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为总结森林乔木层地上生物量研究的进展与前沿热点，基于Web of Science核心数据库，利用R语言“bibliometrix”包对2002—2024年收录的乔木层地上生物量相关文献进行知识图谱分析。通过年度文献、研究领域、影响力期刊、核心作者和关键词的时间趋势等方面，全面综述该领域的研究热点与发展趋势。结果表明，2002—2024年，Web of Science数据库共收录乔木层地上生物量相关文献4604篇，去除发表年份缺失及重复数据后，共有文献3780篇，文献发表数量年均增长率达7.51%。研究表明，中国乔木层地上生物量领域具有较高的学术活跃度，但国内合作论文、国际合作论文、总被引用量和篇均引用量低于美国。乔木层地上生物量文献来源前10的期刊主要集中于农林科学、地球科学、环境科学与生态学领域，其中以农林科学为主，Forest Ecology and Management期刊发表相关文献数量远高于其他期刊。乔木层地上生物量高频文献以Aboveground biomass、Biomass、Lidar、Carbon sequestration和Climate change出现频率最高。从关键词出现频率维度分析，以Aboveground biomass为中心的集群出现频率明显高于其他2个集群，高频关键词为Remote sensing和Landsat,其中遥感技术结合机器学习估算乔木层地上生物量是近年来该领域的研究热点。本研究揭示了森林乔木层地上生物量领域的研究热点与趋势，为未来在遥感、机器学习及碳汇评估等方向的研究提供参考。
Abstract：To summarize the research progress and cutting-edge hotspots in aboveground biomass(AGB) of forest tree layers, this study used the R package "bibliometrix" for knowledge mapping of the publications related to tree-layer AGB in the Web of Science Core Collection from 2002 to 2024. Through comprehensive examination of annual publications, research fields, influential journals, core authors, and temporal trends of keywords, this study systematically reviewed the research hotspots and development trends in this field. The results showed that the Web of Science database included a total of 4604 publications on tree-layer AGB during 2002—2024. After removal of the duplications and records with missing publication years, 3780 publications were retained for analysis. The number of annual publications demonstrated an average growth rate of 7.51%. The study revealed that China showed high academic productivity in this field, while its publications exhibited lower performance than those from the United States in terms of single country publications(SCP), multiple country publications(MCP), total citations, and citations per paper. The top 10 journals publishing studies of tree-layer AGB were primarily concentrated in agricultural and forestry sciences, geosciences, and environmental science & ecology, with agricultural and forestry sciences being the predominant field. Notably, Forest Ecology and Management published significantly more articles in this field than other journals. High-frequency keywords in these publications included "Aboveground biomass", "Biomass", "Lidar", "Carbon sequestration", and "Climate change". Keyword co-occurrence analysis indicated that the cluster centered on "Aboveground biomass" was markedly more prominent than the other two clusters, with "Remote sensing" and "Landsat" being the most frequent keywords. In particular, the integration of remote sensing with machine learning for estimating tree-layer AGB has emerged as a key research focus in recent years. This study identifies the research hotspots and developmental trends in AGB of forest tree layers, offering valuable insights to guide future research in remote sensing applications, machine learning approaches, and carbon sequestration assessment.
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基本信息:
DOI：10.15914/j.cnki.wykx.2025016
中图分类号:S718.5;G353.1
引用信息:
[1]郑晨成,董标,朱静,等.森林乔木层地上生物量研究的知识图谱[J].武夷科学,2026,42(01):70-80.DOI:10.15914/j.cnki.wykx.2025016.
基金信息:
平潭岛森林碳储量遥感长期动态监测技术研究与示范(2024FKJ17); 福建省海岛资源生态监测与保护利用重点实验室开放基金(2023ZD07)
2025-04-18
2025
2025-09-15
2026-01-14
2026
1
2026-01-22
2026-01-22
2026-01-22