Evidence map›Paper›PMID 38792652›Full record

ArticleLife (Basel, Switzerland)2024

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.

Vasiliy Slavskiy, Sergey Matveev, Sergey Sheshnitsan, Daria Litovchenko, Maxim Viktorovich Larionov, Anton Shokurov, Pavel Litovchenko, Nikolay Durmanov

Abstract read
In one paragraph

Article in Life (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Vasiliy SlavskiyFaculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.ORCID 0000-0002-6579-0344
Sergey MatveevFaculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.
Sergey SheshnitsanFaculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.ORCID 0000-0002-8027-855X
Daria LitovchenkoFaculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.
Maxim Viktorovich LarionovDepartment 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 Moscow, Russia.ORCID 0000-0003-0834-2462
Anton ShokurovComputational Methods Laboratory, Mechanics and Mathematics Faculty, Lomonosov Moscow State University, Leninskiye Gory 1, Main Building, GSP-1, 119991 Moscow, Russia.
Pavel LitovchenkoFaculty of Forestry, Voronezh State University of Forestry and Technologies named after G.F. Morozov, 8 Timiryazev Street, 394087 Voronezh, Russia.
Nikolay DurmanovMinistry of Science and Higher Education of the Russian Federation, 11 Tverskaya Street, 21 Bryusov Lane, 125009 Moscow, Russia.

Funding

CSRD VA 1
6 · The paper itself

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.

Indexed as

canopy layercarbon stockforest landscapesforest phytocoenosesforest surveylandscape bioindicationmachine/deep learningmorphometric parameters of treesphytomassremote sensingstate of vegetationsynergy of methodological methods and approachesterrestrial environmental monitoringvegetation indices

Identifiers

PMID38792652
PMCPMC11122659

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.