Digital inventory of agricultural land plots in the Kemerovo Region

Artem Rada; Aleksandr Kuznetsov

doi:10.21603/2308-4057-2022-2-529

UDC 63

DIGITAL INVENTORY OF AGRICULTURAL LAND PLOTS IN THE KEMEROVO REGION Digital inventory of agricultural land plots in the Kemerovo Region

АА Аа

Published in Foods and Raw Materials · Volume 10, Issue 2 · Pages 206–215 · Rubric: Research Article

DOI: https://doi.org/10.21603/2308-4057-2022-2-529

Received: 21.01.2022 Accepted: 14.04.2022 Published: 23.09.2022 Language of publication: ENG

Authors

Artem Rada

ORCID

Aleksandr Kuznetsov

ORCID

Artem Rada

Kemerovo State University Russian Federation · Kemerovo

ORCID

Aleksandr Kuznetsov

Kemerovo State University Russian Federation · Kemerovo

ORCID

ABSTRACT

Cadastral and geodetic land works are expensive, which makes aerial photography extremely valuable for land traceability and inventory. The present research objective was to develop a new digital survey technology for registration of agricultural lands. We assessed the accuracy of the new method and evaluated its decision support options. The study featured the case of the Kemerovo Region (Kuzbass), Russia. The aerial survey took place in 2021 and involved 17 municipalities of the Kemerovo Region. The software and hardware complex included an unmanned aerial vehicle (UAV) and a module for aerial photography. Photogrammetric, cartometric, and satellite methods were used to define the coordinates of feature points. We developed new software (Sovhoz.avi) to perform the land inventory. The photogrammetric and cartographic methods proved efficient in determining the feature points and boundaries of land plots. They also appeared accurate enough for land inventory and decision support. The study updated the available land inventory data. About 30% of all land plots were recorded incorrectly; some plots marked as agricultural appeared to belong to the local forest reserves or urban territories. Incorrect data (1.64%) were excluded from the official inventory. The survey covered a total area of 41 000 ha and revealed 1700 illegally used land plots. The updated inventory of unused lands included 3825 new plots (163 400 ha), which can attract prospective investors. The results can be used by the local authorities to make land management decisions and identify illegal land use.

KEYWORDS

Agricultural land, food, land inventory, unmanned aerial vehicle (UAV), aerial survey, illegal land use

Funding

The research was conducted on the premises of the Research Equipment Sharing Center of Kemerovo State University (KemSU) , agreement No. 075-15-2021-694 dated August 5, 2021, between the Ministry of Science and Higher Education of the Russian Federation (Minobrnauka) and Kemerovo State University (contract identifier RF----2296.61321X0032).

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1. Cooper M, Müller B, Cafiero C, Bayas JSL, Crespo Cuaresma J, Kharas H. Monitoring and projecting global hunger: Are we on track? Global Food Security. 2021;30. https://doi.org/10.1016/j.gfs.2021.100568

2. Béné C, Bakker D, Chavarro MJ, Even B, Melo J, Sonneveld A. Global assessment of the impacts of COVID-19 on food security. Global Food Security. 2021;31. https://doi.org/10.1016/j.gfs.2021.100575

3. Genkin AS, Mikheev AA. Influence of coronavirus crisis on food industry economy. Foods and Raw Materials. 2020;8(2):204-215. https://doi.org/10.21603/2308-4057-2020-2-204-215

4. Vogliano C, Murray L, Coad J, Wham C, Maelaua J, Kafa R, et al. Progress towards SDG 2: Zero hunger in Melanesia - A state of data scoping review. Global Food Security. 2021;29(1). https://doi.org/10.1016/j.gfs.2021.100519

5. Cintina V, Pukite V. Analysis of influencing factors of use of agricultural land. Research for Rural Development. 2018;1:181-187. https://doi.org/10.22616/rrd.24.2018.028

6. Emran S-A, Krupnik TJ, Aravindakshan S, Kumar V, Pittelkow CM. Factors contributing to farm-level productivity and household income generation in coastal Bangladesh’s rice-based farming systems. PLoS ONE. 2021;16(9). https://doi.org/10.1371/journal.pone.0256694

