Automated System for Monitoring the State of Vegetation Cover Based on Satellite Images Recognition

Authors

  • A.G. Gergelezhyu Yuriy Fedkovich Chernivtsi National University
  • M.V. Talakh Yuriy Fedkovich Chernivtsi National University
  • V.V. Dvorzhak Yuriy Fedkovich Chernivtsi National University
  • O.G. Ushenko Yuriy Fedkovich Chernivtsi National University

DOI:

https://doi.org/10.31649/1681-7893-2022-43-1-94-101

Keywords:

Normalized Difference Vegetation Index, Data Processing Algorithms, Satellite Images, Information System, Processing of Space Images, GIS processing technologies, OpenCV

Abstract

Based on the study of approaches and techniques for assessing the state of vegetation, an automated information system has been developed. It allows to perform vegetation indices’ monitoring of the territory on the basis of satellite images. The automation of their handling with the binding of temperature data is carried out. The program gives the ability to visualize the dynamics of vegetation indices and temperature and to evaluate existing connections and type of connections between the investigated factors.

Author Biographies

A.G. Gergelezhyu, Yuriy Fedkovich Chernivtsi National University

master of Computer Science, Computer Science Department

M.V. Talakh, Yuriy Fedkovich Chernivtsi National University

Ph.D., assistant professor of Computer Science Department

V.V. Dvorzhak, Yuriy Fedkovich Chernivtsi National University

Ph.D., assistant professor of Computer Science Department

O.G. Ushenko, Yuriy Fedkovich Chernivtsi National University

D.Sc., Professor, Head of Optics and Publishing Department

References

S.Y.J. Prasetyo, K.D., Hartomo, M.C. Paseleng, D.W. Chandra, E. Winarko. 2020. Satellite imagery and machine learning for aridity disaster classification using vegetation indices. Bulletin of Electrical //Engineering and Informatics 9(3):1149–1158.

J. Wang, X. Yang, X. Yang, L. Jia, S. Fang. Unsupervised change detection between SAR images based on hypergraphs // ISPRS Journal of Photogrammetry and Remote Sensing, 2020 164(7):61–72

Marta Pasternak, Kamila Pawluszek-Filipiak The Evaluation of Spectral Vegetation Indexes and Redundancy Reduction on the Accuracy of Crop Type Detection // Appl. Sci. 2022, 12(10), 50-67.

B. Bardysh. The use of vegetation indices for the identification of objects on the earth's surface / B. Bardysh, Kh. Burshtynska // Current achievements of geodetic science and production. – 2014. – No. 2 (28). - P. 82-88.

S. Koshimura, L. Moya, E. Mas, Y. Bai. Tsunami damage detection with remote sensing: a review. Geosciences 2020 10(5):1–28

Yelu Zeng, Dalei Hao, Alfredo Huete, Benjamin Dechant, Joe Berry, Jing M. Chen, Joanna Joiner, Christian Frankenberg, Ben Bond-Lamberty, Youngryel Ryu, Jingfeng Xiao, Ghassem R. Asrar & Min Chen Optical vegetation indices for monitoring terrestrial ecosystems globally // Nature Reviews Earth & Environment volume 3, pages 477–493 (2022).

L Ma,.; X. Chen,; Q. Zhang, J. Lin,.; C. Yin,.; Y. Ma,.; Q. Yao,.; L. Feng,.; Z. Zhang, etc. Estimation of Nitrogen Content Based on the Hyperspectral Vegetation Indexes of Interannual and Multi-Temporal in Cotton. Agronomy 2022, 12, 1319.

I.O. Pestova Methodology for assessing the state of vegetation in urbanized areas using multispectral space images: dissertation for obtaining the scientific degree of candidate of technical sciences / I.O Pestova. // National Academy of Sciences of Ukraine, Kyiv. - 2015. - 172 p.

I.H. Semenova. Use of vegetation indices to monitor droughts in Ukraine / I.H. Semenova // Ukrainian Hydrometeorological Journal. - 2014. - No. 14. - P. 43-52.

Geological service. USGS [Electronic resource] - Access mode: https://www.usgs.gov/ .

Weather service. Worldweather [Electronic resource] – Access mode: https://www.worldweatheronline.com/

Weather service. rp5 [Electronic resource]. – Aсcess mode: https://rp5.kz.

W. Zhang. Application of Synthetic NDVI Time Series Blended from Landsat and MODIS Data for Grassland Biomass Estimation / V . Zhang, L. Zhang, D. Xi, X. Yin, Ch. Liu, G. Liu // Remote Sens. - 2016. - No. 8 (10). - S. 1-21.

Information Technology in Medical Diagnostics //Waldemar Wójcik, Andrzej Smolarz, July 11, 2017 by CRC Press - 210 Pages.

W. Wójcik, S. Pavlov, M. Kalimoldayev. Information Technology in Medical Diagnostics II. London: (2019). Taylor & Francis Group, CRC Press, Balkema book. – 336 Pages.

Highly linear Microelectronic Sensors Signal Converters Based on Push-Pull Amplifier Circuits / edited by Waldemar Wojcik and Sergii Pavlov, Monograph, (2022) NR 181, Lublin, Comitet Inzynierii Srodowiska PAN, 283 Pages. ISBN 978-83-63714-80-2

Downloads

Abstract views: 113

Published

2022-12-28

How to Cite

[1]
A. Gergelezhyu, M. Talakh, V. Dvorzhak, and O. Ushenko, “Automated System for Monitoring the State of Vegetation Cover Based on Satellite Images Recognition”, Опт-ел. інф-енерг. техн., vol. 43, no. 1, pp. 94–101, Dec. 2022.

Issue

Section

Optical And Optical-Electronic Sensors And Converters In Control And Environmental Monitoring Systems

Metrics

Downloads

Download data is not yet available.

Most read articles by the same author(s)