Estimation of 3D vegetation density with Terrestrial Laser Scanning data using voxels. A sensitivity analysis of influencing parameters

Grau, Eloi; Durrieu, S.; Fournier, Richard; Gastellu-Etchegorry, Jean-Philippe; Yin, Tiangang

doi:10.1016/j.rse.2017.01.032

Cited by 100 publications

(98 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a method of volume visualization of LiDAR points, voxels have already been applied to airborne LiDAR data for improving calculations of forest attributes [87,88]. Voxel size is a key parameter pertaining to the scale of forest structural parameter estimates to the physical dimension of canopy components [89]. Thus, a sensitivity analysis was conducted to investigate the influence of various voxel sizes on forest structural estimations.…”

Section: The Selection Of Voxel Sizesmentioning

confidence: 99%

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

2017

View full text Add to dashboard Cite

Accurate and timely estimation of forest structural parameters plays a key role in the management of forest resources, as well as studies on the carbon cycle and biodiversity. Light Detection and Ranging (LiDAR) is a promising active remote sensing technology capable of providing highly accurate three dimensional and wall-to-wall forest structural characteristics. In this study, we evaluated the utility of standard metrics and canopy metrics derived from airborne LiDAR data for estimating plot-level forest structural parameters individually and in combination, over a subtropical forest in Yushan forest farm, southeastern China. Standard metrics, i.e., height-based and density-based metrics, and canopy metrics extracted from canopy vertical profiles, i.e., canopy volume profile (CVP), canopy height distribution (CHD), and foliage profile (FP), were extracted from LiDAR point clouds. Then the standard metrics and canopy metrics were used for estimating forest structural parameters individually and in combination by multiple regression models, including forest type-specific (coniferous forest, broad-leaved forest, mixed forest) models and general models. Additionally, the synergy of standard metrics and canopy metrics for estimating structural parameters was evaluated using field measured data. Finally, the sensitivity of vertical and horizontal resolution of voxel size for estimating forest structural parameters was assessed. The results showed that, in general, the accuracies of forest type-specific models (Adj-R 2 = 0.44-0.88) were relatively higher than general models (Adj-R 2 = 0.39-0.77). For forest structural parameters, the estimation accuracies of Lorey's mean height (Adj-R 2 = 0.61-0.88) and aboveground biomass (Adj-R 2 = 0.54-0.81) models were the highest, followed by volume (Adj-R 2 = 0.42-0.78), DBH (Adj-R 2 = 0.48-0.74), basal area (Adj-R 2 = 0.41-0.69), whereas stem density (Adj-R 2 = 0.39-0.64) models were relatively lower. The combination models (Adj-R 2 = 0.45-0.88) had higher performance compared with models developed using standard metrics (only) (Adj-R 2 = 0.42-0.84) and canopy metrics (only) (Adj-R 2 = 0.39-0.83). The results also demonstrated that the optimal voxel size was 5 × 5 × 0.5 m 3 for estimating most of the parameters. This study demonstrated that canopy metrics based on canopy vertical profiles can be effectively used to enhance the estimation accuracies of forest structural parameters in subtropical forests.

show abstract

Section: The Selection Of Voxel Sizesmentioning

confidence: 99%

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

2017

View full text Add to dashboard Cite

show abstract

“…The correlation could become saturated, with diminished sensitivity, relying on the relationship between the ratio of footprint diameter to leaf dimension and the LAI. Among all the PNB methods, the LPI weighted approach [28,35,68] is preferable because it has the broadest effective range in adapting various configurations (footprint size, LAI, and clumping), and is the most physically based, which preserves the valid clumping index without the influence of correlation (Table 4). In contrast, all the IB methods listed in Table 2 have the great advantage over the PNB methods of being able to accurately estimate the gap probability without the requirement of a correlation coefficient [α ≈ 1 in Equations (13) and (14)], given that an appropriate radiometric quantity of lidar point is used (either the distance-weighted power integral I or the apparent reflectance ρ a of each return).…”

Section: Discussionmentioning

confidence: 99%

“…Given the influence of ambiguous coefficients and residual radiometric issues, there is considerable controversy over the point-cloud inversion methods used to estimate P gap from a computed laser penetration index (LPI), and in a further step the effective and the true Plant/Leaf Area Index (PAI/LAI) of vegetation [27]. For example, the accuracy of TLS inversions is affected by partial hits that depend on the dimensions of the laser beam and leaves [18,28,29]. Moreover, for ALS, the point density within an area or volume is usually used to estimate the LAI [30][31][32][33][34] or the leaf area density [35].…”

Section: Introductionmentioning

confidence: 99%

Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

Yin

Cook

et al. 2019

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability (P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors.

show abstract

“…Especially the option to simulate arbitrary sensors allowed the implementation of the PASTiS-57 sensor in this study. Although sensor simulation with RTMs is not new, below canopy sensor simulations have been restricted to DHP [56] or TLS [61,62]. Another advantage of DART was the option to simulate heterogeneous canopies, which is crucial for forest radiative transfer modelling.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2018

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Estimation of 3D vegetation density with Terrestrial Laser Scanning data using voxels. A sensitivity analysis of influencing parameters

Cited by 100 publications

References 48 publications

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

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

Contact Info

Product

Resources

About