measurements can be very powerful. Consider Reef Check, which is one of the
internationally recognized field assessment protocols. Substrate surveys using this
protocol include cover types of live hard coral, dead coral, soft coral, fleshy
seaweed, sponge, rock, rubble, sand, silt/clay and other. One approach for linking
remote sensing data with such protocols is to classify the imagery into comparable
cover types. For example, Joyce et al. (2004) classified Landsat ETM+ imagery of
the southern Great Barrier Reef, Australia using the Reef Check substrate types,
but with accuracies ranging from 12 to 74 % for individual reefs. Scopélitis et al.
(2010) further explored the issue, showing that the combination of in situ and
satellite data is best suited for mapping dominant coral morphologies and substrate
types.
In addition to substrate type monitoring, Leiper et al. (2009) showed the possibility of linking remote sensing with CoralWatch data, which compares field
observations of coral color with species-specific Coral Health Charts. The study
showed bleached, medium and dark coral could be discriminated with 72.41 %
overall accuracy, which extends the capabilities of previous studies focused on just
analyzing bleached versus non-bleached corals (Andréfouët et al. 2002a; Elvidge
et al. 2004).
These studies suggest the importance of integrating in situ monitoring protocols
with remote sensing analysis, but reveal that research is still needed to better
establish such links.
3.4.3 Integration with Modeling
The considerable advances in mapping, monitoring and modeling applications
derived from multispectral remote sensing (Table 3.2) encourages not just integration with other sources of data but also interaction among the different applications. For example, a link between in situ monitoring, direct remote sensing
measuring coral decline (Palandro et al. 2008) and indirect remote sensing measuring anomalous environmental variables (Shinn et al. 2000; Abram et al. 2003;
Hu et al. 2003) could be used to identify the cause-and-effect of environmental
change and perturbations (Purkis and Riegl 2005). Integration with susceptibility
modeling and risk assessments is also possible. For instance, incorporating
physical parameters and in situ observations with habitat change trajectories and
monitoring information from direct remote sensing can be used to improve susceptibility modeling (Maina et al. 2008; Fig. 3.5).
Modeling efforts using remote sensing data can also be used to assess biodiversity (Fig. 3.6). Because habitat variability can be assumed a surrogate for
biodiversity in coral reefs (Mumby et al. 2008; Dalleau et al. 2010), habitat decline
may cause loss of biodiversity in coral reefs. For example, Sano et al. (1987)
showed significant decrease in fish species numbers after the destruction of staghorn coral (Acropora cervicornis) communities due to Acanthaster planci infestation. This indicates that mapping habitat change using remote sensing,
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H. Yamano
internationally recognized field assessment protocols. Substrate surveys using this
protocol include cover types of live hard coral, dead coral, soft coral, fleshy
seaweed, sponge, rock, rubble, sand, silt/clay and other. One approach for linking
remote sensing data with such protocols is to classify the imagery into comparable
cover types. For example, Joyce et al. (2004) classified Landsat ETM+ imagery of
the southern Great Barrier Reef, Australia using the Reef Check substrate types,
but with accuracies ranging from 12 to 74 % for individual reefs. Scopélitis et al.
(2010) further explored the issue, showing that the combination of in situ and
satellite data is best suited for mapping dominant coral morphologies and substrate
types.
In addition to substrate type monitoring, Leiper et al. (2009) showed the possibility of linking remote sensing with CoralWatch data, which compares field
observations of coral color with species-specific Coral Health Charts. The study
showed bleached, medium and dark coral could be discriminated with 72.41 %
overall accuracy, which extends the capabilities of previous studies focused on just
analyzing bleached versus non-bleached corals (Andréfouët et al. 2002a; Elvidge
et al. 2004).
These studies suggest the importance of integrating in situ monitoring protocols
with remote sensing analysis, but reveal that research is still needed to better
establish such links.
3.4.3 Integration with Modeling
The considerable advances in mapping, monitoring and modeling applications
derived from multispectral remote sensing (Table 3.2) encourages not just integration with other sources of data but also interaction among the different applications. For example, a link between in situ monitoring, direct remote sensing
measuring coral decline (Palandro et al. 2008) and indirect remote sensing measuring anomalous environmental variables (Shinn et al. 2000; Abram et al. 2003;
Hu et al. 2003) could be used to identify the cause-and-effect of environmental
change and perturbations (Purkis and Riegl 2005). Integration with susceptibility
modeling and risk assessments is also possible. For instance, incorporating
physical parameters and in situ observations with habitat change trajectories and
monitoring information from direct remote sensing can be used to improve susceptibility modeling (Maina et al. 2008; Fig. 3.5).
Modeling efforts using remote sensing data can also be used to assess biodiversity (Fig. 3.6). Because habitat variability can be assumed a surrogate for
biodiversity in coral reefs (Mumby et al. 2008; Dalleau et al. 2010), habitat decline
may cause loss of biodiversity in coral reefs. For example, Sano et al. (1987)
showed significant decrease in fish species numbers after the destruction of staghorn coral (Acropora cervicornis) communities due to Acanthaster planci infestation. This indicates that mapping habitat change using remote sensing,
70
H. Yamano