气候变化领域集成服务门户(CCPortal): Land Cover Classification of Subarctic Wetlands Using Multisource Remotely Sensed Data

CCPortal

DOI	10.1109/JSTARS.2024.3361279
	Land Cover Classification of Subarctic Wetlands Using Multisource Remotely Sensed Data
	Hu, Baoxin; Brown, Glen; Stirling, Callie; Wang, Jianguo
发表日期	2024
ISSN	1939-1404
EISSN	2151-1535
起始页码	17
卷号	17
英文摘要	This study aims to exploit multisource remotely sensed data to improve land cover classification of an area dominated by extensive wetlands with surface cover complexity strongly shaped by permafrost, fine-scale geomorphological and topographic characteristics. Accurate and precise mapping tools are critically needed to track change in wetland ecosystems under climate change. Wetland classification is challenging due to its high intraclass and low interclass variations in remote sensing features. Furthermore, the distribution and seasonal thaw patterns in permafrost influence vegetation cover at broad and fine scales within the study area of Hudson Bay Lowland, Canada, creating further complexity in classification. A systematic analysis was performed to evaluate the contribution of various remote sensing features (e.g., spectral, temporal, and structural features) and to determine vital datasets for effective monitoring of subarctic wetland-dominated ecosystems. Prediction uncertainty was comprehensively studied and reported together with classification accuracy. A decision-level fusion method based on the Dempster-Shaffer (DS) theory was developed. Different classes (i.e., focal elements under DS theory) were considered in this classification, using Sentinel-1 and Sentinel-2 data separately. The overall accuracy of the classification of 13 wetland classes was 0.952, significantly improved compared with the accuracies obtained by using individual data sources and by using feature-based fusion. Furthermore, the percentage of uncertain pixels is reduced as well.
英文关键词	Classification; Dempster-Shaffer (DS) theory; information fusion; multi-source remotely sensed data; prediction uncertainty; random forest; subarctic wetlands
语种	英语
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001175191200011
来源期刊	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296481
作者单位	York University - Canada; Ministry of Natural Resources & Forestry; Trent University
推荐引用方式 GB/T 7714	Hu, Baoxin,Brown, Glen,Stirling, Callie,et al. Land Cover Classification of Subarctic Wetlands Using Multisource Remotely Sensed Data[J],2024,17.
APA	Hu, Baoxin,Brown, Glen,Stirling, Callie,&Wang, Jianguo.(2024).Land Cover Classification of Subarctic Wetlands Using Multisource Remotely Sensed Data.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,17.
MLA	Hu, Baoxin,et al."Land Cover Classification of Subarctic Wetlands Using Multisource Remotely Sensed Data".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 17(2024).