Wilderness protection is increasingly important in the era of the Sixth Extinction and the Anthropocene. Mapping environmental indicators along a continuum of human modification provides key information for wilderness protection. However, uncertainty may occur in identifying wilderness areas by reclassifying wilderness continuum maps. In this study, an approach integrating both Boolean overlay and Weighted Linear Combination (WLC) is used to identify discrete wilderness patches and evaluate their relative wilderness quality. This approach is applied to China with a resolution of 1 km2. The wilderness patches are first identified using Boolean overlay with discrete thresholds for land use, distance from settlements and roads. A Wilderness Quality Index is then created using a WLC model by weighting and combining six wilderness quality indicators including biophysical naturalness, population density, remoteness from settlements, remoteness from roads/railways, settlements density and roads/railways density. An integrated wilderness map is then created by combining the results from the Boolean and WLC models. It is found that China is a highly wild country in parts, containing over 86,000 wilderness patches, with varying relative wilderness qualities, which covers approximately 42% of China’s terrestrial area. About 77% of the existing wilderness patches are not covered by nature reserves, indicating the obvious conservation gaps of China’s wilderness areas. The wilderness maps presented here could potentially support new wilderness protected area designation, connectivity conservation, and monitoring programs. This integrated approach of wilderness mapping is potentially useful for other countries in conducting their own wilderness inventories and developing wilderness conservation policies.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)