Visualization of Snow Cover Changes in the Greater Caucasus Mountains Using Integrated Multi-Sensor Image Fusion
The main goal of the current research was to visualize recent snow cover changes in the parts of the Greater Caucasus Mountains along Shahdag, Bazarduzu, and Tufandag months, using multi-sensor and multi- spectral satellite image processing. Accordingly, accessible satellite imagery from the valid s...
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EDP Sciences
2025-01-01
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| Series: | BIO Web of Conferences |
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| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2025/02/bioconf_mblc2024_02009.pdf |
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| author | Imrani Zaur Rasouli Aliakbar |
| author_facet | Imrani Zaur Rasouli Aliakbar |
| author_sort | Imrani Zaur |
| collection | DOAJ |
| description | The main goal of the current research was to visualize recent snow cover changes in the parts of the Greater Caucasus Mountains along Shahdag, Bazarduzu, and Tufandag months, using multi-sensor and multi- spectral satellite image processing. Accordingly, accessible satellite imagery from the valid sites was obtained, and a few necessary pre-processing operations, such as atmospheric and radiometric corrections, were subjected to available Sentinel-2 and Landsat (8 and 9) images from 2017 to 2023, adjusted to annual, spring-summer months. Next, we employed a harmonized method to combine the Harmonized Landsat and Sentinel (HLS) image bands using object-oriented functions within the eCognition software. This integration led to diverse products, especially High Optimized Natural Color (HONC) images and Scene Classification maps of the study area. Finally, it became possible to evaluate changes in the snow covers with higher accuracy in the annual and monthly time scales. Examining the amount of snow cover, represented by the Normalized Difference Snow Index (NDSI) in recent years shows a significant reduction in snow packs on an annual scale, with rapid melting from the middle of spring to the end of summer months. The final results indicate the fact that the methods of optimization and fusion of Landsat and Sentinel sensors can be very effective in extracting and identifying snow cover with high accuracy on a daily scale. |
| format | Article |
| id | doaj-art-53ad723f05544cc6b4ee21953f2ffe7f |
| institution | Kabale University |
| issn | 2117-4458 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
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| series | BIO Web of Conferences |
| spelling | doaj-art-53ad723f05544cc6b4ee21953f2ffe7f2025-02-05T10:42:41ZengEDP SciencesBIO Web of Conferences2117-44582025-01-011510200910.1051/bioconf/202515102009bioconf_mblc2024_02009Visualization of Snow Cover Changes in the Greater Caucasus Mountains Using Integrated Multi-Sensor Image FusionImrani Zaur0Rasouli Aliakbar1Institute of Geography, MoSEMacquarie University, School of Natural SciencesThe main goal of the current research was to visualize recent snow cover changes in the parts of the Greater Caucasus Mountains along Shahdag, Bazarduzu, and Tufandag months, using multi-sensor and multi- spectral satellite image processing. Accordingly, accessible satellite imagery from the valid sites was obtained, and a few necessary pre-processing operations, such as atmospheric and radiometric corrections, were subjected to available Sentinel-2 and Landsat (8 and 9) images from 2017 to 2023, adjusted to annual, spring-summer months. Next, we employed a harmonized method to combine the Harmonized Landsat and Sentinel (HLS) image bands using object-oriented functions within the eCognition software. This integration led to diverse products, especially High Optimized Natural Color (HONC) images and Scene Classification maps of the study area. Finally, it became possible to evaluate changes in the snow covers with higher accuracy in the annual and monthly time scales. Examining the amount of snow cover, represented by the Normalized Difference Snow Index (NDSI) in recent years shows a significant reduction in snow packs on an annual scale, with rapid melting from the middle of spring to the end of summer months. The final results indicate the fact that the methods of optimization and fusion of Landsat and Sentinel sensors can be very effective in extracting and identifying snow cover with high accuracy on a daily scale.https://www.bio-conferences.org/articles/bioconf/pdf/2025/02/bioconf_mblc2024_02009.pdfsnow cover changesgreater caucasus mountainsazerbaijanhls imagesintegrated processing methods |
| spellingShingle | Imrani Zaur Rasouli Aliakbar Visualization of Snow Cover Changes in the Greater Caucasus Mountains Using Integrated Multi-Sensor Image Fusion BIO Web of Conferences snow cover changes greater caucasus mountains azerbaijan hls images integrated processing methods |
| title | Visualization of Snow Cover Changes in the Greater Caucasus Mountains Using Integrated Multi-Sensor Image Fusion |
| title_full | Visualization of Snow Cover Changes in the Greater Caucasus Mountains Using Integrated Multi-Sensor Image Fusion |
| title_fullStr | Visualization of Snow Cover Changes in the Greater Caucasus Mountains Using Integrated Multi-Sensor Image Fusion |
| title_full_unstemmed | Visualization of Snow Cover Changes in the Greater Caucasus Mountains Using Integrated Multi-Sensor Image Fusion |
| title_short | Visualization of Snow Cover Changes in the Greater Caucasus Mountains Using Integrated Multi-Sensor Image Fusion |
| title_sort | visualization of snow cover changes in the greater caucasus mountains using integrated multi sensor image fusion |
| topic | snow cover changes greater caucasus mountains azerbaijan hls images integrated processing methods |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2025/02/bioconf_mblc2024_02009.pdf |
| work_keys_str_mv | AT imranizaur visualizationofsnowcoverchangesinthegreatercaucasusmountainsusingintegratedmultisensorimagefusion AT rasoulialiakbar visualizationofsnowcoverchangesinthegreatercaucasusmountainsusingintegratedmultisensorimagefusion |