7. Future Developments for Monitoring Coastal and Coral Reef Environments
Using Remotely Sensed Data
7.1 CURRENT STATUS OF REMOTELY SENSED DATA/PROCESSING
TECHNIQUES FOR MONITORING CHANGE IN COASTAL AND CORAL REEF
ECOSYSTEMS
Other Chapters in this volume (e.g. Hendee et al., Newman et al., Skirving et al.,
Webster and Forbes) and recent review papers (Green et al., 2000; Dekker et al.,
2001b; Joyce et al., 2002; Andréfouët et al., 2003; Coppin, 2003; Malthus, 2003)
demonstrate that remote sensing techniques are operational for mapping and monitoring
selected components and processes of coastal aquatic environments. In this context, the
following applications from commercially available image data and image processing
software are operational:
• mapping and monitoring changes of substrate type in relatively clear
waters < 10m deep;
• mapping depth in shallow clear waters < 10m deep;
• mapping selected water quality parameters related to optical properties of
water (e.g. total suspended matter, suspended organic material (e.g.
chlorophyll) and dissolved organic material (e.g. CDOM); and
• mapping sea-surface skin temperature.
Substrate and water quality mapping applications of remote sensing perform
accurately in clear, oceanic Case 1 waters. The accuracy and reliability of these
mapping techniques is reduced significantly in coastal and estuarine waters, which are
often a mix of Case 1 and Case 2 waters, unless hyperspectral data are acquired (see
Chapter 3).
The majority of coastal management and monitoring programs are centered around
measurement of ecosystem health indicators. Hence, it makes sense to focus on such
indicators as a basis for selecting suitable remote sensing approaches towards the
monitoring and management procedures of a region. Ecosystem health or status
indicators often include environmental parameters that can be mapped directly or
indirectly from passive and active image data sets. In this chapter we have presented a
framework for developing remote sensing applications to map and monitor coastal
ecosystem health or status indicators. The framework ensures explicit consideration is
given to selection of an image data set suited to the indicator and its use in
management. In addition, all of the considerations for using remotely sensed data are
included in the evaluation process (data cost, software, hardware, personnel, etc.).
A key component of the use of remote sensing data for monitoring is the
implementation of change and trend detection techniques. Our chapter provided an
overview of the key pre-processing requirements and large range of processing options
that are now available. The framework and change/trend detection sequence is an
“ideal” approach and coastal managers and remote sensing practitioners will often be
faced with a gap that persists between the expectations of both groups pertaining to the
use of data. Our experience in this area, as demonstrated through the L. majuscula
project, is to select one indicator and run through a trial project. It is critical that the
trial project involves management and remote sensing scientists working together on
image and field data collection, data analysis, error assessment, and presentation of
245
Integrated Information Acquisition and Management
Using Remotely Sensed Data
7.1 CURRENT STATUS OF REMOTELY SENSED DATA/PROCESSING
TECHNIQUES FOR MONITORING CHANGE IN COASTAL AND CORAL REEF
ECOSYSTEMS
Other Chapters in this volume (e.g. Hendee et al., Newman et al., Skirving et al.,
Webster and Forbes) and recent review papers (Green et al., 2000; Dekker et al.,
2001b; Joyce et al., 2002; Andréfouët et al., 2003; Coppin, 2003; Malthus, 2003)
demonstrate that remote sensing techniques are operational for mapping and monitoring
selected components and processes of coastal aquatic environments. In this context, the
following applications from commercially available image data and image processing
software are operational:
• mapping and monitoring changes of substrate type in relatively clear
waters < 10m deep;
• mapping depth in shallow clear waters < 10m deep;
• mapping selected water quality parameters related to optical properties of
water (e.g. total suspended matter, suspended organic material (e.g.
chlorophyll) and dissolved organic material (e.g. CDOM); and
• mapping sea-surface skin temperature.
Substrate and water quality mapping applications of remote sensing perform
accurately in clear, oceanic Case 1 waters. The accuracy and reliability of these
mapping techniques is reduced significantly in coastal and estuarine waters, which are
often a mix of Case 1 and Case 2 waters, unless hyperspectral data are acquired (see
Chapter 3).
The majority of coastal management and monitoring programs are centered around
measurement of ecosystem health indicators. Hence, it makes sense to focus on such
indicators as a basis for selecting suitable remote sensing approaches towards the
monitoring and management procedures of a region. Ecosystem health or status
indicators often include environmental parameters that can be mapped directly or
indirectly from passive and active image data sets. In this chapter we have presented a
framework for developing remote sensing applications to map and monitor coastal
ecosystem health or status indicators. The framework ensures explicit consideration is
given to selection of an image data set suited to the indicator and its use in
management. In addition, all of the considerations for using remotely sensed data are
included in the evaluation process (data cost, software, hardware, personnel, etc.).
A key component of the use of remote sensing data for monitoring is the
implementation of change and trend detection techniques. Our chapter provided an
overview of the key pre-processing requirements and large range of processing options
that are now available. The framework and change/trend detection sequence is an
“ideal” approach and coastal managers and remote sensing practitioners will often be
faced with a gap that persists between the expectations of both groups pertaining to the
use of data. Our experience in this area, as demonstrated through the L. majuscula
project, is to select one indicator and run through a trial project. It is critical that the
trial project involves management and remote sensing scientists working together on
image and field data collection, data analysis, error assessment, and presentation of
245
Integrated Information Acquisition and Management