基于Sentinel-1/2的大兴安岭木本沼泽信息提取方法

木本沼泽的遥感信息提取一直是湿地研究的难点之一,在复杂环境地区传统调查方法无法深入,而利用遥感影像提取湿地分布信息大大提高了研究效率,对于理解全球变化具有重要意义.本研究选取位于黑龙江省西北部大兴安岭地区额木尔河流域的一景影像为研究案例,融合应用Sentinel-1的雷达波段和Sen-tinel-2的红边波段,基于红边...

Extent extraction method of swamp in the Greater Khingan Mountains based on Sentinel-1/2 images

Due to environmental and ecosystem complexity, it is difficult to accurately mapping swamp in mountainous regions, especially in the inaccessible areas. Remote sensing provides a potential powerful tool for extracting the extent distribution of swamp, with great significance for understanding global change. In this study, we selected a scene extent of Sentinel imagery covering the Emur River Basin located in the Greater Khingan Mountains as the study area. The radar bands of Sentinel-1 with the red-edge bands of Sentinel-2, and other multispectral bands were combined to establish the feature bundles for delineating swamp. In order to improve the accuracy of swamp delineation, deep learning and support vector machine(SVM) were tested and compared. The results showed that:(1) For the deep learning method, the overall accuracy was improved by 2.3% to 84.3% when adding the red feature to the multispectral feature alone. Furthermore, when radar features were added, the accuracy was improved by another 1.8%. In other words, the combination of multispectral, red-edge, and radar features got an overall accuracy of 86.5% and a Kappa coefficient of 0.84. The highest producer accuracy of each category was 99.9% for woodland, 85.4% for marsh, 69.9% for swamp, and 89.3% for waterbody. The maximum iteration accuracy of the optimal deep learning model was 95.7%.(2) The support vector machine method was used for the control test. The overall accuracies of only multispectral features, followed by the addition of red-edge features and radar features were 74.4%, 75.4% and 77.3%, respectively. The overall accuracy of each scheme was lower than that of deep learning method. In this study, deep learning method could extract the swamp extent information accurately from the Sentinel-1/2 images, with the highest accuracy of 86.5%, which could provide a method reference for future research on the accurate extraction of swamp at broader scale.