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Changes in the coverage and orography of the 4th order basin in relation to the construction of the motorway evaluated from Sentinel-2 satellite data and aerial LiDAR data
ŽŮČEK, Petr
This research is focused on changes of land cover and orography in fourth order river basin. For this purpose, satellite multispectral Sentinel-2 data and airborne LiDAR data were used. The main goal of this work was to verify to which extent can free and publicly available Sentinel-2 data be used to assessment of landscape changes for the use of land planning. To verify the goal the timeseries of the Sentinel-2 satellite data was used for the assessment of the land cover in a relation to the construction of the D3 highway on the selected area. Sentinel-2 data were downloaded, resampled, and classified. Maximum Likelihood method of supervised classification was used. The categories of land cover were created using training areas in the ArcMap software. The accuracy of the classi-fication from 22. 09. 2020 was verified using validation points, which were generated randomly. By field survey classes of land cover were defined. From final classification data, new data about the change of land cover were obtained. The LiDAR data were resampled to the same spatial resolution and differences were evaluated. Areas with significant variances in orography were retrieved. From LiDAR, drainage network models were created. The results of models were compared and discussed. The results of comparison of Sentinel-2 data from 2017 to 2020 shows significant increase in representation of areas with sparse vegetation by 46,39 ha and areas with grass and shrub vegetation by 38,39 ha. Furthermore, there was an increase in meadow areas by 7,02 ha and forest clearing by 1,95 ha. The representation of arable land was decreased by 34,78 ha, forests by 29,05 ha, water areas by 12,12 ha, urbanization by 13,39 ha and areas with ongoing construction by 4,38 ha. The results of Li-DAR data comparison showed several areas with significant orography alteration. The compari-son of drainage network models revealed a distinct variation. Significant part of the runoff water flowed into the neighboring 1-06-03-0030 basin. After the recultivation of former waste pond, divided parts of the Hodějovice stream water gate were connected and the water from the whole basin ends in an outlet of the basin on which this research is focused on. The overall accuracy 0,914 and Kappa coefficient of 0,902 show that used ap-proach of Sentinel-2 data processing provides with sufficiently spatially and themati-cally accurate classification of land cover, apart from the area of urbanization. Classi-fication in built-up area had the user-accuracy of 0,867. Data obtained from Sentinel-2 may be used in several parts of land planning. It is also possible to use them for: updates of land usage, determination of actual growth condition, monitoring of forest complexes or for monitoring of recultivations. LiDAR data may be effectively used for the monitoring of orography variations, modelling of drainage network models, and determining of critical points.
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Possibilities of using satellite data Sentinel-2 in landscape planning
TOMS, Petr
The diploma thesis focuses on the analysis of land cover changes and characteristics of humidity in the Dobřejovický stream basin, using Sentinel-2 data. The aim of the work was to find out how the obtained results can be used for landscape planning. The first part of the thesis deals with the literature search, which is based on the principles of remote sensing, electromagnetic spectrum, spectral expression of objects, multispectral data and and satellite data Sentinel-2, provided by the European Space Agency. The practical part contains the description of the area of interest, the methods used in processing Sentinel-2 data. An important part is focused on the classification of data from which the outputs are created, the results are interpreted and the evaluated accuracy of the classification of land cover changes. Furthermore, the practical part is devoted to the calculation of vegetation indices, thanks to which we can obtain information about humidity characteristics. Part of the work also points to the usability of the obtained results in the forms of landscape planning.
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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...
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Classification of selected agricultural crops from time series of Sentinel-2 and PlanetScope imagery in Kutnohorsko model area
Kuthan, Tomáš ; Kupková, Lucie (advisor) ; Potůčková, Markéta (referee)
Classification of selected agricultural crops from time series of Sentinel-2 and PlanetScope imagery in Kutnohorsko model area Abstract The thesis is focused on the analysis of spectral characteristics of selected agricultural crops druring agriculutural season from time series of Sentinel -2 (A and B) and PlanetScope sensors in the model area situated around the settlements of Kolín and Kutná Hora. It is based on the assumption that the use of multiple dates of image data acquired crops in different phenological phases of the crops allows better identification of crop species (Lu et al., 2004). The aim of the thesis was to analyse the characteristics of the seasonal course of spectral features of selected agricultural crops (sugar beet, spring barley, winter barley, maize, spring wheat, winter wheat, winter rape) and to determine the period of the year suitable for the differentiation of individual crops. Another aim of the thesis was to classify these crops in the model area from time series of two above-mentioned sensors and to compare the accuracy of the pixel and object-oriented classification approach for multitemporal composites and the accuracy for monotemporal image from the term when the individual crops are clearly distinguishable. The training and validation datasets and the classification mask...
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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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Multitemporal segmentation of landscape based on dynamic time warping
Suske, Daniel ; Štych, Přemysl (advisor) ; Kolář, Jan (referee)
The thesis provides new insight into the segmentation processing of freely available satellite time- series data. It is based on open source technologies and offers a new method of unsupervised segmentation, using time series data along with the method originally designed for speech recognition, Dynamic Time Warping (DTW). The thesis deals with the selection of suitable data with sufficient temporal, spatial, and spectral resolution. The most suitable solution in all respects is the Sentinel 2 constellation. The thesis is based on the assumption that the use of time series improves segmentation results compared to segmentation based on one image. The main objective of the work was to find or to create a segmentation method that will take into account not only the time- series data but also the method of calculating the closest relationships using the DTW method. Another goal was to create an algorithm that preprocesses the time series data of the chosen constellation with minimal user input, allows the user to gradually visualize intermediate results and offers a comparison to alternative, currently available segmentation methods used in the scientific community. The main part is the developed segmentation algorithm, whose results can be influenced by tuning the parameters. The results of the work...
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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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