hyperspectral 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
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 multispectral (Cochran-Marquez 2005; Chust et al. 2008; Walker 2009) and hyperspectral 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