Knowledge-Based Approaches to Accurate Mapping of Mangroves from Satellite Data

Abstract

Mangroves are difficult to map accurately from satellite data by means of parametric classification because of their spectral similarity to other coastal vegetation despite their habitat being inside coastal waters. This study aims to improve the mapping accuracy through incorporation of such spatial knowledge about mangroves in the Waitemata Harbor of Auckland, New Zealand, from SPOT data. The spatial knowledge was combined with spectral knowledge in the mapping. Supervised classification was found to map stunted and lush mangroves at an accuracy of, respectively, 46.7 percent and 68.3 percent. These accuracy levels rose, respectively, to 83.3 percent and 96.7 percent after the spatial knowledge was sequentially incorporated into the mapping. A similar accuracy level was achieved from knowledge-based spatial reasoning. If integrated simultaneously with spectral knowledge, spatial knowledge did not improve the accuracy noticeably because of difficulty in gaining quality spectral knowledge. It is concluded that knowledge-based, post-classification processing considerably improves the accuracy of mapping mangroves over parametric classification.