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Temperature characteristics of surface using remote sensing methods
Hofrajtr, Martin ; Štych, Přemysl (advisor) ; Brom, Jakub (referee)
Temperature characteristics of surface using remote sensing methods Abstract The aim of this thesis is to design a methodology for refining the land surface temperature values obtained from Landsat 8 satellite data in areas with diverse land cover. The research section describes factors influencing the radiation of the Earth's surface. Also mentioned are current methods used for processing infrared thermal data and calculate land surface temperature. The practical part describes satellite and airborne data used in the analytical and verification process. All parts of the applied method leading to the subpixel value of the land surface temperature are described in detail in the method part. The results are then compared with airborne verification data with better spatial resolution and with currently used methods. Finally, the pros and cons of this method and its possible improvement in the future are mentioned. Key words: land surface temperature, land surface emissivity, satellite data, Landsat 8, airborne data, subpixel method, Czech Republic
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Tree species classification using sentinel-2 and Landsat 8 data
Havelka, Ondřej ; Štych, Přemysl (advisor) ; Kupková, Lucie (referee)
The main objectives of this master thesis are to evaluate and compare chosen classification algorithm for the tree species classification. With usage of satellite imagery Sentinel-2 and Landsat 8 is examined whether the better spatial resolution affects the quality of the resulted classification. According to past case studies and literature was chosen supervised algorithms Support Vector Machine, Neural Network and Maximum Likelihood. To achieve the best possible results of classification is necessary to find a suitable choice of parameters and rules. Based on literate was applied different settings which were subsequently evaluated by cross validation. All results are accompanied by tables, charts and maps which comprehensively and clearly summarize the answers to the main objectives of the thesis.
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Evaluation of methods and input data for land cover classification: case study of the former military areas Ralsko and Brdy
Paluba, Daniel ; Štych, Přemysl (advisor) ; Brom, Jakub (referee)
Taking advantage of Earth Observation (EO) data for monitoring land cover has attracted the attention of a broad spectrum of researchers and end-users in recent decades. The main reason of increased interest in EO can be found mainly in open data of Landsat and Sentinel archive. The main objective of this study is to evaluate the accuracy of the classification algorithms Maximum Likelihood (ML) and Support Vector Machine (SVM) using Landsat 8 and Sentinel-2 data in the case studies of the former military training areas Brdy and Ralsko, which have undergone a very specific land cover development. The study evaluates the land cover in both case studies in 2016 and based on the obtained results discussing a usefulness of the selected data and methods. The results of the land cover classification achieved satisfactory accuracy - the overall accuracy was higher than 85 %. Based on the expectation, the results of accuracy based on SVM algorithm are higher than results obtained by ML algorithm. The highest accuracy has reached in the land cover classes of water bodies and coniferous forests, on the contrary, the lowest accuracy in built-up areas, sparse vegetation and bare soil. Keywords: Earth Observation, Support Vector Machine, Maximum Likelihood, Czechia, Sentinel-2, Landsat 8
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Classification of forests damaged by disturbance using multispectral satellite data
Šmausová, Barbora ; Štych, Přemysl (advisor) ; Červená, Lucie (referee)
The main objective of this thesis is to create an appropriate methodological procedure for classifying damaged forest in the selected area of Šumava National Park. For this purpose, multispectral imagery WorldView-2 and Landsat 8 are used. Work emphasis on distribution of each phase of forest development affected by bark beetle. According to selected legend, involving multiple stages of damaged but also recovering forest, the images are classified by Neural Network, Support Vector Machine and object classification methods. Application of these methods on selected images required a suitable choice of parameters and rules to achieve optimal results. The results of this thesis compare and evaluate the final classification. Another outcome of this work is to evaluate the influence of the processed images WorldView-2 and Landsat 8 on the final classification performance. All work results are assessed by overall precision, error matrix and kappa coefficient. Powered by TCPDF (www.tcpdf.org)
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