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The Pinus mugo european complex {--} study of genetic structure of populations
KRAJNÍKOVÁ, Eva
The history of taxonomic classification of the complex of Pinus mugo Turra is very complicated. This complex being taxonomically the most complicated group of European pines is represented by the group of evolutionally relatively young taxa that hybridize intensively each other. In addition, the situation is complicated by rising of fertile hybrids or whole hybrid populations with ecologically very plastic species Pinus sylvestris L. The objective of this project is to explore suitability of molecular techniques for classification of intraspecific and interspecific variability in terms of the Pinus mugo complex.
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Deep learning for tree line ecotone mapping from remote sensing data
Dvořák, Jakub ; Potůčková, Markéta (advisor) ; Lefèvre, Sébastien (referee)
Deep learning is growing in popularity in the remote sensing community, especially as a classification algorithm. First part of this thesis describes deep neural networks commonly used for remote sensing classification and their various applications. Capabilities of selected geospatial software suites in relation to deep models are also discussed in this part. Theoretical findings from the first part of the thesis are validated using two deep convolutional Encoder-Decoder networks - U-Net and its proposed adaptation called KrakonosNet. They are used to perform a sematic segmentation of spruce trees and dwarf pine shrubs in the tree line ecotone of the Krkonoše Mountains, Czechia. A normalised digital surface model is employed for creation of sufficiently large amount of training data, while the classification itself is performed using only optical imagery with very high spatial resolution. Resulting classification is compared to a set of traditional remote sensing classifiers, namely Maximum Likelihood, Random Forest, and a Support Vector Machine. Both U-Net and KrakonosNet significantly outperform the other classifiers on this dataset and will be consequently used in a related research project. Key words deep learning, U-Net, Krkonoše mountains, classification, vegetation mapping, picea abies,...
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MICROCLIMATIC INFLUENCES ON WOOD PHENOLOGY OF TREES AND SHRUBS IN TREELINE ECOTONE
Hejda, Tomáš ; Treml, Václav (advisor) ; Chuman, Tomáš (referee)
This thesis deals with the influence of microclimatic conditions (air temperature near tree/shrub top, temperature of the stem and root zone) on the wood phenology of trees (Picea abies) and shrubs (Pinus mugo) growing on the same site in the alpine tree line (ATL) ecotone. Phenological and microclimatic research was carried out in the Giant Mountains at 1,370 m a. s. l. in the 2017 vegetation period, with the aim to compare the course of wood formation depending on the microclimate, to determine the temperature thresholds of wood growth and differences in the morphology of trees and shrubs. Based on the microclimate analysis of trees and shrubs, individual variables were determined (average, average minimum, average maximum, daily amplitude of air, stem and root zone temperatures) characterizing the growing season. The collection of micro-drills of trees and shrubs took place from April to October in an interval of 6 - 11 days, which were subsequently processed and examined in the laboratory. The following phenophases of wood development were distinguished: cambial, enlarging, wall-thickening, mature cells and cells of the previous ring. Subsequently, differences in the number of cells of individual phenophases were identified between trees and shrubs. Furthermore, the temperature thresholds for...
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Klimatický signál v letokruhových chronologiích borovice kleče
Samusevich, Alina ; Treml, Václav (advisor) ; Vejpustková, Monika (referee)
Pinus mugo Turra (sensu lato) is a prostrate shrub growing above the alpine timberline in the mountain ranges of Southern, Central and Eastern Europe. It is well adapted to the harsh alpine environment creating different mechanisms for survival. The research was carried out in Krkonoše Mountains on base of Pinus mugo individuals compared with Picea abies trees. Samples from shrubs were gathered using serial sectioning from four sites in different elevations located on Sněžka Mountain and Smogornia Ridge. Further analysis of the sampled material was made with the help of different detrending methods to see which method will return the best growth response to climate parameters. RCS detrending and detrending via simple averaging reflected the best climate signal contained in dwarf pine chronologies. The advantage of these methods is based on their ability to reflect growth conditions of the particular site. Upper sites showed significant correlations with temperatures of the growing season, while on lower sites the signal was quite unclear. The amount of precipitation plays the significant role on shrub growth during the vegetation period (especially July month) and early spring when water is very important for growth initiation. Upper Pinus mugo sites showed high sensitivity to droughts, especially...
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