Projections
Initially when comparing two datasets, if they are not in the same projection of coordinate system, the data will be shown as misaligned. In addition to distance, direction, area and shape/angles of the data are also affected when data is projected into a different coordinate system. By affected, I mean they can be distorted from the real world. For example, area can be smaller or bigger in ratio compare to the real world depending on the type of projection. In ArcGIS, there are two potential methods of fixing misaligned and improperly reference spatial data. First is projecting-on-the-fly. Projecting-on-the-fly seems like the more “automatic” way of aligning datasets. Once the layers wanted are loaded into the software, the software automatically aligns all the layers without modifying the coordinate system behind each layer. The second method is manually change the projection using ArcToolbox Project and Transformation commands. Through manual modifications, the coordinate systems of each data set is changed and set up to the projection of desire. For example, if I want to preserve angles/shape, conformal projection is recommended. If I want to preserve distance, equidistance projection is preferred. Similarly, if I want to preserve area, equal area projection is favored and azimuthal projection preserves direction. Projecting on the fly and ArcToolbox are similar in that they both align datasets that have incoherent coordinate systems or datum in ArcMap easily and effectively.
Advantages of Landsat Data
There are many advantages to using remotely sensed Landsat data for geographic analysis. In my study, I used Landsat data to compared the effects of the Mt. St. Helen’s 1980 explosion had in the surrounding landscape by comparing images from 1979 and 2002. Since the launch of Landsat in 1972, technology has advanced to now Landsat can scan same area of earth every 16 days. The remote sensor also detects different color bands, corresponds to different energy level from sunlight such as visible light or near infared light. The data collected is particularly useful to monitor and analysis landuse change overtime. For example, the false colour image is commonly used to gauge vegetation growth and health since plants reflect NIR band and absorb visible light. In false color images using NIR band vegetation will show red, barren soil as grey/greenish tint, and water as black or dark blue. Comparing the 1979 and 2002 image, the color of the false color image dramatically changed as in 1979, the image is mostly green and mostly red in 2002 image. The color shift shows the dramatic effect of the eruption. In addition, Landsat data also provide information on the effect of topography. For instance, when comparing the Landsat data between 1979 and 2002, there are obvious changes to reduction of shoreline, debris blocking causing damage and formation of new lake.
Mt. St. Helen is only one application that can be used by Landsat data for geographical analysis. I can also use it to monitor glacier melting rate in future researches. Possible research question could be how much has the glacier area changed in the arctic from 2016-2020? I will divide each year of monitoring into two portions. From March to August, which will represent the summer months, and from September to February as the winter months. Since there is an expected increase in area in winter months and decrease in summer months, by dividing the year into two categories can be easier and more accurately monitor the glacier area change using landsat data. From the summer to winter months, data will vary, but over the years, we can compare both the area change in summer and winter months. There are many other applications and problems that can be solved by Landsat data.
