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Coordinated and published by The Grassroots Institute, the Grassroots Journal of Natural Resources (GJNR) is an international journal dedicated to the latest advancements in natural resources throughout the world. The goal of this journal is to provide a platform for scientists, social scientists, policy analysts, managers and practitioners (on all academic and professional levels) all over the world to promote, discuss and share various new issues and developments in different arenas of natural resources.
Using Multispectral Images to Establish Land Categories
Lev Perovych*1, Ihor Perovych2, Olena Lazarieva3, Andrei Mas4
1Department of Land Management, Petro Mohyla Black Sea National University, Mykolaiv, Ukraine.
Email: levperovych@gmail.com | ORCID: https//orcid.org/0000-0002-0238-7072
2Department of Economic Expertise & Land Management, West Ukrainian National University, Ternopil, Ukraine.
Email: cadastr@gmail.com | ORCID: https//orcid.org/0000-0002-3265-7088
3Department of Land Management, Petro Mohyla Black Sea National University, Mykolaiv, Ukraine.
Email: lazareva95@ukr.net | ORCID: https//orcid.org/0000-0002-1050-7118
4Department of Land Management, Petro Mohyla Black Sea National University, Mykolaiv, Ukraine.
Email: andreimas 1959@gmail.com | ORCID: https//orcid.org/0000-0003-1140-2817
*Corresponding author
Grassroots Journal of Natural Resources, 6(1): 166-176. Doi: https://doi.org/10.33002/nr2581.6853.060108
Received: 24 February 2023
Reviewed: 19 March 2023
Provisionally Accepted: 29 March 2023
Revised: 11 April 2023
Finally Accepted: 15 April 2023
Published: 19 April 2023
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At the present stage, the dominant means of obtaining information is space shooting, which is carried out from space carriers with the help of special shooting equipment, and makes it possible to obtain high-quality images covering a significant area of the earth's surface. Methods combining multi-criteria analysis and GIS technologies can be used to make appropriate environmental decisions. At the same time, an important component for all interested parties is obtaining the original information at the lowest cost. In this regard, this publication provides a methodology for constructing maps of land categories, which is based exclusively on a free basis. This methodology includes free and open FOSS software, space images of the Landsat 8 satellite, and multi-criteria analysis of space image processing. The procedure of the methodology includes the creation of a database based on available land management documents, cadastral plans and maps, satellite images, etc.; processing of the database using multi-criteria analysis; analysis of the results and decision-making. The database is created using QGIS software, and PostgreSQL with the PostGIS extension is used for modeling and data storage. MultiSpec software was used to create multispectral images, perform satellite image classification and evaluation. Using a set of the above software products and Landsat 8 satellite images, a pilot project on an area of 615 km2 was carried out to determine the capabilities of this methodology for establishing land categories. It was established that the multispectral image of the combination of 6-5-2 channels best represents land categories. The accuracy of the classification is 96.2%, and the User Accuracy for arable land is almost 100%, for orchards 55%, and for hayfields and pastures 61.3%.
Land categories; Space images; Multi-criteria analysis
Aran Carrion, J., Espin Estrella, A., Aznar Dols, F., Zamorano Toro, M., Rodriguez, M., and
Ramos Ridao, A. (2008). Environmental decision-support system for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants. Renewable and Sustainable Energy Reviews, 12: 2358–80.
Boryan, C. (2011). Monitoring US agriculture: the US Department of Agriculture, National
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological
Measurement, 20(1): 37–46.
Eastman, J.R., Jin, W., Kyem, P.A.K. and Toledano, J. (1995). Raster Procedures for Multi-
Kereush, D. and Perovych, L. (2017). Technology of optimal site selection for solar photovoltaic
power plants using GIS and Remote Sensing techniques. Interdepartmental Scientific and Technical Review: Geodesy, Cartography and Aerial Photography, 86: 73-79. DOI: https://doi.org/10.23939/istcgcap2017.02.073.
Kereush, D. and Perovych, L. (2019). Technology of optimal site selection for Solar PV power
plants. Monograph. LAP Lambert Academic Publishing.
Koeva, M., Bennett, R. and Persello, C. (2022). Remote Sensing for Land Administration
Maxwell, S.K. (2010). Use of land surface remotely sensed satellite and airborne data for
Sanchez-Lozano, M.J., Teruel-Solano, J., Soto-Elvira, L.P. and Garcia-Cascales, S.M. (2013).
Story, M. and Congalton, R. (1986). Accuracy assessment: a user's perspective. Photogrammetric
Stupen, N., Stupen, M. and Stupen, O. (2018). Electronic agricultural maps formation on the
Tso, B. and Mather, P.M. (2009). Classification Methods for Remotely Sensed Data. 2nd Ed. London:
U.S. Geological Survey (undated). Access mode: https://earthexplorer.usgs.gov/ [accessed on
14 September 2022]
Young, N.E., Anderson, R.S., Chignell, S.M., Vorster, A.G., Lawrence, R. and Evangelista, P.H.
Zhe, Z., Shi, Q. and Su, Y. (2022). Remote sensing of land change: A multifaceted perspective
Perovych, L., Perovych, I., Lazarieva, O. and Mas, A. (2023). Using Multispectral Images to Establish Land Categories. Grassroots Journal of Natural Resources, 6(1): 166-176. Doi: https://doi.org/10.33002/nr2581.6853.060108
Perovych, L., Perovych, I., Lazarieva, O., & Mas, A. (2023). Using Multispectral Images to Establish Land Categories. Grassroots Journal of Natural Resources, 6(1), 166-176. https://doi.org/10.33002/nr2581.6853.060108
Perovych L., Perovych I., Lazarieva O., Mas A. Using Multispectral Images to Establish Land Categories. Grassroots Journal of Natural Resources, 2023, 6 (1), 166-176. https://doi.org/10.33002/nr2581.6853.060108
Perovych, Lev, Perovych, Ihor, Lazarieva, Olena, Mas, Andrei. 2023. “Using Multispectral Images to Establish Land Categories”. Grassroots Journal of Natural Resources, 6 no. 1: 166-176. https://doi.org/10.33002/nr2581.6853.060108
Perovych, Lev, Ihor Perovych, Olena Lazarieva and Andrei Mas. 2023. “Using Multispectral Images to Establish Land Categories”. Grassroots Journal of Natural Resources, 6 (1): 166-176. https://doi.org/10.33002/nr2581.6853.060108
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