producing algae. In each case, the fundamental basis for the signals (i.e. the pigments
themselves) can be detected using remote sensing.
Most of the Case 1 studies on remote sensing of pigments (primarily chlorophyll a)
have focused on optics and algorithm development. The parameter that is deemed most
important is radiative transfer, which is a composite of backscattering, absorption,
transmission, fluorescence, etc. As a result, much of this work results in publications
that are dominated by optical modeling and radiative transfer equations. This approach
is necessary because most water-leaving reflectance spectra in Case 1 waters are quite
similar.
In contrast, the water-leaving reflectance of Case 2 waters is often highly variable
and can be detected with the eye alone. Thus, in a different approach, there is a body of
work on Case 2 algal pigment remote sensing that foregoes preliminary radiative
transfer modeling and instead focuses directly on spectral signatures. Differences in the
spectra themselves then become the basis for algorithm development.
Coastal phytoplankton are often dynamic in terms of phytoplankton population
composition and quantity of cells, both of which can dramatically affect spectral
reflectance. Thus detection of specific spectral patterns, or signatures, can often detect
a specific type of algal bloom. The connection between pigments and algal type is one
of the most promising and applicable examples of the potential for bridging coastal
ecosystem processes and remote sensing.
As mentioned above, one of the benefits of remote sensing for studies of aquatic
coastal zones is the ability of remote sensing to provide synoptic data sets at different
scales. This is accomplished by the availability of sensors with different spatial
resolutions. An enhancement of this capability is the fact that many sensors offer
different spectral, as well as spatial, resolution. The result is that synoptic remote
sensed data can be attained for a given coastal zone that can detect and assess many
different aspects of that particular coastal aquatic ecosystem. Therefore, in addition to
discriminating between phytoplankton types, remote sensing can allow for habitat
mapping, coastal shoreline anomalies, and change detection.
Remote sensing can also detect certain physical properties that are directly or
indirectly crucial to aquatic ecosystem processes. One of the most important of these is
water surface temperature. Temperature is an important factor in the physiological
functioning and health of organisms. It is also a major factor in controlling population
dynamics of many aquatic organisms. The combination of optical signals that can
detect, identify, and quantify different types of aquatic organisms, along with the
capability to detect an important and regulatory factor such as temperature, leads to the
potential for remote sensing as a powerful tool to study and quantify aquatic
ecosystems at the physiologically functional level.
Current research is aimed at using remote sensing to directly scale up aquatic
ecosystem studies at the process level. An example of such an effort is presented by
John Brock and colleagues in Chapter 5. This research group is using a suite of remote
sensors with different spatial and spectral resolution as well as different remotely
sensed factors to measure and extrapolate carbon biogeochemical processes on a coral
reef to the regional scale. Their program includes remotely sensed mapping of the reef
itself (geomorphology), remote sensing based detection of different habitats/groups of
organisms (biotopes), and scaling up of in situ experimental measurements, based on
the remote sensing data, to examine reef “metabolism”. While this chapter is
particularly innovative and beyond the scope of most monitoring and managing
programs, it is included as an example of a feasible and existing remote sensing
application. Another example of this type is found in Chapter 6 by Jim Hendee et al.,
4
Richardson and LeDrew
themselves) can be detected using remote sensing.
Most of the Case 1 studies on remote sensing of pigments (primarily chlorophyll a)
have focused on optics and algorithm development. The parameter that is deemed most
important is radiative transfer, which is a composite of backscattering, absorption,
transmission, fluorescence, etc. As a result, much of this work results in publications
that are dominated by optical modeling and radiative transfer equations. This approach
is necessary because most water-leaving reflectance spectra in Case 1 waters are quite
similar.
In contrast, the water-leaving reflectance of Case 2 waters is often highly variable
and can be detected with the eye alone. Thus, in a different approach, there is a body of
work on Case 2 algal pigment remote sensing that foregoes preliminary radiative
transfer modeling and instead focuses directly on spectral signatures. Differences in the
spectra themselves then become the basis for algorithm development.
Coastal phytoplankton are often dynamic in terms of phytoplankton population
composition and quantity of cells, both of which can dramatically affect spectral
reflectance. Thus detection of specific spectral patterns, or signatures, can often detect
a specific type of algal bloom. The connection between pigments and algal type is one
of the most promising and applicable examples of the potential for bridging coastal
ecosystem processes and remote sensing.
As mentioned above, one of the benefits of remote sensing for studies of aquatic
coastal zones is the ability of remote sensing to provide synoptic data sets at different
scales. This is accomplished by the availability of sensors with different spatial
resolutions. An enhancement of this capability is the fact that many sensors offer
different spectral, as well as spatial, resolution. The result is that synoptic remote
sensed data can be attained for a given coastal zone that can detect and assess many
different aspects of that particular coastal aquatic ecosystem. Therefore, in addition to
discriminating between phytoplankton types, remote sensing can allow for habitat
mapping, coastal shoreline anomalies, and change detection.
Remote sensing can also detect certain physical properties that are directly or
indirectly crucial to aquatic ecosystem processes. One of the most important of these is
water surface temperature. Temperature is an important factor in the physiological
functioning and health of organisms. It is also a major factor in controlling population
dynamics of many aquatic organisms. The combination of optical signals that can
detect, identify, and quantify different types of aquatic organisms, along with the
capability to detect an important and regulatory factor such as temperature, leads to the
potential for remote sensing as a powerful tool to study and quantify aquatic
ecosystems at the physiologically functional level.
Current research is aimed at using remote sensing to directly scale up aquatic
ecosystem studies at the process level. An example of such an effort is presented by
John Brock and colleagues in Chapter 5. This research group is using a suite of remote
sensors with different spatial and spectral resolution as well as different remotely
sensed factors to measure and extrapolate carbon biogeochemical processes on a coral
reef to the regional scale. Their program includes remotely sensed mapping of the reef
itself (geomorphology), remote sensing based detection of different habitats/groups of
organisms (biotopes), and scaling up of in situ experimental measurements, based on
the remote sensing data, to examine reef “metabolism”. While this chapter is
particularly innovative and beyond the scope of most monitoring and managing
programs, it is included as an example of a feasible and existing remote sensing
application. Another example of this type is found in Chapter 6 by Jim Hendee et al.,
4
Richardson and LeDrew