Estimating Biodiversity in Remote Areas, Using Existing Vegetation Data: The Ngamiland Region

Abstract

In data-poor regions, especially when they are large and remote, the measurement of biodiversity presents considerable challenges. This paper explores a way of estimating regional patterns of biodiversity through a combination of land-cover field mapping, remote sensing and interpretative GIS techniques. The results show spatial variations of potential biodiversity in the remote Ngamiland region of Botswana, with areas of higher variability of land-cover classes indicative of higher degrees of biodiversity. The methodology is potentially replicable in other data-poor regions in developing countries.