Mapping forest plantations of the Volgograd region according to remote sensing data using the BSFI and NDWI indices

Remote sensing
UDC: 
528.854.2
DOI: 
10.22389/0016-7126-2023-1000-10-39-49
1 Vasilchenko A.A.
2 Vypritsky A.A.
Year: 
2023
№: 
10
1000
Pages: 
39-49

Federal Scientific Center of Agroecology, Integrated Land Reclamation and Protective Afforestation of the RAS

1, 
2, 
Abstract:
The possibilities of adjusting the results of mapping wood plantations based on the bi-seasonal forest index (BSFI) according to Sentinel-2 data and calculated seasonal composite images of the NDWI index maximum values are presented. It was found out that with using a seasonal composite image of the mentioned values, calculated with the NIR and SWIR2 ranges, the manufacturer`s accuracy in raster cross-validation increases from 82,5 to 92,8 %. At the same time, the initial areas of wood zones decreased by 7 %, and those of false identifications shrank by 67 %. Comparison of mapping results with statistical data showed small differences in areas with a large number of natural forest plantations (in 18 of 33 areas they do not exceed 15 % of the design ones). Moreover, in lands with a large number of protective green spaces, discrepancies can reach up to 210 % of the design values. It is shown that significant distinctions in design and actual mapping materials can be associated with poor preservation, fires, illegal logging, as well as problems of accounting protective forest plantings. A fairly accurate method for adjusting BSFI was proposed (the manufacturer’s accuracy increased from 82,5 to 92,8 %) based on the maximum composite NDWI image calculated using the NIR and SWIR2 bands of the Sentilel-2A and Sentilel-2B satellites in a test area within the Volgograd region
State Assignment of the FRC for Agroecology of the RAS. No. 122020100406-6 “Theoretical foundations and mathematical and cartographic models of the functioning of agroforestry systems in protecting soils from deflation”.
References: 
1.   Agrolesomelioratsiya. Pod red. A. L. Ivanova, K. N. Kulika. – Izd. 5-e, pererab. i dop.. Volgograd: VNIALMI, 2006, 746 p.
2.   Ali M. S., Vorob'ev O. N., Kurbanov E. A. Algoritm «derevo reshenii» dlya klassifikatsii lesov Siriiskoi Arabskoi Respubliki po snimku Sentinel-2. Vestnik Povolzhskogo gos. tekhnologich. un-ta. Ser. «Les. Ekologiya. Prirodopol'zovanie», 2020, no. 1 (45), pp. 5–30. DOI: 10.25686/2306-2827.2020.1.5.
3.   Antonov S. A. Analiz prostranstvennogo polozheniya zashchitnykh lesnykh nasazhdenii na osnove geoinformatsionnykh tekhnologii i dannykh distantsionnogo zondirovaniya Zemli. InterKarto. InterGIS, 2020, Vol. 26, pp. 408–420. DOI: 10.35595/2414-9179-2020-2-26-408-420.
4.   Bartalev S. A., Bogodukhov M. A., Zharko V. O., Sidorenkov V. M. Issledovanie vozmozhnostei ispol'zovaniya dannykh ICESat-2 dlya otsenki vysoty lesov Rossii. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, no. 4, pp. 195–206. DOI: 10.21046/2070-7401-2022-19-4-195-206.
5.   Vladimirov I.N. (2012) Geoinformation modeling of forest vegetation of the North-West Baikal (algorithms experience of geoimages classification. Geodezia i Kartografia, (9), pp. 30-35.
6.   Vypritskii A. A. Sravnitel'nyi analiz sokhrannosti vodorazdel'nykh gosudarstvennykh zashchitnykh lesnykh polos Volgogradskoi oblasti po dannym Sentinel-2 NDVI i informatsionnykh produktov tipov zemnogo pokrova. InterKarto. InterGIS, 2022, Vol. 28, no. 1, pp. 458–470. DOI: 10.35595/2414-9179-2022-1-28-458-470.
7.   Vypritskii A. A., Shinkarenko S. S. Analiz vliyaniya pochvenno-klimaticheskikh uslovii na sokhrannost' gosudarstvennykh zashchitnykh lesnykh polos na osnove dannykh Sentinel-2. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, no. 5, pp. 147–163. DOI: 10.21046/2070-7401-2022-19-5-147-163.
8.   Kurbanov E. A., Borob'ev O. N., Men'shikov S. A., Smirnova L. N. Raspoznavanie lesnykh nasazhdenii i dominiruyushchikh drevesnykh porod Penzenskoi oblasti po dannym sputnika Sentinel-2. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, no. 5, pp. 154–166. DOI: 10.21046/2070-7401-2018-15-5-154-166.
9.   Loupian E. A., Proshin A. A., Burtsev M. A., Balashov I. V., Bartalev S. A., Efremov V. Yu., Kashnitskiy A. V., Mazurov A. A., Matveev A. M., Sudneva O. A., Sychugov I. G., Tolpin V. A., Uvarov I. A. Tsentr kollektivnogo pol'zovaniya sistemami arkhivatsii, obrabotki i analiza sputnikovykh dannykh IKI RAN dlya resheniya zadach izucheniya i monitoringa okruzhayushchei sredy. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, no. 5, pp. 263–284.
