Evidence map›Paper›PMID 38586281›Full record

ArticleInnovation (Cambridge (Mass.))2024

Deforestation in Latin America in the 2000s predominantly occurred outside of typical mature forests.

Zhiyu Zhang, Wenjian Ni, Shaun Quegan, Jingming Chen, Peng Gong, Luiz Carlos Estraviz Rodriguez, Huadong Guo, Jiancheng Shi, Liangyun Liu, Zengyuan Li and 4 more

Abstract read
In one paragraph

Article in Innovation (Cambridge (Mass.)), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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

14 authors.

Zhiyu ZhangKey Laboratory of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
Wenjian NiKey Laboratory of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
Shaun QueganChinal of Mathematics and Statistics, University of Sheffield, Sheffield S3 7RH, UK.
Jingming ChenDepartment of Geography and Program in Planning, University of Toronto, Toronto, ON M5S 3G3, Canada.
Peng GongDepartment of Earth Sciences and Department of Geography, University of Hong Kong, Hong Kong, China.
Luiz Carlos Estraviz RodriguezForest Science Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba 13418-900, Brazil.
Huadong GuoKey Laboratory of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
Jiancheng ShiNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
Liangyun LiuKey Laboratory of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
Zengyuan LiInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China.
Yating HeResearch Institute of Forest Policy and Information, Chinese Academy of Forestry, Beijing 100091, China.
Qinhuo LiuKey Laboratory of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
Yosio ShimabukuroRemote Sensing Department, National Institute for Space Research (INPE), Av. dos Astronautas 1758, São José dos Campos 12227-010, Brazil.
Guoqing SunDepartment of Geographical Sciences, University of Maryland, College Park, MD 20742, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The role of tropical forests in the global carbon budget remains controversial, as carbon emissions from deforestation are highly uncertain. This high uncertainty arises from the use of either fixed forest carbon stock density or maps generated from satellite-based optical reflectance with limited sensitivity to biomass to generate accurate estimates of emissions from deforestation. New space missions aiming to accurately map the carbon stock density rely on direct measurements of the spatial structures of forests using lidar and radar. We found that lost forests are special cases, and their spatial structures can be directly measured by combining archived data acquired before and after deforestation by space missions principally aimed at measuring topography. Thus, using biomass mapping, we obtained new estimates of carbon loss from deforestation ahead of forthcoming space missions. Here, using a high-resolution map of forest loss and the synergy of radar and lidar to estimate the aboveground biomass density of forests, we found that deforestation in the 2000s in Latin America, one of the severely deforested regions, mainly occurred in forests with a significantly lower carbon stock density than typical mature forests. Deforestation areas with carbon stock densities lower than 20.0, 50.0, and 100.0 Mg C/ha accounted for 42.1%, 62.0%, and 83.3% of the entire deforested area, respectively. The average carbon stock density of lost forests was only 49.13 Mg C/ha, which challenges the current knowledge on the carbon stock density of lost forests (with a default value 100 Mg C/ha according to the Intergovernmental Panel on Climate Change Tier 1 estimates, or approximately 112 Mg C/ha used in other studies). This is demonstrated over both the entire region and the footprints of the spaceborne lidar. Consequently, our estimate of carbon loss from deforestation in Latin America in the 2000s was 253.0 ± 21.5 Tg C/year, which was considerably less than existing remote-sensing-based estimates, namely 400-600 Tg C/year. This indicates that forests in Latin America were most likely not a net carbon source in the 2000s compared to established carbon sinks. In previous studies, considerable effort has been devoted to rectify the underestimation of carbon sinks; thus, the overestimation of carbon emissions should be given sufficient consideration in global carbon budgets. Our results also provide solid evidence for the necessity of renewing knowledge on the role of tropical forests in the global carbon budget in the future using observations from new space missions.

Identifiers

PMID38586281
PMCPMC10998227

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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.