Impact of plot size on individual-tree competition measures for growth and yield simulators

2003 ◽  
Vol 33 (3) ◽  
pp. 455-465 ◽  
Author(s):  
Jari Hynynen ◽  
Risto Ojansuu
Keyword(s):  
Picea Abies ◽  
Norway Spruce ◽  
Growth Models ◽  
Model Performance ◽  
Stand Density ◽  
Growth And Yield ◽  
Average Radius ◽  
Sample Plot ◽  
Individual Tree ◽  
Plot Size

The study addresses the effect of sample plot size on the bias related to measured stand density. We analyzed the effect of plot size on model coefficients and model performance in the simulation. Alternative growth models were developed for Norway spruce (Picea abies (L.) Karst.) on the basis of data obtained from permanent inventory sample plots of varying size. The competition measures were estimated from small plots with an average radius of 6 m, large plots with an average radius of 10 m, a cluster of three small plots within a stand, and a cluster of three large plots within a stand. The response of the models to competition varied depending on the plot size. Increasing the plot size increased the sensitivity of the models to the variation of overall stand density and the competitive status of a tree. The development of repeatedly measured, unthinned and thinned Norway spruce sample plots was simulated with the models, and the predictions were compared with the observed development. In the unthinned stand, the model with competition measures based on small plots resulted in a higher and more biased prediction of growth and mortality than the models based on larger plots. In the thinned stand, the differences between the models were negligible.

Scale and spatial structure effects on tree size distributions: implications for growth and yield modelling

2006 ◽  
Vol 36 (11) ◽  
pp. 2983-2993 ◽  
Author(s):  
Oscar García
Keyword(s):  
Spatial Structure ◽  
Growth Models ◽  
Growth And Yield ◽  
Size Distributions ◽  
Spatial Correlations ◽  
Forest Patch ◽  
Individual Tree ◽  
Plot Size ◽  
Central Canada ◽  
Distribution Parameters

Diameter and other size distributions are extensively used in growth modelling. These are usually obtained from sample plot data and assumed to apply both at the stand level, of interest for production planning, and at the forest patch level, the level relevant for tree growth interactions. However, spatial correlation can cause distribution parameters and their estimates to vary with the extent of ground considered. Using mapped tree data from four forest stands in central Canada, it is shown that differences in DBH variance with plot size can be substantial. In addition, size correlations between neighbouring trees were positive, rather than negative as implied by current distance-dependent growth models. Biases in mean DBH are also found. It is proved that plot totals and frequencies are unbiased estimates of stand parameters, but variances and some other statistics are not. The expected variance is expressed in terms of plot size and shape and of second-order stand spatial structure properties. Some possible approaches for reducing bias in stand-level variance estimates are discussed, and the desirability of modelling microsite or genetic spatial correlations in individual-tree simulators is pointed out.

Comparing individual-tree growth models using principles of stand growth for Norway spruce, Scots pine, and European beech

2015 ◽  
Vol 45 (8) ◽  
pp. 1006-1018 ◽  
Author(s):  
Sonja Vospernik ◽  
Robert A. Monserud ◽  
Hubert Sterba
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Tree Growth ◽  
Growth Models ◽  
Basal Area ◽  
European Beech ◽  
Growth And Yield ◽  
Individual Tree ◽  
Individual Tree Growth ◽  
Volume Increment

We examined the relationship between thinning intensity and volume increment predicted by four commonly used individual-tree growth models in Central Europe (i.e., BWIN, Moses, Prognaus, and Silva). We replicated conditions of older growth and yield experiments by selecting 34 young, dense plots of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.), and European beech (Fagus sylvatica L.). At these plots, we simulated growth, with mortality only, to obtain the maximum basal area. Maximum basal area was then decreased by 5% or 10% steps using thinning from below. Maximum density varied considerably between simulators; it was mostly in a reasonable range but partly exceeded the maximum basal area observed by the Austrian National Forest Inventory or the self-thinning line. In almost all cases, simulated volume increment was highest at maximum basal area and then decreased with decreasing basal area. Critical basal area, at which 95% of maximum volume increment can be achieved, ranged from 0.46 to 0.96. For all simulators, critical basal area was lower for the more shade-tolerant species. It increased with age, except for Norway spruce, when simulated with the BWIN model. Age, where mean annual increment culminated, compared well with yield tables.