7. McLaughlin D, Kinzelbach W. Food security and sustainable resource management. Water Resources Research. 2015;51(7):4966-4985. https://doi.org/10.1002/2015WR017053

8. Fitton N, Alexander P, Arnell N, Bajzelj B, Calvin K, Doelman J, et al. The vulnerabilities of agricultural land and food production to future water scarcity. Global Environmental Change. 2019;58. https://doi.org/10.1016/j.gloenvcha.2019.101944

9. Faskhutdinova ER, Osintseva MA, Neverova OA. Prospects of using soil microbiome of mine tips for remediation of anthropogenically disturbed ecosystems. Food Processing: Techniques and Technology. 2021;51(4):883-904. (In Russ.). https://doi.org/10.21603/2074-9414-2021-4-883-904

10. Drozdova MYu, Pozdnyakova AV, Osintseva MA, Burova NV, Minina VI. The microorganism-plant system for remediation of soil exposed to coal mining. Foods and Raw Materials. 2021;9(2):406-418. https://doi.org/10.21603/2308-4057-2021-2-406-418

11. Bolfe ÉL, Jorge LAC, Sanches ID, Júnior AL, da Costa CC, Victoria DC, et al. Precision and digital agriculture: Adoption of technologies and perception of Brazilian farmers. Agriculture. 2020;10(12). https://doi.org/10.3390/agriculture10120653

12. Shang L, Heckelei T, Gerullis MK, Börner J, Rasch S. Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction. Agricultural Systems. 2021;190. https://doi.org/10.1016/j.agsy.2021.103074

13. Rada AO, Fedulova EA, Kosinsky PD. New method for efficiency evaluation of digital technologies in agricultural sector. Food Processing: Techniques and Technology. 2019;49(3):495-504. (In Russ.). https://doi.org/10.21603/2074-9414-2019-3-495-504

14. Balafoutis A, Beck B, Fountas S, Vangeyte J, van der Wal TV, Soto I, et al. Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability. 2017;9(8). https://doi.org/10.3390/su9081339

15. Aune JB, Coulibaly A, Giller KE. Precision farming for increased land and labour productivity in semi-arid West Africa. A review. Agronomy for Sustainable Development. 2017;37(3). https://doi.org/10.1007/s13593-017-0424-z

16. Adenso-Díaz B, Villa G. Crop planning in synchronized crop-demand scenarios: A biobjective optimization formulation. Horticulturae. 2021;7(10). https://doi.org/10.3390/horticulturae7100347

17. Cienciała A, Sobolewska-Mikulska K, Sobura S. Credibility of the cadastral data on land use and the methodology for their verification and update. Land Use Policy. 2021;102. https://doi.org/10.1016/j.landusepol.2020.105204

18. Kocur-Bera K, Frąszczak H. Coherence of cadastral data in land management - a case study of rural areas in Poland. Land. 2021;10(4). https://doi.org/10.3390/land10040399

19. Roić M, Križanović J, Pivac D. An approach to resolve inconsistencies of data in the cadastre. Land. 2021;10(1). https://doi.org/10.3390/land10010070

20. Malashevskyi M, Kuzin N, Palamar A, Malanchuk M. The problems of spatial ordering and taxation of agricultural lands. Geodesy and Cartography. 2017;43(4):166-172. https://doi.org/10.3846/20296991.2017.1412634

21. Lisjak J, Roić M, Tomić H, Mastelić Ivić S. Croatian LADM profile extension for state-owned agricultural land management. Land. 2021;10(2). https://doi.org/10.3390/land10020222

22. García-Morán A, Ulvund S, Unger E-M, Bennett RM. Exploring PPPs in support of fit-for-purpose land administration: A case study from Côte d’Ivoire. Land. 2021;10(9). https://doi.org/10.3390/land10090892

23. Feroz S, Abu Dabous S. UAV-based remote sensing applications for bridge condition assessment. Remote Sensing. 2021;13(9). https://doi.org/10.3390/rs13091809

24. He X, Yang X, Luo Z, Guan T. Application of unmanned aerial vehicle (UAV) thermal infrared remote sensing to identify coal fires in the Huojitu coal mine in Shenmu city, China. Scientific Reports. 2020;10(1). https://doi.org/10.1038/s41598-020-70964-5

