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The superiority of the Adjusted Normalized Difference Snow Index (ANDSI) for mapping glaciers using Sentinel-2 multispectral satellite imagery
Författare
Summary, in English
Accurate monitoring of glaciers’ extents and their dynamics is essential for improving our understanding of the impacts of climate and environmental changes in cold regions. The satellite-based Normalized Difference Snow Index (NDSI) has been widely used for mapping snow cover and glaciers around the globe. However, mapping glaciers in snow-covered areas using existing indices remains a challenging task due to their incapabilities in separating snow, glaciers, and water. This study aimed to evaluate a new satellite-based index and apply machine learning algorithms to improve the accuracy of mapping glaciers. A new index based on satellite data from Sentinel-2 was tested, which we call the Adjusted Normalized Difference Snow Index (ANDSI). ANDSI (besides NDSI) was used with five different machine learning algorithms, namely Artificial Neural Network, C5.0 Decision Tree Algorithm, Naive Bayes classifier, Support Vector Machine, and Extreme Gradient Boosting, to map glaciers, and their performance was evaluated against ground reference data. Four glacierized regions in different countries (Canada, China, Sweden, and Switzerland-Italy) were selected as study sites to evaluate the performance of the proposed ANDSI. Results showed that the proposed ANDSI outperformed the original NDSI, and the C5.0 classifier showed the best overall accuracy and Kappa among the selected five machine learning classifiers in the majority of cases. The original NDSI yielded results with an average overall accuracy of (around) 91% and the proposed ANDSI with (around) 95% for glacier mapping across all models and study regions. This study demonstrates that the proposed ANDSI serves as a superior and improved method for accurately mapping glaciers in cold regions.
Avdelning/ar
- Institutionen för naturgeografi och ekosystemvetenskap
- MECW: The Middle East in the Contemporary World
- Centrum för geografiska informationssystem (GIS-centrum)
- Centrum för Mellanösternstudier (CMES)
- MERGE: ModElling the Regional and Global Earth system
Publiceringsår
2023-09
Språk
Engelska
Publikation/Tidskrift/Serie
GIScience and Remote Sensing
Volym
60
Issue
1
Dokumenttyp
Artikel i tidskrift
Förlag
Taylor & Francis
Ämne
- Remote Sensing
- Physical Geography
Nyckelord
- Adjusted Normalized Difference Snow Index (ANDSI)
- cold regions
- Sentinel-2
- glaciers
- machine learning classifiers
Aktiv
Published
Projekt
- Improving hydrological modelling in cold regions using satellite remote sensing and machine learning techniques
ISBN/ISSN/Övrigt
- ISSN: 1548-1603