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Airborne Imaging Spectroscopy
The data required from airborne imaging spectroscopy to finally generate a map of
plant species that covers all the study area is a geocoded image with all the bands
transformed to ground reflectance. The airborne imaging spectroscopy operator has
a key role in delivering this part of the process and must use an accurate radiometric,
spectrally and geometrically calibrated airborne imaging spectrometer.
(i) An airborne flight campaign comprises several flight lines designed to cover
the study area, constrained by imagery acquisition requisites such as the spatial
resolution, flight time and date. In this sense, HYperspectral REmote Sensing
in Europe specific Support Actions (HYRESA) establishes a user requisites
model that determines the local surveillance area (Reusen et al. 2007). The
plant spectral library could help to indicate the best time of the year for maximum separability among species, but it is also important to take into account
that higher solar elevation angles correspond with high SNR imagery and a
better capacity for discrimination. For mission planning, it is important to
remember that the number of flight lines needed to cover the survey area will
increase with spatial resolution.
(ii) In general, the operator implements the geometric and radiometric algorithms
in a processing and archiving facility (PAF), to integrate an operational workflow that automates the process to transform all the flight lines of the entire
campaign. This facility incorporates all the calibrations and auxiliary parameters required. Methods of direct georeferencing rely on high precision position
and attitude measurements using an onboard Global Position System and
Inertial Navigation System, the bundle adjustment parameters obtained in a
geometric calibration flight, and a high-resolution digital elevation model.
Radiometric corrections include the calibration coefficients to transform the
digital values to at-sensor radiance, and an atmospheric compensation method
to obtain ground reflectance. In this sense, the atmospheric compensation
methodology could be empirical, such as the Empirical Line Correction (Smith
and Milton 1999), or physically-based on radiative transfer models such as
MODTRAN (Berk et al. 2006).
(iii) Data quality is an intrinsic property that evaluates the reliability of acquired
data (International Organization for Standardization (ISO) 2003). In this sense,
airborne spectroscopy imagery must be evaluated against the proposed user
requisites. Typically, a georeferenced pixel must be processed with a geolocation error of less than two pixels and the accuracy of reflectance values within
5% (Biesemans et al. 2007). To achieve this quality, we recommend comparison with ground truth data, ground control points (i.e., crossroads) for georeferenced verification, and field spectra of comparable surfaces (i.e., bare soil)
for reflectance evaluation. ISO 19157:2013 “Geographic Information – Data
quality” establishes the principles to describe the quality of geographic data.
Sub-pixel Mapping of Doñana Shrubland Species
Airborne Imaging Spectroscopy
The data required from airborne imaging spectroscopy to finally generate a map of
plant species that covers all the study area is a geocoded image with all the bands
transformed to ground reflectance. The airborne imaging spectroscopy operator has
a key role in delivering this part of the process and must use an accurate radiometric,
spectrally and geometrically calibrated airborne imaging spectrometer.
(i) An airborne flight campaign comprises several flight lines designed to cover
the study area, constrained by imagery acquisition requisites such as the spatial
resolution, flight time and date. In this sense, HYperspectral REmote Sensing
in Europe specific Support Actions (HYRESA) establishes a user requisites
model that determines the local surveillance area (Reusen et al. 2007). The
plant spectral library could help to indicate the best time of the year for maximum separability among species, but it is also important to take into account
that higher solar elevation angles correspond with high SNR imagery and a
better capacity for discrimination. For mission planning, it is important to
remember that the number of flight lines needed to cover the survey area will
increase with spatial resolution.
(ii) In general, the operator implements the geometric and radiometric algorithms
in a processing and archiving facility (PAF), to integrate an operational workflow that automates the process to transform all the flight lines of the entire
campaign. This facility incorporates all the calibrations and auxiliary parameters required. Methods of direct georeferencing rely on high precision position
and attitude measurements using an onboard Global Position System and
Inertial Navigation System, the bundle adjustment parameters obtained in a
geometric calibration flight, and a high-resolution digital elevation model.
Radiometric corrections include the calibration coefficients to transform the
digital values to at-sensor radiance, and an atmospheric compensation method
to obtain ground reflectance. In this sense, the atmospheric compensation
methodology could be empirical, such as the Empirical Line Correction (Smith
and Milton 1999), or physically-based on radiative transfer models such as
MODTRAN (Berk et al. 2006).
(iii) Data quality is an intrinsic property that evaluates the reliability of acquired
data (International Organization for Standardization (ISO) 2003). In this sense,
airborne spectroscopy imagery must be evaluated against the proposed user
requisites. Typically, a georeferenced pixel must be processed with a geolocation error of less than two pixels and the accuracy of reflectance values within
5% (Biesemans et al. 2007). To achieve this quality, we recommend comparison with ground truth data, ground control points (i.e., crossroads) for georeferenced verification, and field spectra of comparable surfaces (i.e., bare soil)
for reflectance evaluation. ISO 19157:2013 “Geographic Information – Data
quality” establishes the principles to describe the quality of geographic data.
Sub-pixel Mapping of Doñana Shrubland Species