Exploring the remote sensing-based classification of coastal wetlands is significant for their conservation and planning.Hence,this study investigated the Yellow River Delta with the 8-view Landsat8 OIL images from March to October 2019 as the data source.It constructed seven classification schemes based on different features of the images on the Google Earth Engine(GEE)cloud platform.Then,it employed the random forest classifier to classify different feature sets,with the scheme exhibiting the best classification effects selected for mapping the wetland categories of the Yellow River Delta.Considering poor data quality in August and September due to cloud contamination,this study filled in the cloudy zones using the enhanced spatial and temporal adaptive reflectance fusion model(ESTARFM)algorithm.The results show that:① The predicted images generated from the ESTARFM manifested a high correlation with the real image bands,with R values above 0.73,suggesting that the reconstructed images could be used in this study;② The random forest algorithm was used to classify the surface feature types in the study area.Through optimal feature selection,the classification results of Scheme 7 demonstrated an overall accuracy of 92.28%,higher than those of conventional schemes,with a Kappa coefficient of 0.91,aligning with the actual wetland conditions.The results of this study can assist in deeply understanding the spatial distributions of different wetlands in the area,and provide a scientific basis for the conservation and planning of the regional ecological environment.
维普
万方数据