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Title: ANALYSIS OF THE LOCAL ENVIRONMENTAL CONDITIONS OF LEGUMES USING GLOBAL DATASETS
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ANALYSIS OF THE LOCAL ENVIRONMENTAL CONDITIONS OF LEGUMES USING GLOBAL DATASETS
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1314-2704
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English
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17
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21
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The recent development of remote sensing technologies and rapidly accumulating environmental data derived from geographic information systems (GIS) now provide information on the patterns of terrestrial environmental variation at global and continental scales. Moreover, the past decades, an extraordinary amount of work has been undertaken to map species distributions and use the collected information to identify suitable habitats. Remote sensing metrics have become an integral part of SDM studies and contribute the significant amount of spatially explicit data for distribution models given recent development in remote sensing technologies and products. Environmental data relevant for ecophysiology and spatial distribution modelling of legumes includes ecologically important environmental factors. Several geospatial global datasets representing topography, eco-climatological and pedological properties were studied with the main aim to obtain local conditions and to have unified spatial resolution of all environmental factors. Datasets were pre-processed per their key features. Finally, a model for automatic obtaining the data for SDM was developed. Paper presents outcomes of the study, testing and brings comparable metrics between obtained datasets for legume occurrence data.
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conference
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17th International Multidisciplinary Scientific GeoConference SGEM 2017
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17th International Multidisciplinary Scientific GeoConference SGEM 2017, 29 June - 5 July, 2017
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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791-798
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29 June - 5 July, 2017
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website
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cdrom
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3029
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DEM; global geospatial datasets; SDM; legumes; spatial quality
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