cyclones and bleaching (Yamano and Tamura 2004; Collier and Humber 2007).
More specific studies are emerging using a combination of multispectral datasets,
higher spatial resolution images, airborne LiDAR bathymetry, and field data to
map changes in percentage-cover of coral versus algae and to define specific
habitat zones for predicting fish, shell-fish or shell population changes associated
with environmental shifts or management actions (e.g., Hardy 1999; Collier and
Humber 2007; Scopélitis et al. 2009, 2011). Satellite images and aerial photography have also been used to map changes in mean sea-level on coral reefs and
their atolls (Yamano et al. 2000, 2007; Yamano 2007).
Most recent research advances in the area of ‘‘change detection analysis’’ are
now using time series analysis of satellite image and aerial photograph archives
with 10 and 100 s of images to map changes in the composition and biophysical
properties of a coral reef (Purkis and Riegl 2005; Knudby et al. 2009; Scopélitis
et al. 2009, 2011). This allows assessment of natural dynamics of a reef, including
disturbance impact and recovery, in terms of its benthic communities, habitat
zones, and the amount of coral cover. With access to aerial photographic archives
often extending to the 1930s and satellite image archives (Landsat MSS and TM)
to the early 1970s on a global basis, there is substantial potential to extend
applications in this area. Most importantly, these types of datasets and analyses
enable natural variability and processes to be identified and teased apart from the
potential positive or negative effects of human activities.
Remote sensing change detection has been used most frequently for reef
monitoring and management over large areas and time scales in relation to
assessing the biological and physical controls of coral and algal growth, and for
tracking the condition of physical or biological disturbances that will damage
reefs. As shown in Chaps. 11, 12, satellite SST time series data and knowledge of
thermal conditions required for bleaching in certain regions is used to provide
daily updates of the likelihood of bleaching in an area. This near-real-time
approach is based on a long-term global archive of SST data. The long-term
archive enables mean, seasonal, or monthly SST to be defined, while the near-realtime can be compared to the mean or standard conditions to quantify anomalies,
and then adjusted to match the SST anomaly required for bleaching. Other operational applications include prediction and tracking of events that can lead to
significant disturbances on the reef, such as physical impacts (e.g., from tropical
cyclone induced waves) or river flood plumes. Each of these requires a long time
series of images at appropriate temporal scales to track the disturbance. These
applications are, however, usually confined to satellite imagery that provides daily
global coverage, hence pixel sizes range from 250 m to 1 km. Once a disturbance
event is detected, the typical approach has been to obtain higher spatial resolution
data and implement techniques similar to those listed above. Research developments in this area now focus on multiple biological and physical oceanographic
parameters, along with new parameters such as acidity, salinity and aragonite
saturation levels, to allow more detailed assessment of reef growth (Kayanne et al.
2005; Moses et al. 2009).
15 Science and Management
421
More specific studies are emerging using a combination of multispectral datasets,
higher spatial resolution images, airborne LiDAR bathymetry, and field data to
map changes in percentage-cover of coral versus algae and to define specific
habitat zones for predicting fish, shell-fish or shell population changes associated
with environmental shifts or management actions (e.g., Hardy 1999; Collier and
Humber 2007; Scopélitis et al. 2009, 2011). Satellite images and aerial photography have also been used to map changes in mean sea-level on coral reefs and
their atolls (Yamano et al. 2000, 2007; Yamano 2007).
Most recent research advances in the area of ‘‘change detection analysis’’ are
now using time series analysis of satellite image and aerial photograph archives
with 10 and 100 s of images to map changes in the composition and biophysical
properties of a coral reef (Purkis and Riegl 2005; Knudby et al. 2009; Scopélitis
et al. 2009, 2011). This allows assessment of natural dynamics of a reef, including
disturbance impact and recovery, in terms of its benthic communities, habitat
zones, and the amount of coral cover. With access to aerial photographic archives
often extending to the 1930s and satellite image archives (Landsat MSS and TM)
to the early 1970s on a global basis, there is substantial potential to extend
applications in this area. Most importantly, these types of datasets and analyses
enable natural variability and processes to be identified and teased apart from the
potential positive or negative effects of human activities.
Remote sensing change detection has been used most frequently for reef
monitoring and management over large areas and time scales in relation to
assessing the biological and physical controls of coral and algal growth, and for
tracking the condition of physical or biological disturbances that will damage
reefs. As shown in Chaps. 11, 12, satellite SST time series data and knowledge of
thermal conditions required for bleaching in certain regions is used to provide
daily updates of the likelihood of bleaching in an area. This near-real-time
approach is based on a long-term global archive of SST data. The long-term
archive enables mean, seasonal, or monthly SST to be defined, while the near-realtime can be compared to the mean or standard conditions to quantify anomalies,
and then adjusted to match the SST anomaly required for bleaching. Other operational applications include prediction and tracking of events that can lead to
significant disturbances on the reef, such as physical impacts (e.g., from tropical
cyclone induced waves) or river flood plumes. Each of these requires a long time
series of images at appropriate temporal scales to track the disturbance. These
applications are, however, usually confined to satellite imagery that provides daily
global coverage, hence pixel sizes range from 250 m to 1 km. Once a disturbance
event is detected, the typical approach has been to obtain higher spatial resolution
data and implement techniques similar to those listed above. Research developments in this area now focus on multiple biological and physical oceanographic
parameters, along with new parameters such as acidity, salinity and aragonite
saturation levels, to allow more detailed assessment of reef growth (Kayanne et al.
2005; Moses et al. 2009).
15 Science and Management
421