Aerial photographs play an important role in GIS data acquisition and visualization. First, they help provide a solid visual effect. Many people are more able to put spatial concepts into perspective when seeing photos. In addition, the secondary and perhaps more vital role is to provide a basis for gathering spatial information. Examples of this are features such as roads, vegetation, water features. Before this information can be gathered in a way that is useful for a GIS system, the aerial photographs must be prepared in a way that removes distortion from the image. This process is called orthorectification. Without this process you wouldn’t be able to do such functions as make direct measurements of distances, angles, positions, and areas.
What is Orthorectification?
The topographical variations in the surface of the earth and the tilt of the camera affect the distance with which features on the aerial image are display. The more topographically diverse the landscape, the more distortion inherent in the photograph. Thus an aerial photograph taken over a field in Nebraska would contain little or no distortion, while an image of the Cascades would contain a high amount of distortion. As a result, real world distances are not represented uniformly on the photograph. For example, an inch measured in a steep area would relate to a much longer distance than an inch measured over a flat surface such as a plain. Orthorectification is the name of the process used to remove these sources of distortion to equilibrate photo units with real life distances. Once an aerial photo has been orthorectified, it is commonly referred to as an orthophoto. An interesting side note is while orthorectification removes horizontal distortion, vertical relief displacement is still maintained. For example, the sides of a building would still contain distortion.
In a case like the Nebraska example, a simple rectification process like removing the effects of the tilt of the camera may be all that is necessary. This is very rare and in most cases a more involved process is required. After removing the effect of the camera tilt, removing the effects of relief must be accomplished by knowing the elevation of the terrain above (or below) the mapping plane must be known.
There are two methods by which rectification of an aerial photograph can occur. In the first case, Ground Control Points (GCP) are determined either conventional ground surveys, from published maps, by Global Positioning System (GPS) surveys, or by aerotriangulation. These points are taken at visible physical features on the landscape. On the corresponding image, the x, y photo coordinates are then determined for each corresponding GCP. Depending on the type of algorithmic correction to be used, a minimum of 3 to 5 GCP must be established. The relationship of the x, y photo coordinates to the real world GCP is then used to determine the algorithm for resampling the image.
The second method of orthorectification is to use DEMs. These elevations are collected from stereoscopic models by photogrammetric methods to form a digital elevation model (DEM). As with using GCPs, the mathematical relationship between the real world coordinates and the scanned aerial photograph is determined and the digital image is resampled to create the rectified image.
For both cases, the resampling of the digital image involves warping the image so that distance and area are uniform in relationship to real world measurements. This means that with the resampled photo, an inch on the image now measures the same distance on steep terrain as it does in a field.
Depending the on the needs of the aerial imagery in the GIS system, there are advantages and disadvantages to using either method. GCP orthorectification is a faster process and can be accomplished using existing paper maps to establish the GCPs. Using DEMs for orthorectification is a more accurate process by which to geocode digital imagery but require an existing DEM or DTM for processing.
Once an image has been orthorectified it can be used with vector and raster data of the same coordinate system. This image can now have road outlines and street names overlayed onto it. As mentioned before, spatial data can also now be accurately measured in terms of distances and area, allowing for more complex spatial analysis.