3.5 Photogrammetry, UAS, and Image Processing
Key Takeaways
- Photogrammetry extracts measurements from overlapping imagery using camera geometry and control.
- UAS mapping requires flight planning, ground control or check points, image overlap, and quality review.
- Orthophotos remove much perspective distortion, but they are still limited by ground control, terrain model quality, and image resolution.
- FS questions may focus on why imagery is suitable for mapping but not always for boundary or hidden-feature decisions.
Photogrammetry, UAS, and Image Processing
Photogrammetry uses overlapping photographs to derive measurements and map features. Modern projects may use crewed aircraft, unmanned aircraft systems, or close-range cameras. The FS exam includes photogrammetry, remote sensing, UAS, and image processing because surveyors increasingly combine field control with image-based mapping.
The basic idea is geometry. A single photograph shows perspective, but overlapping images taken from different positions can support three-dimensional measurement when camera position, camera calibration, image matching, and ground control are handled properly. Software may produce point clouds, orthomosaics, digital surface models, contours, and planimetric features. The surveyor still has to evaluate whether those products meet the project requirement.
A UAS mapping mission depends on planning. Flight altitude affects ground sample distance, which is the ground size represented by an image pixel. Image overlap affects the ability to match points between photos. Ground control points tie the image model to the project coordinate system. Independent check points help test accuracy without being used to build the model. Poor distribution of control can create localized distortions that are hard to see in the final image.
Image-based mapping controls
| Item | Purpose | Common exam concern |
|---|---|---|
| Forward and side overlap | Supports image matching and 3D reconstruction | Low overlap creates gaps or weak geometry. |
| Ground control points | Ties imagery to the project datum and coordinates | Poor control distribution can warp the product. |
| Check points | Independently test accuracy | Control points alone do not prove accuracy. |
| Ground sample distance | Relates pixel size to ground detail | Fine pixels do not guarantee survey accuracy. |
| Camera calibration | Models lens and sensor geometry | Unmodeled distortion affects measurements. |
| Terrain model | Supports orthorectification | Bad terrain creates positional errors in orthophotos. |
An orthophoto is an image corrected so that it can be used more like a map. Orthorectification reduces tilt and relief displacement using camera geometry and a terrain model. It does not make every visible feature survey-grade. Tall buildings, vegetation, bridges, and steep slopes can still cause issues. Shadows, water, reflective surfaces, and moving objects can reduce feature extraction quality.
Image classification and processing can identify pavement, vegetation, structures, water, or bare ground. These outputs are useful for planning and inventory, but they require review. A classification may confuse dark roofs with pavement, shadows with water, or brush with ground. For boundary work, visible occupation lines in imagery are clues, not legal conclusions. A fence seen in an orthophoto still requires record research, field evidence, and boundary analysis.
For FS questions, look for the quality-control principle. Use check points to evaluate accuracy. Use ground control to connect the model to the project coordinate system. Use overlap and appropriate flight height to support reconstruction. Do not assume a high-resolution image is automatically accurate, current, legally controlling, or suitable for staking.
What is the main purpose of independent check points in a UAS mapping project?
Which statement about orthophotos is most accurate?
A high-resolution UAS image shows a fence near a deed line. What is the best surveying interpretation?