Remote Sensing: Module 2 - Interpreting Aerial Photography

     This module introduces aerial imagery interpretation. There are several elements of interpretation that help in determining what features are in aerial imagery. Tone, texture, size, and shape are among the basic elements while pattern, shadow, and association are more complex methods of interpretation. Independently and in concert these elements help in the analysis of an image.

     Three exercises in this lab aimed at practicing aerial image interpretation. The first exercise deals with the basic elements of interpretation. The second involves slightly more advanced elements such as pattern and association. The third exercise has no map output but involved examining the color of features in true color imagery (colors we can see) versus false color (other wavelengths). What follows is a more detailed discussion of each exercise and the maps created as a result. 

­Exercise 1

     Tone and texture in this exercise are identified and classified based upon increasing tonal values (very light to very dark) and a range of textures (from very fine to very coarse). Identifying tone is intuitive as the lightest and darkest portions of the image are easily identified (see the screenshot below). To aid in assessing the tone differences I used a grey scale. Texture, however, is not as straightforward. While I felt that I appropriately classified texture, another image analyst may feel what I designated as mottled is actually coarse. It seems more subjective than identifying tone. Below is the map I generated showing areas classified within a range of textures and tones. Five areas for each category are included. For tones, the classes are very light, light, medium, dark, very dark. For texture, the classes are very fine, fine, mottled, coarse, and very coarse.   

Exercise Map Product - Tone and Texture
This map shows various regions highlighted and classified by tone and texture.
Tone classifications range from very light to very dark while texture ranges from very fine to very coarse.

Exercise 2
     In this exercise the elements shape, size, shadow, pattern, and association are used to identify various features in this image. Three features are identified in each one of these categories with association being the exception with only two features. In the shape category, I identified a body of water, a road, and a swimming pool (difficult to see in this scaled down map). Each of these features have a distinct shape and size that allowed me to identify them. The swimming pool, for instance, is uniquely shaped and relatively small when compared to the other water features in the image (and also by association, near a residential area). Using their shadows I was able to identify a sign post, pier, and water tower (also a unique shape). Pattern is one of the higher order visual elements and I used different scales to determine the patterns of various features. When the image is at full extent, the coastal vegetation and residential areas are readily apparent but the parking lot looks like an indistinct, grey rectangle-like area. When that area is magnified the white, ordered outlines of the individual parking spots become visible and therefore easier to analyze. The last identification category is association. These features are identified by their context. For instance, I identified what looks like a motel based upon its shape and surrounding features (like signage, a small swimming pool between two mirrored buildings). The beach is also obvious based on its association and proximity with the ocean, pier, coastal vegetation, and strip of buildings lining it.
     The final output is seen in the screenshot below. Each feature is marked by a colored symbol that is representative of the identification element used.   
 

Exercise Map Product - Interpretive Elements
This map displays features identified by shape/size, shadow, pattern, and association
(combining multiple factors to come to an interpretive conclusion). 

Exercise 3

     For this portion of the lab, I looked at a true color image and compared features identified in that image against those same features in a false color version of the image. The goal is to become acquainted with viewing the world in alternative wavelengths/colors. As an example, the true color of vegetation is green but in a false color infrared image (where red light is green, green light is blue, and near infrared is red) those same plants will display in red. This is a function of the type of light that plants reflect (near infrared and green) and absorb (red). It is fun to view the world in alternative color schemes and also incredibly informative. In the plant example above, the redder the vegetation typically the healthier it is. There is much information to be gathered by examining alternative spatial resolutions.  

Map Composition
     I used the Draw tools to create polygons and point locations in their respective exercises. The Editor tool was used to modify the attribute tables of the layers created by using the Draw tool (converting drawings to graphics and saving as .shp files).