Preview

Izvestia Sankt-Peterburgskoj lesotehniceskoj akademii

Advanced search

Geoinformation mapping and classification of hunting grounds based on remote sensing data

https://doi.org/10.21266/2079-4304.2024.250.23-45

Abstract

Remote sensing data and geographic information systems are now an integral part of landscape research and monitoring. Hunting management is based on information about hunting resources and elements of their habitats, i.e. the natural and anthropogenic landscapes. The number of regulative documents of the Russian Federation reflecting modern requirements for the conduct and development of hunting industry, noted that an inventory of the current state of the hunting environment on a unified methodological basis, including monitoring using and analysis of remote sensing and aerial photography of hunting landscapes, is the most effective. The objective of this study was to form the methodological approach to the typological classification of elements of the habitat hunting resources on the basis of the application of an algorithm for the automated classification of satellite images and the geographic information processing of obtaining results from the State Forest Register. The test area of the study was the hunting farm of NP «Hunting farm «Olgino» plot 2 on the territory of the Malovishersky district of the Novgorod region with a total area of 7627.0 hectares. Satellite imagery from the Sentinel-2A L2A HSE, as well as forest inventory and management plans and State Forest Registry data from the WinPLP software complex, were used as source materials. The software included the QGis dzetsaka automated classification plugin of the QGIS i.e. the Classification tool with a classification algorithm based on the Gaussian Mixture Model (GMM) and the MapInfo Pro 17.0 GIS. The results of the processing of the satellite imagery were monitored and refined on the basis of data from the State Forest Register and forest inventory and management plan. As a result, a schematic map of the surveyed hunting farm and a table of the distribution of areas by elements of the habitat hunting resources in accordance with modern regulatory requirements were formed. The proposed approach can be used not only for typological assessment of hunting grounds, but also as a tool for landscape analysis in the practice of operational and retrospective monitoring of natural and anthropogenicmodified territories, including on specially protected natural areas, agricultural lands and forest lands.

About the Authors

E. E. Lukashik
Yaroslav Mudry Novgorod State University; St.Petersburg State Forest Technical University
Russian Federation

Lukashik Evgeny E. – Head of Laboratory of Geographic Information Systems; postgraduate student of the Department of Forest Inventory, Management and GIS

173017. Soviet Army str. 7. Veliky Novgorod



A. S. Alekseev
St.Petersburg State Forest Technical University
Russian Federation

Alekseev Aleksandr S. – DSc (Geography), Professor, Head of the Department of Forest Inventory, Management and GIS

 AuthorID: 150999

194021. Institutsky per. 5. St. Petersburg



References

1. Alekseev A.S., Mihajlova A.A., Chernihovskij D.M., Berezin V.I. Metod opredeleniya taksacionnyh harakteristik nasazhdenij po aerofotosnimkam sverhvysokogo razresheniya. Trudy Sankt-Peterburgskogo nauchno-issledovatel'skogo instituta lesnogo hozyajstva, 2017, no. 2, pp. 67–77. (In Russ.)

2. Alekseev A.S., Nikiforov A.A. Geograficheskie informacionnye sistemy. SPb.: Izdvo SPbGLTU, 2022. 116 p. (In Russ.)

3. Ali M.S., Vorob'yov O.N., Kurbanov E.A. Algoritm «derevo reshenij» dlya klassifikacii lesov Sirijskoj Arabskoj Respubliki po snimku SENTINEL-2. Vestnik Povolzhskogo gosudarstvennogo tekhnologicheskogo universiteta. Ser.: Les. Ekologiya. Prirodopol'zovanie, 2020, no. 1 (45), pp. 5–30. DOI: 10.25686/2306-2827.2020.1.5

4. Borzov S.M., Potaturkin A.O., Potaturkin O.I., Fedotov A.M. Issledovanie effektivnosti klassifikacii giperspektral'nyh sputnikovyh izobrazhenij prirodnyh i antropogennyh territorij. Avtometriya, 2016, 52, no. 1, pp. 3–14. (In Russ.)

