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Vliv vegetace na letní teplotní extrémy ve městech hodnocené z pozemních a leteckých dat
ROHÁČ, Václav
This thesis examines the effect of surface types and vegetation types on ambient temperature during summer temperature extremes. The theoretical part targets the topics of remote sensing, electromagnetic radiation, urban and global climate. Within the practical part, 3 areas of interest were selected from aerial hyperspectral data from the territory of the statutory city of České Budějovice, each representing a different part of the city in terms of land use. By means of data analysis, graphs showing the influence of the relative representation of the variant surfaces on the temperature were created using the linear regression method. From the maps of tree species occurring in the parks of the first two areas, the most abundant species were selected and the average temperature within 2 meters of them was examined. The thesis also includes an evaluation of a questionnaire answered by city residents on the topic of preferences and knowledge about the urban green space.
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Subpixel approach for vegetation classification from hyperspectral and multispectral data in the Krkonoše Mts. tundra
Růžička, Josef ; Kupková, Lucie (advisor) ; Červená, Lucie (referee)
This diploma thesis focuses on the possibilities and potential of using subpixel-based classification methods for hyperspectral and multispectral data capturing selected localities of the tundra in the Krkonoše Mountains, specifically the Bílá louka meadow and Luční hora mountain areas. The thesis presents current methods for collecting and using endmembers as well as methods for the classification itself using the spectral unmixing approach, mainly in connection with the classification of heterogeneous vegetation communities. In the practical part of the thesis, various methods of collecting end members are used, especially the extraction of end member spectra directly from image data using manual, semi-automatic and automatic methods. Envi, EnMAP-Box 3 and MATLAB software are used for collection and subsequent classification. Endmembers collected in different ways are then combined with different classification methods in an attempt to achieve the most accurate result possible, which would be at the level of controlled pixel-based classification. The classification took place on two legend levels. Detailed, classifying individual plant species and less detailed, where species are aggregated into larger groups. The best results were achieved by the classification of the Bílá louka meadow...
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Study of common bean drought response with the use of leaf optical properties
Svitáková, Lenka ; Lhotáková, Zuzana (advisor) ; Zámečník, Jiří (referee)
The negative effects of climate change have affected the conditions of agriculture areas, which leads to hampered cultivation of crops, including the common bean's cultivation. Changes, such as extreme temperature swings and lowered water availability in soil create a big challenge for today's agriculture in seeking solutions for safeguarding the food security for all people on our planet. The aim of this thesis was to study a wider range of genotypes of common bean to quantify their resistance against lowered water availability in soil, and to establish new approaches for detecting drought stress with the use of leaf optical properties. From the methodological point of view, this thesis connects the leaf optical properties with anatomical and biophysical leaf traits. The common bean (Phaseolus vulgaris) and the tepary bean (Phaseolus acutifolius) were employed as the model organisms. Plants were cultivated in two differing environments - in a greenhouse setting at the Faculty of Science of the Charles University in Prague and on the experimental fields in Colombia at the international research institute for tropical agriculture - Alliance of Biodiversity International and CIAT. There were 48 genotypes included into the field experiments in Colombia. These included genotypes from both Mesoamerican...
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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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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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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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The influence of spectral resolution on land cover classification in Krkonoše Mts. tundra
Palúchová, Miroslava ; Červená, Lucie (advisor) ; Kupková, Lucie (referee)
The influence of spectral resolution on land cover classification in Krkonoše Mts. tundra Abstract The aim of this diploma thesis was to specify the spectral resolution requirements for classification and to identify the most important spectral bands to discriminate classes of the predefined legend. Aerial hyperspectral data acquired by AisaDUAL sensor were used. The method applied for the selection of the important bands was discriminant analysis performed in IBM SPSS Statistics. The most discriminative bands were found in intervals 1500-1750 nm (beginning of SWIR), 1100- 1300 nm (longer wavelengths of NIR), 670-760 (red-edge) and 500-600 nm (green light). The classification of the selected bands was realized in ENVI 5.4 using the Support Vector Machine classifier, achieving overall accuracy of 80,54 %, Kappa coefficient 0,7755. The suitability of available satellite data for the classification of tundra vegetation in Krkonoše mountains based on spectral resolution was evaluated as well. Keywords: tundra, Krkonoše, classification, spectral resolution, class separability, discriminant analysis, hyperspectral data
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