National Repository of Grey Literature 103 records found  1 - 10nextend  jump to record: Search took 0.01 seconds. 
Remote sensing for classification of new wilderness vegetation in the hinterland of Kutná Hora
Dančejová, Daniela ; Kupková, Lucie (advisor) ; Červená, Lucie (referee)
Numerous areas in the Czech landscape have been abandoned by human activity, allowing natural processes to take over. Some of these areas have transformed into new wilderness characterized by diverse vegetation compositions, representing va- rious successional stages. The aim of this work is to conduct a comprehensive and accurate classification of vegetation in the new wilderness area utilizing remote sensing techniques. For this purpose, multispectral UAS data with a 5 cm spatial resolution, hyperspectral aerial data with a 60 cm spatial resolution, and botani- cal data collected at three different dates within the area of interest were used. Based on the collected data and the assessment of species separability, three clas- sification legends were proposed to classify the area of interest using Maximum Likelihood, Random Forest and object-based classifiers. The F1-score was used to assess the classification accuracy of vegetation classes. The results demonstrated the suitability of the object classifier for classifying a highly diverse vegetational area at a very high spatial resolution (achieving the highest overall accuracy of 84.06% across 22 classes). The Random Forest classifier yielded better results for vegetation classification on hyperspectral data with a lower spatial resolution...
Analysis of important grass species distribution in the Krkonoše Mts. tundra using remote sensing
Ježek, Vít ; Kupková, Lucie (advisor) ; Červená, Lucie (referee)
Analysis of important grass species distribution in the Krkonoše Mts. tundra using remote sensing Abstract The aim of this thesis was to test the application of maximum likelihood classification, Random forest, Support vector machine and object-oriented classification with the Support vector machine classifier on selected areas in the Krkonoše Mts. relict arctic-alpine tundra for the purpose of mapping the distribution of vegetation with a focus on conservation-important grass species. The research used pre-processed multitemporal hyperspectral data and multispectral data from UAS with a spatial resolution of 0.03 m and 0.06 m and hyperspectral aerial data with a spatial resolution of 0.6 m together with training and validation data collected by botanists directly from the fields using GPS (all data are from 2019-2021). The classifications achieved excellent results. The best overall accuracies were achieved by the object-oriented classification, with accuracies ranging between 80-95 %. Similarly, good results were also achieved by pixel methods - Random forest and Support vector machine (highest overall accuracy 94 %). Of the important grass species, Calamagrostis villosa (producer's accuracy 99.73 %, user's accuracy 99.95 %) and Deschampsia cespitosa (producer's accuracy 99.98 %, user's accuracy 99.33 %)...
Testing possibilities to extract selected landscape characteristics for description of indication-relevant bird species habitats in the Krkonoše Mts. from remote sensing data
Polák, Mojmír ; Kupková, Lucie (advisor) ; Janík, Tomáš (referee)
The thesis uses remote sensing data from two spatial scales (Sentinel-2 with a 10 x 10 m pixel and PlanetScope 3 x 3 m. It explores the possibilities of extracting selected landscape characteristics (spectral indices, land cover type, landscape metrics). In order to evaluate which characteristics and at what scale are statistically significant for the occurrence of 23 selected bird species, species richness in quadrats and the number of species of the order Passeriformes in the Krkonoše Mountains. Data on species occurrence were mapped in the year 2012-2014 The strength of the relationship between characteristics and abundance was determined by Pearson's correlation coefficient. It could not be confirmed that data with higher spatial resolution would be more beneficial for extracting landscape characteristics. Overall, the landscape characteristics did not prove functional relationships for all selected species, but for some species, species richness, and order of Passeriformes, the assumption of elevation and land cover as important factors was confirmed. Land cover was analysed using the Random Forest supervised classification method in Google Earth Engine with an overall accuracy of 78 % for Sentinel-2 data, both in tundra and in the rest of the area, and 77 % for PlanetScoce data in tundra, 66...
Analysis of tundra vegetation developement using a time series of ortoimages in the Krkonoše Mountains
Pajmová, Petra ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
Analysis of tundra vegetation developement using a time series of ortoimages in the Krkonoše Mountains Abstract The aim of this study is to analyse changes in arctic-alpine tundra vegetation in the Krkonoše Mountains using archival and current aerial imagery with red, green and blue bands and spatial resolution of 0.5 m. Three small areas of interest (cca 100  100 m) with different types of vegetation and a one larger area of the eastern tundra were studied. Several classification methods (Maximum likelihood classification, Random forest and object-based classification) were tested to obtain the best classification results. For more detailed analysis of grass species development, unsupervised classification and extended time series (5 orthoimages) were used for the area of Bílá louka. Classification were executed in softwares ENVI 5.5 and R 4.2.1. The highest overall accuracy of the 2020 image classifications were over 70% in all study areas, in some cases over 80%. With the exception of the Luční hora area (58%), the best overall accuracies for 2004 image were above 65%. After comparing classification results between years 2004 and 2020, a possible development trend was revealed. But due to low accuracy of the 2004 data classifications, this cannot be reliably demonstrated. Key words: classification,...
