Evidence map›Paper›PMID 41577751›Full record

ArticleScientific reports2026

Quantifying carbon stock and tree community composition in tropical forests through combining satellite and UAV analyses.

Kotaro Komatsu, Ryuichi Takeshige, Masanori Onishi, Shogoro Fujiki, Nobuo Imai, Kazuki Miyamoto, Shin-Ichiro Aiba, Kanehiro Kitayama, Sandy Tze Lui Tsen, Reuben Nilus and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. 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

14 authors.

Kotaro KomatsuGraduate School of Agriculture, Kyoto University, Oiwake-cho, Kitashirakawa, Sakyo- ku, Kyoto, 606-8502, Japan. koumiwa000507@outlook.jp.
Ryuichi TakeshigeOsaka Metropolitan University, 3-3-138 Sugimoto, Sumiyoshi-ku, Osaka, 558-8585, Japan.
Masanori OnishiGraduate School of Agriculture, Kyoto University, Oiwake-cho, Kitashirakawa, Sakyo- ku, Kyoto, 606-8502, Japan.
Shogoro FujikiBiome Inc, 134 Chudoji-Minamimachi, Shimogyo-ku, Kyoto, 600-8813, Japan.
Nobuo ImaiTokyo University of Agriculture, 1-1-1 Sakuragaoka, Setagaya-ku, Tokyo, 156-8502, Japan.
Kazuki MiyamotoForestry and Forest Products Research Institute, 1 Matsunosato, Tsukuba, 300-1244, Ibaraki, Japan.
Shin-Ichiro AibaHokkaido University, North 8 West 5, Sapporo, 060-0808, Hokkaido, Japan.
Kanehiro KitayamaGraduate School of Agriculture, Kyoto University, Oiwake-cho, Kitashirakawa, Sakyo- ku, Kyoto, 606-8502, Japan.
Sandy Tze Lui TsenForest Research Center, Mile 14, Sepilok Road, Sandakan, Sabah, 90007, Malaysia.
Reuben NilusForest Research Center, Mile 14, Sepilok Road, Sandakan, Sabah, 90007, Malaysia.
Joel DawatForest Research Center, Mile 14, Sepilok Road, Sandakan, Sabah, 90007, Malaysia.
Joan PereiraForest Research Center, Mile 14, Sepilok Road, Sandakan, Sabah, 90007, Malaysia.
Yusuke OnodaGraduate School of Agriculture, Kyoto University, Oiwake-cho, Kitashirakawa, Sakyo- ku, Kyoto, 606-8502, Japan.
Ryota AoyagiGraduate School of Agriculture, Kyoto University, Oiwake-cho, Kitashirakawa, Sakyo- ku, Kyoto, 606-8502, Japan.

Funding

Japan Society for the Promotion of Science 23K19306Japan Society for the Promotion of Science 23KK0120Remote Sensing Technology Center of Japan 203241000037
6 · The paper itself

Abstract

Monitoring tropical ecosystem services such as carbon stock and biodiversity with satellite remote sensing is essential for addressing climate change and biodiversity loss, but collecting ground truth data is costly. We investigated whether Unmanned Aerial Vehicles (UAVs) can reduce the costs. First, we developed a method to estimate Above-Ground Carbon (AGC) and biodiversity index (mixing ratio of pioneer and late-successional species) based on data derived from UAV-RGB images in four Forest Management Units (FMUs) in Malaysia. Second, we tested whether adding UAV-based ground truth (i.e., estimated carbon and biodiversity index) improves satellite-based models. We built machine learning models to estimate AGC and biodiversity index based on Landsat metrics and inventory data across Malaysia and Indonesia (395 plots). Accuracy was low without local inventory data (287 plots outside the four FMUs; R

Identifiers

PMID41577751
PMCPMC12886868

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LicenceCC BY
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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.