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CUA_1701
Hanuš, Jan ; Fabiánek, Tomáš ; Fajmon, Lukáš
Aerial hyperspectral campaign with laser scanning of surface in Litvínov and Bílina dumps area was carried out on May 18., 2017. Flying Laboratory of Imaging Systems of the Global change research institute CAS was used on two location with area of 100 and 60 km2. Surface scanning took place in the spectral range 380–2450nm (in 46 and 141 bands), spatial resolution 0.5 and 1.25m and LiDAR cloud density of 4 point per m2. The resulting product was orthocertificated raster data in ENVI format (with atmospheric correction) and georeferenced LiDAR for physical geographic diversity of the mining area studies.
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CGS_1703
Hanuš, Jan ; Fabiánek, Tomáš ; Fajmon, Lukáš
Data scanned by Flying Laboratory of Imaging Systems of Global change research institut CAS was used for soil degradation modeling. Aerial survey was running on 18. 5. 2018 over Lítov and Silvestr mines. The raster data with spectral range from 380 to 2450nm, thermal data 8000 to 11500nm and 3 point per m2 cloud density of LiDAR sensor. Georeferenced and atmospherically corrected hyperspectral data was passed in spatial resolution 0.8 and 2m.
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Classification of meadow vegetation in the Krkonoše Mts. using aerial hyperspectral data and support vector machines classifier
Hromádková, Lucie ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
Meadow vegetation in the Krkonoše Mountains National Park is classified in this master thesis using aerial hyperspectral data from sensor AISA and Support Vector Machines (SVM) and Neural Networks (NN) classification algorithms. The main goals of the master thesis are to determine the best settings of SVM parameters and to propose an ideal design for a training dataset for this classification algorithm and mapping of the meadows in the Krkonoše mountains. The criterion of the tests will be the result of classification accuracy (confusion matrices and kappa coefficient). The additional goal of the master thesis is to compare performances of both utilized classifiers, especially regarding the amount of training pixels necessary for successful classification of the mountainous meadow vegetation. Classification maps of the area of interest and Python scripts are the main outputs of the master thesis. These outputs will be handed over to the Administration of the Krkonoše Mountains National Park for further utilization in the monitoring and protecting these valuable meadow vegetation communities. Key words: hyperspectral data, AISA, Support Vector Machines, Neural Networks, training dataset, mountainous meadow vegetation
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Hyperspectral data for classification of alpine treeless vegetation in the Krkonoše Mts.
Andrštová, Martina ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
Hyperspectral data for classification of vegetation of alpine treeless in the Krkonoše Mts. ABSTRACT The Master Thesis is a part of the HyMountEcos project, which deals with a complex evaluation of mountain's ecosystems in the Giant Mountains National Park using the hyperspectral data. The area of interest is alpine treeless in the Giant Mountains National Park. The main goal of this thesis was to create detailed methodology for classification of vegetation cover using hyperspectral data from AISA DUAL and APEX sensors, to find a classification method, which would improve the accuracy of the results compared to those found in the literature, and to compare the accuracy reached with these two types of the data. Many different classification algorithms (Spectral Angle Mapper, Linear Spectral Unmixing, Support Vector Machine, MESMA a Neural Net) were applied and the classification results were statistically evaluated and compared in the next part of the work. The classification method Neural Net was found as the most accurate one, as it gives the most accurate results for APEX data (the overall accuracy 96 %, Kappa coefficient 0,95) as well as for AISA DUAL data (the overall accuracy 90 %, Kappa coefficient 0,88). The resulting accuracy of the classification (the overall one and also for some classes) reached...
