## 6 Conclusions  
Human communities rely on glaciers for water supply, agriculture, and drinking water. Rising temperatures due to the climate change cause mountain glaciers to melt and changes the water availability. Hence, modeling glacier variations in response to climate change is a pressing issue. Given that each glacier system is likely to be distinct both in the climate variations that it has experienced and in its response to these variations, a generic model may not be pertinent to model spatiotemporal variations of different glaciers. Moreover, historical measures of surface area or terminus location is essential to predict future spatiotemporal variations of glaciers. However, direct ground measurement is not feasible due to the remote nature of glaciers. Satellite imagery provides a practical approach to measure glacier area or to locate glacier terminus over time through remote sensing.  
This work is in continuation of our previous works and is motivated by expedited recession of many glaciers around the globe while there is not a readily automated or semi-automated system available to quantify variations of a large number of mountain glaciers. In this work, a semi-automated pipeline was introduced to quantify glaciers in multispectral Landsat imagery. Data are gathered as a time sequence of spatially registered multispectral satellite imagery. The geometry of individual glaciers has been obtained using various image processing techniques including supervised classification, edge detection, and region segmentation. Then, two supervised learning methods including a parametric method (multiple regression) and a nonparametric method (generalized additive model) were implemented to identify climate factors that can impact glacier changes. This model can be customized for individual glaciers as the terminus position or surface area of individual glacier systems depend strongly on local basin geometry and local variations in temperature and precipitation. There are limiting factors to the accuracy of the predicted response including the resolution of satellite imagery, the visibility of the glacier in the images, the frequency of imaging, and the signal to noise ratio of satellite sensors. Several factors contribute to partial loss of contrast and variability in the quantified response value. Among them, some can be visually recognized such as the clouds and mountain shades. But some others are more intangible such as debris atop glacial ice, snow, and variations in humidity.  
Conceptualization, E.R., T.T.H., J.W. and N.N.K.; methodology, E.R., T.T.H., J.W. and N.N.K.; software, E.R., T.T.H., J.W. and N.N.N.K.; validation, E.R. and N.N.K.; formal analysis, E.R., T.T.H. and N.N.K.; investigation, E.R., T.T.H., J.W. and N.N.K.; resources, N.N.K.; data curation, E.R., T.T.H., J.W. and N.N.N.K.; writing--original draft preparation, E.R., T.T.H., J.W. and N.N.K.; writing--review and editing, E.R. and N.N.K.; visualization, E.R., T.T.H., J.W. and N.N.K.; supervision, N.N.K.; project administration, N.N.K.; funding acquisition, N.N.K. All authors have read and agreed to the published version of the manuscript.  
This work was done as part of a REU (Research Experiences for Undergraduates) Statistical Models with Applications to Geoscience awarded by NSF (Grant # 1950768).  
MATLAB and R code is available upon reasonable request.  
Authors declare no competing interest.