Segment-Based Spectral Characterisation of Municipal Solid Waste in African Landfills Using HISUI Hyperspectral Imagery

Municipal solid waste management remains a major environmental challenge across Africa, where rapid urbanisation has outpaced formal waste infrastructure and routine landfill monitoring is often absent. Rather than proposing a classification algorithm, this study investigates whether spaceborne hyperspectral imagery can reveal robust spectral fingerprints of landfill surfaces suitable for automated detection. Eight landfill sites across seven African countries were analysed using Hyperspectral Imager Suite (HISUI) data (400–2500 nm, 20 m resolution). A segment-based framework was applied after masking low signal-to-noise regions, combining brightness analysis, L2-normalised spectral shape comparison using Spectral Contrast Angle (SCA), and derivative spectroscopy across 109,275 pixels from six land-cover classes. Brightness-based discrimination exhibited strong inter-site variability, limiting its general applicability. In contrast, shape-based metrices revealed consistent separability between landfill-active surfaces and soil or urban classes in the shortwave infrared (SWIR), particularly within the 1538–1750 nm and 2075–2474 nm regions. Derivative analysis further identified stable extrema near approximately 1700 nm and 2200–2300 nm across all sites, indicating reproducible curvature-based fingerprints associated with exposed municipal solid waste. These results demonstrate that landfill surfaces exhibit intrinsic SWIR spectral characteristics that persist across diverse African environments. This study establishes the first multi-site hyperspectral library of African landfill surfaces, providing a physical basis for developing generalised landfill detection frameworks.