5. Bratkov V.V., Kravchenko I.V., Tuaev G.A., Ataev Z.V., Abdulzhalimov A.A. Primenenie vegetacionnyh indeksov dlya kartografirovaniya landshaftov Bol'shogo Kavkaza. Izvestiya Dagestanskogo gosudarstvennogo pedagogicheskogo universiteta. Estestvennye i tochnye nauki, 2016, iss. 10, no. 4, pp. 97–111. (In Russ.)

6. Buchnev A.A., Pyatkin V.P. Klassifikaciya s obucheniem giperspektral'nyh dannyh distancionnogo zondirovaniya zemli. Interekspo GEO-Sibir'-2017. XIII Mezhdunar. nauch. kongr. : Mezhdunar. nauch. konf. «Distancionnye metody zondirovaniya Zemli i fotogrammetriya, monitoring okruzhayushchej sredy, geoekologiya» : sb. materialov v 2 t. (Novosibirsk, 17–21 aprelya 2017 g.). Novosibirsk : SGUGiT, 2017, iss. 2, pp. 8–12. (In Russ.)

7. Carev P.V., Eskov D.V., Timofeeva E.V. Analiz metodov klassifikacii elementov sredy obitaniya territorii ohotnich'ego ugod'ya. Agroforsajt, 2020, no. 5(29), pp. 29– 34. (In Russ.)

8. Chan-Ryul P., Woo-Shin L. Development of a GIS-based habitat suitability model for wild boar Sus scrofa in the Mt. Baekwoonsan region, Korea. Mammal Study, 2003, 28(1), рр. 17–21.

9. Chernihovskij D.M. Geoinformacionnye sistemy v lesnom dele. SPb.: Izd-vo SPbGLTU, 2022. 88 p. (In Russ.)

10. Clevenger A.P., Wierzchowski J.,Chruszcz B., Gunson K. GIS-Generated, ExpertBased Models for Identifying Wildlife Habitat Linkages and Planning Mitigation Passages. Conservation Biology, 2002, 16, рр. 503–514.

11. Cyndyzhapova S.D., Rozlomij N.G., Belov A.N., Minhajdarov V.Yu. Inventarizaciya mestoobitanij ohotnich'ih zhivotnyh v ugod'yah OO «VKLO» Primorskogo kraya po rezul'tatam mul'tispektral'nyh izobrazhenij. Mnizh, 2021, no. 12-2 (114). (In Russ.)

12. Dmitruk N.G., Shirokova V.A., Nizovcev V.A., Snytko V.A., Frolova N.L., Chesnov V.M., Galkin Yu.S., Ozerova N.A., Shirokov R.S. Msta, Il'men', Volhov – starejshij vodnyj put' Baltijskogo regiona. Ustojchivoe razvitie i geoekologicheskie problemy Baltijskogo regiona: sb. mater. Mezhdunarodnoj nauchno-prakticheskoj konferencii, posvyashchennoj 1150-letiyu Velikogo Novgoroda. Velikij Novgorod, 2009, pp. 51–59. (In Russ.)

13. Elsakov V.V., Shchanov V.M. Razvitie topologicheskih podhodov pri kompleksnyh landshaftnyh issledovaniyah ekosistem Evropejskogo Severa distancionnymi metodami. Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. Fizicheskie osnovy, metody i tekhnologii monitoringa okruzhayushchej sredy i potencial'no opasnyh yavlenij i ob"ektov, 2005, pp. 267–272. (In Russ.)

14. Fan T.K., Nguen Ch.T., Alekseev A.S. i dr. Primenenie distancionnyh metodov i GIS-tekhnologij dlya klassifikacii zemel' Pushkinskogo rajona Sankt-Peterburga. Izvestiya Sankt-Peterburgskoj lesotekhnicheskoj akademii, 2021, no. 235, pp. 84– 102. (In Russ.)

15. Gerrard R., Stine, P., Church, R., Gilpin, M. Habitat evaluation using GIS: a case study applied to the San Joaquin Kit Fox. Landscape and Urban Planning, 2001, 52, рр. 239–255.

