A bibliometric analysis on the use of UAV-LiDAR in forest structure attributes estimation

Unmanned Aerial Vehicle-Light Detection and Ranging (UAV-LiDAR) is preferred for large-scale forest inventories due to its consistent, repeatable monitoring. While separate reviews exist for UAVs or LiDAR in forestry, global documentation on their integrated application remains scarce. Therefore, this study examines UAV-LiDAR application to: i) provide overview analysis, ii) assess authors’ keywords, and iii) identify emerging research themes in UAV-LiDAR application in Forest structure attributes (FSA) estimation. This study applied bibliometric analysis on 66 peer-reviewed English research documents from Scopus and Web of Science (2015–2025). Documents were generated through database navigation, collection and analysis Bibliometrics and Biblioshiny open-source packages readily available in RStudio environment and Google Search Engine platform. The findings indicate “Remote Sensing” and “Forests” are the most frequently publishing journals. Li, X. (9 papers) and Du, H. (6 papers) are the most publishing authors, while Sankey [ 72 ] and Puliti [ 66 ] are the most cited. Institutionally, Zhejiang A&F University and Beijing Forestry University top the list, positioning China as the dominant hub for publications, citations, and collaborations, followed by the USA. Conversely, Africa remains the least involved due to: limited funding, scarce high-resolution datasets, constrained computational infrastructure, and strict aviation policies. The review further reveals emerging topics that include: “LiDAR,” “tree species classification,” and “UAV” point to future research opportunities. Ultimately, this review confirms the strong potential of UAV-LiDAR for FSA estimation, serving as a powerful incentive to explore this technology further, particularly in developing nations like Malawi.