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Synthesis of digital landscape surface data
Šebesta, Michal ; Kahoun, Martin (advisor) ; Křivánek, Jaroslav (referee)
A procedural generation of landscapes often meets a need for real spatial data at finer resolution that data available at the moment. We introduce a method that refines the spatial data at the coarse resolution into the finer resolution utilizing other data sources which are already at the better resolution. We construct weighted local linear statistical models from both the coarse and utility data and use the by- models-learned dependencies between the data sources to predict the needed data at better resolution. To achieve higher computational speed and evade utility data imperfection, we utilize truncated singular value decomposition which reduce a dimensionality of the data space we work with. The~method is highly modifiable and its application shows plausible real-like results. Thanks to this, the method can be of practical use for simulation software development. Powered by TCPDF (www.tcpdf.org)
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Využití dálkového průzkumu pro odhad výnosů zemědělských plodin
Rosendorfská, Eva
Knowledge og the crop yield with sufficient lead time prior to harvest is crucial for the farm management or national agro-food policy. Spectral characteristics provided by satellite based remote sensing have both spatial and temporal resolution which allow crop yields from agricultural fields. The aim of this thesis was to test feasibility of developing crop yield. The study was focused on three major crops in the Czech Republic: spring barely, winter wheat and oilseed rape. The crop yield data were collected from 14 districts that represent regions with more intensive agricultural production and include a variety of climate, topographic and soil conditions. As a main data source for this thesis was series of digital images acquired by MODIS (Moderate Resolution Imaging Spectroradiometr) aboard Terra satellite from 2001-2014 period. Were analyzed two vegetation idices NDVI (Noramized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) from the 16-days composite product with a spatial resolution of 250 m. In most cases, EVI showed higher correlations to the crop yied, which can be explained due to the negative saturation effect of NDVI.
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Synthesis of digital landscape surface data
Šebesta, Michal ; Kahoun, Martin (advisor) ; Křivánek, Jaroslav (referee)
A procedural generation of landscapes often meets a need for real spatial data at finer resolution that data available at the moment. We introduce a method that refines the spatial data at the coarse resolution into the finer resolution utilizing other data sources which are already at the better resolution. We construct weighted local linear statistical models from both the coarse and utility data and use the by- models-learned dependencies between the data sources to predict the needed data at better resolution. To achieve higher computational speed and evade utility data imperfection, we utilize truncated singular value decomposition which reduce a dimensionality of the data space we work with. The~method is highly modifiable and its application shows plausible real-like results. Thanks to this, the method can be of practical use for simulation software development. Powered by TCPDF (www.tcpdf.org)
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Detection of drought events using combination of satellite data and soil moisture modelling
Semerádová, Daniela ; Trnka, Miroslav ; Hlavinka, Petr ; Balek, Jan ; Bohovič, Roman ; Tadesse, T. ; Hayes, M. ; Wardlow, B. ; Žalud, Zdeněk
The use of satellite data offers a potentially well usable tool to accurate drought monitoring. The study examines the space-time possibility of agricultural drought detection using freely available data from the MODIS instrument onboard Terra and Aqua satellites that reflects vegetation condition. Vegetation greenness metrics used in this study are based on the spectral reflectance curves in the visible red and near-infrared part of the spectrum and are expressed in relation to the average for the period of 2000-2014. The results are presented in weekly time step for the whole area of the Czech Republic, and are compared to the drought monitor system, based on the SoilClim dynamic model for soil water content estimates. These data, as well as other parameters, such as soil properties and land use, are integrated at 500 meters spatial resolution.
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Prediction of crop yields using satellite remote sensing
Lukas, V. ; Trnka, Miroslav ; Semerádová, Daniela ; Rajdl, Kamil ; Balek, Jan ; Štěpánek, Petr ; Zahradníček, Pavel ; Hlavinka, Petr ; Žalud, Zdeněk
Knowledge of the crop yield with sufficient lead time prior to harvest is crucial for both the farm management and the agro-food sector policy. The aim of this study was to test feasibility of developing crop yield forecasting model in Czech Republic for winter wheat, spring barley and oilseed rape based on 2000-2014 database of vegetation indices Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI2) from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite in form of 16-days composites. An average yield data were collected for 14 selected districts in the Czech Republic that represent the regions with more intensive agricultural production among varying climate and topographic conditions. The viability of the concept was proven in years with significant yield decline i.e. 2000, 2003, 2006 and 2012, when yields of cereals were significantly affected by occurred drought periods. More stable regression results were achieved in the most productive areas such as Olomouc and Prerov, whilst models in highland regions were influenced by lower acreage of three modelled crops and higher prevalence of fodder crops. In most cases, EVI2 showed higher correlations to the crop yield together with using an average value of all composites during vegetation period.
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