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This study analyses the influence of climate change on the melting of snow on the Chimborazo Mountain peak using remote sensing and a simpler mathematical model. Climate change is undoubtedly real, and one of its leading causes is increased carbon dioxide emissions caused by industrial activities. This climate change phenomenon manifests itself in several ways, such as temperature increase and precipitation variation. We studied the influence of these variables on a mountain peak in Ecuador called Chimborazo. Since Chimborazo Mountain has a considerable size, the area of its ice glacier is sufficiently large enough to be measured and studied. Estimating the glacier area on Chimborazo’s peak was carried out using photointerpretation over Satellite Remote Sensing in a GIS software, applying the best images without cloud per year of Landsat images from 1979 to 2020 because Ecuador has a high cloud density all year. The climate change data are collected from the Intergovernmental Panel on Climate Change (IPCC), matching the years of Remote Sensing data to construct an input dataset for the mathematical model. Then, in the RStudio, the Partial Least Square (PLS) model was executed, where it was determined how many of the combined variables (Climate change data) in the independent vector components could be used in the modelling. Thus, concluding that using the seven components explains 93% of the variation of the results of the area (Remote Sensing extracted area data). The results indicate that the maximum provincial temperature and CO2 country emissions are the variables with the most significant influence on the melting of snow on Chimborazo. Therefore, this melting is influenced by climate change. Additionally, a simulation based on the PLS model is used to compute the Chimborazo snow area until 2050. Thus, a PLS model and Remote Sensing variables can explain the climate change in the snow-capped Ecuadorian mountains in a first approach.
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