The superiority of the Adjusted Normalized Difference Snow Index (ANDSI) for mapping glaciers using Sentinel-2 multispectral satellite imagery
Mohammadi, Babak LU
; Pilesjö, Petter
LU
and Duan, Zheng
LU
(2023)
In GIScience and Remote Sensing
60(1).
- Abstract
- 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).... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/9586fc46-dce0-4e63-a936-c9cc0b3ceb40
- author
- Mohammadi, Babak
LU
; Pilesjö, Petter
LU
and Duan, Zheng
LU
- organization
- publishing date
- 2023-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adjusted Normalized Difference Snow Index (ANDSI), cold regions, Sentinel-2, glaciers, machine learning classifiers
- in
- GIScience and Remote Sensing
- volume
- 60
- issue
- 1
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85171642014
- ISSN
- 1548-1603
- DOI
- 10.1080/15481603.2023.2257978
- project
- Improving hydrological modelling in cold regions using satellite remote sensing and machine learning techniques
- language
- English
- LU publication?
- yes
- id
- 9586fc46-dce0-4e63-a936-c9cc0b3ceb40
- date added to LUP
- 2023-09-19 13:38:03
- date last changed
- 2024-05-30 10:17:05
@article{9586fc46-dce0-4e63-a936-c9cc0b3ceb40,
abstract = {{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.}},
author = {{Mohammadi, Babak and Pilesjö, Petter and Duan, Zheng}},
issn = {{1548-1603}},
keywords = {{Adjusted Normalized Difference Snow Index (ANDSI); cold regions; Sentinel-2; glaciers; machine learning classifiers}},
language = {{eng}},
number = {{1}},
publisher = {{Taylor & Francis}},
series = {{GIScience and Remote Sensing}},
title = {{The superiority of the Adjusted Normalized Difference Snow Index (ANDSI) for mapping glaciers using Sentinel-2 multispectral satellite imagery}},
url = {{http://dx.doi.org/10.1080/15481603.2023.2257978}},
doi = {{10.1080/15481603.2023.2257978}},
volume = {{60}},
year = {{2023}},
}