Page, 2000); 4) coral reef bleaching (Yamano, 2004); 5) water quality monitoring in
estuarine waters (Lavery, 1993); 6) and changes in coral reef communities over
multiple years (Dustan et al., 2001).
Because of the significant length of the Landsat dataset, there have been many
studies in which Landsat images are combined with other air- or spaceborne sensor
types to yield an enhanced data product. Some examples of research that use this
approach are: 1) use of remote sensors to classify coral, algae, and sand as pure and
mixed spectra (Hochberg and Atkinson, 2003); 2) merging Landsat TM and SPOT via
wavelet transformation (Zhou and Civco, 1998); 3) coral reef habitat mapping with
Ikonos and Landsat (Capolsini, 2003); 4) change detection in coral reef environments
using Landsat and SeaWiFS data (Andréfouët et al., 2001); and 5) mapping shallowwater marine environments (Mumby and Edwards, 2002)
2.2.2 Moderate Resolution Data Products
The GLCF also has moderate resolution products and data available, including
Terra (Latin for “land”) MODIS data, United States vegetation index product, Global
MODIS-derived 500 meter tree cover product, MODIS-derived 500 meter 32-day
composite products, and MODIS-derived vegetation product for Central Brazil and
Idaho/Montana at 250 meter resolution. These GLCF products are generally more
appropriate for land based research, and will not be discussed here. However, the
NASA MODIS-derived products can provide researchers with useful information
concerning coastal and aquatic areas. Thus, those aspects of the MODIS instrument are
the focus of this section
MODIS. The MODIS sensor has 36 spectral bands, most of which have 1 km spatial
resolution and a wavelength range of 0.4 µm to 14.4 µm. MODIS is mounted on two
platforms: Aqua and Terra. Terra’s orbit is from North to South in the morning and
Aqua’s orbit is South to North in the early evening. The two MODIS sensors yield
global coverage once every 1-2 days (Townshend et al , 2005). NASA-derived data
products from MODIS can be utilized to detect, monitor, and analyze ecosystem
processes at the local, regional, and global scales. These data products include, but are
not limited to, chlorophyll concentration, organic matter concentration, sea surface
temperature, ocean primary productivity, ocean aerosol properties, and normalized
water-leaving radiance (http://modis.gsfc.nasa.gov/data/dataproducts.html).
MODIS historical data sets remain an important asset to the coastal (global)
research community. Research based on a combination of MODIS with newer datasets
such as Landsat 7 imagery, serve to enhance and validate derived products. Based on
MODIS data, scientists have conducted static and time series analyses focused on
global monitoring of air pollution (Chu et al., 2003), Landsat-derived training data for
al., 2003), and mapping concentrations of total suspended matter in coastal waters
(Miller and McKee, 2004).
MODIS data have also been combined with other data types to enhance and
validate its detection, monitoring and analysis capabilities. Several studies that
exemplify this combination include in situ measurements compared to MODIS-derived
spectral reflectances of snow and sea ice (Zhou and Li 2003), ocean-color observations
of the tropical Pacific Ocean (McClain et al., 2002), measurements of sea-skin
temperature for validation of satellite data (Minnett, 2003), and an investigation of
product accuracy as a function of inputs including Sea Wifs and MODIS data (Wang
et al., 2001).
.
MODIS classifiers (DeFries et al., 1998), validation of ocean color imagery (Chomko
et
188
Gebelein
estuarine waters (Lavery, 1993); 6) and changes in coral reef communities over
multiple years (Dustan et al., 2001).
Because of the significant length of the Landsat dataset, there have been many
studies in which Landsat images are combined with other air- or spaceborne sensor
types to yield an enhanced data product. Some examples of research that use this
approach are: 1) use of remote sensors to classify coral, algae, and sand as pure and
mixed spectra (Hochberg and Atkinson, 2003); 2) merging Landsat TM and SPOT via
wavelet transformation (Zhou and Civco, 1998); 3) coral reef habitat mapping with
Ikonos and Landsat (Capolsini, 2003); 4) change detection in coral reef environments
using Landsat and SeaWiFS data (Andréfouët et al., 2001); and 5) mapping shallowwater marine environments (Mumby and Edwards, 2002)
2.2.2 Moderate Resolution Data Products
The GLCF also has moderate resolution products and data available, including
Terra (Latin for “land”) MODIS data, United States vegetation index product, Global
MODIS-derived 500 meter tree cover product, MODIS-derived 500 meter 32-day
composite products, and MODIS-derived vegetation product for Central Brazil and
Idaho/Montana at 250 meter resolution. These GLCF products are generally more
appropriate for land based research, and will not be discussed here. However, the
NASA MODIS-derived products can provide researchers with useful information
concerning coastal and aquatic areas. Thus, those aspects of the MODIS instrument are
the focus of this section
MODIS. The MODIS sensor has 36 spectral bands, most of which have 1 km spatial
resolution and a wavelength range of 0.4 µm to 14.4 µm. MODIS is mounted on two
platforms: Aqua and Terra. Terra’s orbit is from North to South in the morning and
Aqua’s orbit is South to North in the early evening. The two MODIS sensors yield
global coverage once every 1-2 days (Townshend et al , 2005). NASA-derived data
products from MODIS can be utilized to detect, monitor, and analyze ecosystem
processes at the local, regional, and global scales. These data products include, but are
not limited to, chlorophyll concentration, organic matter concentration, sea surface
temperature, ocean primary productivity, ocean aerosol properties, and normalized
water-leaving radiance (http://modis.gsfc.nasa.gov/data/dataproducts.html).
MODIS historical data sets remain an important asset to the coastal (global)
research community. Research based on a combination of MODIS with newer datasets
such as Landsat 7 imagery, serve to enhance and validate derived products. Based on
MODIS data, scientists have conducted static and time series analyses focused on
global monitoring of air pollution (Chu et al., 2003), Landsat-derived training data for
al., 2003), and mapping concentrations of total suspended matter in coastal waters
(Miller and McKee, 2004).
MODIS data have also been combined with other data types to enhance and
validate its detection, monitoring and analysis capabilities. Several studies that
exemplify this combination include in situ measurements compared to MODIS-derived
spectral reflectances of snow and sea ice (Zhou and Li 2003), ocean-color observations
of the tropical Pacific Ocean (McClain et al., 2002), measurements of sea-skin
temperature for validation of satellite data (Minnett, 2003), and an investigation of
product accuracy as a function of inputs including Sea Wifs and MODIS data (Wang
et al., 2001).
.
MODIS classifiers (DeFries et al., 1998), validation of ocean color imagery (Chomko
et
188
Gebelein