Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Pedersen, Rune Østergaard; Skovsgaard, Jens Peter

doi:10.1016/j.ecolmodel.2009.06.048

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…A bimodal distribution in total carbon storage amongst the sample sites was found, the cause of which is likely attributable to the study design [34]; in this case, the sampling of two populations. Under a recently closed canopy of exotic species, tea tree growth rates are stunted.…”

Section: Bimodal Distributionmentioning

confidence: 94%

“…This means that regression models or confidence intervals cannot be effectively applied to the data. Pedersen and Skovsgaard [34] state that due to the nature of the data, 'nonlinearity is often addressed, but rarely quantified' in the discipline of forestry science. Being a biological system, relationships between variables are often complex, resulting in data that do not follow a recognized distribution pattern [35].…”

Section: Sample Quantilesmentioning

confidence: 99%

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Comparing the Carbon Storage Potential of Naturally Regenerated Tea Trees with Default New Zealand Carbon Look-Up Tables: A Case Study

2023

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The New Zealand Emissions Trading Scheme allows landowners to be remunerated for the carbon sequestration capabilities of eligible forests established post 1990. For afforested areas of 100 hectares or fewer, carbon sequestration is estimated with the use of default carbon look-up tables administered by the Ministry for Primary Industries. However, a disparity exists between exotic pines (Pinus radiata), where carbon sequestration predictions are regionally differentiated, and native species, where carbon sequestration estimations are neither distinguished by species or locality. This paper aims to highlight this inequality by comparing the calculated carbon storage of endemic tree species with the ‘Indigenous Forest’ category in the carbon look-up tables. The carbon storage of 12-year-old naturally regenerated tea trees (Leptospermum scoparium and Kunzea ericoides) was calculated using allometric measurements and compared to the look-up tables. The results suggest that carbon look-up tables underestimate the carbon sequestration of native tea trees by 81.8%. A bimodal data distribution suggests that carbon sequestration is heavily dependent on light interception levels. It is recommended that carbon sequestration data for specific native species in different environments are collected and integrated into such tables.

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Section: Bimodal Distributionmentioning

confidence: 94%

Section: Sample Quantilesmentioning

confidence: 99%

Comparing the Carbon Storage Potential of Naturally Regenerated Tea Trees with Default New Zealand Carbon Look-Up Tables: A Case Study

2023

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“…Moreover, multivariable regression analysis theory and methods were applied to model many other processes very well (e.g., chemical systems, scattered data sets in computational physics, the volume computation problem of forest trees, and major depressive disorder problems, etc.) 38 39 40 41 42 43 . In order to solve the key technical problem of requiring large data sets in executing the HMPR, we propose a statistical formula ( equation (4) in Methods) to construct the simulated data sets, which is larger than the experimental data sets.…”

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confidence: 99%

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

Jie

Chen

Liu

et al. 2016

Sci Rep

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From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

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“…It is known from control inventories that random measurement errors, for variables such as diameter and height, can be substantial even if the systematic errors generally are small (Daamen 1980). Random errors can also lead to increased random variation and systematic errors (bias) in predicted variables due to model non-linearity (for example, Gertner 1991, Pedersen & Skovsgaard 2009). The magnitude of this type of errors can be expected to be different on permanent sample plots compared to temporary sample plots.…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the described methods for growth estimation at tree level is affected by the sampling design, the correctness of the functions including model selection, the parameter estimation and the material used, non-explained residual variability, and measurement or classification errors in the input variables in applications (cf. Gertner & Dzialowy 1984, Smith & Burkhart 1984, Gertner 1986, Kangas 1996, Kangas 1998, Eid 2000, Canavan & Hann 2004, Pedersen & Skovsgaard 2009, Mäkinen et al 2010a. Measurement errors generally receive less attention and are assumed to be relatively small compared to sampling errors and regression function errors (Gertner 1990).…”

Section: Introductionmentioning

confidence: 99%

Assessment of bias due to random measurement errors in stem volume growth estimation by the Swedish National Forest Inventory

Suty

Nyström

Ståhl

2013

Scandinavian Journal of Forest Research

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We evaluated the performance of two methods for estimating stem volume increment at individual tree level with respect to bias due to random measurement errors. Here, growth is either predicted as the difference between two consecutive volume estimates where single-tree volume functions are applied to data from repeated measurements or by a regression model that is applied to data from a single survey and includes radial increment. In national forest inventories (NFIs), the first method is typically used for permanent plots, the second for temporary plots. The Swedish NFI combines estimates from both plot types to assess growth at national and regional scales and it is therefore important that the two methods provide similar results. The accuracy of these estimates is affected by random measurement errors in the independent variables, which may lead to systematic errors in predicted variables due to model non-linearity. Using Taylor series expansion and empirical data from the Swedish NFI we compared the expected bias in stem volume growth estimates for different diameter classes of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). Our results indicate that both methods are fairly insensitive to random measurement errors of the size that occur in the Swedish NFI. The empirical comparison between the two methods showed greater differences for large diameter trees of both pine and spruce. A likely explanation is that the regressions are uncertain because few large trees were available for developing the models.

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Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Cited by 4 publications

References 9 publications

Comparing the Carbon Storage Potential of Naturally Regenerated Tea Trees with Default New Zealand Carbon Look-Up Tables: A Case Study

Comparing the Carbon Storage Potential of Naturally Regenerated Tea Trees with Default New Zealand Carbon Look-Up Tables: A Case Study

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

Assessment of bias due to random measurement errors in stem volume growth estimation by the Swedish National Forest Inventory

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