Atmospheric correction of landsat-8 / Oli data using the flaash algorithm: Obtaining information about agricultural crops
Yerzhanova A.E. Kerimkhulle S.Y. Abdikerimova G.B. Makhanov M. Beglerova S.T. Taszhurekova Z.K.
15 July 2021Little Lion Scientific
Journal of Theoretical and Applied Information Technology
2021#99Issue 133110 - 3119 pp.
The article presents studies and proposes methods for determining the objects of the underlying surface such as soil, water, soil moisture, agricultural crops and their diseases, weeds, and monitoring plant growth over vegetative periods based on the analysis of the spectral brightness coefficient of space images. Recognition of plant species, soils, and territories from satellite images is an applied task that allows you to implement many processes in agriculture and automate the activities of farmers and large farms. These studies are aimed at creating a scientific and methodological basis for an information system in the form of a computer application on gadgets. The main tool for analyzing satellite imagery data is the clustering of data that uniquely identifies the desired objects and changes associated with various reasons. Based on the data obtained in the course of experiments on obtaining numerical values of SLC, which are published in the press, the regularities of the behavior of the processes of reflection of vegetation, factors that impede the normal growth of plants, and the proposed clustering of the spectral ranges of wave distribution, by which the type of objects under consideration can be determined. Recognition of these causes through the analysis of the spectral brightness coefficient of satellite images will allow creating an information system for monitoring the state of plants and events to eliminate negative causes. SLC data is divided into nonoverlapping ranges, i.e., they form clusters reflecting the normal development of plant species and deviations associated with negative causes. If there are deviations, then there is an algorithm that determines the cause of the deviation and proposes an action plan to eliminate the defect. To accomplish this task, the dependence of the state of plants on the types of soils, their moisture content, the identification of weeds, the detection of diseases, and the lack of mineral and organic fertilizers were taken into account. Several negative causes associated with plant diseases require ground monitoring due to the lack of experimental data on spectral analysis of these diseases. It should be noted that the distribution of brightness spectra depends on the climatic and geographical conditions of the plant species and is unique for each region. This study refers to the Northern Kazakhstan region, where crops are grown. In all types of space and ground monitoring of plant growth, measures are proposed to eliminate negative causes.
Atmospheric Correction , Band , Earth Remote Sensing , ENVI , ERS , FLAASH , Landsat-8 , Spectral Brightness Coefficients , Wavelength
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L.N. Gumilyov Eurasian National University, Department of Information Systems, Astana, Kazakhstan
L.N. Gumilyov Eurasian National University, Department of Transport, transport equipment and technologies, Nur-Sultan, Kazakhstan
Non-profit limited company Taraz Regional University named after M.KH. Dulaty, Taraz, Kazakhstan
L.N. Gumilyov Eurasian National University
L.N. Gumilyov Eurasian National University
Non-profit limited company Taraz Regional University named after M.KH. Dulaty
10 лет помогаем публиковать статьи Международный издатель
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