Photo Interpretation and Remote Sensing Module 4

This week we learned about using ground truthing to confirm land cover/land use classifications, and about calculating the accuracy of an LULC map base on ground truthing. Since we couldn't actually visit our sample sites for the lab assignment, we used Google Maps and Google Street View to confirm/correct classifications. Below is my map from last week, overlain with "ground truthing" points color-coded according to whether or not my original classification was correct at that location. Some points were more of a challenge to identify than others, particularly a few that had obviously changed between the time the photo was taken and the time the Google Maps imagery was last updated.

Photo Interpretation and Remote Sensing Module 3

This week was all about land use and land cover classification using aerial photos, and the lab assignment was to visually classify an image. We only needed to classify to Level II (out of four levels), so it's much less detailed and specific than it could be. Even so, it takes a lot of time to examine the image and identify various features to determine in which category they belong. 

My final map is not as thorough as I would have liked, but my time was limited. Here's what I came up with:

Photo Interpretation and Remote Sensing Module 2

We're starting off the course on Photo Interpretation and Remote Sensing by examining aerial photographs, and this week we specifically looked at different methods of identifying the features in an image. 

The image below explores the range of tones and textures that can exist in a single photograph. Tone and texture can help in identifying a feature.

Features can also be identified by their size and shape, by the shape of the shadows they cast, because they form a pattern, or based on their association with other identifiable features. The image below shows some examples.