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

Lazaro J. Mangewa (), Patrick A. Ndakidemi, Richard D. Alward, Hamza K. Kija, Emmanuel R. Nasolwa and Linus K. Munishi
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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
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
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
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

Resources, 2024, vol. 13, issue 8, 1-30

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.

Keywords: community wildlife management areas; random forest algorithm; remote sensing technologies; Sentinel-2; pre-UAV flight ground data; unmanned aerial vehicles (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2024
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