Ms. Veronika Kopacková Profile

Veronika Kopacková

at Czech Geological Survey

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Author

Publications (5)

SPIE Journal Paper | 29 June 2012

Utilization of hyperspectral image optical indices to assess the Norway spruce forest health status

Jan Misurec, Veronika Kopacková, Zuzana Lhotakova, Jana Albrechtova, Jan Hanus, Joerg Weyermann, Petya Entcheva-Campbell

JARS, Vol. 6, Issue 01, 063545, (June 2012) https://doi.org/10.1117/12.10.1117/1.JRS.6.063545

KEYWORDS: Data modeling, Hyperspectral imaging, Vegetation, Reflectivity, Magnesium, Statistical modeling, Calibration, Absorption, Statistical analysis, Data acquisition

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Proceedings Article | 29 October 2011 Paper

Air pollution detection using MODIS data

Jan Harbula, Veronika Kopacková

Proceedings Volume 8181, 81811E (2011) https://doi.org/10.1117/12.898107

KEYWORDS: MODIS, Aerosols, Statistical analysis, Atmospheric modeling, Air contamination, Data modeling, Atmospheric particles, Clouds, Pollution, Particles

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Proceedings Article | 27 October 2011 Paper

Spectroscopy as a tool for geochemical modeling

Veronika Kopacková, Stephane Chevrel, Anna Bourguignon

Proceedings Volume 8181, 818106 (2011) https://doi.org/10.1117/12.898404

KEYWORDS: Image segmentation, Absorption, Data modeling, Minerals, Reflectivity, Mining, Metals, Spectroscopy, Spectrometers, Iron

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Proceedings Article | 27 October 2011 Paper

Change detection over Sokolov open-pit mining area, Czech Republic, using multi-temporal HyMAP data (2009-2010)

S. Adar, G. Notesco, A. Brook, I. Livne, P. Rojik, V. Kopacková, K. Zelenkova, J. Misurec, A. Bourguignon, S. Chevrel, C. Ehrler, C. Fisher, J. Hanus, Y. Shkolnisky, E. Ben Dor

Proceedings Volume 8180, 81800T (2011) https://doi.org/10.1117/12.898748

KEYWORDS: Mining, Principal component analysis, Sensors, Algorithm development, Image processing, Minerals, Reflectivity, Eye, Error analysis, Visible radiation

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Two HyMap images acquired over the same lignite open-pit mining site in Sokolov, Czech Republic, during the summers of 2009 and 2010 (12 months apart), were investigated in this study. The site selected for this research is one of three test sites (the others being in South Africa and Kyrgyzstan) within the framework of the EO-MINERS FP7 Project (http://www.eo-miners.eu). The goal of EO-MINERS is to "integrate new and existing Earth Observation tools to improve best practice in mining activities and to reduce the mining related environmental and societal footprint". Accordingly, the main objective of the current study was to develop hyperspectral-based means for the detection of small spectral changes and to relate these changes to possible degradation or reclamation indicators of the area under investigation. To ensure significant detection of small spectral changes, the temporal domain was investigated along with careful generation of reflectance information. Thus, intensive spectroradiometric ground measurements were carried out to ensure calibration and validation aspects during both overflights. The performance of these corrections was assessed using the Quality Indicators setup developed under a different FP7 project-EUFAR (http://www.eufar.net), which helped select the highest quality data for further work. This approach allows direct distinction of the real information from noise. The reflectance images were used as input for the application of spectral-based change-detection algorithms and indices to account for small and reliable changes. The related algorithms were then developed and applied on a pixel-by-pixel basis to map spectral changes over the space of a year. Using field spectroscopy and ground truth measurements on both overpass dates, it was possible to explain the results and allocate spatial kinetic processes of the environmental changes during the time elapsed between the flights. It was found, for instance, that significant spectral changes are capable of revealing mineral processes, vegetation status and soil formation long before these are apparent to the naked eye. Further study is being conducted under the above initiative to extend this approach to other mining areas worldwide and to improve the robustness of the developed algorithm.

Proceedings Article | 29 October 2007 Paper

An approach for GIS-based statistical landslide susceptibility zonation: with a case study in the northern part of El Salvador

V. Kopackova, J. Sebesta

Proceedings Volume 6749, 67492R (2007) https://doi.org/10.1117/12.737835

KEYWORDS: Landslide (networking), Statistical analysis, Electroluminescence, Geographic information systems, Principal component analysis, Photography, Earthquakes, Hazard analysis, Earth observing sensors, Landsat

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The present study deals with evaluation of landslide prone zones in the northern part of El Salvador. The study area falls onto a tectonically and seismically active zone of Central America with on-going neo-tectonic activities. Focus has been put on applying the technique that allows a fast assessment of large regions. The analysis was based on digital data sets including various derivatives of digital elevation models (DEMs) as well as Landsat-based information such as micro-lineament density and landcover; seismic database, geological and morphological maps. Spatial multi-layered information has been used for landslide susceptibility analysis. Here, an inventory map of 363 landslides induced in 1998 by hurricane Mitch were used to produce a dependent variable, the statistical hazard analysis has been carried out while the zonal statistics was used to assign the weights for individual classes of the studied factors. Thus, all the relevant thematic layers representing various independent factors (slope, aspect, relative relief, lithology, drainage density, micro-lineament density and land cover) were relatively weighted and classified due to its disposition to cause landslides. Principle Component Analyses (PCA) was used as a multivariate statistical method that allowed decorrelation of the individual hazard triggers. It has been observed that the high potential zones were found to have very high lineament density, high relative relief and drainage density areas. On the young volcanic pyroclastic deposits, heavy rainfall and sparse vegetation cover cause persistent recurrence of landslides along this region. As result, a landslide susceptibility map integrating morphological, lithological and hydrological information was computed. Delineated hazard zones were again validated with the landslide inventory map and both, the model and terrain mapping, showed a good agreement as the highest class occupied the 64% of the landslide areas and the two highest classes together occupied 90% of the landslide areas, on the other hand none of the landslides fell into the lowest class.

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