Evidence map›Paper›PMID 40069206›Full record

ArticleScientific data2025

A global urban tree leaf area index dataset for urban climate modeling.

Wenzong Dong, Hua Yuan, Wanyi Lin, Zhuo Liu, Jiayi Xiang, Zhongwang Wei, Lu Li, Qingliang Li, Yongjiu Dai

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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.

Wenzong DongSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China.
Hua YuanSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China. yuanh25@mail.sysu.edu.cn.
Wanyi LinSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China.
Zhuo LiuSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China.
Jiayi XiangSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China.
Zhongwang WeiSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China.
Lu LiSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China.
Qingliang LiCollege of Computer Science and Technology, Changchun Normal University, Changchun, China.
Yongjiu DaiSouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Urban trees are recognized for mitigating urban thermal stress, therefore incorporating their effects is crucial for urban climate research. However, due to the limitation of remote sensing, the LAI in urban areas is generally masked (e.g., MODIS), which in turn limits its application in Urban Canopy Models (UCMs). To address this gap, we developed a high-resolution (500 m) and long-time-series (2000-2022) urban tree LAI dataset derived through the Random Forest model trained with MODIS LAI data, with the help of meteorological variables and tree height datasets. The results show that our dataset has high accuracy when validated against site reference maps, with R of 0.85 and RMSE of 1.03 m

Identifiers

PMID40069206
PMCPMC11897131

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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.