A Comparative use of UAV and Satellite Images in discrimination and estimation of cashew plantation areas
DOI:
https://doi.org/10.58825/jog.2024.18.2.147Keywords:
cashew plantations, UAV, Landsat, Sentinel-2, Random Forest, data fusionAbstract
Cashew plantations generate significant interest in Benin due to their high socioeconomic value for the population. A thorough understanding of the spatial distribution of these plantations is crucial for comprehending their environmental and socioeconomic impacts. In this study, various types of multi-sensor imagery were compared to assess each sensor's capabilities in mapping plantation areas. The study was conducted in the Savè commune, a major industrial cashew-producing region. Multispectral sensors from Landsat-8 Operational Land Imager (OLI), Sentinel-2A, and UAV multispectral platforms, along with ground surveys, were fused and classified using the Random Forest algorithm. The study results allowed for the assessment of uncertainties associated with different platforms in detecting cashew plantations in the test area. Classification using Random Forest algorithms on UAV, Sentinel, and Landsat platform images yielded overall accuracies of 83%, 65%, and 48%, respectively. Producer and user accuracies were 94% and 75% for the UAV platform, 98% and 71% for the Sentinel platform, and 91% and 77% for the Landsat platform in cashew tree detection. This study demonstrates the complementarity among various platforms in detecting and mapping cashew plantations.
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