Knowledge-Based Raster Mapping Approach to Wetland Assessment: a Case Study in Suzhou, China

We present a knowledge-based raster mapping (KBRM) approach to assessing wetlands based on the wetland-environment model, which is built by local experts using their knowledge and experience. The Delphi method and the Analytic Hierarchy Process (AHP) were employed to improve the consistency and the quality of the elicited expert knowledge. The environmental factors selected by local experts include edge density, aquatic connectivity, water-body density, road density, urban area density, vegetation screening index, and slope gradient. The experts assigned weights to those environmental factors and created optimality function curves to characterize the relationships between individual factors and the wetland condition. At each raster cell, an optimality value for each environmental factor was calculated based on the local value of that factor, and an overall optimality score was then calculated as the weighted average of the optimality values for individual factors. As a case study, we implemented this approach in assessing the wetlands in Suzhou, China. While our approach does not directly require field information, its result is comparable with the result of the rapid field method that is directly based on field data. In addition, our approach is advantageous in representing interior details of a wetland.