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• W2034215227 abstract "Field surveys of plants and animals were combined with satellite remote sensing of broad vegetation types to map biodiversity and thereby help plan conservation in the Sango Bay area, some 30 by 100 km bordering Lake Victoria in Uganda. A statistical classifier applied to satellite images identified 14 land-cover classes including water, swamp, dry grasslands, degraded woody vegetation, semi-natural forest classes and intensive land uses. Validation, using 240 sample sites, recorded 86% correspondence between field and map data. Intensive land use makes up 23% of the area, water and swamps 27%, dry grasslands 29%, woody vegetation 21%, with semi-natural forests covering 15% of the area. The species data from sample-based field surveys included flowering plant species, dragon/lies, butterflies, fish, amphibians, reptiles, birds and mammals. The species data were used to generate biodiversity ratings, based on species `richness' and `rarity', which could be related to the vegetation cover. This inter-relation helped to generate a biodiversity map of the Sango Bay area which has since been used to aid conservation planning." @default.
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• W2034215227 date "1998-12-01" @default.
• W2034215227 modified "2023-10-03" @default.
• W2034215227 title "The integration of field survey and remote sensing for biodiversity assessment: a case study in the tropical forests and wetlands of Sango Bay, Uganda" @default.
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• W2034215227 doi "https://doi.org/10.1016/s0006-3207(98)00005-6" @default.
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