Browsing by Subject "Point cloud"

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  • Yrttimaa, Tuomas; Saarinen, Ninni; Luoma, Ville; Tanhuanpaa, Topi; Kankare, Ville; Liang, Xinlian; Hyyppa, Juha; Holopainen, Markus; Vastaranta, Mikko (2019)
    Dead wood is a key forest structural component for maintaining biodiversity and storing carbon. Despite its important role in a forest ecosystem, quantifying dead wood alongside standing trees has often neglected when investigating the feasibility of terrestrial laser scanning (TLS) in forest inventories. The objective of this study was therefore to develop an automatic method for detecting and characterizing downed dead wood with a diameter exceeding 5 cm using multi-scan TLS data. The developed four-stage algorithm included (1) RANSAC-cylinder filtering, (2) point cloud rasterization, (3) raster image segmentation, and (4) dead wood trunk positioning. For each detected trunk, geometry-related quality attributes such as dimensions and volume were automatically determined from the point cloud. For method development and validation, reference data were collected from 20 sample plots representing diverse southern boreal forest conditions. Using the developed method, the downed dead wood trunks were detected with an overall completeness of 33% and correctness of 76%. Up to 92% of the downed dead wood volume were detected at plot level with mean value of 68%. We were able to improve the detection accuracy of individual trunks with visual interpretation of the point cloud, in which case the overall completeness was increased to 72% with mean proportion of detected dead wood volume of 83%. Downed dead wood volume was automatically estimated with an RMSE of 15.0 m(3)/ha (59.3%), which was reduced to 6.4 m(3)/ha (25.3%) as visual interpretation was utilized to aid the trunk detection. The reliability of TLS-based dead wood mapping was found to increase as the dimensions of dead wood trunks increased. Dense vegetation caused occlusion and reduced the trunk detection accuracy. Therefore, when collecting the data, attention must be paid to the point cloud quality. Nevertheless, the results of this study strengthen the feasibility of TLS-based approaches in mapping biodiversity indicators by demonstrating an improved performance in quantifying ecologically most valuable downed dead wood in diverse forest conditions.
  • Liang, Xinlian; Hyyppä, Juha; Kaartinen, Harri; Lehtomäki, Matti; Pyörälä, Jiri; Pfeifer, Norbert; Holopainen, Markus; Brolly, Gábor; Francesco, Pirotti; Hackenberg, Jan; Huang, Huabing; Jo, Hyun-Woo; Katoh, Masato; Liu, Luxia; Mokroš, Martin; Morel, Jules; Olofsson, Kenneth; Poveda-Lopez, Jose; Trochta, Jan; Wang, Di; Wang, Jinhu; Xi, Zhouxi; Yang, Bisheng; Zheng, Guang; Kankare, Ville; Luoma, Ville; Yu, Xiaowei; Chen, Liang; Vastaranta, Mikko; Saarinen, Ninni; Wang, Yunsheng (2018)
    The last two decades have witnessed increasing awareness of the potential of terrestrial laser scanning (TLS) in forest applications in both public and commercial sectors, along with tremendous research efforts and progress. It is time to inspect the achievements of and the remaining barriers to TLS-based forest investigations, so further research and application are clearly orientated in operational uses of TLS. In such context, the international TLS benchmarking project was launched in 2014 by the European Spatial Data Research Organization and coordinated by the Finnish Geospatial Research Institute. The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction. The project is designed to benchmark the TLS algorithms by processing identical TLS datasets for a standardized set of forest attribute criteria and by evaluating the results through a common procedure respecting reliable references. Benchmarking results reflect large variances in estimating accuracies, which were unveiled through the 18 compared algorithms and through the evaluation framework, i.e., forest complexity categories, TLS data acquisition approaches, tree attributes and evaluation procedures. The evaluation framework includes three new criteria proposed in this benchmarking and the algorithm performances are investigated through combining two or more criteria (e.g., the accuracy of the individual tree attributes are inspected in conjunction with plot-level completeness) in order to reveal algorithms’ overall performance. The results also reveal some best available forest attribute estimates at this time, which clarify the status quo of TLS-based forest investigations. Some results are well expected, while some are new, e.g., the variances of estimating accuracies between single-/multi-scan, the principle of the algorithm designs and the possibility of a computer outperforming human operation. With single-scan data, i.e., one hemispherical scan per plot, most of the recent algorithms are capable of achieving stem detection with approximately 75% completeness and 90% correctness in the easy forest stands (easy plots: 600 stems/ha, 20 cm mean DBH). The detection rate decreases when the stem density increases and the average DBH decreases, i.e., 60% completeness with 90% correctness (medium plots: 1000 stem/ha, 15 cm mean DBH) and 30% completeness with 90% correctness (difficult plots: 2000 stems/ha, 10 cm mean DBH). The application of the multi-scan approach, i.e., five scans per plot at the center and four quadrant angles, is more effective in complex stands, increasing the completeness to approximately 90% for medium plots and to approximately 70% for difficult plots, with almost 100% correctness. The results of this benchmarking also show that the TLS-based approaches can provide the estimates of the DBH and the stem curve at a 1–2 cm accuracy that are close to what is required in practical applications, e.g., national forest inventories (NFIs). In terms of algorithm development, a high level of automation is a commonly shared standard, but a bottleneck occurs at stem detection and tree height estimation, especially in multilayer and dense forest stands. The greatest challenge is that even with the multi-scan approach, it is still hard to completely and accurately record stems of all trees in a plot due to the occlusion effects of the trees and bushes in forests. Future development must address the redundant yet incomplete point clouds of forest sample plots and recognize trees more accurately and efficiently. It is worth noting that TLS currently provides the best quality terrestrial point clouds in comparison with all other technologies, meaning that all the benchmarks labeled in this paper can also serve as a reference for other terrestrial point clouds sources.
  • Yrttimaa, Tuomas; Saarinen, Ninni; Kankare, Ville; Hynynen, Jari; Huuskonen, Saija; Holopainen, Markus; Hyyppä, Juha; Vastaranta, Mikko (2020)
    There is a limited understanding of how forest structure affects the performance of methods based on terrestrial laser scanning (TLS) in characterizing trees and forest environments. We aim to improve this understanding by studying how different forest management activities that shape tree size distributions affect the TLS-based forest characterization accuracy in managed Scots pine (Pinus sylvestris L.) stands. For that purpose, we investigated 27 sample plots consisting of three different thinning types, two thinning intensities as well as control plots without any treatments. Multi-scan TLS point clouds were collected from the sample plots, and a point cloud processing algorithm was used to segment individual trees and classify the segmented point clouds into stem and crown points. The classified point clouds were further used to estimate tree and forest structural attributes. With the TLS-based forest characterization, almost 100% completeness in tree detection, 0.7 cm (3.4%) root-mean-square- error (RMSE) in diameter-at-breast-height measurements, 0.9–1.4 m (4.5–7.3%) RMSE in tree height measure-ments, and <6% relative RMSE in the estimates of forest structural attributes (i.e. mean basal area, number of trees per hectare, mean volume, basal area-weighted mean diameter and height) were obtained depending on the applied thinning type. Thinnings decreased variation in horizontal and vertical forest structure, which especially favoured the TLS-based tree detection and tree height measurements, enabling reliable estimates for forest structural attributes. A considerably lower performance was recorded for the control plots. Thinning intensity was noticed to affect more on the accuracy of TLS-based forest characterization than thinning type. The number of trees per hectare and the proportion of suppressed trees were recognized as the main factors affecting the accuracy of TLS-based forest characterization. The more variation there was in the tree size distribution, the more challenging it was for the TLS-based method to capture all the trees and derive the tree and forest structural attributes. In general, consistent accuracy and reliability in the estimates of tree and forest attributes can be expected when using TLS for characterizing managed boreal forests.
  • Wang, Yunsheng; Kukko, Antero; Hyyppä, Eric; Hakala, Teemu; Pyörälä, Jiri; Lehtomäki, Matti; El Issaoui, Aimad; Yu, Xiaowei; Kaartinen, Harri; Liang, Xinlian; Hyyppä, Juha (2021)
    BackgroundCurrent automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost.ResultsIn the experiment, an approximately 0.5ha forest was covered in ca. 10min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2-4cm RMSE of the diameter at the breast height estimates, and a 4-7cm RMSE of the stem curve estimates.ConclusionsResults of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.
  • Wang, Yunsheng; Kukko, Antero; Hyyppä, Eric; Hakala, Teemu; Pyörälä, Jiri; Lehtomäki, Matti; El Issaoui, Aimad; Yu, Xiaowei; Kaartinen, Harri; Liang, Xinlian; Hyyppä, Juha (Springer Singapore, 2021)
    Abstract Background Current automated forest investigation is facing a dilemma over how to achieve high tree- and plot-level completeness while maintaining a high cost and labor efficiency. This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle (UAV) that flies above and under canopies in a single operation. The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight, thus grants the access to simultaneous high completeness, high efficiency, and low cost. Results In the experiment, an approximately 0.5 ha forest was covered in ca. 10 min from takeoff to landing. The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems, which leads to a 2–4 cm RMSE of the diameter at the breast height estimates, and a 4–7 cm RMSE of the stem curve estimates. Conclusions Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective. Thus, it is a solution to combine the advantages of the terrestrial static, the mobile, and the above-canopy UAV observations, which is a promising step forward to achieve a fully autonomous in situ forest inventory. Future studies should be aimed to further improve the platform positioning, and to automatize the UAV operation.
  • Liang, Xinlian; Kankare, Ville; Hyyppä, Juha; Wang, Yunsheng; Kukko, Antero; Haggren, Henrik; Yu, Xiaowei; Kaartinen, Harri; Jaakkola, Anttoni; Guan, Fengying; Holopainen, Markus; Vastaranta, Mikko (2016)
    Decision making on forest resources relies on the precise information that is collected using inventory. There are many different kinds of forest inventory techniques that can be applied depending on the goal, scale, resources and the required accuracy. Most of the forest inventories are based on field sample. Therefore, the accuracy of the forest inventories depends on the quality and quantity of the field sample. Conventionally, field sample has been measured using simple tools. When map is required, remote sensing materials are needed. Terrestrial laser scanning (TLS) provides a measurement technique that can acquire millimeter-level of detail from the surrounding area, which allows rapid, automatic and periodical estimates of many important forest inventory attributes. It is expected that TLS will be operationally used in forest inventories as soon as the appropriate software becomes available, best practices become known and general knowledge of these findings becomes more wide spread. Meanwhile, mobile laser scanning, personal laser scanning, and image-based point clouds became capable of capturing similar terrestrial point cloud data as TLS. This paper reviews the advances of applying TLS in forest inventories, discusses its properties with reference to other related techniques and discusses the future prospects of this technique. (C) 2016 The Authors. Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licensesiby-nc-nd/11.0/).