Evaluation of Methods to Predict Weibull Parameters for Characterizing Diameter Distributions

Poudel, Krishna P.; Cao, Quang V.

doi:10.5849/forsci.12-001

Cited by 114 publications

(74 citation statements)

References 87 publications

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“…Previous studies have implemented ABAs, ITD methods or a hybrid of the two approaches to predict stem diameter distributions (e.g., [12][13][14][15][16][17][18][19][21][22][23][24][25]28]). We have used an allometry-based modelling approach coupled with Bayesian model-fitting techniques to predict stem diameter distributions and proportions of broadleaved and coniferous species from airborne LiDAR ( Figure 6).…”

Section: Discussionmentioning

confidence: 99%

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

et al. 2017

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Abstract:We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R 2 ≥ 0.75, and another 23% had 0.5 ≤ R 2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m 2 /ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution.

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Section: Discussionmentioning

confidence: 99%

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

et al. 2017

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“…While parametric strategies are advantageous in being able to represent a complete distribution with a few parameters when the empirical distribution is unimodal, they may have limited ability to represent complex and mixed species stands that may not have unimodal densities [18]. Empirical strategies which retain the original data or relative densities by dbh bins provide more flexibility to accommodate many types of stand tables [17], however, some prediction strategies for multiple dbh percentiles can result in illogical behavior, benefitting from the introduction of constraints [19].…”

Section: Introductionmentioning

confidence: 99%

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

Strunk

Gould

Packalén

et al. 2017

Forests

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While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R 2 ), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km 2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

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“…Baldwin and Feduccia 1987;Borders et al 1987;Maltamo et al 2000;Cao 2004;Poudel and Cao 2013). However, few studies, to date, have pointed out the relationship between measures of variability (i.e.…”

Section: Discussionmentioning

confidence: 99%

“…Baldwin and Feduccia 1987;Cao 2004;Poudel and Cao 2013;Siipilehto and Mehtatalo 2013). The recurring conclusion from these studies is that the PRM seems to be a better approach.…”

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confidence: 99%

Modelling Stem Diameter Variability in Pinus caribaea (Morelet) Plantations in South West Nigeria

Adesoye¹

2016

Journal of Forest and Environmental Science

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Stem diameter variability is an essential inventory result that provides useful information in forest management decisions. Little has been done to explore the modelling potentials of standard deviation (SDD) and coefficient of variation (CVD) of diameter at breast height (dbh). This study, therefore, was aimed at developing and testing models for predicting ). The results of the LMM indicated that location and plot effects were not significant. The CVD and SDD models having only measures of percentiles (i.e. P24 and P93) as predictors produced better predictions than others. However, CVD model produced the overall best predictions, because of the lower RSE and stability in measuring variability across different stand developments. The results demonstrate the potentials of CVD in modelling stem diameter variability in relationship with percentiles variables.

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Evaluation of Methods to Predict Weibull Parameters for Characterizing Diameter Distributions

Cited by 114 publications

References 87 publications

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

An Alternative Approach to Using LiDAR Remote Sensing Data to Predict Stem Diameter Distributions across a Temperate Forest Landscape

An Examination of Diameter Density Prediction with k-NN and Airborne Lidar

Modelling Stem Diameter Variability in Pinus caribaea (Morelet) Plantations in South West Nigeria

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