## 3 Methods
### Quantification of Glacier Area and Terminus  
Terminal point was located using a suit of image processing techniques over temporal sequence of images collected for each glacier. A graphical user interface was developed to zoom on the glacier's geographical location on the image using glacier's latitude and longitude. A bonding box as region of interest (ROI) encompassing the glacier's area was drawn. The ROI was used to crop the glacier through entire sequence of original Landsat images. Terminal point location was then marked in each cropped image of the sequence.  
Because only two glaciers were studied in this project, the manual terminus detection was an effective and rapid approach to locate the terminus and mark it by a single pixel on the glacier image. This manual detection method allowed a swift process of a large sequence of images for each glacier by avoiding issues such as partial obstruction of the glacier in the image.  
Although an expedient method was developed for the identification of the terminus location, segmentation of glacier area proved to be a more challenging problem. This difficulty stem from the fact that finding the entire outline of the glacier is a two-dimensional problem in contrast with the detection of the terminal point that is a zero-dimensional task. The segmentation of cropped image to glacier and non-glacier segments with sharp boundaries is rather challenging due mainly to debris and mud blending into the ice near glacier's boundaries.  
Region growing and edge detection methods were preliminary attempted to segment the area of the glacier. Each of these methods were slightly successful to segment the glacier area. Region growing method creates homogenous regions based upon a specific threshold, while edge detection looks for abrupt changes in pixel intensity to identify an edge. A mutual challenge among both segmentation methods is to find the threshold for optimal segmentation of glacier boundary in each image in the sequence, one by one. Hence, a hybrid method was implemented by combination of region growing and edge detection techniques. Figure 7 shows some partially successful application of this hybrid Region Growing-Edge Detection approach to both glaciers in this study. Some regions containing both glacier and non-glacier areas can be observed in Figure 7(Left).  
Figure 6: Schematic of Proposed Method.  
To further improve the segmentation results, a multi-level thresholding technique ([PERSON]'s method) was applied. [PERSON]'s method finds the optimum global threshold for the image segmentation by maximizing the between-class variance of the pixel intensities in the partitioned regions, here glacier vs. non-glacier. It demonstrated limited success in producing somewhat detailed depictions of the glacier in some images, but often could not produce sufficient separation between glacier and neighboring non-glacier areas.  
The final approach contained three steps and outperformed the previous methods that were used for glacier segmentation in this study. In the first step, the images were cropped to remove as much of the glacier's surroundings as possible. This cropping process has the virtue of reducing the surrounding non-glacier features to improve the segmentation results. The second step was to binarize the cropped image for preliminary segmentation of image to glacier and non-glacier partitions. The final step was the application of [PERSON]'s method for a detailed segmentation by finding the optimal global threshold. Different glacial regions in the binarized image were compared (by counting the number of pixels in each) to choose the region comprising the largest connected glacier area. The selective cropping in the first step greatly facilitated the performance. The main advantage of this technique was the combination of initial and final elimination of non-glacier areas.