Mapping tree genera using discrete LiDAR and geometric tree metrics

Abstract

SUMMARYMaps of tree genera are useful in applications including forest inventory, urban planning, and the maintenance of utility transmission line infrastructure. We present a case study of using high density airborne LiDAR data for tree genera mapping along the right of way (ROW) of a utility transmission line corridor. Our goal was to identify single trees that showed or posed potential threats to transmission line infrastructure. Using the three dimensional mapping capability of LiDAR, we derived tree metrics that are related to the geometry of the trees (tree forms). For example, the dominant growth direction of trees is useful in identifying trees that are leaning towards transmission lines. We also derived other geometric indices that are useful in determining tree genera; these metrics included their height, crown shape, size, and branching structures. Our pilot study was situated north of Thessalon, Ontario, Canada along a major utility corridor ROW and surrounding woodlots. The geometric features used for general classification could be categorized into five broad categories related to: 1) lines, 2) clusters, 3) volumes, 4) 3D buffers of points, and 5) overall tree shape that provide parameters as an input for the Random Forest classifier.Key words: Airborne LiDAR, tree genera mapping, tree geometry, Random Forest Classification. RESUMENLos mapas de géneros de árboles son útiles para el inventario forestal, planificación urbana y el mantenimiento de la infraestructura de líneas de transmisión. Se presenta un estudio de caso de uso de datos LiDAR de alta densidad para el mapeo de géneros de árboles a lo largo del derecho de paso (ROW) de un corredor de línea de transmisión. El objetivo de la investigación fue identificar árboles individuales que mostraban o poseían una amenaza potencial a la infraestructura de la línea de transmisión. Mediante el uso de mapas tridimensionales de LiDAR se derivaron métricas de árboles que están relacionadas con la geometría de éstos (formas del árbol). Por ejemplo, la dirección del crecimiento dominante de los árboles es útil para identificar árboles que crecen inclinados hacia las líneas de transmisión. También se derivaron otras métricas geométricas que son útiles para determinar los géneros de los árboles, tales como altura, forma de la copa, tamaño y estructura de ramas. El área de estudio se ubicó al norte de Thessalon, Ontario, Canadá, a lo largo de los principales corredores de ROW y en los bosques aledaños. Los atributos geométricos usados para la clasificación de los géneros fueron categorizados en cinco amplias clases: 1) líneas, 2) agrupamiento, 3) volúmenes, 4) amortiguamiento en 3D de puntos, y 5) forma general del árbol que provee parámetros como una entrada para el clasificador forestal aleatorio.Palabras clave: LiDAR aéreo, mapeo de género de árboles, geometría de árbol, clasificación forestal aleatoria.

Automatic Clearance Anomaly Detection for Transmission Line Corridors Utilizing UAV-Borne LIDAR Data

“…Airborne laser scanning (ALS) has become one of the primary information sources for the ROW inspection. Compared with the image-based techniques, ALS method directly generates dense 3D point clouds describing the geometry with auxiliary information (e.g., multiple echoes and intensity) rather than recover the 3D structure from stereo vision [5,[40][41][42][43]. Various approaches have been reported on the ROW point clouds classification as well as the subsequent power facilities modeling including power lines, pylons, etc.…”

A Multiple Sensors Platform Method for Power Line Inspection Based on a Large Unmanned Helicopter

“…However, limited by the short flight time of UAVs, it is unfeasible to conduct long-distance inspection on power lines. The third category is inspection methods using typical photogrammetric or LiDAR techniques based on manned aerial vehicles, which are more effective and can be used for long distance inspection, but the details of the power line components cannot be obtained by these methods [20,21,22,23]. …”

Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests

“…The use of lidar as single data for tree species classification has been tested with very few species, and methods usually relied on geometric and vertical distribution features used to detect differences in stems and crowns structure (Hovi et al, 2016;Holmgren and Persson, 2004;Ko et al, 2012;Korpela et al, 2010;Li et al, 2013;Vaughn et al, 2012). Innovative approaches for information extraction include the use of computational geometry, and the development of metrics related to texture, foliage clustering and gap distribution (Kent et al, 2015;Li et al, 2013;Vauhkonen et al, 2009).…”