TLSpy: An Open-Source Addition to Terrestrial Lidar Workflows

Terrestrial lidar scanners (TLS) that capture three dimensional (3D) geometry with cm scale precision present many new opportunities in the Earth Sciences and related fields. However, the lack of domain specific tools impedes full and efficient utilization of the information contained in these datasets. Most processing and analysis is performed using a variety of manufacturing, surveying, airborne lidar, and GIS software. Although much overlap exists, inevitably some needs are not addressed by these applications. TLSpy provides a plugin driven framework with 3D visualization capabilities that encourages researchers to fill these gaps. The goal is to free researchers from the intellectual overhead imposed by user and data interface design, enabling rapid development of TLS specific processing and analysis algorithms. We present two plugins as examples of problems that TLSpy is being applied to. The first plugin corrects for the strong influence of target orientation on TLS measured reflectance intensities. It calculates the distribution of incidence angles and intensities in an input scan and assists the user in fitting a reflectance model to the distribution. The model is then used to normalize input intensities, minimizing the impact of surface orientation and simplifying the extraction of quantitative data from reflectance measurements. Although reasonable default models can be determined the large number of factors influencing reflectance values require that the plugin be designed for maximum flexibility, allowing the user to adjust all model parameters and define new reflectance models as needed. The second plugin helps eliminate multipath reflections from water surfaces. Characterized by a lower intensity mirror image of the subaerial bank appearing below the water surface, these reflections are a common problem in scans containing water. These erroneous reflections can be removed by manually selecting points that lie on the waterline, fitting a plane to the points, and deleting points below that plane. This plugin simplifies the process by automatically identifying waterline points using characteristic changes in geometry and intensity. Automatic identification is often faster and more reliable than manual identification, however, manual control is retained as a fallback for degenerate cases.