Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning

Conto, Tiago de; Olofsson, Kenneth; Görgens, Eric Bastos; Rodriguez, Luiz Carlos Estraviz; Almeida, Gilberto Machado de

doi:10.1016/j.compag.2017.10.019

Cited by 75 publications

(54 citation statements)

References 27 publications

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“…O BTAINING accurate timber inventory is crucial for forest managers. Foresters currently use Terrestrial Laser Scanning (TLS) sensors [1], [2] to extract tree metrics with high accuracy and reduced manpower [1]. However, these sensors need to be moved around to measure the timber inventory at multiple locations for full coverage of the forest, requiring additional time and manpower.…”

Section: Introductionmentioning

confidence: 99%

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Chen

Nardari

Lee

et al. 2020

IEEE Robot. Autom. Lett.

143

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This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping. Accurate mapping of this type of environment is challenging since the ground and the trees are surrounded by leaves, thorns and vines, and the sensor typically experiences extreme motion. We propose a semantic feature based pose optimization that simultaneously refines the tree models while estimating the robot pose. The pipeline utilizes a custom virtual reality tool for labeling 3D scans that is used to train a semantic segmentation network. The masked point cloud is used to compute a trellis graph that identifies individual instances and extracts relevant features that are used by the SLAM module. We show that traditional lidar and image based methods fail in the forest environment on both Unmanned Aerial Vehicle (UAV) and hand-carry systems, while our method is more robust, scalable, and automatically generates tree diameter estimations.

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Section: Introductionmentioning

confidence: 99%

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Chen

Nardari

Lee

et al. 2020

IEEE Robot. Autom. Lett.

143

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“…Although various types of forest information have been investigated in previous studies, the diameter at breast height (dbh) is the most representative characteristic of a tree and have been measured in most field inventories [33]. To the best of our knowledge, most of the work developed for the direct measurement of dbh was developed using terrestrial lidar (also known as a terrestrial laser scanner-TLS) [34][35][36][37][38][39], since these systems gets high-density-point clouds and can provide more detailed structural information within the forest [36].…”

Section: Introductionmentioning

confidence: 99%

“…TLS has advantages for accurate forest inventory. Previous research [40] explored the methods of using TLS to obtain point cloud data and estimate the height and diameter of individual trees at breast height (dbh), and found a mean RMSE for dbh of 1.28 cm and a mean RMSE for tree height of 0.95 m. In [39], researchers reported RMSE ranging from 1.06 to 3.21 cm for Scots pine (Pinus sylvestris) and 1.19 to 3.58 cm for Norway spruce (Picea abies) using a small set of trees, performing scans specifically designed for single trees, making field measurements from various heights and testing many different modeling methods. Researchers [37] obtained a mean error of less than 1 cm in the lower parts of the loblolly pines (Pinus taeda), which were planted in rows with regular spacing.…”

Section: Introductionmentioning

confidence: 99%

Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

et al. 2020

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Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

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“…A random tree was chosen and segmented from the point cloud for further analysis. Segmenting the stem within the point cloud at the typical diameter at breast height (DBH) of 1.3 m (see Figure 10, left) shows that it is possible to measure the diameter using, for example, stem-modelling algorithms (de Conto, Olofsson, Görgens, Rodriguez, & Almeida, 2017). As height increases however, noise in the point cloud becomes more prominent, which could result in stem diameter estimation inaccuracies (see Figure 10, right).…”

Section: Qualitative Analysis Of the Resulting Point Cloudmentioning

confidence: 99%

Aerial and Terrestrial Photogrammetric Point Cloud Fusion for Intensive Forest Monitoring

Krause¹

2019

giforum

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Remote sensing methods for forest monitoring are evolving rapidly thanks to recent advances in Unmanned Aerial Vehicle technology and digital photogrammetry. Photogrammetric point clouds allow the non-destructive derivation of individual tree parameters at a low cost. The fusion of aerial and terrestrial photogrammetry for creating full-tree point clouds is of utility for forest research, as tree volume could be assessed more economically and efficiently than by traditional methods. However, this is challenging to implement due to difficulties with co-registration and issues of occlusion. This study explores the possibility of using spherical targets typically used for Terrestrial Laser Scanning to accomplish the co-registration of UAV-based and terrestrial photogrammetric datasets. Results show a full-tree point cloud derived from UAV oblique imagery in combination with terrestrial imagery. Despite issues of noise produced from the sky in terrestrial imagery, the methodology is promising for aerial and terrestrial point cloud fusion.

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Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning

Cited by 75 publications

References 27 publications

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory

Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

Aerial and Terrestrial Photogrammetric Point Cloud Fusion for Intensive Forest Monitoring

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