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Use of hyperspectral data for detection and classification of selected anthropogenic materials
Novotná, Kateřina ; Kupková, Lucie (advisor) ; Batistová, Jana (referee)
The thesis deals with use of hyperspectral data from APEX and AISA sensors for detection and classification of anthropogenic materials in the areas of Čáslav, Rokytnice nad Jizerou and Harrachov. The main goal is to propose methodology for the detection and classification of roof materials and road surface materials based on established spectral libraries. Another goal is to evaluate applicability of spectral libraries for classification, to compare possibilities of hyperspectral data with larger and smaller spectral range and to create maps of anthropogenic materials above. The methodological approach including masks of anthropogenic materials for roads surface materials and roof materials creation, settings of four classifications algorithms (Linear Spectral Unmixing, Multiple endmember spectral mixture analysis, Spectral Angle Mapper, Spectral Information Divergence) parameters and assessment of classification results, is in the methodology part. The results are visualized and evaluated using overall accuracy and percentage of classified pixels. Finally the results are compared with existing studies and possible improvements for further research are proposed. Powered by TCPDF (www.tcpdf.org)
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Laboratory and image spectroscopy for mapping of selected rocks in peak areas of the Krkonoše Mountains
Kubečková, Jana ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
Laboratory and image spectroscopy for mapping of selected rocks in peak areas of the Krkonoše Mountains Abstract This thesis deals with geological mapping of selected rocks in peak areas of the Krkonoše Mountains. Four areas of interest were situated in two parts of Krkonše Mountains - on the west side it is the area of Vysoké kolo and Harrachovy kameny and on the east side there is the area of Sněžka and the area of Kozí hřbety. The main data were acquired by the hyperspectral sensor APEX. Ground spectral measurments of selected rocks and block fields were executed and the laboratory spectral measurments of geological samples and lichens were executed. Practical part aims at classification of rocks and lichens in selected areas using four classification methods: SAM, SID, MESMA and LSU. The spectral library is one of the outputs of this thesis. This spectral library contains the spectra of pure rocks and lichens and mixtured spectra of rocks and lichens. The output of this thesis is the comparation of used classification methods, the analysis of spatial and geological accuracy and evaluation of lichens influence on the classification results, spectral library and maps of classified rocks occurrence. Keywords: classification, block fields, hyperspectral data, spectral mixture, lichens, The Krkonoše Mountains
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Determination of soluble phenolics in common spruce stands using hyperspectral data
Buřičová, Michaela ; Kupková, Lucie (advisor) ; Petruchová, Jana (referee)
The thesis deals with lignin and soluble phenolic determination in Norway spruce foliace using hyperspectral data. A literature overview is focused on the analysis of lignin and soluble phenolics. The practical part focuses on the determination of wavelenghts intervals which are suitable for the detection of lignin and soluble phenolics. There is applied regression analysis for the determination of relationship between the foliage spectra and the content of biochemical substances for the chosen spektrum intervals. Indexes NDLI, mNDLI and RLI were than calculated. HyMap hyperspectral airborne images from 2009 and 2010 for the area of Sokolov, spectral curves of dry matter and fresh branches of Norway spruce and laboratory determination of lignin and soluble phenolics content were the inputs for the analyses. Maps of lignin content in Norway spruce are the final output of the work. Keywords: Norway spruce (Picea Abies), lignin, soluble phenolics, PLS (partial least square) method, multiple Stepwise regression, NDLI
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ÚPOL Rudice – 1602
Hanuš, Jan ; Fajmon, Lukáš ; Fabiánek, Tomáš
The object of the hyperspectral campaign UPOL Rudice 1602 was collect data for estimation of soil parameters. During scanning was used sensors CASI1500 and SASI600 from FLIS – Flying Laboratory of Imaging Spectroscopy. Selection of locality and term for aircraft scanning was established according of vegetation cover status.
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VUMOP - Pesticides
Hanuš, Jan ; Fabiánek, Tomáš
The object of the hyperspectral campaign VUMOP Pesticidy was used remote sensing to determination of selected pesticides in agriculture soils. During scanning was used FLIS – Flying Laboratory of Imaging Spectroscopy in VNIR and VTIR spectral range. Selection of locality and term for aircraft scanning was established according of subsequent application
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NPŠ - 2015
Hanuš, Jan
Aim of airborne campaign NPŠ (National Park Šumava) 2015 was acquisition of georefernced hyperspectral datasets of (1) whole area National park Šumava and (2) selected sites in sufficient spatial and spectral (VNIR) resolution. Settings both datasets was adapted for use in subsequent applications.
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