scholarly journals Mixed-effects basal area increment models for tree species in the boreal forest of Ontario, Canada using an ecological land classification approach to incorporate site effects

2012 ◽  
Vol 85 (2) ◽  
pp. 255-270 ◽  
Author(s):  
B. Pokharel ◽  
J. P. Dech
Keyword(s):  
Boreal Forest ◽  
Tree Species ◽  
Site Effects ◽  
Basal Area ◽  
Mixed Effects ◽  
Basal Area Increment ◽  
Land Classification ◽  
Classification Approach ◽  
Ecological Land Classification

scholarly journals Zuwachs und Klimasensitivität von Baumarten im Ökogramm der kollinen und submontanen Stufe

2015 ◽  
Vol 166 (6) ◽  
pp. 380-388 ◽  
Author(s):  
Pascale Weber ◽  
Caroline Heiri ◽  
Mathieu Lévesque ◽  
Tanja Sanders ◽  
Volodymyr Trotsiuk ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Properties ◽  
Climate Sensitivity ◽  
Tree Species ◽  
Ring Width ◽  
Basal Area ◽  
Growth Potential ◽  
Basal Area Increment ◽  
Tree Ring Data ◽  
Mean Sensitivity

Growth potential and climate sensitivity of tree species in the ecogram for the colline and submontane zone In forestry practice a large amount of empirical knowledge exists about the productivity of individual tree species in relation to site properties. However, so far, only few scientific studies have investigated the influence of soil properties on the growth potential of various tree species along gradients of soil water as well as nutrient availability. Thus, there is a research gap to estimate the productivity and climate sensitivity of tree species under climate change, especially regarding productive sites and forest ad-mixtures in the lower elevations. Using what we call a «growth ecogram», we demonstrate species- and site-specific patterns of mean annual basal area increment and mean sensitivity of ring width (strength of year-to-year variation) for Fagus sylvatica, Quercus spp., Fraxinus excelsior, Picea abies, Abies alba and Pinus sylvestris, based on tree-ring data from 508 (co-)dominant trees on 27 locations. For beech, annual basal area increment ( average 1957–2006) was significantly correlated with tree height of the dominant sampling trees and proved itself as a possible alternative for assessing site quality. The fact that dominant trees of the different tree species showed partly similar growth potential within the same ecotype indicates comparable growth limitation by site conditions. Mean sensitivity of ring width – a measure of climate sensitivity – had decreased for oak and ash, while it had increased in pine. Beech showed diverging reactions with increasing sensitivity at productive sites (as measured by the C:N ratio of the topsoil), suggesting an increasing limitation by climate at these sites. Hence, we derive an important role of soil properties in the response of forests to climate change at lower elevations, which should be taken into account when estimating future forest productivity.

scholarly journals Development of a Mixed-Effects Individual-Tree Basal Area Increment Model for Oaks (Quercus spp.) Considering Forest Structural Diversity

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 474 ◽  
Author(s):  
Wenwen Wang ◽  
Xinyun Chen ◽  
Weisheng Zeng ◽  
Jianjun Wang ◽  
Jinghui Meng
Keyword(s):  
Basal Area ◽  
Information Criterion ◽  
Structural Diversity ◽  
Mixed Effects ◽  
Mixed Effects Model ◽  
Central China ◽  
Basal Area Increment ◽  
Individual Tree ◽  
Final Model ◽  
Quercus Spp

In the context of uneven-aged mixed-species forest management, an individual-tree basal area increment model considering forest structural diversity was developed for oaks (Quercus spp.) using data collected from 11,860 observations in 845 sample plots from the 7th (2004), 8th (2009), and 9th (2014) Chinese National Forest Inventory in Hunan Province, south-central China. Since the data was longitudinal and had a nested structure, we used a linear mixed-effects approach to construct the model. We also used the variance function and an autocorrelation structure to describe within-plot heteroscedasticity and autocorrelation. Finally, the optimal mixed-effects model was determined based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), log-likelihood (Loglik) and the likelihood ratio test (LRT). The results indicate that the reciprocal transformation of initial diameter at breast height (1/DBH), relative density index (RD), number of trees per hectare (NT), elevation (EL) and Gini coefficient (GC) had a significant impact on the individual-tree basal area increment. In comparison to the basic model developed using least absolute shrinkage and selection operator (LASSO) regression, the mixed-effects model performance was greatly improved. In addition, we observed that the heteroscedasticity was successfully removed by the exponent function and autocorrelation was significantly corrected by AR(1). Our final model also indicated that forest structural diversity significantly affected tree growth and hence should not be neglected. We hope that our final model will contribute to the scientific management of oak-dominated forests.

