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Geometric Aspects of Detecting Grassland Mowing in Krkonoše Mountains Based on Sentinel-1 Coherence
Dvořák, Jakub ; Potůčková, Markéta (advisor) ; Mouratidis, Antonios (referee)
Grassland mowing is a common management practice used in European grasslands for livestock fodder production and to enhance biodiversity. To support a less intensive use of grasslands, public agencies look for a reliable way to monitor the management performed on the grasslands. Satellite remote sensing is a key tool for monitoring over large areas, with SAR remote sensing being especially useful in areas with high cloud cover. However, grassland monitoring using SAR in complex terrain is not fully understood and may come with challenges related to topography and sensor geometry. To explore these potential challenges, this thesis detected mowing events using a high-resolution DEM for precise coregistration and terrain correction of Sentinel-1 SAR imagery. Effect of local incidence angle on detection accuracy from interferometric coherence was also explored. The hypotheses were tested on 61 grassland plots in Krkonoše mountains, Czechia. Detection accuracies in this thesis were higher than in previous studies when only considering SAR detections. The improvement was most likely caused by counting detections from individual orbits to assess the certainty of each detection. A deeper analysis showed that using a high-resolution DEM led to a horizontal shift in computed coherence, but the shift had no...
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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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Multi-temporal classification of agricultural crops using Sentinel-1
Kopecký, Kamil ; Štych, Přemysl (advisor) ; Mouratidis, Antonios (referee)
Multi-temporal classification of agricultural crops using Sentinel-1 Abstract This diploma thesis aimed on the exploration of the reflective behavior of individual agricultural crops during the vegetation season. Statistical analysis of agricultural crops was carried out on the basis of multi-temporal SAR C-band Sentinel-1 data. The crop's backscatter was observed during the year 2016. Classification rules were made from detected characteristics. Achieved knowledge was applied and crops separation was done. The result of separation was successful in class Maize. Spring and Winter grains was impossible to distinguish. The possible reasons of poor results are mentioned and further improvements are suggested. Keywords: SAR, C-Band, crops, object-based classification, SENTINEL-1
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