About: National University of Life and Environmental Sciences of Ukraine is a education organization based out in Kyiv, Ukraine. It is known for research contribution in the topics: Population & Sustainable development. The organization has 1784 authors who have published 1689 publications receiving 6030 citations.
Papers published on a yearly basis
TL;DR: Liu et al. as mentioned in this paper studied the distribution and land use of Mollisols in the world and found that they are most prevalent in the mid-latitudes of North America, Eurasia, and South America.
Abstract: Liu, X., Burras, C. L., Kravchenko, Y. S., Duran, A., Huffman, T., Morras, H., Studdert, G., Zhang, X., Cruse, R. M. and Yuan, X. 2012. Overview of Mollisols in the world: Distribution, land use and management. Can. J. Soil Sci. 92: 383–402. Mollisols – a.k.a., Black Soils or Prairie Soils – make up about 916 million ha, which is 7% of the world's ice-free land surface. Their distribution strongly correlates with native prairie ecosystems, but is not limited to them. They are most prevalent in the mid-latitudes of North America, Eurasia, and South America. In North America, they cover 200 million ha of the United States, more than 40 million ha of Canada and 50 million ha of Mexico. Across Eurasia they cover around 450 million ha, extending from the western 148 million ha in southern Russia and 34 million ha in Ukraine to the eastern 35 million ha in northeast China. They are common to South America's Argentina and Uruguay, covering about 89 million and 13 million ha, respectively. Mollisols are often rec...
TL;DR: It is concluded that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.
Abstract: Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April–May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2–3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April–May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha −1 in June and 0.4 t ha −1 in April, while performance of three approaches for 2011 was almost the same (0.5–0.6 t ha −1 in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets.
TL;DR: It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images.
Abstract: Ukraine is one of the most developed agricultural countries in the world. For many applications, it is extremely important to provide reliable crop maps taking into account diversity of cropping systems used in Ukraine. The use of optical imagery only is limited due to cloud cover, and previous studies showed particular difficulties in discriminating summer crops in Ukraine such as maize, soybeans, sunflower, and sugar beet. This paper focuses on exploring feasibility and assessing efficiency of using multitemporal satellite synthetic-aperture radar (SAR) acquired in C-band and optical images for crop classification in Ukraine. Both optical (Landsat-8/OLI) and SAR (Radarsat-2) images are used to assess the impact of adding backscattering intensity from SAR images for classification purposes. SAR intensity information is very important due to availability of Sentinel-1 imagery over Ukraine starting March 2015. Different combinations of optical and SAR images, as well as SAR modes and polarizations, are assessed for better discrimination of crops. A committee of neural networks, in particular multilayer perceptrons (MLPs), is used to improve classification accuracy compared to several standard classifiers. It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images. Considering the summer crops, the major impact of adding backscatter intensity information from SAR images is in better separation of sunflower, soybeans, and maize.
TL;DR: This review highlights the recent progress in research on the potential of using 2D non-graphene materials and similar oxide nanostructures for different types of biosensors (optical and electrochemical).
Abstract: The discovery of graphene and its unique properties has inspired researchers to try to invent other two-dimensional (2D) materials. After considerable research effort, a distinct "beyond graphene" domain has been established, comprising the library of non-graphene 2D materials. It is significant that some 2D non-graphene materials possess solid advantages over their predecessor, such as having a direct band gap, and therefore are highly promising for a number of applications. These applications are not limited to nano- and opto-electronics, but have a strong potential in biosensing technologies, as one example. However, since most of the 2D non-graphene materials have been newly discovered, most of the research efforts are concentrated on material synthesis and the investigation of the properties of the material. Applications of 2D non-graphene materials are still at the embryonic stage, and the integration of 2D non-graphene materials into devices is scarcely reported. However, in recent years, numerous reports have blossomed about 2D material-based biosensors, evidencing the growing potential of 2D non-graphene materials for biosensing applications. This review highlights the recent progress in research on the potential of using 2D non-graphene materials and similar oxide nanostructures for different types of biosensors (optical and electrochemical). A wide range of biological targets, such as glucose, dopamine, cortisol, DNA, IgG, bisphenol, ascorbic acid, cytochrome and estradiol, has been reported to be successfully detected by biosensors with transducers made of 2D non-graphene materials.
Moscow State Forest University1, International Institute for Applied Systems Analysis2, Polish Academy of Sciences3, Lviv Polytechnic4, University of Canterbury5, National University of Life and Environmental Sciences of Ukraine6, Delft University of Technology7, National Institute for Environmental Studies8
TL;DR: In this paper, the authors applied geographically weighted regression (GWR) to integrate eight different forest products into three global hybrid forest cover maps at a 1-km resolution for the reference year 2000.
Abstract: A number of global and regional maps of forest extent are available, but when compared spatially, there are large areas of disagreement. Moreover, there is currently no global forest map that is consistent with forest statistics from FAO (Food and Agriculture Organization of the United Nations). By combining these diverse data sources into a single forest cover product, it is possible to produce a global forest map that is more accurate than the individual input layers and to produce a map that is consistent with FAO statistics. In this paper we applied geographically weighted regression (GWR) to integrate eight different forest products into three global hybrid forest cover maps at a 1 km resolution for the reference year 2000. Input products included global land cover and forest maps at varying resolutions from 30 m to 1 km, mosaics of regional land use/land cover products where available, and the MODIS Vegetation Continuous Fields product. The GWR was trained using crowdsourced data collected via the Geo-Wiki platform and the hybrid maps were then validated using an independent dataset collected via the same system. Three different hybrid maps were produced: two consistent with FAO statistics, one at the country and one at the regional level, and a “best guess” forest cover map that is independent of FAO. Independent validation showed that the “best guess” hybrid product had the best overall accuracy of 93% when compared with the individual input datasets. The global hybrid forest cover maps are available at http://biomass.geo-wiki.org .
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|R. S. Boiko||19||68||1114|
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