Improving the Accuracy of Mangrove Species Discrimination using Object Based and High Spatial Resolution Imagery: A Case Study in Pak Phanang, Thailand.

Abstract

Detailed mangrove species maps are often required for proper management of mangrove forests. Recent reports on tropical mangrove species classification based on modern earth observation satellite data strongly support the potential use of remote sensing technology in mangrove forest management. Nevertheless, difficulty has remained in distinguishing between two dominant species, Rhizophora apiculata and Rizophora mucronata. The aim of this study is to improve the discrimination accuracy between these two species using an object-based classification method along with very high resolution, Quickbird images. Texture analyses and object-based classification were applied to the indistinguishable areas. The results showed an improvement in overall accuracy from 92 percent to 97 percent. Moreover, the producer’s accuracy of Rhizophora apicularta and Rizophora mucronata increased from 68 percent to 94 percent and from 94 percent to 95 percent, respectively. This outcome supports the use of the proposed method for resolving confusion between the two species. Additional data such as physiological parameters and vegetation indices may also be integrated in future studies for more accurate results.