Individual Tree Species Classification Based on Terrestrial Laser Scanning Using Curvature Estimation and Convolutional Neural Network

Abstract

<p><strong>Abstract.</strong> In this paper, we propose a new method for specifying individual tree species based on depth and curvature image creation from point cloud captured by terrestrial laser scanner and Convolutional Neural Network (CNN). Given a point cloud of an individual tree, the proposed method first extracts the subset of points corresponding to a trunk at breast-height. Then branches and leaves are removed from the extracted points by RANSAC -based circle fitting, and the depth image is created by globally fitting a cubic polynomial surface to the remaining trunk points. Furthermore, principal curvatures are estimated at each scanned point by locally fitting a quadratic surface to its neighbouring points. Depth images clearly capture the bark texture involved by its split and tear-off, but its computation is unstable and may fail to acquire bark shape in the resulting images. In contrast, curvature estimation enables stable computation of surface concavity and convexity, and thus it can well represent local geometry of bark texture in the curvature images. In comparison to the depth image, the curvature image enables accurate classification for slanted trees with many branches and leaves. We also evaluated the effectiveness of a multi-modal approach for species classification in which depth and curvature images are analysed together using CNN and support vector machine. We verified the superior performance of our proposed method for point cloud of Japanese cedar and cypress trees.</p>

Predicting Tree Species From 3D Laser Scanning Point Clouds Using Deep Learning

“…While the bark structure may be a useful feature for species identification in very close-range scans (cf. Othmani et al, 2013 ; Mizoguchi et al, 2019 ) it may not appear in required detail at greater scanning distances. We are also not aware of studies that utilized solely leaf characteristics for tree species classification from laser scan data, even though leaf area index, a trait of all leaves together rather than individuals, was used in a pioneering study ( Lin and Herold, 2016 ).…”

“…With regard to TLS, Zou et al (2017) introduced an approach for species classification that reached up to 95.6% accuracy, using automatically extracted individual 3D tree point clouds that were transformed into 2D images before classification into four different species. Similarly, Mizoguchi et al (2019) performed a transformation of 3D point clouds into images in order to facilitate classification tasks based on the bark surface of two species. They reached classification accuracies also often greater than 90%.…”

Enhancing Tree Species Identification in Forestry and Urban Forests through Light Detection and Ranging Point Cloud Structural Features and Machine Learning

“…In this study, we hypothesized that bark features traditionally used by botanists, like ridges, crevices, and smoothness, could be described mathematically and applied to species identification from TLS point clouds. The approach we presented differs from the very few other recently published approaches, such as that of [44], in the kind and number of bark features considered.…”

“…While [44,63] have already used structural bark features, this paper tries to address this critical gap by exploring the utility of structural features traditionally used by experts to identify tree species based on their bark and stem characteristics. Apart from bark color, botanists use structural features like ridges, fissures, peeling, or scales, which have been described, amongst others, in [64].…”

The shape of trees: Reimagining forest ecology in three dimensions with remote sensing

“…Ayrey & Hayes, 2018; Chen et al, 2021; Krisanski et al, 2021; Luo et al, 2021; Xi & Hopkinson, 2021) provide the most promising way forward to increase automation within the processing workflows needed to make use of three‐dimensional data practical at scale. For example, neural network approaches have been used to automate tree crown detection from both RGB aerial imagery (Bosch, 2020; Weinstein et al, 2019), and from aerial LiDAR (Windrim & Bryson, 2020), and to identify species based on whole tree point clouds (Seidel et al, 2021), stem and bark properties (Mizoguchi et al, 2019) and processed, interpretable features (Terryn et al, 2020). The additional inclusion of ecologically realistic information to constrain processing algorithms, including through the use of scaling rules, can further improve performance (Brummer et al, 2021; Tao et al, 2015); however, the need for training data to build these models means that increased data sharing across the community may be needed, as well as adoption of approaches such as transfer learning and data augmentation.…”