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

Pimont, François; Soma, Maxime; Dupuy, Jean‐Luc

doi:10.3390/rs11131580

Cited by 15 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Integrating these laser-dependent factors to the PATH model is feasible, but solving the integral equations would become difficult. An alternative approach provided by [59] is to discard Beer’s law and model the statistical form, relating leaf area distribution, laser path length, density attenuation, and footprint size. Such a statistical model is solvable with a maximum likelihood estimator (MLE).…”

Section: Resultsmentioning

confidence: 99%

A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring

Hopkinson

Rood

et al. 2019

Sensors

View full text Add to dashboard Cite

A growing need for sampling environmental spaces in high detail is driving the rapid development of non-destructive three-dimensional (3D) sensing technologies. LiDAR sensors, capable of precise 3D measurement at various scales from indoor to landscape, still lack affordable and portable products for broad-scale and multi-temporal monitoring. This study aims to configure a compact and low-cost 3D fusion scanning system (FSS) with a multi-segment Leddar (light emitting diode detection and ranging, LeddarTech), a monocular camera, and rotational robotics to recover hemispherical, colored point clouds. This includes an entire framework of calibration and fusion algorithms utilizing Leddar depth measurements and image parallax information. The FSS was applied to scan a cottonwood (Populus spp.) stand repeatedly during autumnal leaf drop. Results show that the calibration error based on bundle adjustment is between 1 and 3 pixels. The FSS scans exhibit a similar canopy volume profile to the benchmarking terrestrial laser scans, with an r2 between 0.5 and 0.7 in varying stages of leaf cover. The 3D point distribution information from FSS also provides a valuable correction factor for the leaf area index (LAI) estimation. The consistency of corrected LAI measurement demonstrates the practical value of deploying FSS for canopy foliage monitoring.

show abstract

Section: Resultsmentioning

confidence: 99%

A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring

Hopkinson

Rood

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…From a LiDAR point cloud, certain forest parameters can be determined, such as: aboveground forest biomass [7][8][9], Leaf Area Index (LAI) [10][11][12][13][14] or canopy density [15,16]. Furthermore, LiDAR covered by dense forest vegetation).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

Cățeanu

Ciubotaru

2020

IJGI

View full text Add to dashboard Cite

A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.data can be used for tree inventories [17][18][19][20], monitoring vegetation health [21][22][23][24] or generating Canopy Height Models (CHMs) [16,25].The main advantage of LiDAR, with regard to forestry, is the fact that laser pulses can penetrate the canopy cover, with some of the pulses reaching the ground level. Therefore, geomorphological data is collected even if the ground is covered by forest vegetation, allowing the generation of a detailed and accurate ground surface model.In general, a data structure that models the elevation over an area is called a Digital Elevation Model (DEM). In short, a DEM is simply a surface that represents the elevation of a certain area in reference to a common vertical datum. When this type of model is used for a representation of the bare-earth surface, it can also be termed as Digital Terrain Model (DTM). In this paper, we opted for the term DTM, in order to emphasize the fact that the models we analyse, while generally speaking classify as DEMs, are meant specifically to represent the surface of the bare-earth.To obtain such a representation of the ground surface, a LiDAR point cloud must initially be filtered-a process which involves the separation of ground-level points from the orig...

show abstract

“…Among these, only one paper exclusively uses passive RS data [21], while 29 papers use at least one LiDAR dataset in the analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][22][23][24][25][26][27][28][29][30]. Ten papers exclusively use airborne laser scanning (ALS) data [4,6,7,10,11,13,18,23,26,27], nine papers exclusively use terrestrial laser scanning (TLS) data in the analysis [3,9,15,16,20,22,24,25,30], two papers exclusively use mobile laser scanning (MLS) data …”

mentioning

confidence: 99%

“…Finally, five papers use combined active and passive remote sensing data sets [2,14,17,19,28]. Regarding the scale of the analysis, 18 of the studies perform individual tree level (ITL) analysis [1][2][3][8][9][10][11][12][14][15][16]19,20,[23][24][25][26]30], eight papers report stand level (SL) analysis [6,7,17,18,21,22,27,29] and four report a combination of ITL and SL [4,5,13,28]. Tree position, diameter at breast height (DBH) and individual tree height (h) are the most common variables of interest, analyzed in nine, six and six papers, respectively, while the most commonly used methods are 3D reconstruction, point filtering and statistical modelling, which are used in eight, five and five papers, respectively (see Table 1).…”

mentioning

confidence: 99%

“…The SM include 30 published manuscripts (see Table 1): 28 research papers [1,2,[4][5][6][7][8][9][10][11][12][13][14][15][17][18][19][20][21][22][23][24][25][26][27][28][29][30], one letter [16] and one technical note [3]. Among these, only one paper exclusively uses passive RS data [21], while 29 papers use at least one LiDAR dataset in the analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]…”

mentioning

confidence: 99%

See 1 more Smart Citation

3D Point Clouds in Forest Remote Sensing

Varela

González-Ferreiro

2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

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

Cited by 15 publications

References 28 publications

A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring

A Lightweight Leddar Optical Fusion Scanning System (FSS) for Canopy Foliage Monitoring

Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

3D Point Clouds in Forest Remote Sensing

Contact Info

Product

Resources

About