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Data acquisition with the APEX hyperspectral sensor

100%
Vreys K., Iordache M.-D., Bomans B., Meuleman K.
Miscellanea Geographica. Regional Studies on Development
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2016
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vol. 20
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issue 1
5-10
EN
APEX (Airborne Prism EXperiment) is a high spectral and spatial resolution hyperspectral sensor developed by a Swiss-Belgian consortium on behalf of the European Space Agency. Since the acceptance of the instrument in 2010, it has been operated jointly by the Flemish Institute for Technological Research (VITO, Mol, Belgium) and the Remote Sensing Laboratories (RSL, Zurich, Switzerland). During this period, several flight campaigns have been performed across Europe, gathering over 4 Terabytes of raw data. Following radiometric, geometric and atmospheric processing, this data has been provided to a multitude of Belgian and European researchers, institutes and agencies, including the European Space Agency (ESA), the European Facility for Airborne Research (EUFAR) and the Belgian Science Policy Office (BelSPO). The applications of APEX data span a wide range of research topics, e.g. landcover mapping (mountainous, coastal, countryside and urban regions), the assessment of important structural and (bio)physical characteristics of vegetative and non-vegetative species, the tracing of atmospheric gases, and water content analysis (chlorophyll, suspended matter). Recurrent instrument calibration, accurate flight planning and preparation, and experienced pilots and instrument operators are crucial to successful data acquisition campaigns. In this paper, we highlight in detail these practical aspects of a typical APEX data acquisition campaign.
2
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Forest species mapping using airborne hyperspectral APEX data

86%
Tagliabue G., Panigada C., Colombo R., Fava F., Cilia C., Baret F., Vreys K., Meuleman K., Rossini M.
Miscellanea Geographica. Regional Studies on Development
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2016
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vol. 20
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issue 1
28-33
EN
The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer’s accuracy ranging from 60% to 86% and user’s accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way.
3
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Geometric correction of APEX hyperspectral data

86%
Vreys K., Iordache M.-D., Biesemans J., Meuleman K.
Miscellanea Geographica. Regional Studies on Development
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2016
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vol. 20
|
issue 1
11-15
EN
Hyperspectral imagery originating from airborne sensors is nowadays widely used for the detailed characterization of land surface. The correct mapping of the pixel positions to ground locations largely contributes to the success of the applications. Accurate geometric correction, also referred to as “orthorectification”, is thus an important prerequisite which must be performed prior to using airborne imagery for evaluations like change detection, or mapping or overlaying the imagery with existing data sets or maps. A so-called “ortho-image” provides an accurate representation of the earth’s surface, having been adjusted for lens distortions, camera tilt and topographic relief. In this paper, we describe the different steps in the geometric correction process of APEX hyperspectral data, as applied in the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). APEX ortho-images are generated through direct georeferencing of the raw images, thereby making use of sensor interior and exterior orientation data, boresight calibration data and elevation data. They can be referenced to any userspecified output projection system and can be resampled to any output pixel size.
4
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Atmospheric correction of APEX hyperspectral data

86%
Sterckx S., Vreys K., Biesemans J., Iordache M.-D., Bertels L., Meuleman K.
Miscellanea Geographica. Regional Studies on Development
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2016
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vol. 20
|
issue 1
16-20
EN
Atmospheric correction plays a crucial role among the processing steps applied to remotely sensed hyperspectral data. Atmospheric correction comprises a group of procedures needed to remove atmospheric effects from observed spectra, i.e. the transformation from at-sensor radiances to at-surface radiances or reflectances. In this paper we present the different steps in the atmospheric correction process for APEX hyperspectral data as applied by the Central Data Processing Center (CDPC) at the Flemish Institute for Technological Research (VITO, Mol, Belgium). The MODerate resolution atmospheric TRANsmission program (MODTRAN) is used to determine the source of radiation and for applying the actual atmospheric correction. As part of the overall correction process, supporting algorithms are provided in order to derive MODTRAN configuration parameters and to account for specific effects, e.g. correction for adjacency effects, haze and shadow correction, and topographic BRDF correction. The methods and theory underlying these corrections and an example of an application are presented.
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