Drone Mapping - General Concepts

  • Updated

Digital Surface Model (DSM): This model represents the Earth's surface, including all natural and man-made objects like trees, buildings, and bridges. It provides a 3D representation of the area with elevations of all surface features.

 

Digital Terrain Model (DTM): Unlike DSM, a DTM represents the bare Earth, excluding surface objects. It is useful for understanding the actual terrain and is often used in applications like flood modeling and land use planning.

 

Direct Georeferencing: Direct Georeferencing is the process of associating a digital image or map with real-world geographic coordinates. This ensures that every point on the map corresponds accurately to its location on the Earth's surface. An example of direct georeferncing is Drone Sampling applied for agricultural applications

 

Geometric Accuracy: Geometric Accuracy refers to how close the measurements are to the true values. High accuracy is crucial for applications like cadastral mapping and construction.

 

Geometric Precision: Geometric Precision refers to the consistency of measurements. High precision ensures that repeated measurements yield similar results.

 

Ground Control Points (GCPs): GCPs are physical markers placed on the ground with known geographic coordinates. They are used to improve the accuracy of drone mapping by providing reference points for georeferencing and aligning the drone imagery with real-world coordinates.

 

Ground Sample Distance (GSD): GSD is a measure of the distance between the centers of two adjacent pixels on the ground. It represents the spatial resolution of an image. A lower GSD means higher image resolution and more detail. GSD is influenced by factors such as camera sensor size, focal length, and flight altitude.

 

LiDAR (Light Detection and Ranging): LiDAR is a remote sensing technology that uses laser pulses to measure distances. Drones equipped with LiDAR sensors can capture highly detailed 3D data of the Earth's surface. LiDAR is particularly useful for creating accurate elevation models, especially in areas with dense vegetation.

 

Orthomosaic: Orthomosaic or Ortho is a georeferenced, high-resolution image that is created by stitching together many individual images taken by a drone. Each pixel in an orthomosaic contains accurate geographical coordinates. Orthomosaics are free from distortions and can be used for precise measurements and mapping.

 

Overlap: Overlap refers to the amount of common area between consecutive images taken during a drone flight. There are two types of overlap:

  • Front overlap (longitudinal overlap): The overlap between images taken along the flight path.
  • Side overlap (lateral overlap): The overlap between images taken across the flight path.

An optimal overlap ensures seamless stitching of images and accurate 3D reconstruction. Typically, a 70-80% overlap is recommended for most projects.

 

Point Clouds: A point cloud is a collection of data points in a three-dimensional coordinate system. Each point represents a specific location on the Earth's surface. Point clouds are generated using photogrammetry or LiDAR (Light Detection and Ranging) and are used to create 3D models, measure distances, and analyze terrain.

 

Post-Processing Kinematic (PPK): A GPS correction technique that provides corrections to the drone's GPS data. These corrections are applied after the flight during data processing. PPK can be more accurate but requires additional processing time in comparison to RTK.

 

Radiometric Calibration: Radiometric Calibration ensures that the images captured by the drone's camera are consistent in terms of color and brightness. This is important for applications like vegetation monitoring, where accurate color representation is crucial for analyzing plant health and growth.

 

Real-Time Kinematic (RTK): A GPS correction technique that provides real-time corrections to the drone's GPS data, ensuring high positional accuracy during the flight.

 

Structure from Motion (SfM) is a photogrammetric technique used to create 3D models and maps from 2D images. By capturing overlapping images from different angles, SfM algorithms can reconstruct the 3D structure of the scene. This method is widely used in drone mapping to generate detailed topographic maps and models.