James, Mike R.
Arranz, Jose Juan
de Lima, Isabel Pedroso
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Earth System Observation and Modelling
Environmental Science and Engineering Program
Water Desalination and Reuse Research Center (WDRC)
Permanent link to this recordhttp://hdl.handle.net/10754/662263
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AbstractWith the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.
CitationTmušić, G., Manfreda, S., Aasen, H., James, M. R., Gonçalves, G., Ben-Dor, E., … McCabe, M. F. (2020). Current Practices in UAS-based Environmental Monitoring. Remote Sensing, 12(6), 1001. doi:10.3390/rs12061001
SponsorsThis research was funded by the COST Action CA16219 “HARMONIOUS—Harmonization of UAS techniques for agricultural and natural ecosystems monitoring”. H.A. is supported by the Swiss National Science Foundation (SNSF), within the framework of the National Research Programme “Sustainable Economy: resource-friendly, future-oriented, innovative” (NRP 73), in the InnoFarm project, Grant-N◦ 407340_172433. K.J., Y.M. and M.F.M. were supported by the King Abdullah University of Science and Technology
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