Bias in lidar-based canopy gap fraction estimates

Vaccari, Simone; Leeuwen, Martin van; Calders, Kim; Coops, Nicholas C.; Herold, Martin

doi:10.1080/2150704x.2012.742211

Cited by 23 publications

(10 citation statements)

References 16 publications

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“…Studies have also focused on gap fraction and leaf area index (LAI), such as Danson et al (2007), who derived canopy gap fractions from TLS data, and work of Huang and Pretzsch (2010), who developed a method to predict LAI that incorporates nonuniformity of the foliage distribution. Vaccari et al (2013) also examined gap fraction from TLS and developed correction factors based on canopy perimeter to account for instrument bias when the laser footprint covers a mixture of canopy elements and gaps. Full-waveform TLS can FIG.…”

Section: Terrestrial Laser Scanningmentioning

confidence: 99%

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

White

Coops

Wulder

et al. 2016

Canadian Journal of Remote Sensing

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614

354

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Abstract. Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time.

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Section: Terrestrial Laser Scanningmentioning

confidence: 99%

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

White

Coops

Wulder

et al. 2016

Canadian Journal of Remote Sensing

Self Cite

614

354

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“…The outcome can then be used for vegetation structure analysis, when the objective is to accurately model, for example, the branches and trunks within forest canopies [7]. In summary, considering the aforementioned recommendations, the developed method in this study will not only remove ghost points at the edge of canopy perimeters when gaps are distinguishable [23], but will also delete ghost points from scenes with overlapping objects and consequently improve continuous-wave TLS data quality.…”

Section: Discussionmentioning

confidence: 99%

“…A manual selection and correction of the point cloud during the TLS data preprocessing procedure has also been tested [9], but this technique is not efficient to delete ghost points on large datasets (e.g., real forest canopies). More recent studies, however, have improved quality of TLS data collected in forest environments by investigating the morphology of canopy gaps and correcting for the bias introduced by points on time-of-flight TLS data [23]. Improved data is also obtained when default and customized filters (i.e., threshold filters for range and intensity) are used to remove ghost points on datasets from continuous-wave and time-of-flight TLS systems [7].…”

Section: Introductionmentioning

confidence: 99%

Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups

Cifuentes

Zande

Salas

et al. 2014

Forests

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Terrestrial laser scanning (TLS) data makes possible to directly characterize the three-dimensional (3D) distribution of canopy foliage elements. The scanned edges of these elements may result in incorrectly point measurements (i.e., "ghost points") impacting the quality of point cloud data. Therefore, estimation of the ghost points' spatial visibilities, measurement of their characteristics and their removal are essential. In order to quantify the improvements on data quality, a method is developed in this study to efficiently correct for ghost points. Since the occurrence of ghost points is governed by a number of factors, (e.g., scanning resolution and distance, object properties, incident angle); the developed method is based on the analysis of the effects of these factors under controlled conditions where canopy-like objects (i.e., leaves, branches and layers of leaves) were scanned using a continuous-wave TLS system that employs phase-shift technology. Manual extraction of ghost points was done in order to calculate the relative amount of ghost points per scan, or ghost points ratio (gpr). The gpr values were computed in order to: (i) analyze their relationships with variables representing the above factors; and (ii) be used as a reference to evaluate the performance of filters used for extraction of ghost points. The ghost points' occurrence was modeled by fitting regression models using different predictor variables OPEN ACCESSForests 2014, 5 1566 that represent the variables under study. The obtained results indicated that reduced models with three predictors were suitable for gpr estimation in artificial leaves and in artificial branches, with a relative root mean squared error (RMSE) of 4.7% and 3.7%, respectively; while the full model with four predictors was appropriate for artificial layers of leaves, with relative RMSE of 4.5%. According to the statistical analysis, scanning distance was identified as the most important variable for modeling ghost points occurrence. Results indicated that optimized distance-based filters relative to the scanning distance have improved the outcomes in ghost points detection, in comparison to standard filtering criteria. These results suggest that more accurate characterization of forest canopy 3D structures can be achieved by removing ghost points using the new developed method.

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“…This results in high precision time series, i.e., with low noise in the temporal domain. On the other side, partial hits lead to underestimation of gap fraction by these kind of sensors [46,48]. Partial hits result from objects that only partially cover the laser instantaneous field of view, but are registered as full interceptions by the waveform analysis methods of commercial suppliers to maximise point cloud density.…”

Section: Reference Datasetsmentioning

confidence: 99%

Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57

et al. 2018

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Land Surface Phenology (LSP) and Leaf Area Index (LAI) are important variables that describe the photosynthetically active phase and capacity of vegetation. Both are derived on the global scale from optical satellite sensors and require robust validation based on in situ sensors at high temporal resolution. This study assesses the PAI Autonomous System from Transmittance Sensors at 57 • (PASTiS-57) instrument as a low-cost transmittance sensor for simultaneous monitoring of LSP and LAI in forest ecosystems. In a field experiment, spring leaf flush and autumn senescence in a Dutch beech forest were observed with PASTiS-57 and illumination independent, multi-temporal Terrestrial Laser Scanning (TLS) measurements in five plots. Both time series agreed to less than a day in Start Of Season (SOS) and End Of Season (EOS). LAI magnitude was strongly correlated with a Pearson correlation coefficient of 0.98. PASTiS-57 summer and winter LAI were on average 0.41 m 2 m −2 and 1.43 m 2 m −2 lower than TLS. This can be explained by previously reported overestimation of TLS. Additionally, PASTiS-57 was implemented in the Discrete Anisotropic Radiative Transfer (DART) Radiative Transfer Model (RTM) model for sensitivity analysis. This confirmed the robustness of the retrieval with respect to non-structural canopy properties and illumination conditions. Generally, PASTiS-57 fulfilled the CEOS LPV requirement of 20% accuracy in LAI for a wide range of biochemical and illumination conditions for turbid medium canopies. However, canopy non-randomness in discrete tree models led to strong biases. Overall, PASTiS-57 demonstrated the potential of autonomous devices for monitoring of phenology and LAI at daily temporal resolution as required for validation of satellite products that can be derived from ESA Copernicus' optical missions, Sentinel-2 and -3.

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Bias in lidar-based canopy gap fraction estimates

Cited by 23 publications

References 16 publications

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

Remote Sensing Technologies for Enhancing Forest Inventories: A Review

Correction of Erroneous LiDAR Measurements in Artificial Forest Canopy Experimental Setups

Monitoring Forest Phenology and Leaf Area Index with the Autonomous, Low-Cost Transmittance Sensor PASTiS-57

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