25. Tatum MC, Liu J. Unmanned aircraft system applications in construction. Procedia Engineering. 2017;196:167-175. https://doi.org/10.1016/j.proeng.2017.07.187

26. Prosekov A, Kuznetsov A, Rada A, Ivanova S. Methods for monitoring large terrestrial animals in the wild. Forests. 2020;11(8). https://doi.org/10.3390/f11080808

27. Tuśnio N, Wróblewski W. The efficiency of drones usage for safety and rescue operations in an open area: A case from Poland. Sustainability. 2022;14(1). https://doi.org/10.3390/su14010327

28. Mohamed N, Al-Jaroodi J, Jawhar I, Idries A, Mohammed F. Unmanned aerial vehicles applications in future smart cities. Technological Forecasting and Social Change. 2020;153. https://doi.org/10.1016/j.techfore.2018.05.004

29. Montgomery K, Henry JB, Vann MC, Huseth AS, Mitasova H. Measures of canopy structure from low-cost UAS for monitoring crop nutrient status. Drones. 2020;4(3). https://doi.org/10.3390/drones4030036

30. Puniach E, Bieda A, Ćwiąkała P, Kwartnik-Pruc A, Parzych P. Use of unmanned aerial vehicles (UAVs) for updating farmland cadastral data in areas subject to landslides. ISPRS International Journal of Geo-Information. 2018;7(8). https://doi.org/10.3390/ijgi7080331

31. Mantey S, Tagoe ND. Suitability of unmanned aerial vehicles for cadastral surveys. Ghana Mining Journal. 2019;19(1):1-8. https://doi.org/10.4314/gm.v19i1.1

32. Šafář V, Potůčková M, Karas J, Tlustý J, Štefanová E, Jančovič M, et al. The use of UAV in cadastral mapping of the Czech Republic. ISPRS International Journal of Geo-Information. 2021;10(6). https://doi.org/10.3390/ijgi10060380

33. Karabin M, Bakuła K, Łuczyński R. Verification of the geometrical representation of buildings in cadastre using UAV photogrammetry. Geomatics and Environmental Engineering. 2021;15(4):81-99. https://doi.org/10.7494/geom.2021.15.4.81

34. Puniach E, Kwartnik-Pruc A. The use of laser scanning and unmanned aerial vehicles in construction surveying in the light of legal regulations in Poland. Journal of Applied Engineering Sciences. 2018;8(2):79-88.

35. Brookman-Amissah M, Mantey S, Kumi-Boateng B, Aikins BE, Masoud AB. Developing a workflow for the use of unmanned aerial vehicles for cadastral mapping in Ghana. Journal of Environment and Earth Science. 2021;11(2):23-31. https://doi.org/10.7176/JEES/11-2-04

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GOST

Rada A. O., Kuznetsov A. D. Digital inventory of agricultural land plots in the Kemerovo Region // Foods and Raw Materials. 2022. no. 2. pp. 206-215. DOI: https://doi.org/10.21603/2308-4057-2022-2-529. (Date of access 25.10.2026).

MLA

Artem Rada, Kuznetsov A. D.. "Digital inventory of agricultural land plots in the Kemerovo Region." Foods and Raw Materials 10.2 (2022): 206-215. DOI: https://doi.org/10.21603/2308-4057-2022-2-529.

APA

Rada A. O., Kuznetsov A. D. (2022). Digital inventory of agricultural land plots in the Kemerovo Region. Foods and Raw Materials, 10(2), 206-215. https://doi.org/10.21603/2308-4057-2022-2-529

Chicago

Artem Rada, Kuznetsov A. D.. "Digital inventory of agricultural land plots in the Kemerovo Region." Foods and Raw Materials 10, no. 2 (2022): 206-215. https://doi.org/10.21603/2308-4057-2022-2-529.

WIKI

{{cite journal | author = Rada A. O., Kuznetsov A. D. | title = Digital inventory of agricultural land plots in the Kemerovo Region | journal = Foods and Raw Materials | year = 2022 | volume = 10 | issue = 2 | pages = 206-215 | doi = 10.21603/2308-4057-2022-2-529 }}

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DIGITAL INVENTORY OF AGRICULTURAL LAND PLOTS IN THE KEMEROVO REGION Digital inventory of agricultural land plots in the Kemerovo Region

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