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Land Use/Cover Classification of Large Conservation Areas Using a Ground-Linked High-Resolution Unmanned Aerial Vehicle

Author

Listed:
  • Lazaro J. Mangewa

    (School of Life Sciences and Bio-Engineering (LISBE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha P.O. Box 447, Tanzania
    College of Forestry, Wildlife, and Tourism (CFWT), Sokoine University of Agriculture (SUA), Morogoro P.O. Box 3009, Tanzania
    These authors contributed equally to this work.)

  • Patrick A. Ndakidemi

    (School of Life Sciences and Bio-Engineering (LISBE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha P.O. Box 447, Tanzania
    These authors contributed equally to this work.)

  • Richard D. Alward

    (School of Life Sciences and Bio-Engineering (LISBE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha P.O. Box 447, Tanzania
    Aridlands, LLC, Grand Junction, CO 81507, USA
    These authors contributed equally to this work.)

  • Hamza K. Kija

    (Conservation Information Monitoring Section (CIMS), Tanzania Wildlife Research Institute (TAWIRI), Arusha P.O. Box 661, Tanzania)

  • Emmanuel R. Nasolwa

    (School of Life Sciences and Bio-Engineering (LISBE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha P.O. Box 447, Tanzania)

  • Linus K. Munishi

    (School of Life Sciences and Bio-Engineering (LISBE), Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha P.O. Box 447, Tanzania
    These authors contributed equally to this work.)

Abstract

High-resolution remote sensing platforms are crucial to map land use/cover (LULC) types. Unmanned aerial vehicle (UAV) technology has been widely used in the northern hemisphere, addressing the challenges facing low- to medium-resolution satellite platforms. This study establishes the scalability of Sentinel-2 LULC classification with ground-linked UAV orthoimages to large African ecosystems, particularly the Burunge Wildlife Management Area in Tanzania. It involved UAV flights in 19 ground-surveyed plots followed by upscaling orthoimages to a 10 m × 10 m resolution to guide Sentinel-2 LULC classification. The results were compared with unguided Sentinel-2 using the best classifier (random forest, RFC) compared to support vector machines (SVMs) and maximum likelihood classification (MLC). The guided classification approach, with an overall accuracy (OA) of 94% and a kappa coefficient ( k ) of 0.92, outperformed the unguided classification approach (OA = 90%; k = 0.87). It registered grasslands (55.2%) as a major vegetated class, followed by woodlands (7.6%) and shrublands (4.7%). The unguided approach registered grasslands (43.3%), followed by shrublands (27.4%) and woodlands (1.7%). Powerful ground-linked UAV-based training samples and RFC improved the performance. The area size, heterogeneity, pre-UAV flight ground data, and UAV-based woody plant encroachment detection contribute to the study’s novelty. The findings are useful in conservation planning and rangelands management. Thus, they are recommended for similar conservation areas.

Suggested Citation

  • Lazaro J. Mangewa & Patrick A. Ndakidemi & Richard D. Alward & Hamza K. Kija & Emmanuel R. Nasolwa & Linus K. Munishi, 2024. "Land Use/Cover Classification of Large Conservation Areas Using a Ground-Linked High-Resolution Unmanned Aerial Vehicle," Resources, MDPI, vol. 13(8), pages 1-30, August.
    • Handle: RePEc:gam:jresou:v:13:y:2024:i:8:p:113-:d:1461893
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    References listed on IDEAS

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    1. Roshan Sharma & Bhagawat Rimal & Himlal Baral & Udo Nehren & Kiran Paudyal & Sunil Sharma & Sushila Rijal & Surendra Ranpal & Ram Prasad Acharya & Amer A. Alenazy & Prashid Kandel, 2019. "Impact of Land Cover Change on Ecosystem Services in a Tropical Forested Landscape," Resources, MDPI, vol. 8(1), pages 1-13, January.
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