There and Back Again

This was a fun lab. We did photo interpretation on several aerial photos in black and white, true color, and false color. In the first exercise, we identified areas by tone and texture, looking for very light to very dark areas, and very fine to very rough areas: In the next section, we had a black and white aerial photo with significant shadows present, and used clues such as shape and size, pattern, shadow, and association to make educated decisions about what was visible: In the final task, we compared landmarks on a true color photograph to the same areas on a false color IR photo and logged how they compared, making note of how they transformed between photos and what visual data was more or less visible.

This was one of the more interesting labs I've done in this program. Here, we explored how scale and resolution affect the amount and fidelity of spatial data. In the first exercise, we compared the number of waterbodies and the length of hydrographic lines (i.e., rivers and streams) visible at different scales, and graphed out how much is lost between each one. Then we made simple maps with a layer for each one, to visualize what was present or omitted. In the second part, we took a Digital Elevation Model and used the resample tool to change the pixel size of the raster and created layers of different scales, then used the slope tool to create a slope grid of the resampled DEM, and then created histograms to determine the average slope for each layer. Starting with an original dataset but progressively lowering the resoltion produces a clear downward trend in average slope. In the next, we explored the Modifiable Areal Unit Problem. To do this, we took socioeconomic data an...

This was a fun lab, we got to experiment with various interpolation methods to produce continuous data from discrete data points. A significant part of the lab was comparing and contrasting each interpolation method, and finding out ways to recognize and account for the possible ways interpolation could distort or misrepresent data. For example, spline interpolation is like a plane that is forced to meet every data point, and distorts itself to do so. One of the exercises featured us having a spline reult with anomalously high concentrations of pollution, and finding a way to normalize the data. IDW is an interpolation method that uses a weighted average of known data points, proportional to the distance they are from the interpolated area. We also created Thiessen polygons, which are irregularly shaped polygons where every point inside the polygon is closer to the sampling point than any other point.