16. Grekov O.A. Vnedrenie geoinformacionnyh tekhnologij v praktiku ohotnich'ego hozyajstva. Biodiagnostika sostoyaniya prirodnyh i prirodno-tekhnogennyh sistem : mater. HIX Vserossijskoj nauchno-prakticheskoj konferencii c mezhdunarodnym uchastiem, Kirov, 25 noyabrya 2021 goda. Kirov: Vyatskij gosudarstvennyj universitet, 2021.

17. Grzegorz Szewczyk & Lipka, Krzysztof & Wezyk, Piotr & Zięba-Kulawik, Karolina & Winczek, Monika. Methods of Landscape Valorization and Possibilities of Its Application in Hunting Area Categorisation. 10.5772/intechopen.94048, 2020.

18. Hamel S., Garel M., Festa-Bianchet M., Gaillard J.M., Côté S.D. Spring normalized difference vegetation index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates. J. ApplEcol, 2009, 46, рр. 582–589.

19. Jaskula J., Sojka M., Wicher-Dysarz J. Analysis of the vegetation Process in a Two-stage Reservior on the Basis of Satellite Imagery – a Case Study: Razyny Reservior on the Sama River. Rocznik Ochrona Środowiska, 2018, 20, рр. 203–220.

20. Kunovac S., Omanović M. Game habitats modeling. International Conference Structure and dynamics of ecosystems «Dinarides – status, possibilities and prospects», 15–16. June 2011, Sarajevo, Bosnia and Herzegovina, Department of Natural Sciences and Mathematics, Proceedings, 2012, 23, рр. 127–134.

21. Lupyan E.A., Bartalev S.A., Ershov D.V. [i dr.]. Organizaciya raboty so sputnikovymi dannymi v informacionnoj sisteme distancionnogo monitoringa lesnyh pozharov Federal'nogo agentstva lesnogo hozyajstva (ISDM-Rosleskhoz). Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa, 2015, iss. 12, no. 5, pp. 222–250. (In Russ.)

22. Lyubimov A.V., Selivanov A.A., Kryuchkov A.N. [i dr.]. Analiz priznakov deshifrirovaniya taksacionnyh pokazatelej lesov s ispol'zovaniem veroyatnostnyh metodov. Izvestiya Samarskogo nauchnogo centra Rossijskoj akademii nauk, 2018, iss. 20, no. 2(82), pp. 85–90. (In Russ.)

23. Malinnikov V.A., Bartalev S.S. Vozmozhnosti regional'noj ekologicheskoj ocenki lesov po dannym sputnikovyh nablyudenij. Izvestiya vysshih uchebnyh zavedenij. Geodeziya i aerofotos"emka, 2006, no. 6, pp. 3–18. (In Russ.)

24. Melnikov V.V., Melnikov V.K. Upravlenie ohotnich'im hozyajstvom ili ohotoj. Sovremennye problemy prirodopol'zovaniya, ohotovedeniya i zverovodstva, 2007, no. 1 Myshlyakov S.G. Osobennosti deshifrirovaniya landshaftov po mul'tispektral'nym kosmicheskim snimkam dlya sozdaniya karty elementov sredy obitaniya ohotnich'ih resursov. Geomatika, 2013, no. 1, pp. 53–62. (In Russ.)

25. Ostapchuk A.M. Normativno – pravovaya baza deyatel'nosti ohotpol'zovatelej v SSSR, RSFSR, RF. Mezhdunarodnyj zhurnal teorii i nauchnoj praktiki, 2019, iss. 2, no. 2, pp. 53–67. (In Russ.)

26. Pettorelli N., Pelletier F., von Hardenberg A., Festa-Bianchet M., Cote S.D. Early onset of vegetation growth versus rapid green-up: impacts on juvenile mountain ungulates. Ecology, 2007, 88, рр. 381–390.

27. Radeloff V.C., Pidgeon A.M., Hostert P. Habitat and population modelling of roe deer using an interactive geographic information system. Ecological Modelling, 1999, 114(2-3), рр. 287–304.

28. Samsonov E.V., Samsonova A.M., Berlin N.G., Simbirceva Yu.V. Vydelenie taksonomicheskih edinic pri inventarizacii ohotnich'ego ugod'ya. Agroforsajt, 2017, no. 5(11), p. 3. (In Russ.)

