Zhanna Alexandrovna Assylkhanova

Mrs. Zhanna Alexandrovna Assylkhanova


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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21 June 2023 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Verified
Journal Article
Open access
PDF file
MTM. Toguzova
M. Toguzova
BSB. Shaimardanova
B. Shaimardanova
ZSZ. Shaimardanov
Z. Shaimardanov
ZAZhanna Alexandrovna Assylkhanova
Zhanna Alexandrovna Assylkhanova
MRM. Rakhymberdina
M. Rakhymberdina
Agriculture is one of the main sectors of the economy, which provides food security of the country. The development and implementation of modern geoinformation technologies in agricultural production is the basis for transition to precision farming. Application of elements of precision farming system in agricultural enterprises is impossible without high-quality information and technical support. The purpose of the study is to analyze the use of digital technologies in the farms of the Eastern region of Kazakhstan, identify key factors hindering their implementation and prospects for further development. The information basis for the study was the results of a questionnaire survey of 40.3% of representatives of agricultural enterprises in the Eastern region. The SWOT-analysis, reflecting internal and external factors and conditions for the development of precision farming, was made on the basis of the results. There are: a low level of development of information and communication infrastructure in rural areas (65%), the predominance of small agricultural producers (80 %), insufficiently effective tools of state support, insufficient influence of regional public authorities on the processes of system informatization of agricultural production, weak spread of digital technologies in agriculture. It is concluded that, despite the relevance and importance of digital technologies in modern agriculture, the digital transformation of agriculture is characterized by a significant lag behind the advanced sectors of the economy, so a unified approach, including a set of measures to stimulate agricultural enterprises to innovative development, government support, a set of differentiated mechanisms for testing and implementing IT technologies, adapted to the conditions of the intra-branch environment is needed.

20 November 2024 in Proceedings of the International conference “InterCarto/InterGIS”
Verified
Journal Article
Closed access
EAElibek Asangaliyev
Elibek Asangaliyev
BABaitak Apshikur
Baitak Apshikur
SLSergey Lutay
Sergey Lutay
ZAZhanna Alexandrovna Assylkhanova
Zhanna Alexandrovna Assylkhanova
In 2023 there was a severe forest fire on the territory of the Semipalatinsk Forest. More than 60 thousand ha were burned. This resulted in high environmental costs, destroying trees, real estate, recreational and even human lives. One of the most pressing issues was the determination of the origin and spread of forest fires. In such cases, remote sensing data is a spatial and temporal measure to obtain fast and accurate data to prevent the further spread of the fire and to neutralize this natural disaster. Using such geospatial information, it is possible to prioritize preventive measures to reduce the risk of forest fires and identify mitigating factors to increase the likelihood of immediate fire suppression in threatened areas. This work proposes to assess the fire potential and to determine the hazard potential. This is done by analyzing and mapping the area of the fire that has occurred. The mapping of the fire potential was approached from a remote sensing point of view by estimating and mapping the total vegetation cover using Landsat-8–9 OLI/TIRS DATA and the geographic information systems QGIS, SAGAGIS. A morphometric, spatial analysis of conditions was also conducted, taking into account many factors affecting fire potential—land exposure, aspect, wind direction, fire statistics, population density, climatic characteristics, etc. As a result, an attempt was made to create indices RBR, RdNBR, dNDVI, dGNDI, GEMI and BAI, which could be the basis for the determination of fire potential. These indices are based on the type of tree, the vegetation and the topographic features. These characteristics make it possible, after the classification of the Landsat images, to evaluate the reliability of the information obtained by determining the area of the fire through the Object Based Image Analysis segmentation method and by assessing the accuracy of the detected data. The index values identified were consistent with reliable information for identifying fire locations and monitoring estimated fire risk. They could be used to map fire potential from regional to local scales.