DETERMINATION OF THE NUMBER OF CLUSTERS OF NORMALIZED VEGETATION INDICES USING THE K-MEANS ALGORITHM

Mimenbayeva A. Artykbayev S. Suleimenova R. Abdygalikova G. Naizagarayeva A. Ismailova A.
2023 Technology Center

Eastern-European Journal of Enterprise Technologies
2023 #5 Issue 2(125)42 - 55 pp.

The process of clustering of normalized vegetation indices in five regions with a total area of 2565 hectares of the North Kazakhstan region was studied. A methodological approach to organizing the clustering process is proposed using the vegetation indices NDVI, MSAVI, ReCI, NDWI and NDRE, taking into account individual characteristics in the three main phases of spring wheat development As a result of the research, vegetation indices were grouped into 3 classes using the k-means clustering method. The first cluster contained vegetation indices whose maximum values occupied about 33.98 % of the total area of the study area. It was found that NDVImax located in the first cluster was positively correlated with soil-corrected vegetation indices MSAVI and crop moisture indicators NDMI (R2=0.92). The second cluster is characterized by minimum values of NDVImax coefficients at the germination, tillering and ripening phases (from 0.53 to 0.55). The lowest values of vegetation indices occupied 35.9 % in the germination phase, 37.9 % in the tillering phase, and 40.1 % of the field from the total area. The third cluster is characterized by average values of vegetation indices in all three phases. A correlation matrix was also constructed to assess the closeness of the relationship between actual yield and NDVI vegetation indices. The maximum coefficient was obtained at the germination phase, R=0.94 with a minimum significance coefficient p=0.018. The approach used in this study can be useful in the analysis of satellite data, as it can improve the sensitivity of the constellation procedure. From a practical point of view, the results obtained make it possible to assess the condition of agricultural crops in the early stages of the growing season, which makes it possible to improve their productivity based on the results of cluster analysis.

cluster analysis , k-means algorithm , NVDI , remote sensing data , vegetation index

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Department of Computational and Data Sciences, Astana IT University, Mangilik ave., 55/11, C1, Astana, 010017, Kazakhstan
Department of Information Systems, S.Seifullin Kazakh Agro Technical Research University, Zhenis ave., 62, Astana, 010011, Kazakhstan
Department of Information Systems, S.Seifullin Kazakh Agro Technical Research University, Zhenis ave., 62, Astana, 010011, Kazakhstan
Department of Information Systems, S.Seifullin Kazakh Agro Technical Research University, Zhenis ave., 62, Astana, 010011, Kazakhstan
Department of Information Systems, S.Seifullin Kazakh Agro Technical Research University, Zhenis ave., 62, Astana, 010011, Kazakhstan
Department of Information Systems, S.Seifullin Kazakh Agro Technical Research University, Zhenis ave., 62, Astana, 010011, Kazakhstan

Department of Computational and Data Sciences
Department of Information Systems
Department of Information Systems
Department of Information Systems
Department of Information Systems
Department of Information Systems

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