Variables of Lidar Data that Influence Change Detection Between Datasets

Abstract:

Lidar data has been used to understand change with respect to many types of morphological change. Within Quantum Spatial’s experience, limitations and best practices have been devised to both understand what change is reliably measured. Measuring change between lidar datasets is largely dependent on plans for data collection, ground survey, error budgets, and processing methods. Flight plans ought to consider appropriate point density, ensuring the target features are sufficiently captured. Many flights are now being collected at 20 points per square meter ppsm or greater to ensure best possible ground density. Flying height is important as laser scanner error decreases in correlation with flying height. Ground survey plays a role in measuring lidar accuracy. Accuracy statistics allow users to understand the error budget within a single dataset. A typical error budget of a fixed wing lidar collection contains an error budget of 15 to 20 cm. Understanding the error of each lidar survey is important to reliable change detection. Processing methods are also important for understanding change. It is challenging to direct all aspects of a lidar processing, one might satisfy such understanding through a handful of checks. Even the best automated ground algorithms contain error. Error is likely to take place in locations where model assumptions cannot accurately describe variable targets and conditions of the data. Examples of model failure are areas of low ground point density, variable terrain, hydrologic features, and extreme terrain. A visual quality assurance step in the lidar processing is a valuable tool for ensuring bare earth models accurately represent terrain surfaces.