Social Network and Bibliometric Analysis of Unmanned Aerial Vehicle Remote Sensing Applications from 2010 to 2021

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 15; p. 2912
• Main Authors: Wang, Jingrui, Wang, Shuqing, Zou, Dongxiao, Chen, Huimin, Zhong, Run, Li, Hanliang, Zhou, Wei, Yan, Kai
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2021
• Subjects: affordability; Artificial intelligence; Artificial neural networks; Bibliometric; bibliometric analysis; Bibliometrics; China; Citation analysis; Citations; Cocitation; Cooperation; Documents; Environmental science; Keywords; Network analysis; Neural networks; Remote sensing; Remote Sensing (RS); satellites; Scientometric; Social network analysis; Social networks; Social organization; Trends; Unmanned Aerial Vehicle (UAV); Unmanned aerial vehicles; Visualization; United Kingdom > UK; United States > US; China
• DOI: 10.3390/rs13152912
• ISSN: 2072-4292

Unmanned Aerial Vehicle (UAV) Remote sensing (RS) has unique advantages over traditional satellite RS, including convenience, high resolution, affordability and fast acquisition speed, making it widely used in many fields. To provide an overview of the development of UAV RS applications during the past decade, we screened related publications from the Web of Science core database from 2010 to 2021, built co-author networks, a discipline interaction network, a keywords timeline view, a co-citation cluster, and detected burst citations using bibliometrics and social network analysis. Our results show that: (1) The number of UAV RS publications had an increasing trend, with explosive growth in the past five years. The number of papers published by China and the United States (US) is far ahead in this field; (2) The US has currently the greatest influence in this field through the largest number of international cooperations. Cooperation is mainly concentrated in countries and institutions with a large number of publications but is not widely distributed. (3) The application of UAV RS involves multiple interdisciplinary subjects, among which “Environmental Science and Ecology” ranks first; (4) Future research trends of UAV RS are expected to be related to artificial intelligence (e.g., artificial neural networks-based research). This paper provides a scientific basis and guidance for future developments of UAV RS applications, which can help the research community to better grasp the developments of this field.