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Assessment of Phytomass and Carbon Stock in the Ecosystems of the Central Forest Steppe of the East European Plain: Integrated Approach of Terrestrial Environmental Monitoring and Remote Sensing with Unmanned Aerial Vehicles.
Slavskiy, Vasiliy; Matveev, Sergey; Sheshnitsan, Sergey; Litovchenko, Daria; Larionov, Maxim Viktorovich; Shokurov, Anton; Litovchenko, Pavel; Durmanov, Nikolay.
Afiliação
  • Slavskiy V; Faculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.
  • Matveev S; Faculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.
  • Sheshnitsan S; Faculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.
  • Litovchenko D; Faculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.
  • Larionov MV; Department of Bioecology and Biological Safety, Institute of Veterinary Medicine, Veterinary and Sanitary Expertise and Agricultural Safety, Federal State Budgetary Educational Institution of Higher Education Russian Biotechnological University (ROSBIOTEC'H University), 1 Volokolamsk Highway, 125080
  • Shokurov A; Computational Methods Laboratory, Mechanics and Mathematics Faculty, Lomonosov Moscow State University, Leninskiye Gory 1, Main Building, GSP-1, 119991 Moscow, Russia.
  • Litovchenko P; Interdisciplinary Scientific and Educational School of Moscow University "Brain, Cognitive Systems, Artificial Intelligence", Leninskiye Gory 1, Main Building, GSP-1, 119991 Moscow, Russia.
  • Durmanov N; Faculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.
Life (Basel) ; 14(5)2024 May 15.
Article em En | MEDLINE | ID: mdl-38792652
ABSTRACT
The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches based on remote sensing and ecological modelling have been increasingly applied. The aim is to develop the scientific and methodological framework for the remote sensing estimation of qualitative and quantitative characteristics of forest stands, using the combination of surveys and machine learning models to determine phytomass of forest stands and calculate the carbon balance. Even-aged stands of different tree species growing in the forest steppe zone of the East European Plain were chosen as test objects. We have applied the modernized methodological approaches to compare and integrate forest and tree stand characteristics obtained by ground-based and UAV-based comprehensive surveys; additionally, we developed computer vision models and methods for determining the same characteristics by remote sensing methods. The key advantage of the proposed methodology for remote monitoring and carbon balance control over existing analogues is the minimization of the amount of groundwork and, consequently, the reduction inlabor costs without loss of information quality. Reliable data on phytomass volumes will allow for operational control of the forest carbon storage, which is essential for decision-making processes. This is important for the environmental monitoring of forests and green spaces of various economic categories. The proposed methodology is necessary for the monitoring and control of ecological-climatic and anthropogenic-technogenic transformations in various landscapes. The development is useful for organizing the management of ecosystems, environmental protection, and managing the recreational and economic resources of landscapes with natural forests and forest plantations.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article