The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 958, P. 178132 - 178132
Published: Dec. 17, 2024
Language: Английский
The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 958, P. 178132 - 178132
Published: Dec. 17, 2024
Language: Английский
Published: Jan. 1, 2025
Language: Английский
Citations
0Forestry Engineering Journal, Journal Year: 2025, Volume and Issue: 14(4), P. 63 - 84
Published: May 7, 2025
The development of Earth remote sensing methods, neural network technologies, creation machine learning models, etc. will allow developing new algorithms, indicators and criteria that significantly increase the efficiency forest monitoring help reduce financial costs. basis for work was verification ultra-high- high-resolution satellite imagery data based on in-situ survey materials conducted permanent test plots in Voronezh region. generated elements training samples using a classifier automated, highly accu-rate determination stand taxation data. When verifying amount aboveground phytomass calculated materials, significant similarity presented results revealed. In 67% cases, average values determined by different methods did not differ from each other (at significance level p<0.05). Reliable differences were found decid-uous stands with high horizontal canopy density, presence second tier abundant understory vegetation. As result work, theoretical laid further research conducting field for-est conservation, protection reproduction. material is useful building multidisciplinary practical areas restoration biological diversity phytocenoses, as well ensuring integrity ecological stability forests, under modern trends carbon cycles climate changes.
Language: Английский
Citations
0The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 958, P. 178132 - 178132
Published: Dec. 17, 2024
Language: Английский
Citations
1