Evidence map›Paper›PMID 40773067›Full record

ReviewCarbon balance and management2025

Quantifying terrestrial carbon in the context of climate change: a review of common and novel technologies and methods.

Samuel Gameiro, Manuel Eduardo Ferreira, Luis Fernando Chimelo Ruiz, Gillian L Galford, Mojtaba Zeraatpisheh, Victor Fernandez Nascimento, Rosane Garcia Collevatti

Abstract readReview
In one paragraph

Review in Carbon balance and management, 2025. 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

7 authors.

Samuel GameiroRemote Sensing and GIS Lab (LAPIG), Environmental Science Graduate Program, Federal University of Goiás, Goiânia, Brazil. samuel_gameiro@outlook.com.ORCID http://orcid.org/0000-0002-4339-6725
Manuel Eduardo FerreiraRemote Sensing and GIS Lab (LAPIG), Environmental Science Graduate Program, Federal University of Goiás, Goiânia, Brazil.
Luis Fernando Chimelo RuizResearch and Development (R&D) at Santos Lab Digital, Rio de Janeiro, Brazil.
Gillian L GalfordRubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, 05405, USA.
Mojtaba ZeraatpishehRubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, 05405, USA.
Victor Fernandez NascimentoEngineering, Modelling, and Applied Social Sciences Center, Federal University of ABC, Santo André, Brazil.
Rosane Garcia CollevattiRemote Sensing and GIS Lab (LAPIG), Environmental Science Graduate Program, Federal University of Goiás, Goiânia, Brazil.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 140673/2021Conselho Nacional de Desenvolvimento Científico e Tecnológico 315699/2020-5Conselho Nacional de Desenvolvimento Científico e Tecnológico 404767/2021-4Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 88881.846255/2023-01University of Vermont 80NSSC23K0537
6 · The paper itself

Abstract

backgroundUnderstanding carbon dynamics in Earth's ecosystem is necessary for mitigating climate change. With recent advancements in technologies, it is important to understand both how carbon quantification in soil and vegetation is measured and how it can be improved. Therefore, this study conducted a bibliometric and bibliographic review of the most common carbon quantification methodologies.

resultsAmong the most widely used techniques, the Walkley-Black method and Elemental Analysis stand out for measuring below-ground carbon, while forest inventories are prominent for assessing above-ground carbon. Additionally, we found that the United States and China have the largest number of publications on this topic, with forest and agricultural areas being the most studied, followed by grasslands and mangroves. However, it should be noted that despite being indirect techniques, remote sensing, regression analysis, and machine learning have increasingly been used to generate geo-environmental carbon models for various areas. Landsat satellite images are the most widely used in remote sensing, followed by LiDAR digital models.

conclusionsThese results demonstrate that while new technologies do yet not replace analytical techniques, they are valuable allies working in conjunction with the current carbon quantification process.

Indexed as

Analytical techniquesCarbon stockPRISMARemote sensing

Identifiers

PMID40773067
PMCPMC12330144

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.