A meta-analysis and review of unmanned aircraft system (UAS) imagery for terrestrial applications

ABSTRACT

Over the past decade, the remote-sensing community has eagerly adopted unmanned aircraft systems (UAS) as a cost-effective means to capture imagery at spatial and temporal resolutions not typically feasible with manned aircraft and satellites. The rapid adoption has outpaced our understanding of the relationships between data collection methods and data quality, causing uncertainties in data and products derived from UAS and necessitating exploration into how researchers are using UAS for terrestrial applications. We synthesize these procedures through a meta-analysis of UAS applications alongside a review of recent, basic science research surrounding theory and method development. We performed a search of the Web of Science (WoS) database on 17 May 2017 using UAS-related keywords to identify all peer-reviewed studies indexed by WoS. We manually filtered the results to retain only terrestrial studies ($n=412$) and further categorized results into basic theoretical studies ($n=63$), method development ($n=63$), and applications ($n=286$). After randomly selecting a subset of applications ($n=108$), we performed an in-depth content analysis to examine platforms, sensors, data capture parameters (e.g. flight altitude, spatial resolution, imagery overlap, etc.), preprocessing procedures (e.g. radiometric and geometric corrections), and analysis techniques. Our findings show considerable variation in UAS practices, suggesting a need for establishing standardized image collection and processing procedures. We reviewed basic research and methodological developments to assess how data quality and uncertainty issues are being addressed and found those findings are not necessarily being considered in application studies.

Acknowledgment

We gratefully acknowledge financial support from the National Science Foundation EPSCoR program [Grant IIA-1531070]. We acknowledge the contribution of Chi Cheng (Finn) Yip from Oklahoma State University for help with data cleaning and mapping. Finally, we thank Anna Klevtcova from the Center for Geospatial Analytics at North Carolina State University for her assistance in literature search, valuable comments, and feedback on the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science Foundation [IIA-1539070].