This research analyses the changes in coverage Mt Kenya glaciers in a bid find what has been causing the retreat of these glaciers. Optical Landsat data for 1984 to 2017 and Climatic data of the same years were used. Glaciers and forest coverage were extracted from Landsat images and its thermal band was used to extract temperature data. Correlation with the respective year’s climatic data and forest cover area were done to justify the assumption that the shrinkage in the glaciers coverage has been caused by changes in climate and/or deforestation. Then using the historical EC Earth model climate data predictions for 1984-2017 and historical observed data for the same years, bias correction factors were computed and used to correct the future model data for the years (2018-2045). Since the data was extracted for only four points around Mt Kenya, Interpolation was then done to obtain the Precipitation and Temperature for the mountain peak (since the glaciers are found at the peak) using the IDW technique. Prediction of glacier area coverage was then done using these interpolated climate data. In order to predict the future glacier cover, linear equations of the form y = a1x1 + a2x2 +bo of the interpolated climate data (for 2018-2045) and computed glacier areas for (1984-2017) were formed. The a1 a2 and bo in the equation are constants obtained from SPSS (a statistical software). X1 and x2 are the predicted Temperature and Precipitation respectively. Predictions were done for RCP scenarios 8.5 and 4.5. The results of prediction showed that the current trend of glacier thinning is going to continue but at a slower rate compared to the rapid melting that was observed for the period 1984-2017. However, Mt Kenya glaciers are likely to have completely disappeared by the year 2100.
| DOI | 10.11648/j.ajese.20180201.11 |
| Published in | American Journal of Environmental Science and Engineering (Volume 2, Issue 1, March 2018) |
| Page(s) | 1-16 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Glaciers and Forest Coverage, Climate, Prediction
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APA Style
Purity Njeri Mwaniki, David Kuria, Charles Ndegwa Mundia, Godfrey Makokha. (2018). Analysis of Mt Kenya Glaciers Recession Using GIS and Remote Sensing. American Journal of Environmental Science and Engineering, 2(1), 1-16. https://doi.org/10.11648/j.ajese.20180201.11
ACS Style
Purity Njeri Mwaniki; David Kuria; Charles Ndegwa Mundia; Godfrey Makokha. Analysis of Mt Kenya Glaciers Recession Using GIS and Remote Sensing. Am. J. Environ. Sci. Eng. 2018, 2(1), 1-16. doi: 10.11648/j.ajese.20180201.11
AMA Style
Purity Njeri Mwaniki, David Kuria, Charles Ndegwa Mundia, Godfrey Makokha. Analysis of Mt Kenya Glaciers Recession Using GIS and Remote Sensing. Am J Environ Sci Eng. 2018;2(1):1-16. doi: 10.11648/j.ajese.20180201.11
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Download@article{10.11648/j.ajese.20180201.11,
author = {Purity Njeri Mwaniki and David Kuria and Charles Ndegwa Mundia and Godfrey Makokha},
title = {Analysis of Mt Kenya Glaciers Recession Using GIS and Remote Sensing},
journal = {American Journal of Environmental Science and Engineering},
volume = {2},
number = {1},
pages = {1-16},
doi = {10.11648/j.ajese.20180201.11},
url = {https://doi.org/10.11648/j.ajese.20180201.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20180201.11},
abstract = {This research analyses the changes in coverage Mt Kenya glaciers in a bid find what has been causing the retreat of these glaciers. Optical Landsat data for 1984 to 2017 and Climatic data of the same years were used. Glaciers and forest coverage were extracted from Landsat images and its thermal band was used to extract temperature data. Correlation with the respective year’s climatic data and forest cover area were done to justify the assumption that the shrinkage in the glaciers coverage has been caused by changes in climate and/or deforestation. Then using the historical EC Earth model climate data predictions for 1984-2017 and historical observed data for the same years, bias correction factors were computed and used to correct the future model data for the years (2018-2045). Since the data was extracted for only four points around Mt Kenya, Interpolation was then done to obtain the Precipitation and Temperature for the mountain peak (since the glaciers are found at the peak) using the IDW technique. Prediction of glacier area coverage was then done using these interpolated climate data. In order to predict the future glacier cover, linear equations of the form y = a1x1 + a2x2 +bo of the interpolated climate data (for 2018-2045) and computed glacier areas for (1984-2017) were formed. The a1 a2 and bo in the equation are constants obtained from SPSS (a statistical software). X1 and x2 are the predicted Temperature and Precipitation respectively. Predictions were done for RCP scenarios 8.5 and 4.5. The results of prediction showed that the current trend of glacier thinning is going to continue but at a slower rate compared to the rapid melting that was observed for the period 1984-2017. However, Mt Kenya glaciers are likely to have completely disappeared by the year 2100.},
year = {2018}
}
TY - JOUR T1 - Analysis of Mt Kenya Glaciers Recession Using GIS and Remote Sensing AU - Purity Njeri Mwaniki AU - David Kuria AU - Charles Ndegwa Mundia AU - Godfrey Makokha Y1 - 2018/05/17 PY - 2018 N1 - https://doi.org/10.11648/j.ajese.20180201.11 DO - 10.11648/j.ajese.20180201.11 T2 - American Journal of Environmental Science and Engineering JF - American Journal of Environmental Science and Engineering JO - American Journal of Environmental Science and Engineering SP - 1 EP - 16 PB - Science Publishing Group SN - 2578-7993 UR - https://doi.org/10.11648/j.ajese.20180201.11 AB - This research analyses the changes in coverage Mt Kenya glaciers in a bid find what has been causing the retreat of these glaciers. Optical Landsat data for 1984 to 2017 and Climatic data of the same years were used. Glaciers and forest coverage were extracted from Landsat images and its thermal band was used to extract temperature data. Correlation with the respective year’s climatic data and forest cover area were done to justify the assumption that the shrinkage in the glaciers coverage has been caused by changes in climate and/or deforestation. Then using the historical EC Earth model climate data predictions for 1984-2017 and historical observed data for the same years, bias correction factors were computed and used to correct the future model data for the years (2018-2045). Since the data was extracted for only four points around Mt Kenya, Interpolation was then done to obtain the Precipitation and Temperature for the mountain peak (since the glaciers are found at the peak) using the IDW technique. Prediction of glacier area coverage was then done using these interpolated climate data. In order to predict the future glacier cover, linear equations of the form y = a1x1 + a2x2 +bo of the interpolated climate data (for 2018-2045) and computed glacier areas for (1984-2017) were formed. The a1 a2 and bo in the equation are constants obtained from SPSS (a statistical software). X1 and x2 are the predicted Temperature and Precipitation respectively. Predictions were done for RCP scenarios 8.5 and 4.5. The results of prediction showed that the current trend of glacier thinning is going to continue but at a slower rate compared to the rapid melting that was observed for the period 1984-2017. However, Mt Kenya glaciers are likely to have completely disappeared by the year 2100. VL - 2 IS - 1 ER -
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