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Determination of evapotranspiration from small catchments
Toušková, Jitka ; Šípek, Václav (advisor) ; Možný, Martin (referee) ; Brom, Jakub (referee)
Evapotranspiration (ET) plays a significant role in the hydrological balance. The terms potential (PET) and reference (RET) evapotranspiration are often used while estimating its rate. The doctoral thesis deals with the estimation of PET, RET and other selected processes. First, the influence of net longwave radiation (the component of radiation balance) on the rate of PET was examined. It was found that the standard methods result in the significant differences in PET estimation due to the absence of model calibration to local conditions. The original model caused distinction in the PET evaluation for the Liz experimental catchment by up to 100 mm/year. Calibration of the parameters of two commonly used methods for calculating net longwave radiation reduced the error in PET evaluation to less than 20 mm/year. PET or RET estimation itself can be performed by many direct or indirect methods. Their accuracy is highly discussed. This work focused on selection of suitable methods and their further testing on conditions of 18 stations in the Czech Republic. 37 methods were compared with measured data. It was proven, that the best results in this region were achieved by combination methods (with average RMSE of 1.2 mm/day, 18.6 mm/month, and 33.3 mm/year). Among individual models, the radiation-based...
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Evaluation of forest vegetation based on time series of remote sensing data
Laštovička, Josef ; Štych, Přemysl (advisor) ; Brom, Jakub (referee) ; Bucha, Tomáš (referee)
Příloha k disertační práci: Abstrakt v AJ (Mgr. Josef Laštovička) Abstract This dissertation thesis deals with the study of forest ecosystems in the central Europe with the time series of multispectral optical satellite data. These forest ecosystems have been influenced by biotic and abiotic disturbances for the last decade. The time series of the satellite data with high spatial resolution allow the detection and analysis of forest disturbances. This thesis is mainly focused primally on free available Landsat and Sentinel-2 data, these two data types were compared. From methods, the difference time series analyses / algorithms were used. The whole thesis can be divided into two main parts. The first one analyses usability of classifiers for detection of forest ecosystems with per-pixel and sub-pixel methods. Specifically, the Neural Network, the Support Vector Machine and the Maximum Likelihood per-pixel classifiers were used and compared for different types of data (for data with high spatial resolution - Landsat or Sentinel-2; very high spatial resolution - WorldView-2) and for classification of protected forest areas. The Support Vector Machine were selected as the most suitable method for forest classifications (with most accurate outputs) from the list of selected per-pixel classifiers. Also, Spectral...
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Evaluation of forest vegetation based on time series of remote sensing data
Laštovička, Josef ; Štych, Přemysl (advisor) ; Brom, Jakub (referee) ; Bucha, Tomáš (referee)
Příloha k disertační práci: Abstrakt v AJ (Mgr. Josef Laštovička) Abstract This dissertation thesis deals with the study of forest ecosystems in the central Europe with the time series of multispectral optical satellite data. These forest ecosystems have been influenced by biotic and abiotic disturbances for the last decade. The time series of the satellite data with high spatial resolution allow the detection and analysis of forest disturbances. This thesis is mainly focused primally on free available Landsat and Sentinel-2 data, these two data types were compared. From methods, the difference time series analyses / algorithms were used. The whole thesis can be divided into two main parts. The first one analyses usability of classifiers for detection of forest ecosystems with per-pixel and sub-pixel methods. Specifically, the Neural Network, the Support Vector Machine and the Maximum Likelihood per-pixel classifiers were used and compared for different types of data (for data with high spatial resolution - Landsat or Sentinel-2; very high spatial resolution - WorldView-2) and for classification of protected forest areas. The Support Vector Machine were selected as the most suitable method for forest classifications (with most accurate outputs) from the list of selected per-pixel classifiers. Also, Spectral...
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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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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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