A new approach to calculate Plant Area Density (PAD) using 3D ground-based lidar

2016 ◽  
Author(s):  
Leila Taheriazad ◽  
Hamid Moghadas ◽  
Arturo Sanchez-Azofeifa
Keyword(s):  
New Approach ◽  
Area Density ◽  
Plant Area ◽  
Ground Based Lidar

Estimating the plant area density of a Japanese larch (Larix kaempferi Sarg.) plantation using a ground-based laser scanner

2008 ◽  
Vol 148 (3) ◽  
pp. 428-438 ◽  
Author(s):  
Tomomi Takeda ◽  
Hiroyuki Oguma ◽  
Tomohito Sano ◽  
Yasumichi Yone ◽  
Yasumi Fujinuma
Keyword(s):  
Laser Scanner ◽  
Japanese Larch ◽  
Larix Kaempferi ◽  
Area Density ◽  
Plant Area

scholarly journals Robust processing of airborne laser scans to plant area density profiles

2020 ◽  
Vol 17 (23) ◽  
pp. 5939-5952
Author(s):  
Johan Arnqvist ◽  
Julia Freier ◽  
Ebba Dellwik
Keyword(s):  
Beech Forest ◽  
Airborne Lidar ◽  
Small Scale ◽  
Average Intensity ◽  
Forest Site ◽  
Area Index ◽  
Area Density ◽  
Intensity Information ◽  
Plant Area Index ◽  
Plant Area

Abstract. We present a new algorithm for the estimation of the plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods do not use the intensity information at all, use only average intensity values, or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new algorithm to three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full-leaf) and winter (bare-tree) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites. Specific weak points regarding the estimation of the PAD from airborne lidar data are addressed including the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is reduced in the new algorithm, by combining benefits of earlier algorithms. We further show that low-resolution gridding of the PAD will lead to a negative bias in the resulting estimate according to Jensen's inequality for convex functions and that the severity of this bias is method dependent. As a result, the PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for the PAD gridding of airborne lidar scans.

scholarly journals Estimators and confidence intervals for plant area density at voxel scale with T-LiDAR

2018 ◽  
Vol 215 ◽  
pp. 343-370 ◽  
Author(s):  
François Pimont ◽  
Denis Allard ◽  
Maxime Soma ◽  
Jean-Luc Dupuy
Keyword(s):  
Confidence Intervals ◽  
Area Density ◽  
Plant Area

scholarly journals Robust processing of airborne laser scans to plant area density profiles

2020 ◽  
Author(s):  
Johan Arnqvist ◽  
Julia Freier ◽  
Ebba Dellwik
Keyword(s):  
Beech Forest ◽  
Airborne Lidar ◽  
Small Scale ◽  
Average Intensity ◽  
Forest Site ◽  
Area Index ◽  
Area Density ◽  
Intensity Information ◽  
Plant Area Index ◽  
Plant Area

Abstract. We present a new algorithm for the estimation of plant area density (PAD) profiles and plant area index (PAI) for forested areas based on data from airborne lidar. The new element in the algorithm is to scale and average returned lidar intensities for each lidar pulse, whereas other methods either do not use the intensity information at all, only use average intensity values or do not scale the intensity information, which can cause problems for heterogeneous vegetation. We compare the performance of the new and three previously published algorithms over two contrasting types of forest: a boreal coniferous forest with a relatively open structure and a dense beech forest. For the beech forest site, both summer (full leaf) and winter (bare trees) scans are analyzed, thereby testing the algorithm over a wide spectrum of PAIs. Whereas all tested algorithms give qualitatively similar results, absolute differences are large (up to 400 % for the average PAI at one site). A comparison with ground-based estimates shows that the new algorithm performs well for the tested sites, and further and more importantly – it never produces clearly dubious results. Specific weak points for estimation of PAD from airborne lidar data are addressed; the influence of ground reflections and the effect of small-scale heterogeneity, and we show how the effect of these points is minimized using the new algorithm. We further show that low-resolution gridding of PAD will lead to a negative bias in the resulting estimate according to Jensen’s inequality for concave functions, and that the severity of this bias is method-dependent. As a result, PAI magnitude as well as heterogeneity scales should be carefully considered when setting the resolution for PAD gridding of airborne lidar scans.

scholarly journals Estimators and Confidence Intervals for Plant Area Density at Voxel Scale with T-LiDAR

