A mapping methodology for wetland categories of the Yellow River Delta based on optimal feature selection and spatio-temporal fusion algorithm
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.
Landsat8multitemporal dataYellow River Delta wetlandimage fusionGoogle Earth Engineran-dom forest
冯倩、张佳华、邓帆、吴贞江、赵恩灵、郑培鑫、韩杨
展开 >
长江大学地球科学学院,武汉 430100
中国科学院空天信息创新研究院中国科学院数字地球重点实验室,北京 100094
中国科学院大学地球与行星科学学院,北京 100049
Landsat8 多时相数据 黄河三角洲湿地 图像融合 Google Earth Engine 随机森林
万方数据
维普
