Hand-Held Personal Laser Scanning

Balenović, Ivan; Liang, Xinlian; Jurjević, Luka; Hyyppä, Juha; Seletković, Ante; Kukko, Antero

doi:10.5552/crojfe.2021.858

Cited by 61 publications

(33 citation statements)

References 19 publications

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“…According to the results presented above, the efficiency in data acquisition of PLS systems as compared to TLS systems or traditional fieldwork seems clear, an observation that has also arisen in the other recent studies focused on this topic (Balenović et al 2021;Bauwens et al 2016;Chen et al 2019;Gollob et al 2021;Chiappini et al 2022). This is a key point when it comes to ensuring the possibility of incorporating LiDAR data into the forest inventory reference data.…”

Section: Discussionmentioning

confidence: 61%

Automatic tree detection and attribute characterization using portable terrestrial lidar

Solares-Canal

Alonso

Picos

et al. 2023

Trees

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Key message This study details a methodology to automatically detect the positions of and dasometric information about individual Eucalyptus trees from a point cloud acquired with a portable LiDAR system. Abstract Currently, the implementation of portable laser scanners (PLS) in forest inventories is being studied, since they allow for significantly reduced field-work time and costs when compared to the traditional inventory methods and other LiDAR systems. However, it has been shown that their operability and efficiency are dependent upon the species assessed, and therefore, there is a need for more research assessing different types of stands and species. Additionally, a few studies have been conducted in Eucalyptus stands, one of the tree genus that is most commonly planted around the world. In this study, a PLS system was tested in a Eucalyptus globulus stand to obtain different metrics of individual trees. An automatic methodology to obtain inventory data (individual tree positions, DBH, diameter at different heights, and height of individual trees) was developed using public domain software. The results were compared to results obtained with a static terrestrial laser scanner (TLS). The methodology was able to identify 100% of the trees present in the stand in both the PLS and TLS point clouds. For the PLS point cloud, the RMSE of the DBH obtained was 0.0716, and for the TLS point cloud, it was 0.176. The RMSE for height for the PLS point cloud was 3.415 m, while for the PLS point cloud, it was 10.712 m. This study demonstrates the applicability of PLS systems for the estimation of the metrics of individual trees in adult Eucalyptus globulus stands.

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Section: Discussionmentioning

confidence: 61%

Automatic tree detection and attribute characterization using portable terrestrial lidar

Solares-Canal

Alonso

Picos

et al. 2023

Trees

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“…Recent findings demonstrate that the geometric accuracy and point density of an MLS point cloud generally fall short of the respective characteristics of a multi-scan TLS point cloud (Balenović et al 2021). Tree density and the presence of undergrowth vegetation complicate the forest structure and make it challenging for any terrestrial close-range sensing technique to provide unoccluded point cloud representation of trees, but with MLS, this effect is even more prominent (Ryding et al 2015;Bauwens et al 2016;Liang, Kukko, et al 2018).…”

Section: Terrestrial Close-range Sensing Methods To Characterize Treesmentioning

confidence: 99%

Characterizing tree communities in space and time using point clouds

Yrttimaa¹

2021

Diss. For.

