A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data

Abstract

a b s t r a c tThis work proposes a segmentation method that isolates individual tree crowns using airborne LiDAR data. The proposed approach captures the topological structure of the forest in hierarchical data structures, quantifies topological relationships of tree crown components in a weighted graph, and finally partitions the graph to separate individual tree crowns. This novel bottom-up segmentation strategy is based on several quantifiable cohesion criteria that act as a measure of belief on weather two crown components belong to the same tree. An added flexibility is provided by a set of weights that balance the contribution of each criterion, thus effectively allowing the algorithm to adjust to different forest structures.The LiDAR data used for testing was acquired in Louisiana, inside the Clear Creek Wildlife management area with a RIEGL LMS-Q680i airborne laser scanner. Three 1 ha forest areas of different conditions and increasing complexity were segmented and assessed in terms of an accuracy index (AI) accounting for both omission and commission. The three areas were segmented under optimum parameterization with an AI of 98.98%, 92.25% and 74.75% respectively, revealing the excellent potential of the algorithm. When segmentation parameters are optimized locally using plot references the AI drops to 98.23%, 89.24%, and 68.04% on average with plot sizes of 1000 m 2 and 97.68%, 87.78% and 61.1% on average with plot sizes of 500 m 2 .More than introducing a segmentation algorithm, this paper proposes a powerful framework featuring flexibility to support a series of segmentation methods including some of those recurring in the tree segmentation literature. The segmentation method may extend its applications to any data of topological nature or data that has a topological equivalent.

aTrunk—An ALS-Based Trunk Detection Algorithm

“…Remote sensing based single tree characterisation procedures have been proven suitable to enhance forest inventories regarding, for example, ecology, wildlife or biodiversity (e.g., [2,3]). Moreover, growth simulations require single tree models (with features such as height, crown size, tree species, spatial distribution) as input variables [4].…”

“…An adequate identification of suppressed trees relies on the analysis of full-waveform or laser-point data ( [11,12,17]). A 3D delineation is possible using voxel-based or vector-based approaches, for example multi-layer imaging techniques [18], variants of k-means clustering [19][20][21], adaptive 3D clustering [22], multi-scale clustering [23] or graph-based methods [2,12]. Generally the 3D approaches result in improved detection rates in vertically inhomogeneous forest stands and more complex and realistic crown shapes [9] compared to raster-based approaches.…”

Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [<i>Picea abies</i> (L.) Karst.] Test Site in Romania

“…More recently, a new segmentation method based on a graph segmentation algorithm was proposed in order to identify individual tree crowns using LiDAR data (Strîmbu and Strîmbu, 2015).…”

Analytical approaches for modeling tree crown volume in black wattle (Acacia mearnsii De Wild.) stands

“…Recently, remote sensing methods provide competitive approaches for acquiring not only faster measurements than previous methods, but also accurate estimates of crown (Strîmbu and Strîmbu, 2015;Hu et al, 2014;Eerikäinen, 2009;Naesset, 2002;Hyyppä et al, 2000;Naesset, 1997). However, modern estimation methods based on remotely sensed acquired data may not be affordable in many circumstances due to challenges presented by high computational power, big data, logistics, data acquisition costs (Strîmbu and Strîmbu, 2015;Wulder et al 2012) and software licenses (Sönmez, 2009;Zhang et al, 2007;Song et al, 2003).…”

“…However, modern estimation methods based on remotely sensed acquired data may not be affordable in many circumstances due to challenges presented by high computational power, big data, logistics, data acquisition costs (Strîmbu and Strîmbu, 2015;Wulder et al 2012) and software licenses (Sönmez, 2009;Zhang et al, 2007;Song et al, 2003). Therefore, traditional methods of estimation still show potential.…”