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Assessment of vegetation phenology using Sentinel-2 time series data
Danilchyk, Tatsiana ; Štych, Přemysl (advisor) ; Bohovic, Roman (referee)
This work aims to evaluate the detection of phenological phases of vegetation based on phenometric parameters according to archival Sentinel-2 data in the selected areas over the period 2018-2020. The first part of the work describes literature review of the relevant publications, which is followed by the description of the suggested methodology. Then, there are the results with the graphic material and description for each monitored site. In the final part of the work, advantages and disadvantages of the developed algorithm are discussed followed up by suggestions for future research and improvement. The developed algorithm consists of two parts. Masking out cloudy and cloud shadow pixels and generation on the vegetation indices time series is done in the GEE platform. The time series analysis and detection of SOS and EOS as well as statistical analysis are done in the R environment. The study areas of size 20 x 20 m represent different species of perennial vegetation across the Czech Republic. For the assessment of the phenophases detection are selected NDVI, RENDVI, NDRE, NDMI and MCARI. The Asymmetric Gaussian function and Double Logistic function are fitted to the time series of each vegetation season in each tested site, the phenology metrics are derived based on threshold or derivatives...
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Landcover classification of selected parts of Ethiopia based on machine learning method
Valchářová, Daniela ; Štych, Přemysl (advisor) ; Nedbal, Václav (referee)
Diploma thesis deals with the land cover classification in Sidama region of Ethiopia and 2 kebeles, Chancho and Dangora Morocho. High resolution Sentinel-2 and very high resolution PlanetScope satellite images are used. The development of the classification algorithm is done in the Google Earth Engine cloud based environment. Ten combinations of the 4 most important parameters of the Random Forest classification method are tested. The defined legend contains 8 land cover classes, namely built-up, crops, grassland/pasture, forest, scrubland, bareland, wetland and water body. The training dataset is collected in the field during the fall 2020. The classification results of the two data types at two scales are compared. The highest overall accuracy for land cover classification of Sidama region came out to be 84.1% and kappa index of 0.797, with Random Forest method parameters of 100 trees, 4 spectral bands entering each tree, value of 1 for leaf population and 40% of training data used for each tree. For the land cover classification of Chancho and Dangora Morocho kebele with the same method settings, the overall accuracy came out to be 66.00 and 73.73% and kappa index of 0.545 and 0.601. For the classification of Chancho kebele, a different combination of parameters (80, 3, 1, 0.4) worked out better...
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A correction of the local incidence angle of SAR data: a land cover specific approach for time series analysis
Paluba, Daniel ; Štych, Přemysl (advisor) ; Mouratidis, Antonios (referee)
To ensure the highest possible temporal resolution of SAR data, it is necessary to use all the available acquisition orbits and paths of a selected area. This can be a challenge in a mountainous terrain, where the side-looking geometry of space-borne SAR satellites in combination with different slope and aspect angles of terrain can strongly affect the backscatter intensity. These errors/noises caused by terrain need to be eliminated. Although there have been methods described in the literature that address this problem, none of these methods is prepared for operable and easily accessible time series analysis in the mountainous areas. This study deals with a land cover-specific local incidence angle (LIA) correction method for time-series analysis of forests in mountainous areas. The methodology is based on the use of a linear relationship between backscatter and LIA, which is calculated for each image separately. Using the combination of CORINE and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual image. The algorithm is prepared and tested in cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, SRTM digital elevation model, and CORINE and Hansen Global Forest databases. The method was tested...
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Zavádění technologií precizního zemědělství na rodinné farmě
Konečný, Petr
The subject of the bachelor thesis is focused on theoretical description of all the parts, which are the part of the precision agriculture technology. The sources of informations contained the experience of experts and the domestic and foreign literature. This knowledge was theoretically applicated in the environment of the small family farm in Haná region with 195 ha. The best possible way to introduce the technologies of precision agriculture were estimated after consultations with the owner. The part of this thesis is the description of the making of the nitrogen variability map workflow.
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