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To better understand the underlying processes of many natural phenomena, accurate observations and measurements must be carried out in space and time. Considering forest ecosystems, monitoring the development and dynamics of tree characteristics is essential in this regard. An era of three-dimensional (3D) sensing techniques and point clouds has revolutionized individual tree observations, enabling measurements at an unprecedented level of detail. The feasibility of using point clouds to characterize trees and tree communities in space and their development in time was investigated in this thesis. The objective was to develop point cloud-based methods for distinguishing and characterizing trees and downed dead wood and to test the feasibility of the developed methods in boreal forest conditions.Point cloud-based methods for detecting and characterizing forest structure were developed in studies I-III. Downed dead wood trunks could be distinguished from the undergrowth vegetation and near-ground objects by means of their regular, cylindrical geometry. Smooth, cylindrical surfaces and vertical continuity, on the other hand, were the key characteristics of point cloud structures to separate woody structures of standing trees from foliage and a tree stem from branches. The methods were tested in diverse boreal forest structures to validate these methodological principles.The feasibility of the developed methods for characterizing trees and tree communities in space and time was tested in studies II-V. The structural complexity of a tree community was noted as the most important factor affecting tree-detection accuracy. High performance of the point cloud-based method was achieved on managed forest stands with a low degree of variation in tree size distribution. In controlled thinning experiments, thinning intensity was found to be a more significant factor affecting the performance than thinning type (i.e. thinning from below, thinning from above, and systematic thinning). The hemispherical measurement geometry of terrestrial point clouds was successfully complemented with aerial point clouds acquired from above the canopy to improve the vertical characterization of trees and tree communities. Finally, the capacity of bitemporal terrestrial point clouds to characterize changes in the structure of trees and tree communities was demonstrated. If there was an increase or decrease in the attributes of trees within a tree community detected with conventional forest mensuration techniques, a similar outcome was achieved with the point clouds.The findings of this thesis improve the current knowledge of the feasibility of using point cloud-based methods in observing tree characteristics. Detailed 3D reconstruction of forests expands the spectrum of tree observations, as the dynamics of trees and tree communities can be monitored in more detail. This increases the understanding of processes shaping ecosystems and provides new approaches to improve ecological knowledge.

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“…Car-mounted laser scanner devices (Mobile Mapping 2020) can provide high-pulse density point clouds, too, and there have been novel approaches to Hand-Held Mobile Laser Scanning (HMLS), too (Kaartinen 2012;Ryding et al 2015, Bauwens et al 2016, Balenović 2021) not only to forest inventories, but enabling this to be used as an alternative tool for road assessments. Mobile Laser Scanning (MLS) offers opportunities for road research using both UAV and car-mounted sensors (Jaakkola 2015).…”

Section: Forest Road Inventoriesmentioning

confidence: 99%

Unpaved forest road quality assessment using airborne LiDAR data

Waga¹

2021

Diss. For.

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This study creates new methods for assessing unpaved forest road quality using airborne laser scanning (ALS) data. The low and high pulse density ALS data were first processed and digital elevation models (DEMs) created at several resolutions from 0.2 m to 1 m. Different interpolation methods such as IDW, NN, Spline and Kriging were compared in the first phase, and IDW was chosen for further calculations. The work focuses on road quality properties such as surface flatness, surface wear quality, road structure, ditch quality, road drying properties and water accumulation, and also the vegetation cover on and beside the road.The roads were divided into three categories using the Metsäteho forest road quality assessment system. Active/deactivated road status was assessed on Vancouver Island, Canada. Linear discriminant analysis was used to find the best predictors of the road quality classes, the result being validated using confusion matrices, by k-fold cross-validation, and/or by calculating kappa values. A combination of surface indices, the topographic wetness index and soil information provided high precision (81.6-89.8%) information about unpaved forest road quality. Simultaneously, the indices individually showed promising results when applied to high pulse density data. The classification based on vegetation growth was up to 73% correct, while the presence of a ditch system and its status as mapped using the high resolution LiDAR data was up to 92% correct.The findings indicate that the use of LiDAR data can help forest managers gain more information about the quality and status of forest roads in remote areas without spending extra resources (time, transportation costs, personnel) on checking the road network manually. Although the use of ALS data for road quality assessment cannot yet replace field visits, it opens up possibilities for further research and offer the option of combining these novel approaches with other road assessments.

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Hand-Held Personal Laser Scanning

Cited by 61 publications

References 19 publications

Automatic tree detection and attribute characterization using portable terrestrial lidar

Automatic tree detection and attribute characterization using portable terrestrial lidar

Characterizing tree communities in space and time using point clouds

Unpaved forest road quality assessment using airborne LiDAR data

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