Abstract
Numerous environmental issues are being faced by the world today as a result of unfavorable climate changes brought on by global warming and other man-made factors, as well as natural earth imbalances. Consequently, the temperature is rising every day, and as a result of the melting of land, snow, or ice sheets, the world's sea levels are rising. One of the largest difficulties we have is the rising rates of melting of land-based snow and ice sheets, which we must regulate or address as soon as feasible. So, utilizing a few digital image processing techniques and a few other geospatial techniques applied to high resolution snow-covered satellite photos, an effort is made in this work to research and build a system that can extract the snow-covered area using K-means clustering. Using temporal satellite imageries, this work went further to investigate and comprehend changes in snow-covered Himalayan ranges from the years 1984 to 2022.
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Acknowledgements
I am very much grateful to Google, ESRI and Bhuvan (ISRO) for providing primary data (high resolution satellite images) for this study. Similarly, I am also thankful to QGIS team for providing such a great tool for geo-spatial research and development.
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The presented work deals with some digital image processing, geo-informatic tools and K-Means clustering like techniques to extract the snow covered area from high resolution satellite imageries. Further the work is extended to do a comparative analysis of extracted snow covered areas from time series satellite images (1984-2022) of Himalayan ranges.
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Being an author I declare that, I have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Communicated by: H. Babaie
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Kodge, B.G. Extraction and analysis of snow covered area from high resolution satellite imageries using K-means clustering. Earth Sci Inform 16, 4285–4291 (2023). https://doi.org/10.1007/s12145-023-01108-2
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DOI: https://doi.org/10.1007/s12145-023-01108-2