METHODOLOGY OF ZONING APPLICATION FOR AGRICULTURAL CROPS CULTIVATION ON THE BASIS OF SPACE IMAGERY

One has carried out the monitoring of rapeseed cultivation on the example of land-use area of 69.7 hectares, located on the territory of Busk district of Lviv region outside the settlement Baluchyn according to the artificial satellite Sentinel-2. One has identified the state of its sowing, which is...

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Bibliographic Details
Main Authors: Mykhailo STUPEN, Nazar STUPEN, Zoriana RYZHOK, Ruslana TARATULA
Format: Article
Language:English
Published: University of Agricultural Sciences and Veterinary Medicine, Bucharest 2020-01-01
Series:Scientific Papers Series : Management, Economic Engineering in Agriculture and Rural Development
Online Access:https://managementjournal.usamv.ro/pdf/vol.20_1/Art70.pdf
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Summary:One has carried out the monitoring of rapeseed cultivation on the example of land-use area of 69.7 hectares, located on the territory of Busk district of Lviv region outside the settlement Baluchyn according to the artificial satellite Sentinel-2. One has identified the state of its sowing, which is characterized by the maximum value of the NDVI vegetation index on July 1, 2019, and indicates the developed vegetation for harvesting. Areas with high, medium and low rapeseed vegetation are displayed using the numerical taxonomy method of optical brightness based on the analysis of multispectral land-use scan data in the Crop Monitoring geo-information system. One has done biological yielding capacity prediction using obtained values of vegetation index NDVI, NDRE, MSAVI and RECI in each zone of land use, which indicates the sparse vegetation of rapeseed with low yield and therefore requires the application of additional organic and mineral fertilizers in the low vegetation area with an average of 41 hectares per the research object. The obtained results of the use of geo-information technologies according to space monitoring data are proposed to be applied for the estimation of the state of sowing, yielding capacity prediction, the performance of agrarian and technical operations at all stages of agricultural development.
ISSN:2284-7995
2285-3952