2017 ◽  
Author(s):  
Francois Pimont ◽  
Denis Allard ◽  
Maxime Soma ◽  
Jean-Luc Dupuy
Keyword(s):  
Confidence Intervals ◽  
Research Field ◽  
Airborne Lidar ◽  
Area Density ◽  
Terrestrial Lidar ◽  
Unbiased Estimators ◽  
Vegetation Heterogeneity ◽  
Plant Area ◽  
Path Lengths ◽  
Vegetation Samples

Terrestrial LiDAR becomes more and more popular to estimate leaf and plant area density. Voxel-based approaches account for this vegetation heterogeneity and significant work has been done in this recent research field, but no general theoretical analysis is available. Although estimators have been proposed and several causes of biases have been identified, their consistency and efficiency have not been evaluated. Also, confidence intervals are almost never provided. In the present paper, we solve the transmittance equation and use the Maximum Likelihood Estimation (MLE), to derive unbiased estimators and confidence intervals for the attenuation coefficient, which is proportional to leaf area density. The new estimators and confidence intervals are defined at voxel scale, and account for the number of beams crossing the voxel, the inequality of path lengths in voxel, the size of vegetation elements, as well as for the variability of element positions between vegetation samples. They are completed by numerous numerical simulations for the evaluation of estimator consistency and efficiency, as well as the assessment of the coverage probabilities of confidence intervals. • Although commonly used when the beam number is low, the usual estimators are strongly biased and the 95% confidence intervals can be ≈±100% of the estimate. • Our unbiased estimators are consistent in a wider range of validity than the usual ones, especially for the unbiased MLE, which is consistent when the beam number is as low as 5. The unbiased MLE is efficient, meaning it reaches the lowest residual errors that can be expected (for an unbiased estimator). Also the unbiased MLE does not require any bias correction when path lengths are unequal. • When elements are small (or voxel is large), 103 beams entering the voxel leads to some confidence intervals ≈±10%, but when elements are larger (or voxel smaller), it can remain wider than ±50%, even for a large beam number. This is explained by the variability of element positions between vegetation samples. Such a result shows that a significant part of residual error can be explained by random effects. • Confidence intervals are much smaller (±5 to 10%) when LAD estimates are averaged over several small voxels, typically within a horizontal layer or in the crown of individual plants. In this context, our unbiased estimators show a reduction of 50% of the radius of confidence intervals, in comparison to usual estimators. Our study provides some new ready-to-use estimators and confidence intervals for attenuation coefficients, which are consistent and efficient within a fairly large range of parameter values. The consistency is achieved for a low beam number, which is promising for application to airborne LiDAR data. They entail to raise the level of understanding and confidence on LAD estimation. Among other applications, their usage should help determine the most suitable voxel size, for given vegetation types and scanning density, whereas existing guidelines are highly variable among studies, probably because of differences in vegetation, scanning design and estimators.

scholarly journals Accounting for Wood, Foliage Properties, and Laser Effective Footprint in Estimations of Leaf Area Density from Multiview-LiDAR Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1580 ◽  
Author(s):  
François Pimont ◽  
Maxime Soma ◽  
Jean-Luc Dupuy
Keyword(s):  
Leaf Area ◽  
Point Clouds ◽  
Tree Canopy ◽  
New Approach ◽  
Area Density ◽  
Leaf Area Density ◽  
Previous Formulation ◽  
Lad Estimation ◽  
New Formulation ◽  
Correction Terms

The spatial distribution of Leaf Area Density (LAD) in a tree canopy has fundamental functions in ecosystems. It can be measured through a variety of methods, including voxel-based methods applied to LiDAR point clouds. A theoretical study recently compared the numerical errors of these methods and showed that the bias-corrected Maximum Likelihood Estimator was the most efficient. However, it ignored (i) wood volumes, (ii) vegetation sub-grid clumping, (iii) the instrument effective footprint, and (iv) was limited to a single viewpoint. In practice, retrieving LAD is not straightforward, because vegetation is not randomly distributed in sub-grids, beams are divergent, and forestry plots are sampled from more than one viewpoint to mitigate occlusion. In the present article, we extend the previous formulation to (i) account for both wood volumes and hits, (ii) rigorously include correction terms for vegetation and instrument characteristics, and (iii) integrate multiview data. Two numerical experiments showed that the new approach entailed reduction of bias and errors, especially in the presence of wood volumes or when multiview data are available for poorly-explored volumes. In addition to its conciseness, completeness, and efficiency, this new formulation can be applied to multiview TLS—and also potentially to UAV LiDAR scanning—to reduce errors in LAD estimation.

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