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Mapping fractional vegetation cover in UAS RGB and multispectral imagery in semi-arid Australian ecosystems using CNN-based semantic segmentation

Abstract Context Monitoring fractional vegetation cover (FVC) is crucial for assessing ecosystem health and sustainably managing semi-arid rangelands. Field-based methods are resource-intensive, while moderate-resolution satellite imagery lacks the spatial detail needed to capture ecosystem complexity. This study leverages centimetre-scale UAS RGB and multispectral imagery with convolutional neural networks (CNN)-based U-net segmentation framework to address these challenges. Objectives We tested the integration of UAS multispectral imagery and CNN models for mapping FVC in semi-arid rangelands with varying vegetation types. Methods We trained and evaluated site-specific and generic experimental U-net CNN models to classify FVC into five classes: bare ground (BE), photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), shadow (SI), and water (WI). Data from three semi-arid sites with varying vegetation structures were …

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