Evidence map›Paper›PMID 39753724›Full record

ArticleScientific reports2025

Estimating forest aboveground carbon sink based on landsat time series and its response to climate change.

Kun Yang, Kai Luo, Jialong Zhang, Bo Qiu, Feiping Wang, Qinglin Xiao, Jun Cao, Yunrun He, Jian Yang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 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

9 authors.

Kun YangThe Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China.
Kai LuoThe Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China.
Jialong ZhangThe Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China. jialongzhang@swfu.edu.cn.
Bo QiuThe Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China.
Feiping WangGuangxi State-owned Gaofeng Forest Farm, Nanning, 530001, Guangxi, China.
Qinglin XiaoThe Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China.
Jun CaoThe Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China.
Yunrun HeThe Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China.
Jian YangState-owned Jiaozuo Forest Farm, Jiaozuo, 454000, Henan, China.

Funding

Innovation Programs of Southwest Forestry University LXXK-2023Z06National Natural Science Foundation of China 32260390"Young Top Talents" special project of the high-level talent training support program of Yunnan province, China, in 2020 YNWR-QNBJ-2020-164
6 · The paper itself

Abstract

Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are critical to achieving carbon neutrality and sustainable development. Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. The climate-driven mechanisms in Shangri-La have yet to be explored. In this study, a genetic algorithm (GA) was used to optimize the parameters of random forest (RF) to establish dynamic models to estimate the carbon sink intensity (CSI) of Pinus densata in Shangri-La and analyze the combined effects of multi-climatic factors on CSI. We found that (1) GA can effectively improve the estimation accuracy of RF, the R

Indexed as

Climate changeDynamic modelingForest aboveground carbon sinkGenetic algorithmTime series

Identifiers

PMID39753724
PMCPMC11698910

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Registered trials

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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.