Land cover classification of the Yellow River Delta wetland based on multiple end member spectral mixture analysis and a Random Forest classifier
ABSTRACT As an important ecosystem, wetlands play a crucial role in both regional and global environments. Accurate land cover classification can facilitate the management and understanding of wetlands. Considering the timely and cost effective characteristics of remote sensing, this technique was used to obtain land cover information for the Yellow River Delta (YRD) wetland in this investigation. Landsat 8 Operational Land Imager (OLI) sensor data were selected for the data set in this study. A combined approach of multiple end member spectral mixture analysis (MESMA) and Random Forest (RF) was developed for land cover classification mapping of the YRD wetland. This study aimed (1) to determine whether the MESMA technique in combination with RF significantly improves the accuracy of classification in complex landscapes such as the YRD wetland, (2) to determine whether the RF classifier shows good performance in land cover classification of the YRD wetland, and (3) to compare the proposed method with the traditional Maximum Likelihood Classifier (MLC) The proposed hybrid method showed good performance, with an overall accuracy of 89.5% and a kappa coefficient (k) of 0.88. The inclusion of fractional information derived from MESMA can improve the classification accuracy by 2 3% In addition, through a comparison with traditional maximum likelihood (ML) methodology, the effectiveness of the proposed approach was evaluated. Overall, the proposed approach in this study can relatively accurately delineate a land cover classification map of the YRD wetland with Landsat 8 OLI remotely sensed data.