Remote Sensing and Photogrammetry
Remote Sensing and Photogrammetry
This domain tests how imagery and active sensors are acquired, geometrically processed, and interpreted, including resolution limits and classification assumptions.
Core rules and concepts
- Resolution types are spatial, spectral, radiometric, and temporal. High spatial resolution does not guarantee high positional accuracy.
- Multispectral indices combine bands; NDVI = (NIR − Red)/(NIR + Red), with values typically from −1 to +1. Healthy vegetation generally gives higher positive values.
- Photogrammetry uses overlapping images and parallax. Bundle adjustment solves camera pose and object coordinates; orthorectification removes terrain and sensor distortion.
- A DSM includes vegetation and buildings; a DTM represents bare earth. LiDAR intensity and multiple returns support surface characterization.
- Classification accuracy is assessed with a confusion matrix, overall accuracy, and kappa or class-specific errors.
Worked example
An aerial photograph has a scale of 1:10,000. A feature measures 5 cm on the image. Ground distance = image distance × scale denominator = 0.05 m × 10,000 = 500 m. If the same project instead uses a digital sensor, ground sample distance is calculated from pixel size, flying height, and focal length; scale alone is not enough.
Traps
- Confusing DSM with DTM when calculating terrain slope or flood depth.
- Treating NDVI as a direct measure of biomass without calibration.
- Ignoring shadows, mixed pixels, and atmospheric effects in classification.
- Assuming a smaller pixel means a more accurate coordinate.