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

Autor: Lazaro J. Mangewa, Patrick A. Ndakidemi, Richard D. Alward, Hamza K. Kija, Emmanuel R. Nasolwa, Linus K. Munishi
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Resources, Vol 13, Iss 8, p 113 (2024)
Druh dokumentu: article
ISSN: 13080113
2079-9276
DOI: 10.3390/resources13080113
Popis: 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.
Databáze: Directory of Open Access Journals
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