Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Default user image.

Petter Pilesjö

Prefekt

Default user image.

The superiority of the Adjusted Normalized Difference Snow Index (ANDSI) for mapping glaciers using Sentinel-2 multispectral satellite imagery

Författare

  • Babak Mohammadi
  • Petter Pilesjö
  • Zheng Duan

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

Avvikelse

1

Dokumenttyp

Artikel i tidskrift

Förlag

Taylor & Francis

Ämne

  • Earth Observation
  • 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