presents an overview, aimed at managers, that discusses the benefits and potential of
remote sensing for management of coastal aquatic ecosystems, as well as a discussion
of cost and financial issues. Chapter 11 by Candace Newman et al. extends this
discussion to specifically address technology transfer of remote sensing to
underdeveloped countries, including local communities. These concepts are further
extended to policy making in Chapter 10 by Stuart Phinn and colleagues.
One of the most important and encompassing rotes of managers is integration of
data within and between the diverse areas of social, natural resource, economic, and
environmental frameworks. Incorporation of remote sensing at the management level
also includes data integration, but with a different aim – that of not only extracting and
integrating data from various sensors, but transferring this knowledge to the wider
community. Such topics are addressed, and examples of successful efforts are
provided, in Chapter 12 by Julie Robinson et al.
7. Integrating remote sensing, science, monitoring, and management
The integration of science and remote sensing is now a reality. One of the best
examples of success is the use of remote sensing to predict coral bleaching, as
presented by William Skirving and colleagues in Chapter 2. The science-based data
that have proven the connection between physiological thermal stress and coral
bleaching are now fully integrated into automated remote sensing data analyses that
allow real-time world-wide coral bleaching “alerts”. The accuracy of these alerts in
predicting bleaching is extremely high. Thus in this case scientists have provided a
quantitative link between an aquatic ecosystem process and a factor (sea surface
temperature) detectable at global scales using remote sensing. Managers now routinely
access real-time satellite-derived predictive data that is directly relevant to the health of
the ecosystem they are managing – coral reefs.
This approach has been extended using another type of remote sensing instrument
package – permanently moored buoys that support an array of different sensors that
measure factors of importance to aquatic scientists, in particular biologists, and
managers. A state of the art system is described here in Chapter 6 by Jim Hendee and
colleagues. In addition to an overview of the system and examples of how these data
can support science applications, a guideline for design and deployment of the system is
provided for managers.
Another example is the use of remote sensing in support of management of coastal
flooding. In Chapter 7, Tim Webster and Donald Forbes present a detailed case study
in which remote sensing is being used to mitigate the effects of coastal flooding based
on the results of light detection and ranging (LIDAR). Thus in this case, as opposed to
those discussed above, purely physical features (details of varying coastal elevations)
are integrated with historical data bases to predict flooding effects. City managers are
actively integrating these remote sensing data with planning.
8. Summary
All of the chapters in this book are meant to serve as resources for both scientists
and managers. In addition to the specific examples of remote sensing in science,
monitoring, and management briefly summarized above, the chapters by lead authors
Jennifer Gebelein, Brian Whitehouse, Stuart Phinn, Candace Newman, and Julie
Robinson include overviews of data bases, comparisons of sensor capabilities, and the
6
Richardson and LeDrew
remote sensing for management of coastal aquatic ecosystems, as well as a discussion
of cost and financial issues. Chapter 11 by Candace Newman et al. extends this
discussion to specifically address technology transfer of remote sensing to
underdeveloped countries, including local communities. These concepts are further
extended to policy making in Chapter 10 by Stuart Phinn and colleagues.
One of the most important and encompassing rotes of managers is integration of
data within and between the diverse areas of social, natural resource, economic, and
environmental frameworks. Incorporation of remote sensing at the management level
also includes data integration, but with a different aim – that of not only extracting and
integrating data from various sensors, but transferring this knowledge to the wider
community. Such topics are addressed, and examples of successful efforts are
provided, in Chapter 12 by Julie Robinson et al.
7. Integrating remote sensing, science, monitoring, and management
The integration of science and remote sensing is now a reality. One of the best
examples of success is the use of remote sensing to predict coral bleaching, as
presented by William Skirving and colleagues in Chapter 2. The science-based data
that have proven the connection between physiological thermal stress and coral
bleaching are now fully integrated into automated remote sensing data analyses that
allow real-time world-wide coral bleaching “alerts”. The accuracy of these alerts in
predicting bleaching is extremely high. Thus in this case scientists have provided a
quantitative link between an aquatic ecosystem process and a factor (sea surface
temperature) detectable at global scales using remote sensing. Managers now routinely
access real-time satellite-derived predictive data that is directly relevant to the health of
the ecosystem they are managing – coral reefs.
This approach has been extended using another type of remote sensing instrument
package – permanently moored buoys that support an array of different sensors that
measure factors of importance to aquatic scientists, in particular biologists, and
managers. A state of the art system is described here in Chapter 6 by Jim Hendee and
colleagues. In addition to an overview of the system and examples of how these data
can support science applications, a guideline for design and deployment of the system is
provided for managers.
Another example is the use of remote sensing in support of management of coastal
flooding. In Chapter 7, Tim Webster and Donald Forbes present a detailed case study
in which remote sensing is being used to mitigate the effects of coastal flooding based
on the results of light detection and ranging (LIDAR). Thus in this case, as opposed to
those discussed above, purely physical features (details of varying coastal elevations)
are integrated with historical data bases to predict flooding effects. City managers are
actively integrating these remote sensing data with planning.
8. Summary
All of the chapters in this book are meant to serve as resources for both scientists
and managers. In addition to the specific examples of remote sensing in science,
monitoring, and management briefly summarized above, the chapters by lead authors
Jennifer Gebelein, Brian Whitehouse, Stuart Phinn, Candace Newman, and Julie
Robinson include overviews of data bases, comparisons of sensor capabilities, and the
6
Richardson and LeDrew