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Supraglacial lakes detection and volume estimation from remote sensing data
Rusnák, Samo ; Brodský, Lukáš (advisor) ; Šobr, Miroslav (referee)
Supraglacial lakes detection and volume estimation from remote sensing data Abstract Supraglacial lakes play an important role in understanding glacier dynamics, including their response to climate change. This thesis explores the problematics of estimating lake depth and volume using a physical model. This brings challenges in considering the influence of various factors, such as cryoconite on glacier surface and suspended particular matter, which influences physical model, which is in research mostly neglected. Regression analysis of the g parameter of a physical model, representing light attenuation coefficient, and supervised classification of supraglacial lakes is applied in this thesis. The results reveal the variability of parameter Ad, representing lake bottom albedo reflectance, and its impact on predicted supraglacial lakes depth and volume. The results highlight the problem of global parameterisation of the physical model of supraglacial lakes and the need for further research to improve its accuracy and explore future possibilities in this field. Keywords: supraglacial lake, remote sensing, machine learning, physical model, depth estimation, regression analysis
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Využití pozemní senzorové sítě pro kalibraci družicových dat dálkového průzkumu
Doubrava, Pavel ; Batrlová, Iva ; Kvapil, Jiří ; Seidlová, Jana
Dálkový průzkum Země je disciplína založená na fyzikálních principech. Naměřená data jsou zpracovávána matematicko-fyzikálními a matematickými metodami. Základním fyzikálním jevem, na kterém je dálkový průzkum založen, je interakce elektromagnetického záření se zkoumaným objektem. Informace o tom, jak probíhá tato interakce, nám udává energie, kterou zaznamenává detektor v přesně vymezených intervalech vlnových délek v daném prostorovém úhlu. Takto definovaná interakce je výchozí pro určování hledaných vlastností daných objektů. Každý objekt na zemském povrchu má vliv na naměřené hodnoty energie, a to jak v čase, tak v prostoru. Proto je nutné, aby tato energie byla měřena prostřednictvím kvantitativních fyzikálních veličin.
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Status of implementation of the National Inventory of Contaminated Sites project
Suchánek, Zdeněk ; Řeřicha, Jaroslav ; Krhovský, Jan
By mid-April 2021, the inventory of contaminated sites in 52 districts out of the 77 districts of the Czech Republic was completed. 6403 records are already stored in the SEKM, ie 70.7 % of the planned parameter (target number of at least 9053 records). The inventory already covers 68.5 % of the inventory area. The inventory in the regions has already been completed with reports for the Jihočeský, Liberecký, Karlovarský, Královéhradecký and Pardubický regions. Monitoring and control of the “yield” of individual sources (SEKM, indications obtained by remote sensing, new sites) resulted in ongoing methodological guidance of inventory teams (control days, training, consultations, manuals and methodological recommendations). The project management gradually adopted 18 adjustments to the work schedule, ie. without affecting the project completion date. Linear extrapolation of the number of existing approved SEKM records on the entire territory of the Czech Republic makes it possible to assume that the prescribed parameter - 9053 records of contaminated sites with evaluated priority - will be achieved on time. The project will be completed by 31 December 2021.
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Hodnocení míry znečištění vodních nádrží sinicemi metodami dálkového průzkumu Země
NOVÁK, Matěj
The thesis deals with evaluating the pollution of selected water reservoirs by cyanobacteria and algae using their spectral characteristics. The thesis compares data measured by a spectroradiometer ASD Fieldspec 4 with laboratory-evaluated data collected in situ. Spectral algorithms 709/620 and 709/665 were chosen to evaluate the degree of pollution by cyanobacteria and algae. The values of the spectral algorithms 709/620 and 709/665 showed a close linear relationship with the laboratory-evaluated concentrations of phycocyanin and chlorophyll-a (709/620 with PC and 709/665 with Chl-a) and the regression models achieved considerable accuracy (R2PC = 0.7339; R2Chl-a = 0.7899). In the case of the relationship of the spectral algorithm 709/665 with TP, PP and PN, a close linear dependence was also achieved. And the regression models again achieved considerable accuracy (R2TP = 0.7273, R2PP = 0.7466, R2PN = 0.7491). This indicates that the use of the ASD Fieldspec spectrometer, and thus other DPZ methods using spectral reflectance for evaluation, offers in practice a suitable alternative to evaluating surface water status through laboratory evaluation of samples.
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Predictability of yields of the main crops in Macedonia (FYROM)
Trajchevski, Hristijan
The objective of this thesis is to investigate yield predictability of the selected crops at national, regional and municipality level in Republic of North Macedonia. Emphasis is placed on wheat, maize, tobacco and grape, due to their significance for the economy of the country, both as exporting goods and for internal use. Specifically, the aim of the thesis is to investigate if there are possibilities for predicting the yield of the selected crops early in the season. The methods used in this thesis are yield estimation model based on linear regression using one variable and yield predicting model based on Artificial Neural Network (ANN) using meteorological data and remotely sensed data, individually. From the obtained results it is possible to conclude that the model based on linear regression indicates more accurate estimations for the yield of wheat and maize, than for the grape or tobacco. However, this model is not suitable for yield forecasting, whereas ANN is more suitable, because it manages to work with non-linear and very complex relationships. From the results of the yield predicting model it is possible to conclude that the model based on ANN using meteorological data showed more accurate results compared with the model based on ANN using remotely sensed data. Both of these model types generated highly accurate predictions, proving that ANN is powerful tool for yield prediction and forecast, even several months before the harvest.
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Carbon dynamics in the chernozem region after half a century of intensive cultivation
Juřicová, Anna ; Chuman, Tomáš (advisor) ; Dostál, Tomáš (referee) ; Maerker, Michael (referee)
Soils are vital for global food security and recently they have also received attention as a tool potentially mitigating climate change due to their ability to store large amounts of carbon (C) and provide other vital non-productive functions. However, several forms of degradation threat these functions. In agricultural soils, the decline of soil organic carbon (SOC) and soil erosion are two of the degradation processes. Soil erosion laterally redistributes large amounts of SOC in hilly agricultural landscapes. Lateral fluxes further impact the SOC storage in soils and indirectly modulate the vertical C fluxes between the terrestrial sphere and the atmosphere. The aim of this thesis is to investigate the long-term (> 50 years) regional SOC changes and analyze the impacts of soil redistribution in C dynamics in a hilly agricultural landscape. We consider soil redistribution via water and tillage as the main erosion agents. The study uses a comparison of recently resampled and legacy data from historical monitoring (the 1960s) together with coupled erosion and C dynamics modelling using the SPEROC-C model. The study is conducted in the chernozem region, south Moravia, the Czech Republic. Our results showed that the change in SOC content tends to be positive after more than 50 years. Moreover,...
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