Land Cover Classification of Subarctic Wetlands Using Multisource Remotely Sensed Data

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...

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Bibliographic Details
Published in:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Main Authors: Baoxin Hu, Glen Brown, Callie Stirling, Jianguo Wang
Format: Article in Journal/Newspaper
Language:English
Published: IEEE 2024
Subjects:
Classification
Dempster-Shaffer (DS) theory
information fusion
multi-source remotely sensed data
prediction uncertainty
random forest
Ocean engineering
TC1501-1800
Geophysics. Cosmic physics
QC801-809
Hudson Bay
permafrost
Subarctic
Online Access:https://doi.org/10.1109/JSTARS.2024.3361279
https://doaj.org/article/d78d27f098bf4bee9dee4806e4da9fef
Description
Summary: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.

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