Coastal wetland classification with multiseasonal high-spatial resolution satellite imagery

Accurate mapping of wetland distribution is required for wetland conservation, management, and restoration, but remains a challenge due to the complexity of wetland landscapes. This research employed four seasons of multispectral images from Gaofen-1 satellite to map wetland land-cover distribution in Hangzhou bay coastal wetland (245 km2) in China. Maximum likelihood classifier (MLC), random forest (RF), and the expert-based approach were examined based on spectral, spatial, and phenological features. The results showed that land-cover classification accuracies of 83.9% using RF and 90.3% using the expert-based approach were obtained, and they had higher accuracy than MLC, which had an overall accuracy of only 63.3%. The high classification accuracy for nine land-cover classes using the expert-based approach indicated the important role of expert knowledge from the phenological features in improving wetland classification accuracy. As high spatial resolution satellite images become more easily obtainable, effective use of temporal information of different sensor data will be valuable for detailed land-cover classification with higher accuracy. The approach to establish expert rules from multitemporal images provides a new way to improve land-cover classification in different terrestrial ecosystems.