Statistical models that describe species-environmental relationships are important components within many wildlife conservation strategies. These models are typically developed from studies conducted on small geographic scales (hundreds of square kilometres), representing a relatively small range in environmental conditions. Such local models from local studies are often then extrapolated to predict the suitability of other unsampled regions.

Substantial differences in the size of landholdings among cocoa farmers in the Western Region – the last cocoa “frontier” in Ghana – are primarily a result of inheritance practices and the purchase of vast tracts of land by migrants in the initial period of the cocoa boom. Individual accumulation of land over the last decade has mainly taken place via inheritance (among indigenous farmers) without takeovers of land and dispossession of small-scale farmers outside the extended family. Land accumulation among migrant farmers is rare beyond the initial acquisition.

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates.