image pixel to transform the pixel value from a measurement of reflectance to a
measurement of a biophysical property of the coral reef or surrounding water
column, atmosphere or land (Phinn et al. 2010). These approaches are based on the
assumption that the measured spectral reflectance in certain bands has a direct
relationship to the biophysical property being estimated. For example, absorption
of light at specific wavelengths have known relationships to: water column depth;
concentrations of absorbing and scattering organic and inorganic materials; concentrations of photosynthetic and non-photosynthetic pigments in coral, seagrass
and algae; and processes such as photosynthesis (Mobley 1994; Hedley and
Mumby 2002).
Several approaches are commonly used to deliver maps of coral reef biophysical properties. In the first case, the relative area of each pixel occupied by a
set of coral reef benthic cover types (e.g., coral, sand, algae) is estimated using
‘‘unmixing’’ techniques. These techniques assume the image pixel is larger than
the features to be mapped and are applied to images which have had the influence
of the water column removed (Hedley and Mumby 2003; Hedley et al. 2004;
Goodman and Ustin 2007; Lesser and Mobley 2007). The mathematical solutions
required for these techniques become more accurate as the number of un-correlated input variables (spectral bands in this case) increases; hence hyperspectral
image data are used predominantly in this approach. The remaining approaches,
commonly referred to as ‘‘inversion’’ techniques use empirical or analytic mathematical solutions to extract biophysical information from image pixels, including
water depth, concentrations of organic and inorganic material in the water column,
and benthic/substrate reflectance signatures. Empirical approaches are mainly used
for estimating depth or bathymetric surfaces, require calibration against field
measured depths, and typically only function accurately over homogeneous substrates to depths of 5–10 m. These techniques can be applied to both multispectral
and hyperspectral data. Analytic and semi-analytic approaches function more
effectively on hyperspectral image data sets, and often require locally specific field
data on optical properties of the water column and benthic spectral reflectance
signatures to produce accurate results. These results, however, are more robust
than empirical approaches and produce accurate maps to depths of 20–25 m in
areas with heterogeneous benthic and substrate features (Kutser et al. 2006;
Dekker et al. 2011).
1.4 Future Directions
Advances in science and technology will affect the sensors, data types, data
accessibility, processing techniques and, collectively, our ability to transform
remotely sensed images into maps of coral reef biophysical properties. Scientific
advances pertain to the ongoing development and testing of image processing
algorithms to more accurately map and monitor biophysical properties of coral
22
S. R. Phinn et al.
measurement of a biophysical property of the coral reef or surrounding water
column, atmosphere or land (Phinn et al. 2010). These approaches are based on the
assumption that the measured spectral reflectance in certain bands has a direct
relationship to the biophysical property being estimated. For example, absorption
of light at specific wavelengths have known relationships to: water column depth;
concentrations of absorbing and scattering organic and inorganic materials; concentrations of photosynthetic and non-photosynthetic pigments in coral, seagrass
and algae; and processes such as photosynthesis (Mobley 1994; Hedley and
Mumby 2002).
Several approaches are commonly used to deliver maps of coral reef biophysical properties. In the first case, the relative area of each pixel occupied by a
set of coral reef benthic cover types (e.g., coral, sand, algae) is estimated using
‘‘unmixing’’ techniques. These techniques assume the image pixel is larger than
the features to be mapped and are applied to images which have had the influence
of the water column removed (Hedley and Mumby 2003; Hedley et al. 2004;
Goodman and Ustin 2007; Lesser and Mobley 2007). The mathematical solutions
required for these techniques become more accurate as the number of un-correlated input variables (spectral bands in this case) increases; hence hyperspectral
image data are used predominantly in this approach. The remaining approaches,
commonly referred to as ‘‘inversion’’ techniques use empirical or analytic mathematical solutions to extract biophysical information from image pixels, including
water depth, concentrations of organic and inorganic material in the water column,
and benthic/substrate reflectance signatures. Empirical approaches are mainly used
for estimating depth or bathymetric surfaces, require calibration against field
measured depths, and typically only function accurately over homogeneous substrates to depths of 5–10 m. These techniques can be applied to both multispectral
and hyperspectral data. Analytic and semi-analytic approaches function more
effectively on hyperspectral image data sets, and often require locally specific field
data on optical properties of the water column and benthic spectral reflectance
signatures to produce accurate results. These results, however, are more robust
than empirical approaches and produce accurate maps to depths of 20–25 m in
areas with heterogeneous benthic and substrate features (Kutser et al. 2006;
Dekker et al. 2011).
1.4 Future Directions
Advances in science and technology will affect the sensors, data types, data
accessibility, processing techniques and, collectively, our ability to transform
remotely sensed images into maps of coral reef biophysical properties. Scientific
advances pertain to the ongoing development and testing of image processing
algorithms to more accurately map and monitor biophysical properties of coral
22
S. R. Phinn et al.