scholarly journals A Novel Approach to Modelling Stand-Level Growth of an Even-Aged Forest Using a Volume Productivity Index with Application to New Zealand-Grown Coast Redwood

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1155 ◽  
Author(s):  
Mark O. Kimberley ◽  
Michael S. Watt
Keyword(s):  
New Zealand ◽  
Growth Model ◽  
Growth Models ◽  
Stand Density ◽  
Site Index ◽  
Growth And Yield ◽  
Productivity Index ◽  
Site Quality ◽  
Spatial And Temporal Variation ◽  
Coast Redwood

Empirical growth models are widely used to predict the growth and yield of plantation tree species, and the precise estimation of site quality is an important component of these models. The most commonly used proxy for site quality in growth models is Site Index (SI), which describes the mean height of dominant trees at a specified base age. Although SI is widely used, considerable research shows significant site-dependent variation in height for a given volume, with this latter variable more closely reflecting actual site productivity. Using a national dataset, this study develops and describes a stand-level growth and yield model for even-aged New Zealand-grown coast redwood (Sequoia sempervirens). We used a novel modelling approach that quantifies site quality using SI and a volume-based index termed the 300 Index, defined as the volume mean annual increment at age 30 years for a reference regime of 300 stems ha−1. The growth model includes a number of interrelated components. Mean top height is modelled from age and SI using a polymorphic Korf function. A modified anamorphic Korf function is used to describe tree quadratic mean diameter (Dq) as a function of age, stand density, SI and a diameter site index. As the Dq model includes stand density in its formulation, it can predict tree growth for different stand densities and thinning regimes. The mortality model is based on a simple attritional equation improved through incorporation of the Reineke stand density index to account for competition-induced mortality. Using these components, the model precisely estimates stand-level volume. The developed model will be of considerable value to growers for yield projection and regime evaluation. By more robustly describing the site effect, the growth model provides researchers with an improved framework for quantifying and understanding the causes of spatial and temporal variation in plantation productivity.

Assessing model performance in forecasting long-term individual tree diameter versus basal area increment for the primary Acadian tree species

2011 ◽  
Vol 41 (12) ◽  
pp. 2267-2275 ◽  
Author(s):  
Matthew B. Russell ◽  
Aaron R. Weiskittel ◽  
John A. Kershaw
Keyword(s):  
Random Effects ◽  
Mean Squared Error ◽  
Growth Models ◽  
Secondary Growth ◽  
Model Performance ◽  
Basal Area ◽  
Nonlinear Mixed Effects Models ◽  
Absolute Deviation ◽  
Individual Tree

Tree basal area (ba) or diameter at breast height (dbh) are universally used to represent tree secondary growth in individual tree based growth models. However, the long-term implications of using either ba or dbh for predictions are rarely fully assessed. In this analysis, Δba and Δdbh increment equations were fit to identical datasets gathered from six conifer and four hardwood species grown in central Maine. The performance of Δba and Δdbh predictions from nonlinear mixed-effects models were then compared with observed growth measurements of up to 29 years via a Monte Carlo simulation. Two evaluation statistics indicated substantial improvement in forecasting dbh using Δdbh rather than Δba. Root mean squared error (RMSE) and percentage mean absolute deviation (MAD%) were reduced by 14% and 15% on average, respectively, across all projection length intervals (5–29 years) when Δdbh was used over Δba. Differences were especially noted as projection lengths increased. RMSE and MAD% were reduced by 24% when Δdbh was employed over Δba at longer projection lengths (up to 29 years). Simulations found that simulating random effects rather than using local estimates for random effects performed as well or better at longer interval lengths. These results highlight the implications that selecting a growth model dependent variable can have and the importance of incorporating model uncertainty into the growth projections of individual tree based models.

scholarly journals Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

2019 ◽  
Vol 11 (3) ◽  
pp. 261 ◽  
Author(s):  
Darío Domingo ◽  
Rafael Alonso ◽  
María Teresa Lamelas ◽  
Antonio Luis Montealegre ◽  
Francisco Rodríguez ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Growth Models ◽  
Model Performance ◽  
Basal Area ◽  
Stand Density ◽  
Airborne Laser Scanning ◽  
Support Vector ◽  
Low Density ◽  
Airborne Laser ◽  
Forest Attributes

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.

Sign in / Sign up

Export Citation Format

Share Document