Extension of Copernicus Urban Atlas to Non-European Countries

National Research Foundation of Ukraine from the state budget 2020.02/0284 “Geospatial models and information technologies of satellite monitoring of smart city problems” (NRFU Competition “Leading and Young Scientists Research Support”), and trans-national project SMURBS (Grant Agreement N. 689443), funded under the EU Horizon 2020 programOne of the parts of the Land Monitoring Service is Copernicus Urban Atlas, which provides reliable and comparable land use maps with high accuracy for large number European functional urban areas and their neighbors for every 6 years (2006, 2012, 2018). Unfortunately, there is no available such products for Ukrainian cities and there is no possibility to reproduce the technology by which they are obtained. This is due to the unavailability of sufficient high resolution satellite data information at the cities level, which is an integral part of the European methodology for obtaining the Urban Atlas. That is why we have proposed new approach on the base of open data which can be applicable to any other city. Kyiv (Ukraine) became the first city outside the Europe, for which the methodology by creating Urban Atlas was developed, which is compliant in structure and functionality to the European Copernicus Urban Atlas. The methodology was scaled for Lviv City, as well as applied and tested for other cities, in particular for Rivne, Irpin (Ukraine) and Lublin (Poland). In addition to the main management tasks that the Urban Atlas helps to solve, the obtained products can be used to unify and air quality monitoring in cities, and as a base for assessing the sustainable development goals indicator 11.6.2 “Annual mean levels of fine particulate matter in cities”

Regional scale crop mapping using multi-temporal satellite imagery

One of the problems in dealing with optical images for large territories (more than 10,000 sq. km) is the presence of clouds and shadows that result in having missing values in data sets. In this paper, a new approach to classification of multi-temporal optical satellite imagery with missing data due to clouds and shadows is proposed. First, self-organizing Kohonen maps (SOMs) are used to restore missing pixel values in a time series of satellite imagery. SOMs are trained for each spectral band separately using nonmissing values. Missing values are restored through a special procedure that substitutes input sample's missing components with neuron's weight coefficients. After missing data restoration, a supervised classification is performed for multi-temporal satellite images. An ensemble of neural networks, in particular multilayer perceptrons (MLPs), is proposed. Ensembling of neural networks is done by the technique of average committee, i.e. to calculate the average class probability over classifiers and select the class with the highest average posterior probability for the given input sample. The proposed approach is applied for regional scale crop classification using multi temporal Landsat-8 images for the JECAM test site in Ukraine in 2013. It is shown that ensemble of MLPs provides better performance than a single neural network in terms of overall classification accuracy, kappa coefficient, and producer's and user's accuracies for separate classes. The overall accuracy more than 85% is achieved. The obtained classification map is also validated through estimated crop areas and comparison to official statistics

Assessment of the land cover based on geoprostoral data

<p>Assessment of the land cover based on geoprostoral data</p

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST