南京林业大学学报（自然科学版）2024，Vol.48Issue(2)：19-26,8.DOI:10.12302/j.issn.1000-2006.202212016
基于Sentinel-1和Sentinel-2影像的洪泽湖国家湿地公园水生植被信息提取
Extraction of aquatic vegetation in Hongze Lake National Wetland Park based on Sentinel-1 and Sentinel-2 images
摘要
Abstract
[Objective]The objective of this study was to explore the extraction of spatio-temporal distribution of aquatic vegetation in lake wetlands using Sentinel-1 and Sentinel-2 data.[Method]Hongze Lake National Wetland Park was chosen as the research area.Based on the combination of Sentinel-2 MSI images and Sentinel-1 SAR images,the object-oriented image analysis was used.The feature set was constructed by using EVSI,NDVI,SR feature index and contextual features between objects,as well as differences in the backscatter coefficients of the SAR images corresponding to differences in the height of the emergent vegetation types.A decision-tree model was established at the object level to classify the wetland,and the spatio-temporal distribution of the aquatic vegetation and the emergent vegetation in the Hongze Lake National Wetland Park was acquired.[Result]The classification accuracy and the Kappa coefficient of aquatic vegetation were observed to be 89％and 0.85,respectively,and that of the emergent vegetation was 85.2％and 0.76,respectively.The results showed that,compared with the results of the pixel-based analysis method,the accuracy of object-based image analysis was higher.The wetland aquatic vegetation was dominated by submerged and emergent vegetation;among the emergent vegetation,lotus leaves and reeds were dominant.[Conclusion]The methods proposed in this study were feasible,and the results could provide a scientific basis for managers and planners of wetlands.关键词
水生植被/Sentinel-1/Sentinel-2/决策树/植被特征指数/后向散射系数/洪泽湖国家湿地公园Key words
aquatic vegetation/Sentinel-1/Sentinel-2/decision tree/vegetation characteristic index/backscatter coefficient/Hongze Lake National Wetland Park分类
农业科技引用本文复制引用
韩森,阮仁宗,傅巧妮,许捍卫,衡雪彪..基于Sentinel-1和Sentinel-2影像的洪泽湖国家湿地公园水生植被信息提取[J].南京林业大学学报（自然科学版）,2024,48(2):19-26,8.基金项目
应急管理部国家减灾中心-中国人民财产保险股份有限公司联合实验室2020年度开放基金(NDRCCPICC202003). （NDRCCPICC202003）
