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Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review

Author

Listed:
  • Benjamin T. Fraser

    (Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA)

  • Christine L. Bunyon

    (Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA)

  • Sarah Reny

    (Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA)

  • Isabelle Sophia Lopez

    (Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA)

  • Russell G. Congalton

    (Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA)

Abstract

Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in a range of disciplines. Despite these achievements, only minimal progress has been made in (1) establishing standard operating practices and (2) communicating both the limitations and necessary next steps for future research. In this review of literature published between 2016 and 2022, UAS applications in forestry, freshwater ecosystems, grasslands and shrublands, and agriculture were synthesized to discuss the status and trends in UAS sensor data collection and processing. Two distinct conclusions were summarized from the over 120 UAS applications reviewed for this research. First, while each discipline exhibited similarities among their data collection and processing methods, best practices were not referenced in most instances. Second, there is still a considerable variability in the UAS sensor data methods described in UAS applications in natural resources, with fewer than half of the publications including an incomplete level of detail to replicate the study. If UAS are to increasingly provide data for important or complex challenges, they must be effectively utilized.

Suggested Citation

  • Benjamin T. Fraser & Christine L. Bunyon & Sarah Reny & Isabelle Sophia Lopez & Russell G. Congalton, 2022. "Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review," Geographies, MDPI, vol. 2(2), pages 1-38, June.
    • Handle: RePEc:gam:jgeogr:v:2:y:2022:i:2:p:21-340:d:834145
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    References listed on IDEAS

    as
    1. Jérôme Théau & Étienne Lauzier-Hudon & Lydiane Aubé & Nicolas Devillers, 2021. "Estimation of forage biomass and vegetation cover in grasslands using UAV imagery," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-18, January.
    2. Clement E. Akumu & Eze O. Amadi & Samuel Dennis, 2021. "Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding," Land, MDPI, vol. 10(3), pages 1-13, March.
    3. Mohammad Fatin Fatihur Rahman & Shurui Fan & Yan Zhang & Lei Chen, 2021. "A Comparative Study on Application of Unmanned Aerial Vehicle Systems in Agriculture," Agriculture, MDPI, vol. 11(1), pages 1-26, January.
    4. Tainá T. Guimarães & Maurício R. Veronez & Emilie C. Koste & Eniuce M. Souza & Diego Brum & Luiz Gonzaga & Frederico F. Mauad, 2019. "Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images," Sustainability, MDPI, vol. 11(9), pages 1-13, May.
    5. Nahina Islam & Md Mamunur Rashid & Santoso Wibowo & Cheng-Yuan Xu & Ahsan Morshed & Saleh A. Wasimi & Steven Moore & Sk Mostafizur Rahman, 2021. "Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm," Agriculture, MDPI, vol. 11(5), pages 1-13, April.
    6. Santesteban, L.G. & Di Gennaro, S.F. & Herrero-Langreo, A. & Miranda, C. & Royo, J.B. & Matese, A., 2017. "High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard," Agricultural Water Management, Elsevier, vol. 183(C), pages 49-59.
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