29. Shihov A.N., Gerasimov A.P., Ponomarchuk A.I., Perminova E.S. i dr. Tematicheskoe deshifrirovanie i interpretaciya kosmicheskih snimkov srednego i vysokogo prostranstvennogo razresheniya: ucheb. posobie; Permskij gosudarstvennyj nacional'nyj issledovatel'skij universitet. Perm', 2020. 191 p. (In Russ.)

30. Shihov A.N., Gerasimov A.P., Ponomarchuk A.I., Perminova E.S. Tematicheskoe deshifrirovanie i interpretaciya kosmicheskih snimkov srednego i vysokogo prostranstvennogo razresheniya: uchebnoe posobie; Permskij gosudarstvennyj nacional'nyj issledovatel'skij universitet. Elektronnye dannye. Perm', 2020. 191 p. (In Russ.)

31. Smirnov S.I. Vnutrihozyajstvennoe i territorial'noe ohotustrojstvo: sovremennye problemy i perspektivy razvitiya. Ohotnich'e hozyajstvo i racional'noe prirodopol'zovanie v usloviyah sovremennoj global'noj transformacii (Chteniya pamyati A.A. Silant'eva): materialy Vserossijskoj nauchno-prakticheskoj konferencii, Sankt-Peterburg, 06 oktyabrya 2022 goda. SPb.: SPbGLTU, 2022. (In Russ.)

32. Suchant R., Baritz R., Braunisch V. Wildlife habitat analysis: a multidimensional habitat management model. J. Nat. Conserv. 2003, 10, рр. 253–268.

33. Suhih V.I. Ajerokosmicheskie metody v lesnom hozjajstve i landshaftnom stroitel'stve. Joshkar-Ola: MarGTU, 2005. 392 p. (In Russ.)

34. Timothy G. Whiteside, Guy S. Boggs, Stefan W. Maier. Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 2011, vol. 13, iss. 6, рр. 884–893.

35. Ukrainskij P.A., Terekhin E.A., Pavlyuk Ya.V. Fragmentaciya lesov verhnej chasti bassejna reki Vorskla s konca XVIII veka. Vestnik Moskovskogo universiteta. Seriya 5: Geografiya, 2017, no. 1, pp. 82–91. (In Russ.)

36. Usova I.P. Ocenka fragmentacii lesov s ispol'zovaniem landshaftnyh indeksov (na primere vostochno-belorusskoj landshaftnoj provincii). Aktual'nye problemy geobotaniki: mater. III Vserossijskoj shkoly-konferencii. II chast'. Petrozavodsk: KarNC RAN, 2007, pp. 250–253. (In Russ.)

37. Vinober A.V. Ohotovedenie i ohotnich'e hozyajstvo: v poiskah identichnosti, sinhronizacii i proliferacii idej (k 70 letiyu fakul'teta ohotovedeniya (1950–2020 gg.). «Siberia Land Congress» Biosphere and Agriculture Economies Support and Development Fund, 2019, no. 6(18), pp. 5–17. (In Russ.)

38. Xin Pan, Ce Zhang, Jun Xu, Jian Zhao. Simplified object-based deep neural network for very high resolution remote sensing image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, vol. 181, рр. 218–237. URL: https://doi.org/10.1016/j.isprsjprs.2021.09.014.

39. Yang, He & Ma, Ben & Du, Qian. Decision fusion for supervised and unsupervised hyperspectral image classification. International Geoscience and Remote Sensing Symposium (IGARSS), 2009, 4. IV-948. 10.1109/IGARSS.2009.5417535.

40. Zenkin G.Yu. Ispol'zovanie kart Google Maps v zadache identifikacii tochek na sputnikovyh izobrazheniyah srednego prostranstvennogo razresheniya. I-methods, 2012, no. 1.


Review

For citations:


Lukashik E.E., Alekseev A.S. Geoinformation mapping and classification of hunting grounds based on remote sensing data. Izvestia Sankt-Peterburgskoj lesotehniceskoj akademii. 2024;(250):23-45. (In Russ.) https://doi.org/10.21266/2079-4304.2024.250.23-45

Views: 101


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2079-4304 (Print)
ISSN 2658-5871 (Online)