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

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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 ([Inline formula]) and further categorized results into basic theoretical studies ([Inline formula]), method development ([Inline formula]), and applications ([Inline formula]). After randomly selecting a subset of applications ([Inline formula]), 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.
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Document Type: Research Article

Affiliations: 1: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA 2: Center for Applications of Remote Sensing, Department of Geography, Oklahoma State University, Stillwater, OK, USA

Publication date: August 18, 2018

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