TY - GEN
T1 - Quantitative Analysis of Climatic Variability in Relation to Surface Loss with Landsat Data in Peruvian Snow-Capped Mountains 2010–2020
AU - Kancha, Anthony Flores
AU - Agüero, Jair Torres
AU - Soria, Juan J.
AU - Poma, Orlando
AU - Huaranga, Milda Cruz
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Climate change is affecting the planet due to lack of information regarding its temperatures, precipitation and decrease in the surface area of the snow-capped mountains, which causes a high impact on the melting of the snow-capped mountains, for this reason it is important to have predictive models of multivariate regressions that allow forecasting the surface area of snow-capped mountains over time. In the investigation, the multivariate regression model was used for each snowfall under study, having as regressive variables the temperature, precipitation and as a dependent variable the surface area of the snowfall with data obtained through Landsat 4–5, 7 and 8 images during the years 2010–2020. A model of the snow-capped Huaytapallana was obtained in the form y = 34.274 − 2.3197 X1 + 0.135 X2 with p-value = 0.034 of significance and the snow-capped Coropuna with a model of the shape y = 101.3487− 6.720 X1 + 0.331 X2 having p-value = 0.036 being significant for your prediction. The mean of the highest surface area was Coropuna (53.92 km2) with a SD of 14.94, and the lowest was Verónica (16.16 km2) with a SD of 37.77. The Huaytapallana mountain showed the least reduction in surface area, decreasing by 7%, while the Verónica mountain was reduced by 52%, being the most affected of the 4 snow-capped mountains, due to the melting under study during the 10 years of data analyzed.
AB - Climate change is affecting the planet due to lack of information regarding its temperatures, precipitation and decrease in the surface area of the snow-capped mountains, which causes a high impact on the melting of the snow-capped mountains, for this reason it is important to have predictive models of multivariate regressions that allow forecasting the surface area of snow-capped mountains over time. In the investigation, the multivariate regression model was used for each snowfall under study, having as regressive variables the temperature, precipitation and as a dependent variable the surface area of the snowfall with data obtained through Landsat 4–5, 7 and 8 images during the years 2010–2020. A model of the snow-capped Huaytapallana was obtained in the form y = 34.274 − 2.3197 X1 + 0.135 X2 with p-value = 0.034 of significance and the snow-capped Coropuna with a model of the shape y = 101.3487− 6.720 X1 + 0.331 X2 having p-value = 0.036 being significant for your prediction. The mean of the highest surface area was Coropuna (53.92 km2) with a SD of 14.94, and the lowest was Verónica (16.16 km2) with a SD of 37.77. The Huaytapallana mountain showed the least reduction in surface area, decreasing by 7%, while the Verónica mountain was reduced by 52%, being the most affected of the 4 snow-capped mountains, due to the melting under study during the 10 years of data analyzed.
KW - Climate variability
KW - Glacier retreat
KW - Landsat images
KW - Multiple regression
KW - Peruvian glaciers
UR - http://www.scopus.com/inward/record.url?scp=85135091016&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-09073-8_47
DO - 10.1007/978-3-031-09073-8_47
M3 - Conference contribution
AN - SCOPUS:85135091016
SN - 9783031090721
T3 - Lecture Notes in Networks and Systems
SP - 551
EP - 565
BT - Cybernetics Perspectives in Systems - Proceedings of 11th Computer Science On-line Conference, CSOC 2022, Vol 3
A2 - Silhavy, Radek
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Computer Science On-line Conference, CSOC 2022
Y2 - 26 April 2022 through 26 April 2022
ER -
