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Open access

Kristin Vreys, Marian-Daniel Iordache, Jan Biesemans and Koen Meuleman

Abstract

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.

Open access

Kristin Vreys, Marian-Daniel Iordache, Bart Bomans and Koen Meuleman

Abstract

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.

Open access

Sindy Sterckx, Kristin Vreys, Jan Biesemans, Marian-Daniel Iordache, Luc Bertels and Koen Meuleman

Abstract

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.

Open access

Adriana Marcinkowska, Bogdan Zagajewski, Adrian Ochtyra, Anna Jarocińska, Edwin Raczko, Lucie Kupková, Premysl Stych and Koen Meuleman

Abstract

This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity.

The research area covers the Szrenica Mount region on the border between Poland and the Czech Republic - the most important part of the Western Karkonosze and one of the main areas in the Karkonosze National Park (M&B Reserve of the UNESCO).

The APEX hyperspectral data that was classified in this study was acquired on 10th September 2012 by the German Aerospace Center (DLR) in the framework of the EUFAR HyMountEcos project. This airborne scanner is a 288-channel imaging spectrometer operating in the wavelength range 0.4-2.5 μm.

For reference patterns of forest and non-forest vegetation, maps (provided by the Polish Karkonosze National Park) were chosen. Terrain recognition was based on field walks with a Trimble GeoXT GPS receiver. It allowed test and validation dominant polygons of 15 classes of vegetation communities to be selected, which were used in the Support Vector Machines (SVM) classification. The SVM classifier is a type of machine used for pattern recognition. The result is a post classification map with statistics (total, user, producer accuracies, kappa coefficient and error matrix). Assessment of the statistics shows that almost all the classes were properly recognised, excluding the fern community. The overall classification accuracy is 79.13% and the kappa coefficient is 0.77. This shows that hyperspectral images and remote sensing methods can be support tools for the identification of the dominant plant communities of mountain areas.

Open access

Anna M. Jarocińska, Monika Kacprzyk, Adriana Marcinkowska-Ochtyra, Adrian Ochtyra, Bogdan Zagajewski and Koen Meuleman

Abstract

Information about vegetation condition is needed for the effective management of natural resources and the estimation of the effectiveness of nature conservation. The aim of the study was to analyse the condition of non-forest mountain communities: synanthropic communities and natural grasslands. UNESCO’s M&B Karkonosze Transboundary Biosphere Reserve was selected as the research area. The analysis was based on 40 field test polygons and APEX hyperspectral images. The field measurements allowed the collection of biophysical parameters - Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and chlorophyll content - which were correlated with vegetation indices calculated using the APEX images. Correlations were observed between the vegetation indices (general condition, plant structure) and total area of leaves (LAI), as well as fraction of Absorbed Photosynthetically Active Radiation (fAPAR). The outcomes show that the non-forest communities in the Karkonosze are in good condition, with the synanthropic communities characterised by better condition compared to the natural communities.

Open access

Giulia Tagliabue, Cinzia Panigada, Roberto Colombo, Francesco Fava, Chiara Cilia, Frédéric Baret, Kristin Vreys, Koen Meuleman and Micol Rossini

Abstract

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.

Open access

Małgorzata Krówczyńska, Ewa Wilk, Piotr Pabjanek, Bogdan Zagajewski and Koen Meuleman

Abstract

Asbestos and asbestos containing products are harmful to human health, and therefore its use has been legally forbidden in the EU. Since there is no adequate data on the amount of asbestos-cement roofing in Poland, the objective of this study was to map asbestos-cement roofing with the use of hyperspectral APEX data (288 bands at the spatial resolution of 2.7 m) in the Karpacz area (southwest Poland). A field survey constituted the basis for training and verification polygons in the classification process. A SAM classification method was performed with the following classification results: 62% producer’s accuracy, 73% user’s accuracy and an overall accuracy of 95%. The asbestos-cement roofing for buildings may be discriminated with a high classification accuracy with the use of hyperspectral imagery. The vast majority of the classified buildings were characterised by their small area (i.e. residential type buildings), which reduced the overall accuracy of the classification.