St. Petersburg State Forest Technical University named after S.M. Kirov
Voronezh, Russian Federation
Minsk, Belarus
UDC 630
The paper presents the concept and preliminary results of the development of an automated system for monitoring the condition and productivity of varietal forest crops using the example of the common pine variety "Negorelskaya". The system is based on the integration of multispectral satellite data from Earth remote sensing (ERS), ground-based measurements of chlorophyll fluorescence, and biometric parameters within the "target plant" paradigm. Special attention is paid to the role of the Negorelskaya variety as a key component for creating sustainable environmental protection plantations in the context of climate change, in particular, for the formation of the ecological framework and recreational belt of St. Petersburg. The proposed approach allows for a transition from traditional forest management to precision forestry, providing an operational assessment of the selection effect, forecasting productivity, and identifying stress conditions in plantations.
variety "Negorelskaya", remote sensing of the Earth (RS), multispectral data, automated monitoring, chlorophyll fluorescence, target plant, environmental protection plantations, Saint Petersburg
1. Kondratov, P. A. Sovremennye tendencii ocenki investicionnoy privlekatel'nosti proektov v lesnom hozyaystve / P. A. Kondratov // Ekonomicheskoe razvitie Rossii. – 2025. – T. 32, № 9. – S. 139-145. – EDN MJHSBE.
2. Paramonov, A. A. Issledovanie struktury tropicheskogo ciklona na osnove analiza dannyh sputnikovogo videoryada s primeneniem metoda pochti periodicheskogo analiza / A. A. Paramonov, A. V. Kalach, A. S. Kravchenko // Modelirovanie sistem i processov. – 2024. – T. 17, № 4. – S. 67-76. – DOIhttps://doi.org/10.12737/2219-0767-2024-17-4-67-76. – EDN BOGSTY.
3. Kalach, A. V. O vozmozhnostyah primeneniya metoda pochti-periodicheskogo analiza dlya obrabotki izobrazheniy / A. V. Kalach, A. A. Paramonov, S. L. Saharov // Modelirovanie sistem i processov. – 2024. – T. 17, № 3. – S. 44-52. – DOIhttps://doi.org/10.12737/2219-0767-2024-42-50. – EDN HKBSAV.
4. Sokolov, S. V. Novye optoelektronnye sistemy ekspress-analiza semyan v lesohozyaystvennom proizvodstve / S. V. Sokolov, A. I. Novikov // Lesotehnicheskiy zhurnal. – 2019. – T. 9, № 2(34). – S. 5-13. – DOIhttps://doi.org/10.34220/issn.2222-7962/2019.2/1. – EDN CNXAWZ.
5. Deep-learning approach for fusarium head blight detection in wheat seeds using low-cost imaging technology / R. C. Bernardes, A. De Medeiros, L. Da Silva [et al.] // Agriculture. – 2022. – Vol. 12, No. 11. – P. 1801. – DOIhttps://doi.org/10.3390/agriculture12111801. – EDN HKVPRE.
6. Ranniy rost kul'tur (Pinus sylvestris L. var. Negorelskaya), poluchennyh iz semyan s izvestnymi spektrometricheskimi i morfometricheskimi parametrami / T. P. Novikova, S. V. Rebko, E. P. Petrischev, A. I. Novikov // Izvestiya Sankt-Peterburgskoy lesotehnicheskoy akademii. – 2025. – № 255. – S. 196-224. – DOIhttps://doi.org/10.21266/2079-4304.2025.255.196-224. – EDN CBXCGT.
7. Prospects assessment of origins of scots pine in a changing climate / S. U. Rabko, L. F. Paplauskaya, P. V. Tupik [et al.] //12–14 noyabrya 2025 goda, 2025. – P. 119-121. – EDN XHQKWW.