10.   Mabiala I. Zh., Malinnikov V. A. Metodika vyyavleniya i kartografirovaniya izmenenii sostoyaniya lesnykh massivov territorii Lac Télé (Respublika Kongo) po materialam mnogozonal'nykh kosmicheskikh s"emok. Monitoring. Nauka i tekhnologii, 2022, no. 3 (53), pp. 22–33. DOI: 10.25714/MNT.2022.53.002.
11.   Rulev A. S., Yuferev V. G., Anopin V. N., Rulev G. A. Geoinformatsionnyi analiz sostoyaniya pridorozhnykh lesnykh nasazhdenii. Izvestiya Orenburgskogo gos. agrarnogo un-ta, 2014, no. 3 (47), pp. 42–45.
12.   Tarasova L. V., Kurbanov E. A., Vorob'ev O. N. Lezhnin S. A., Dergunov D. M. Ispol'zovanie raznosezonnykh izobrazhenii Sentinel-2 dlya kartografirovaniya vodookhrannykh lesov Mariiskogo Zavolzh'ya. Lesnye ekosistemy v usloviyakh izmeneniya klimata: biologicheskaya produktivnost' i distantsionnyi monitoring, 2022, no. 8, pp. 76-94. DOI: 10.25686/2022.79.42.008.
13.   Tkachenko N. A., Koshelev A. V. Kartografirovanie zashchitnoi lesistosti agrolandshaftov Volgogradskogo Zavolzh'ya. Vestnik APK Stavropol'ya, 2017, no. 2 (26), pp. 137–143.
14.   Cheplyanskii I. Ya., Turchin T. Ya., Ermolova A. S. Distantsionnyi monitoring gosudarstvennykh zashchitnykh lesnykh polos stepnoi zony evropeiskoi chasti Rossii. Izv. vuzov «Lesnoi zhurnal», 2022, no. 3, pp. 44–59. DOI: 10.37482/0536-1036-2022-3-44-59.
15.   Chimitdorzhiev T. N., Dmitriev A. V., Kirbizhekova I. I., Sherkhoeva A. A., Baltukhaev A. K., Dagurov P. N. Distantsionnye optiko-mikrovolnovye izmereniya parametrov lesa: sovremennoe sostoyanie issledovanii i eksperimental'naya otsenka vozmozhnostei. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, no. 4, pp. 9–24. DOI: 10.21046/2070-7401-2018-15-4-9-24.
16.   Shinkarenko S. S., Bartalev S. A., Vasil'chenko A. A. Metod kartografirovaniya zashchitnykh lesnykh nasazhdenii na osnove raznovremennykh sputnikovykh izobrazhenii vysokogo prostranstvennogo razresheniya i bisezonnogo indeksa lesa. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, no. 4, pp. 207–222. DOI: 10.21046/2070-7401-2022-19-4-207-222.
17.   Shinkarenko S. S., Berdengalieva A. N. Analiz mnogoletnei dinamiki stepnykh pozharov v Volgogradskoi oblasti. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, no. 2, pp. 98–110. DOI: 10.21046/2070-7401-2019-16-2-98-110.
18.   Shinkarenko S. S., Doroshenko V. V., Berdengalieva A. N. Dinamika ploshchadi garei v zonal'nykh landshaftakh yugo-vostoka evropeiskoi chasti Rossii. Izvestiya RAN. Ser. Geograficheskaya, 2022, Vol. 86, no. 1, pp. 1–12. DOI: 10.31857/S2587556622010113.
19.   Gao B. C. (1996) NDWI – a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, Volume 58, no. 3, pp. 257–266. DOI: 10.1016/S0034-4257(96)00067-3.
20.   Koshelev A. V., Tkachenko N. A., Shatrovskaya M. O. (2021) Decoding of forest belts using satellite images. IOP Conference Series: Earth and Environmental Science, Volume 875, no. 1, pp. 12–65. DOI: 10.1088/1755-1315/875/1/012065.
21.   Loupian E. A., Bourtsev M. A., Proshin A. A., Kashnitskii A. V., Balashov I. V., Bartalev S. A., Konstantinova A. M., Kobets D. A., Radchenko M. V., Tolpin V. A., Uvarov I. A. (2022) Usage experience and capabilities of the VEGA-science system. Remote Sensing, Volume 14, no. 1, pp. 77–96. DOI: 10.3390/rs14010077.
22.   McFeeters S. K. (1996) The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens, Volume 17, no. 7, pp. 1425-1432.
23.   Özelkan E. (2020) Water body detection analysis using NDWI indices derived from Landsat-8 OLI. Polish Journal of Environmental Studies, no. 29, 2, pp. 1759–1769. DOI: 10.15244/pjoes/110447.
24.   Rulev A. S., Pugacheva A. M. (2019) Formation of a new agroforestry paradigm. Herald of the Russian Academy of Sciences, Volume 89, no. 5, pp. 495–501. DOI: 10.1134/S1019331619050071.
25.   Smirnov V. O., Zelentsova M. G., Krainyuk E. S. (2021) Practical application peculiarities of geo-information technologies while planning the protective forest belts. IOP Conference Series: Earth and Environmental Science, Volume 666, no. 042005, DOI: 10.1088/1755-1315/666/4/042005.
26.   Vassilev K. V., Assenov A. I., Velev N. I., Grigorov B. G., Borissova B. B. (2019) Distribution, characteristics and ecological role of protective forest belts in Silistra municipality. Northeastern Bulgaria. Ecologia Balcanica, Volume 11, no. 1, pp. 191–204.
Citation:
Vasilchenko A.A., 
Vypritsky A.A., 
(2023) Mapping forest plantations of the Volgograd region according to remote sensing data using the BSFI and NDWI indices. Geodesy and cartography = Geodezia i Kartografia, 84(10), pp. 39-49. (In Russian). DOI: 10.22389/0016-7126-2023-1000-10-39-49
Publication History
Received: 23.08.2023
Accepted: 16.10.2023
Published: 20.11.2023

Content

2023 October DOI:
10.22389/0016-7126-2023-1000-10