Subpixel approach for vegetation classification from hyperspectral and multispectral data in the Krkonoše Mts. tundra
Růžička, Josef ; Kupková, Lucie (advisor) ; Červená, Lucie (referee)
This diploma thesis focuses on the possibilities and potential of using subpixel-based classification methods for hyperspectral and multispectral data capturing selected localities of the tundra in the Krkonoše Mountains, specifically the Bílá louka meadow and Luční hora mountain areas. The thesis presents current methods for collecting and using endmembers as well as methods for the classification itself using the spectral unmixing approach, mainly in connection with the classification of heterogeneous vegetation communities. In the practical part of the thesis, various methods of collecting end members are used, especially the extraction of end member spectra directly from image data using manual, semi-automatic and automatic methods. Envi, EnMAP-Box 3 and MATLAB software are used for collection and subsequent classification. Endmembers collected in different ways are then combined with different classification methods in an attempt to achieve the most accurate result possible, which would be at the level of controlled pixel-based classification. The classification took place on two legend levels. Detailed, classifying individual plant species and less detailed, where species are aggregated into larger groups. The best results were achieved by the classification of the Bílá louka meadow...
Land cover classfication using artificial neural networks
Oubrechtová, Veronika ; Štych, Přemysl (advisor) ; Kupková, Lucie (referee)
Land cover classification using artificial neural networks Abstract This Diploma thesis deals with automatic classification of the satellite high spatial resolution image in the field of land cover. The first half of the work contains the theoretical information about remote sensing and classification methods. The biggest attention is given to the artificial neural networks. In practical part of Diploma thesis are these methods used for the classification of SPOT satellite image. Keywords: remote sensing, image classification, artificial neural networks, SPOT
Land cover changes in District Nachod using remote sensing data
Červená, Lucie ; Štych, Přemysl (advisor) ; Kupková, Lucie (referee)
Land cover changes in District Nachod using remote sensing data Abstract The purpose of this project was to create a classification of the land cover of Náchod district for years 1979, 1991 and 2001 based on multi-spectral images gained from publicly available archive images database provided by Landsat satellites. Data used in this paper are described in details. The created classification system is based on CORINE Land Cover and adjusted to a measured area and data available. The method used for images classification was method of supervised classification in PCI Geomatics program and classification algorithm of Maximum Likelihood Classification. The result was smoothed by majority filter and converted to the vector form. Accuracy of the classification was evaluated in details, based on the check points. Overall accuracy was quite low (2001 - 82 %, 1991 - 74 %, 1979 - 67 %), depending on the quality (mainly spectral and spatial resolution) of the images and also availability of other reference data. Land cover changes for the whole time period were therefore evaluated using just the balance method (i.e. overall classes distributions in district were compared between separate years). For years 1991 and 2001 it was also tried to overlap their final vector land cover layouts, however target Change areas in...
Development of selected invasive species and meadow vegetation classification algorithm in the Krkonoše Mountains using hyperspectral data
Jelének, Jan ; Kupková, Lucie (advisor) ; Halabuk, Andrej (referee)
Development of selected invasive species and meadow vegetation classification algorithm in the Krkonoše Mountains using hyperspectral data Abstract The thesis deals with utilization of airbone APEX hyperspectral image data for selected invasive species and meadow vegetation classification in the study area of the Krkonoše Mountains National Park. The mian goal of the thesis was to develop of classification algorithm based on proposed vegetation indices. The approach was based on the utilization of in-situ LAI, fAPAR, chlorophyll content data and analysis of their relation with vegetation spectral properties. The work also deals with several problems regarding LAI - vegetation indices relationship, namely saturation of LAI and mutual correlation of LAI and chlorophyll content. Tha classification was focued on invasive species Rumex alpinus and Lupinus polyphyllus, meadow vegetation with dominant Nardus stricta and dominant Trisetum flavescens and cutted lawns. Besides the proposed approach, the presented work resulted in several classification maps of study area and in spectral libraries, containing ground level spectra of studied invasive species, meadow vegetation types and several other meadow species. Keywords: hyperspectral image data, APEX, LAI, fAPAR, vegetation indices, invasive species, meadow...

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