Planning the Perfect Survey
Before a LiDAR aircraft takes off, survey designers must balance competing requirements: point density sufficient for the project's accuracy needs, swath coverage wide enough to minimize the number of flight lines, altitude appropriate for the terrain and airspace, and total flight time within budget. The fundamental equation ρ = PRR / (v × SW) links these parameters, and this simulation lets you explore the trade-offs interactively — adjusting any parameter and seeing the cascading effects on coverage and density.
Pulse Rate Revolution
Twenty years ago, airborne LiDAR systems fired 10,000-50,000 pulses per second. Today's sensors exceed 2,000,000 pulses per second, enabling point densities of 20-50 pts/m² from standard survey altitudes. This revolution in pulse rate has transformed LiDAR from a terrain-mapping tool to a precision measurement system capable of detecting power line sag, railway track deformation, and individual tree branches. Higher pulse rates require faster digitization electronics and generate massive data volumes.
Swath Geometry
The scan mechanism sweeps the laser beam across-track, illuminating a swath on the ground. The swath width SW = 2H × tan(α) determines how much ground is covered per flight line. Wider scan angles produce broader swaths (fewer flight lines) but introduce geometric distortion: points at the edges have elongated footprints, reduced intensity, and degraded vertical accuracy. Most surveys use 15-25° half-angles as a practical compromise between coverage and quality.
From Specifications to Flight Plan
Project specifications dictate the minimum point density, which in turn constrains the allowable combinations of altitude, speed, scan angle, and pulse rate. The survey planner then designs flight lines to cover the project area with appropriate overlap (typically 50-60%) to ensure seamless coverage and provide redundancy. Modern flight management systems optimize the plan for efficiency, accounting for terrain relief, airspace restrictions, and fuel constraints. The result is a flight plan that delivers the required data quality at minimum cost.