A full-waveform airborne laser scanning metric extraction tool for forest structure modelling. Do scan angle and radiometric correction matter?

[EN] In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0°¿5° and 15°¿20°) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.This research was funded by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the projects ForeStructure (CGL2013-46387-C2-1-R) and FIRMACARTO (CGL2016-80705-R).Crespo-Peremarch, P.; Ruiz Fernández, LÁ. (2020). A full-waveform airborne laser scanning metric extraction tool for forest structure modelling. Do scan angle and radiometric correction matter?. Remote Sensing. 12(2):1-17. https://doi.org/10.3390/rs12020292S11712

Reconstrucción digital de estructuras de tejados históricos: desarrollo de un flujo de trabajo de análisis altamente automatizado

[EN] Planning on adaptive reuse, maintenance and restoration of historic timber structuresrequiresextensive architectural and structural analysis of the actual condition. Current methods for a modellingof roof constructions consist of several manual steps including the time-consuming dimensional modelling. The continuous development of terrestrial laser scanners increases the accuracy, comfort and speed of the surveying work inroof constructions. Resultingpoint clouds enabledetailed visualisation of theconstructionsrepresented by single points or polygonal meshes, but in fact donot containinformation about the structural system and the beam elements. The developed workflow containsseveral processing steps on the point cloud dataset. The most important among them arethenormal vector computation, the segmentation of points to extract planarfaces, a classification of planarsegmentsto detect the beam side facesand finally theparametric modelling of the beams on the basis of classified segments. Thisenablesa highly automated transitionfrom raw point cloud data to a geometric model containing beams of the structural system. The geometric model,as well as additional information about the structural properties of involved wooden beams and their joints,is necessaryinput for a furtherstructural modellingof timber constructions. The results of the workflow confirm that the proposed methods work well for beams with a rectangularcross-section and minor deformations. Scan shadows and occlusionof beamsby additional installationsor interlockingbeamsdecreases the modelling performance, but in generala high level ofaccuracy and completeness isachieved ata high degree of automation.[ES] Las estructuras históricas de madera requieren un análisis arquitectónico y estructural exhaustivo de su condición real en aras de planificar la reutilización flexible, el mantenimiento y la restauración. Los métodos actuales que modelan las construcciones de cubiertas pasan por aplicar varias etapas en modo manual, que incluye el lento modelado dimensional. El desarrollo continuo de escáneres láser terrestres aumenta la exactitud, la comodidad y la velocidad del trabajo topográfico en construcciones de tejados. Las nubes de puntos resultantes permiten la visualización detallada de las construcciones representadas por puntos o mallas poligonales, pero de hecho no contienen información sobre el sistema estructural y los elementos del travesaño. El flujo de trabajo desarrollado contiene varias etapas de procesamiento en el conjunto de datos de la nube de puntos. Los más importantes son el cálculo del vector normal, la segmentación de puntos que extraen caras planas, la clasificación de segmentos planos que detectan las caras laterales del travesaño y, finalmente, el modelado paramétrico de los travesaños en función de los segmentos clasificados. Esto permite una transición altamente automatizada de los datos de la nube de puntos brutos a un modelo geométrico que contiene los travesaños del sistema estructural. El modelo geométrico, así como la información adicional sobre las propiedades estructurales de las vigas de madera involucradas y de sus juntas, es información necesaria de entrada para el modelado estructural eventual de las construcciones de madera. Los resultados del flujo de trabajo confirman que los métodos propuestos funcionan bien en travesaños que presentan secciones transversales rectangulares y deformaciones menores. Las sombras en los escaneados y las oclusiones de los travesaños a partir de instalaciones adicionales o vigas entrelazados disminuye el rendimiento del modelado, pero en general se logra un nivel de exactitud e integridad elevado con un alto grado de automatización.Pöchtrager, M.; Styhler-Aydın, G.; Döring-Williams, M.; Pfeifer, N. (2018). Digital reconstruction of historic roof structures: developing a workflow for a highly automated analysis. Virtual Archaeology Review. 9(19):21-33. doi:10.4995/var.2018.8855SWORD2133919Attene, M., & Spagnuolo, M. (2000). Automatic surface reconstruction from point sets in space. Computer Graphics Forum, 19(3), 457-465. doi:10.1111/1467-8659.00438Baik, A., Yaagoubi, R., & Boehm, J. (2015). 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Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods