Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

Abegg, Meinrad; Kükenbrink, Daniel; Zell, Jürgen; Schaepman, Michael E.; Morsdorf, Felix

doi:10.3390/f8060184

Cited by 77 publications

(91 citation statements)

References 32 publications

(39 reference statements)

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“…Note that nominal values are per flight strip and actual point density is computed using all returns, including strip overlap and multiple returns. 22rsfs.royalsocietypublishing.org Interface Focus 8: 20170046 experiment in a temperate mixed forest agree very well with the simulation experiment from Abegg et al[35], who were able to show that TLS suffers from lower sampling density towards the top-of-canopy, which only can be mitigated by a large increase of the number of scans, which might come at prohibitively high costs.On the other hand, having more points does not always mean getting more information. Comparing plot-level canopy profiles of the UAV and traditional ALS data, very high correlations were observed, despite the large difference in point density of about 15 m 22 for ALS and 3200 m 22 for UAVLS…”

supporting

confidence: 85%

“…As Abegg et al [35] showed in their simulation study, the point density above TLS scanners is the lowest when compared with all considered elevation angles and decreases with distance from the scanner. Thus, this under-sampling of the upper canopy is partly system imminent and can only be mitigated by very dense placement of scanner locations, i.e.…”

Section: A Change Of Perspective: Terrestrial Laser Scanners From a Cmentioning

confidence: 73%

“…Using too high a scan resolution or inappropriate scanner locations can result in tremendous costs. Consequently, Abegg et al [35] used a virtual modelling set-up within the Blender software to test different scanner location patterns and scanning resolution settings in more than 2000 simulated stands. They were able to show that the scanner locations in a multi-scan design need to be evenly distributed within a plot, and not placed at the plot edges.…”

Section: Abstracting Reality: Sensors In the Virtual Domainmentioning

confidence: 99%

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Close-range laser scanning in forests: towards physically based semantics across scales

et al. 2018

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Laser scanning with its unique measurement concept holds the potential to revolutionize the way we assess and quantify three-dimensional vegetation structure. Modern laser systems used at close range, be it on terrestrial, mobile or unmanned aerial platforms, provide dense and accurate three-dimensional data whose information just waits to be harvested. However, the transformation of such data to information is not as straightforward as for airborne and space-borne approaches, where typically empirical models are built using ground truth of target variables. Simpler variables, such as diameter at breast height, can be readily derived and validated. More complex variables, e.g. leaf area index, need a thorough understanding and consideration of the physical particularities of the measurement process and semantic labelling of the point cloud. Quantified structural models provide a framework for such labelling by deriving stem and branch architecture, a basis for many of the more complex structural variables. The physical information of the laser scanning process is still underused and we show how it could play a vital role in conjunction with three-dimensional radiative transfer models to shape the information retrieval methods of the future. Using such a combined forward and physically based approach will make methods robust and transferable. In addition, it avoids replacing observer bias from field inventories with instrument bias from different laser instruments. Still, an intensive dialogue with the users of the derived information is mandatory to potentially re-design structural concepts and variables so that they profit most of the rich data that close-range laser scanning provides.

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supporting

confidence: 85%

Section: A Change Of Perspective: Terrestrial Laser Scanners From a Cmentioning

confidence: 73%

Section: Abstracting Reality: Sensors In the Virtual Domainmentioning

confidence: 99%

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Close-range laser scanning in forests: towards physically based semantics across scales

et al. 2018

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“…In addition, RS technologies can be used to derive a large range of forest health indicators [6,[15][16][17]. Studies increasingly refer to the importance of combining national forest inventory field plots and RS data for assessing forest health [18,19], but only a limited number of countries [20] and operational programs support plot-based national forest inventories monitoring by using close-range and air and spaceborne RS data.A study on the significance of RS in national forest inventories [20] showed that approximately 71% (32 out of 45 countries) consider their national forest inventory (NFI) to depend on or partly depend on earth observation data. Approximately 31% of those asked (14 countries) found the use of aerial images for NFI to be indispensable.…”

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confidence: 99%

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

et al. 2018

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Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.forest ecosystem change through effective global coordination [3] good observations, indicators and scenarios of biodiversity and ecosystem services change [4].There is still a large discrepancy between the information required by forest managers and scientists and the information that is available for understanding and assessing the complexity and multidimensionality of forest health drivers, stressors, disturbances, and effects. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded ...

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“…Previous research has reported that the quality and completeness of the point cloud is highly 177 dependent on scanner distance and occlusion (Abegg et al 2017, Pyörälä et al 2018b). Here, we 178 examined to what degree scanner distance and self-occlusion affected branch detection.…”

Section: Errors 176mentioning

confidence: 99%

Assessing branching structure for biomass and wood quality estimation using terrestrial laser scanning point clouds

Pyörälä

Liang

Saarinen

et al. 2018

Canadian Journal of Remote Sensing

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12Terrestrial laser scanning (TLS) accompanied by quantitative tree-modeling algorithms can 13 potentially acquire branching data non-destructively from a forest environment and aid the 14 development and calibration of allometric crown biomass and wood quality equations for species and 15 geographical regions with inadequate models. However, TLS's coverage in capturing individual 16 branches still lacks evaluation. We acquired TLS data from 158 Scots pine (Pinus sylvestris L.) trees 17 and investigated the performance of a quantitative branch detection and modeling approach for 18 extracting key branching parameters, namely the number of branches, branch diameter (bd) and 19 branch insertion angle (bα) in various crown sections. We used manual point cloud measurements as 20 references. The accuracy of quantitative branch detections decreased significantly above the live 21 crown base height, principally due to the increasing scanner distance as opposed to occlusion effects 22 caused by the foliage. bd was generally underestimated, when comparing to the manual reference, 23while bα was estimated accurately: tree-specific biases were 0.89 cm and 1.98°, respectively. Our 24 results indicate that full branching structure remains challenging to capture by TLS alone. 25Nevertheless, the retrievable branching parameters are potential inputs into allometric biomass and 26 wood quality equations. 27

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Terrestrial Laser Scanning for Forest Inventories—Tree Diameter Distribution and Scanner Location Impact on Occlusion

Cited by 77 publications

References 32 publications

Close-range laser scanning in forests: towards physically based semantics across scales

Close-range laser scanning in forests: towards physically based semantics across scales

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

Assessing branching structure for biomass and wood quality estimation using terrestrial laser scanning point clouds

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