Evidence map›Paper›PMID 41667528›Full record

ArticleScientific data2026

A 10 m High-precision Canopy Height Product for Nanping City, Fujian Province, China.

Ling Yi, Xiaojing Yao, Aixia Yang, Liqiang Zhang, Dacheng Wang, Yue Jiao, Yaoliang Chen, Shufu Liu, Gang Chen, Yalan Liu

Abstract read
In one paragraph

Article in Scientific data, 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

10 authors.

Ling YiAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Xiaojing YaoAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China. yaoxj@aircas.ac.cn.
Aixia YangAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Liqiang ZhangBeijing Normal University, Beijing, 100875, China.
Dacheng WangAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Yue JiaoAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Yaoliang ChenSchool of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China.
Shufu LiuAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Gang ChenAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China.
Yalan LiuAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China. liuyl@aircas.ac.cn.

Funding

National Key Research and Development Program of China 2022YFF0606400Science and Technology Service Network Initiative, Chinese Academy of Sciences 2023T3080
6 · The paper itself

Abstract

As a key ecological barrier and core carbon sink area in Southern China, Nanping City presents significant challenges for high-accuracy forest canopy height mapping due to its unique vegetation structure and complex topography. The current products for canopy height couldn't meet the requirements for accurate carbon stock estimation. This study calibrated the biases in GEDI data by proposing an innovative Bias Calibration Model and combined with multi-source data including Sentinel-1, 2, UAV LiDAR, and topographic data, produced 10-meter canopy height products for 2022 and 2023 for Nanping City using a random forest model. The generated data products achieved R² = 0.62 for both 2022 and 2023 when validated against independent UAV data, with RMSE = 2.88 m and 3.09 m for 2022 and 2023 respectively, accurately characterizing the vertical structural attributes of the forest This study provides a reliable data foundation for the estimation of subsequent forest biomass and carbon stock.

Identifiers

PMID41667528
PMCPMC13149869

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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.