THE FULL-WAVEFORM LIDAR RIEGL LMS-Q680I: FROM REVERSE ENGINEERING TO SENSOR MODELING

The development of new data processing methods requires access to the raw data. Unfortunately some LiDAR manufacturers do not provide information about the format and the users can only rely on proprietary software to do their processing. Even if using black boxes might be sufficient for some simple applications, it might be an impediment to scientific research, as the processing would be limited to state of the art methods and their current implementation. In existing full-waveform LiDAR software there is a lack of error propagation methods that might be an issue when making quantitative measurements of topography, reflectance, or vegetation parameters. This problem can only be addressed at the lowest level by working directly on the waveforms. Moreover, to improve range measurement and feature extraction techniques, and compute error bars correctly, one also needs an instrument model describing the data acquisition process. Here we focus on the Riegl LMS-Q680i airborne LiDAR sensor. We acquired 200 km 2 of data (nearly 100 GB of undocumented binary files). We performed a reverse engineering to understand how the timestamps, look angles and waveforms were stored. Then we developed a model of this particular sensor: the two nonlinear detector channels, the asymmetric amplifier impulse response and the ringing effect. Assuming this model, not only were we able to match the output of the proprietary software, but we also managed to compute the range uncertainty, and we opened the way to new methodologies to improve the reliability and the accuracy of echo extraction.