Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions

Roberts, Scott D.; Dean, Thomas J.; Evans, David L.; McCombs, John; Harrington, R. L.; Glass, Patrick A.

doi:10.1016/j.foreco.2005.03.025

Cited by 103 publications

(73 citation statements)

References 30 publications

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“…The equation incorporated in the model was based on the regression model of Roberts et al [69], with average tree height and height to the middle of the live crown (crown length divided by 2) as predictor variables. This model was developed with the intention of calculating the individual leaf area from LiDAR measured heights in loblolly pine plantations in order to test for treatment effects on LAI using remotely sensed data [70]. Other methods for estimating the leaf area index remotely have been proposed, such as regressing various statistical moments on ground-based measurements of the leaf area index or passive reflectance values [71] (p. 353).…”

Section: Mean Leaf Area Per Treementioning

confidence: 99%

Neighbor and Height Effects on Crown Properties Associated with the Uniform-Stress Principle of Stem Formation

Dean

2018

Forests

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According to the uniform-stress principle of stem formation, the amount of leaf area a tree carries and the leverage it exerts on the stem determine the stem dimensions. Within an even-aged monoculture, the leaf area per tree and the leverage placed on the stem are functions of tree density and tree height. The uniform-stress principle presents the means to translate density effects on crown characteristics into stem dimensions and total standing volume. This approach is truly a top-down method of simulating growth tree and stand growth because leaf area and other crown properties must be determined before stem size and taper can be calculated. Each crown property influences either the sail area or the leverage placed on the stem, but the degree to which a specific crown property affects these parameters changes with stand density and height. Leverage is the more complicated of the two variables, being a function of the height to the base of the live crown and the vertical distribution of leaf area. The purpose of this brief review is to summarize the effects of stand density on the height to the base of the live tree and the vertical distribution of leaf area and the various ways these variables have been quantified.

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Section: Mean Leaf Area Per Treementioning

confidence: 99%

Neighbor and Height Effects on Crown Properties Associated with the Uniform-Stress Principle of Stem Formation

Dean

2018

Forests

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“…Since the first application of airborne LiDAR in forestry over a decade ago (Nilsson 1996), the technology has been widely used to quantify the spatial variation in tree height and crown dimensions at resolutions ranging from stand level (Hall et al 2005, Naesset & Bjerknes 2001, to plot level (Holmgren et al 2003, Lim & Treitz 2004, Popescu et al 2004) and individual tree level (Chen et al 2006, Coops et al 2004, Holmgren & Persson 2004, Popescu & Zhao 2008, Roberts et al 2005. Comparison of the maximum heights estimated from the extreme value distributions with the maximum heights measured by LiDAR at individual tree level is useful, mainly for error calibration, which enables recalculation of all heights measured by LiDAR and estimation of the stand structure.…”

Section: Iforest -Biogeosciences and Forestrymentioning

confidence: 99%

Modeling extreme values for height distributions in Pinus pinaster, Pinus radiata and Eucalyptus globulus stands in northwestern Spain

Gorgoso-Varela¹,

García-Villabrille²,

Alboreca³

2016

iForest

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Methods of estimating extreme height values can be used in forest modeling to improve fits to the marginal distribution of heights in the following bivariate diameter-height models: the SBB Johnson's distribution, the bivariate beta (GDB-2) distribution, the bivariate Logit-Logistic (LL-2) distribution and the power-normal (PN) distribution. Some applications to LiDAR derived data are also possible, e.g., for error calibration. Practical applications in forest management may also be considered, e.g., for pruning. In probability theory and statistics, the generalized extreme value (GEV) distribution, also known as the Fisher-Tippett distribution, is a family of continuous probability distributions that combine the Gumbel, Fréchet and Weibull distributions. This study compared the three distributions for fitting extreme values of tree heights (maximum and minimum heights), which were measured in 185 permanent research plots in Pinus pinaster Ait.

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“…The vast majority of research in the field of remote sensing of tree geometric parameters is devoted to the forest inventory. LiDAR data are commonly used for the estimation of tree heights [Morsdorf et al, 2004;Yu et al, 2004;Andersen et al, 2006;Hopkinson et al, 2007;Edson and Wing, 2011;Saremi et al, 2014], crown base heights [Vauhkonen, 2010], crown dimensions [Means et al, 2000;Popescu and Zhao, 2008], crown volume [Hinsley et al, 2002;Riaño et al, 2004], stem diameter [Popescu, 2007;Saremi et al, 2014], stem volume [Persson et al, 2002;Straub and Koch, 2011], and leaf area index [Roberts et al, 2005;Pope and Treitz, 2013;Sabol et al, 2014]. These parameters can be used for the indirect estimation of the biomass volume [Hauglin et al, 2013;Kankare et al, 2013], in order to avoid destructive methods for direct biomass volume measurement by cutting and weighing the pieces of wood [Araújo et al, 1999;Velázquez-Martí et al, 2010].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of tree geometric parameters depending on the LiDAR data density

Hadas

Estornell

2016

European Journal of Remote Sensing

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The aim of this study was to compare geometric parameters of olive trees (tree height, crown base height, crown diameters, crown area), using LiDAR data of different densities: 0.5, 3.5 and 9 points m -2 . Two strategies were proposed and verified with a focus on raster and raw data analysis. Statistical tests have shown, that for the tree height and crown base height estimation, the choice of strategy was irrelevant, but denser LiDAR data provided more accurate results. The raster analysis strategy applied for sparse and dense LiDAR datasets allowed crown shape to be determined with a similar accuracy which means raster data are useful for estimating other indirect tree parameters. The quality of results was independent from the tree size.

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Estimating individual tree leaf area in loblolly pine plantations using LiDAR-derived measurements of height and crown dimensions

Cited by 103 publications

References 30 publications

Neighbor and Height Effects on Crown Properties Associated with the Uniform-Stress Principle of Stem Formation

Neighbor and Height Effects on Crown Properties Associated with the Uniform-Stress Principle of Stem Formation

Modeling extreme values for height distributions in Pinus pinaster, Pinus radiata and Eucalyptus globulus stands in northwestern Spain

Accuracy of tree geometric parameters depending on the LiDAR data density

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