Estimating Leaf Bulk Density Distribution in  a Tree Canopy Using Terrestrial LiDAR and  a Straightforward Calibration Procedure

Pimont, François; Dupuy, Jean‐Luc; Rigolot, Éric; Prat, Vincent; Piboule, Alexandre

doi:10.3390/rs70607995

Cited by 27 publications

(26 citation statements)

References 34 publications

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“…Visualization of the uncorrected 905 nm data as recorded by the scanner system did not allow clear dinstinction of vegetation characteristics. The same observation has been confirmed by Pimont et al (2015), where a detailed discussion of this phenomenon can be found. However, this motivated us to experiment with the automation of intensity correction methods directly from the field data.…”

Section: Datasupporting

confidence: 68%

TLS Field Data Based Intensity Correction for Forest Environments

Heinzel¹,

Huber²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Terrestrial laser scanning (TLS) is increasingly used for forestry applications. Besides the three dimensional point coordinates, the 'intensity' of the reflected signal plays an important role in forestry and vegetation studies. The benefit of the signal intensity is caused by the wavelength of the laser that is within the near infrared (NIR) for most scanners. The NIR is highly indicative for various vegetation characteristics. However, the intensity as recorded by most terrestrial scanners is distorted by both external and scanner specific factors. Since details about system internal alteration of the signal are often unknown to the user, model driven approaches are impractical. On the other hand, existing data driven calibration procedures require laborious acquisition of separate reference datasets or areas of homogenous reflection characteristics from the field data. In order to fill this gap, the present study introduces an approach to correct unwanted intensity variations directly from the point cloud of the field data. The focus is on the variation over range and sensor specific distortions. Instead of an absolute calibration of the values, a relative correction within the dataset is sufficient for most forestry applications. Finally, a method similar to time series detrending is presented with the only pre-condition of a relative equal distribution of forest objects and materials over range. Our test data covers 50 terrestrial scans captured with a FARO Focus 3D S120 scanner using a laser wavelength of 905 nm. Practical tests demonstrate that our correction method removes range and scanner based alterations of the intensity.

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Section: Datasupporting

confidence: 68%

TLS Field Data Based Intensity Correction for Forest Environments

Heinzel¹,

Huber²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Various algorithms have been successfully developed to estimate tree height [26], leaf area index [27,28], aboveground biomass [29], canopy cover [7,30,31] at both individual tree [29,32,33] and forest stand scales [34,35]. However, studies on LiDAR-based CBH estimation are still insufficient, particularly at individual-tree scale.…”

Section: Introductionmentioning

confidence: 99%

Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Lai-ping

Zhai

et al. 2018

Opt. Express

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Abstract:Crown base height (CBH) is an essential tree biophysical parameter for many applications in forest management, forest fuel treatment, wildfire modeling, ecosystem modeling and global climate change studies. Accurate and automatic estimation of CBH for individual trees is still a challenging task. Airborne light detection and ranging (LiDAR) provides reliable and promising data for estimating CBH. Various methods have been developed to calculate CBH indirectly using regression-based means from airborne LiDAR data and field measurements. However, little attention has been paid to directly calculate CBH at the individual tree scale in mixed-species forests without field measurements. In this study, we propose a new method for directly estimating individual-tree CBH from airborne LiDAR data. Our method involves two main strategies: 1) removing noise and understory vegetation for each tree; and 2) estimating CBH by generating percentile ranking profile for each tree and using a spline curve to identify its inflection points. These two strategies lend our method the advantages of no requirement of field measurements and being efficient and effective in mixed-species forests. The proposed method was applied to a mixed conifer forest in the Sierra Nevada, California and was validated by field measurements. The results showed that our method can directly estimate CBH at individual tree level with a root-mean-squared error of 1.62 m, a coefficient of determination of 0.88 and a relative bias of 3.36%. Furthermore, we systematically analyzed the accuracies among different height groups and tree species by comparing with field measurements. Our results implied that taller trees had relatively higher uncertainties than shorter trees. Our findings also show that the accuracy for CBH estimation was the highest for black oak trees, with an RMSE of 0.52 m. The conifer species results were also good with uniformly high R 2 ranging from 0.82 to 0.93. In general, our method has demonstrated high accuracy for individual tree CBH estimation and strong potential for applications in mixed species over large areas. References and links1. E. Naesset and T. Økland, "Estimating tree height and tree crown properties using airborne scanning laser in a boreal nature reserve," Remote Sens. Environ. 79, 105-115 (2002). 2. S. C. Popescu and K. Zhao, "A voxel-based lidar method for estimating crown base height for deciduous and pine trees," Remote Sens.

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“…The cases we explored here were intended as a simple first step, rather than a comprehensive evaluation; many aspects of fuel heterogeneity that can be represented in STANDFIRE and FuelManager were not used to their full effect in these examples. This capacity for finer detail should also enable use of more detailed fuel mapping approaches (Loudermilk et al 2012;Pimont et al 2015;Almeida et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Modeling thinning effects on fire behavior with STANDFIRE

Parsons

Pimont

Wells

et al. 2018

Annals of Forest Science

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& Key message We describe a modeling system that enables detailed, 3D fire simulations in forest fuels. Using data from three sites, we analyze thinning fuel treatments on fire behavior and fire effects and compare outputs with a more commonly used model. & Context Thinning is considered useful in altering fire behavior, reducing fire severity, and restoring resilient ecosystems. Yet, few tools currently exist that enable detailed analysis of such efforts. & Aims The study aims to describe and demonstrate a new modeling system. A second goal is to put its capabilities in context of previous work through comparisons with established models. & Methods The modeling system, built in Python and Java, uses data from a widely used forest model to develop spatially explicit fuel inputs to two 3D physics-based fire models. Using forest data from three sites in Montana, USA, we explore effects of thinning on fire behavior and fire effects and compare model outputs. & Results The study demonstrates new capabilities in assessing fire behavior and fire effects changes from thinning. While both models showed some increases in fire behavior relating to higher winds within the stand following thinning, results were quite different in terms of tree mortality. These different outcomes illustrate the need for continuing refinement of decision support tools for forest management. & Conclusion This system enables researchers and managers to use measured forest fuel data in dynamic, 3D fire simulations, improving capabilities for quantitative assessment of fuel treatments, and facilitating further refinement in physics-based fire modeling.

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Estimating Leaf Bulk Density Distribution in a Tree Canopy Using Terrestrial LiDAR and a Straightforward Calibration Procedure

Cited by 27 publications

References 34 publications

TLS Field Data Based Intensity Correction for Forest Environments

TLS Field Data Based Intensity Correction for Forest Environments

Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Modeling thinning effects on fire behavior with STANDFIRE

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