Delineating Individual Trees from Lidar Data: A Comparison of Vector- and Raster-based Segmentation Approaches

Abstract

Light detection and ranging (lidar) data is increasingly being used for ecosystem monitoring across geographic scales. This work concentrates on delineating individual trees in topographically-complex, mixed conifer forest across the California's Sierra Nevada. We delineated individual trees using vector data and a 3D lidar point cloud segmentation algorithm, and using raster data with an object-based image analysis (OBIA) of a canopy height model (CHM). The two approaches are compared to each other and to ground reference data. We used high density (9 pulses/m 2 ), discreet lidar data and WorldView-2 imagery to delineate individual trees, and to classify them by species or species types. We also identified a new method to correct artifacts in a high-resolution CHM. Our main focus was to determine the difference between the two types of approaches and to identify the one that produces more realistic results. We compared the delineations via tree detection, tree heights, and the shape of the generated polygons. The tree height agreement was high between the two approaches and the ground data (r 2 : 0.93-0.96). Tree detection rates increased for more dominant trees (8-100 percent). The two approaches delineated tree boundaries that differed in shape: the lidar-approach produced fewer, more complex, and larger polygons that more closely resembled real forest structure.

Forest Parameter Prediction Using an Image-Based Point Cloud: A Comparison of Semi-ITC with ABA

“…Many crown segmentation algorithms have been developed for ALS, e.g., [27,28]; however, no study has yet investigated tree crown delineation with image-based point cloud data. Since no optimized segmentation algorithm for image-based 3D data exists, we chose to use a simple watershed algorithm [29], which we adjusted to the present data.…”

Detecting Individual Tree Position and Height Using Airborne LiDAR Data in Chollipo Arboretum, South Korea

“…The Triangulated Irregular Network (TIN) interpolation method was performed to generate DSM, DTM, and CHM in the ArcGIS 10.1 program. This process converted LiDAR points into raster format Jakubowski et al 2013). …”

“…It is essential to apply remote sensing techniques for forest carbon quantification to solve these issues (Popescu 2007;Park et al 2011). Aerial optical imagery has been widely applied for decades in the previous forest investigation studies (Jakubowski et al 2013). It is difficult to quantify forest stocks because optical imagery collects spectral information in 2D (Cui et al 2012).…”

Mapping forests with Lidar provides flexible, accurate data with many uses

“…Delineating the individual trees is done by segmenting the Lidar-derived canopy height modelthe raster image interpolated from Lidar points depicting the top of the vegetation canopy (e.g., Chen et al 2006) -by delineating the trees directly from the point cloud (Li et al 2012) or by a combination of these methods (Jakubowski, Li et al 2013). After accurate segmentation, relationships can be derived between Lidarand field-measured structural attributes such as tree height, crown diameter and canopy base height, which are directly measured, and basal area, diameter at breast height, wood volume, biomass and species type, which are derived by correlations (Chen et al 2006;Chen et al 2007).…”