New applications for LiDAR technology appear every day. Regardless of the process or technological innovation, every application needs to be specific about point density, precision, and categorization requirements. It is vital to succinctly understand the objective and the way the information will be used to precisely convey an end-state decisional product to the stakeholders, the buyer of the report. Early identification of the data requirements will save time and money in potential rework and post-processing data correction.
Based upon the application, point densities may vary between 1 point per square meter to 100 points per square meter. Probably one of the essential facets of designing a successful LiDAR project will always be to get a crystal-clear picture of exactly needs to be modeled. Users thinking about bare-earth information will require less point density, whereas additional features, such as vegetation, railroad tracks or pipeline, would necessitate 100 points per square meter. From a Drone Aerial LiDAR perspective, this also has collection savings. Lower density requirements mean higher altitudes, broader swaths, faster speeds, and more area covered per sortie.
Vertical accuracy of 2ft contour topography has been 18.5cm RMSE for decades. With sensor miniaturization, better timing systems, and sophisticated software algorithm, we are seeing RTK/PPK mobile lidar accuracy achieving single digit centimeter accuracies. Depending on use case, FEMA and USGS standards may suffice. However with the newer technology, increasingly there is a blurring line between traditional aerial lidar products and terrestrial scanner quality. Being clear on what decisional data product needs to be achieved is essential. This is especially true in infrastructure mapping, where depending on data requirements centimeter or millimeter accuracy may be required to determine material fatigue or settling conditions. The reward is if centimeter accuracy required, a multirotor drone solution may dramatically decrease collection times, even over terrestrial scanners.
What is unique about LiDAR data is the ability to add a classification to each point in the dataset. ASPRS drafted a classification schema which allows basic standards but also custom codes. Bare-earth should require no designation, where vegetation or asset management need more. The more classification requirements increase costs associated with data storage, analysis, and classification quality verification. To keep a competitive advantage and profit margin that you collect and process your data for what is exactly needed.
With all the technological advancement over the past few years, it is becoming increasingly important to remember the basics. When first getting into lidar mapping many help people feel it is essential to collect as much information as they can. It is not until they start seeing how much time, energy and storage are required for these large datasets that they take the time to get clear on what exactly is needed for their client. We hope this helps you ask the right questions when writing a statement of work for a prospective client or collection plan for your own engineer/ surveying company. No one likes leaving money on the table.