It’s widely predicted that drone adoption rates will continue to go in one direction (up!) and this is largely based on the assumption that non drone-related enterprises, eg. construction companies, will start to add and scale in-house drone capability to conduct their work in a safer, more efficient manner. However, it’s our view that the drone industry needs to help facilitate this by talking in a language that the non drone-using fraternity can easily understand and relate to. This couldn’t be more prevalent for one of the current uses of drones: Mapping.
So, with this in mind, we’ve put together a glossary of the common terms you’ll come across when exploring drone mapping.
The science of taking measurements from photographs. In the case of drone-based photogrammetry, the photographs are taken from the air with a camera-equipped drone.
This refers to an image or set of images that have been taken when the camera is pointing directly down. Nadir imagery is absolutely critical to the generation of photogrammetric maps.
In contrast to Nadir imagery, oblique imagery is taken with the camera at an angle that is not perpendicular to the ground. Oblique imagery is critical to the generation of 3-dimensional models.
An aerial image, which is made up of many individual images. Unlike a single image, an orthomosaic has been rectified so that the scale of the image is uniform throughout. This is what allows us to make accurate measurements within the image.
The collection of georeferenced points on an object’s surface. These “points” can then be transformed into a 3-dimensional digital twin of the object.
Ground Sample Distance (GSD)
Ground Sample Distance tells us the physical area on the ground that is represented by each individual pixel in the image. For example a GSD of 1cm in an orthomosaic tells us that each pixel translates to an area of 1cm² in the real world, on the ground. Needless to say, the lower the GSD, the higher the accuracy of the orthomosaic.
Sometimes referred to as “Local Accuracy”, this tells us the accuracy of a measurement between 2 or more points on a photogrammetric map relative to the same points in the real world. For example, if you knew the actual (real world) distance between two distinctive points on the map to be 25 metres, and the same measurement that you applied on the map resulted in 25.1 metres, the relative accuracy would be 10 cm.
Sometimes referred to as “Global Accuracy”, this tells us the accuracy of the position of any given point on a photogrammetric map relative to its position in a fixed coordinate system in the real world. For example, if you knew the actual (real world) position of a specific point on the map, and the same point on the map was displayed 5cm to the left, the absolute accuracy of that point on the map is 5cm.
Our first acronym! GCP stands for Ground Control Points. This is a technique used to improve absolute accuracy of a photogrammetric map or model. GCPs are essentially physical markers that are visible from the sky and that are placed strategically around the area to be mapped. The GPS coordinates of each GCP are recorded and then assigned within the photogrammetric map. This technique ensures that the entire map (and critically, the objects within it) can by correctly placed in their real world positions.
Real-Time Kinematics. This is a satellite navigation technique used to improve positioning accuracy. An RTK system consists of a fixed base station, which transmits real-time correction signals to a moving receiver – which in our case would be on the drone. The benefit of RTK in a drone mapping application is that the images taken by the drone now have highly accurate positioning data assigned to them. In turn, this greatly improves the absolute accuracy of photogrammetric maps and models.
Post-Processing Kinematics. Another satellite navigation technique used to improve positioning accuracy. The key difference between RTK and PPK is the point at which the positioning corrections are made to the image files. Unlike RTK, which adds the accurate positioning data to the images in real-time, with a PPK workflow we have to assign the accurate positioning data to the images after the flight.
Normalised Difference Vegetation Index is mathematical equation that can be applied to drone images containing the Near-Infrared (NIR) light band. When the NDVI algorithm is applied to an orthomosaic of NIR images, each pixel of the photogrammetric map is assigned a colour based on the amount of light that is reflected from the vegetation. This enables the user to very quickly differentiate areas of healthy vegetation from potentially unhealthy or zero vegetation.
A band of light that is below the visible light waveband. The invisible NIR light reflects off chlorophyll in a plant, so when used in combination with the NDVI algorithm, can be a powerful tool to help with earlier detection of potential crop health issues.
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