A new approach to ecological land classification for the Canadian boreal forest that integrates disturbances

2013 ◽  
Vol 29 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Pierre Grondin ◽  
Sylvie Gauthier ◽  
Daniel Borcard ◽  
Yves Bergeron ◽  
Jean Noël
Keyword(s):  
Boreal Forest ◽  
Land Classification ◽  
New Approach ◽  
Ecological Land Classification ◽  
Canadian Boreal Forest

scholarly journals An individual-tree linear mixed-effects model for predicting the basal area increment of major forest species in Southern Europe

2020 ◽  
Vol 29 (3) ◽  
pp. e019
Author(s):  
Lucio Di Cosmo ◽  
Diego Giuliani ◽  
Maria Michela Dickson ◽  
Patrizia Gasparini
Keyword(s):  
Forest Inventory ◽  
Basal Area ◽  
Ordinary Least Squares ◽  
Mixed Effects ◽  
Forest Growth ◽  
Mixed Effects Model ◽  
Basal Area Increment ◽  
Individual Tree ◽  
Diameter At Breast Height ◽  
Breast Height

Aims of the study. Assessment of growth is essential to support sustainability of forest management and forest policies. The objective of the study was to develop a species-specific model to predict the annual increment of tree basal area through variables recorded by forest surveys, to assess forest growth directly or in the context of more complex forest growth and yield simulation models.Area of the study. Italy.Material and methods. Data on 34638 trees of 31 different forest species collected in 5162 plots of the Italian National Forest Inventory were used; the data were recorded between 2004 and 2006. To account for the hierarchical structure of the data due to trees nested within plots, a two-level mixed-effects modelling approach was used.Main results. The final result is an individual-tree linear mixed-effects model with species as dummy variables. Tree size is the main predictor, but the model also integrates geographical and topographic predictors and includes competition. The model fitting is good (McFadden’s Pseudo-R2 0.536), and the variance of the random effect at the plot level is significant (intra-class correlation coefficient 0.512). Compared to the ordinary least squares regression, the mixed-effects model allowed reducing the mean absolute error of estimates in the plots by 64.5% in average.Research highlights. A single tree-level model for predicting the basal area increment of different species was developed using forest inventory data. The data used for the modelling cover 31 species and a great variety of growing conditions, and the model seems suitable to be applied in the wider context of Southern Europe.   Keywords: Tree growth; forest growth modelling; forest inventory; hierarchical data structure; Italy.Abbreviations used: BA - basal area; BAI – five-year periodic basal area increment; BALT - basal area of trees larger than the subject tree; BASPratio - ratio of subject tree species basal area to stand basal area; BASTratio - ratio of subject tree basal area to stand basal area; CRATIO - crown ratio; DBH – diameter at breast height ; DBH0– diameter at breast height corresponding to five years before the survey year; DBHt– diameter at breast height measured in the survey year; DI5 - five-year, inside bark, DBH increment; HDOM - dominant height; LULUCF - Land Use, Land Use Changes and Forestry; ME - mean error; MAE - mean absolute error; MPD - mean percent deviation; MPSE - mean percent standard error; NFI(s) - National Forest Inventory/ies; OLS - ordinary least squares regression; RMSE - root mean squared error; UNFCCC - United Nation Framework Convention on Climate Change.

Utility of hyperspectral remote sensing data in estimating biomass and structure variables in boreal forest of Finland

2020 ◽  
Author(s):  
Eelis Halme ◽  
Petri Pellikka ◽  
Matti Mõttus
Keyword(s):  
Remote Sensing ◽  
Boreal Forest ◽  
Tree Species ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Sensing Data ◽  
Stem Biomass ◽  
Forestry Industry ◽  
Multispectral Remote Sensing

<p>Three-quarters of Finland’s land surface area (22.8 million hectares) is filled with forests. The role of remote sensing in large area inventories is crucial. The forests of Finland serve as an important resource for the nation’s nature conservation as well as for the forestry industry. Furthermore, forests are significant carbon sinks and play a great role in climate change mitigation. Research on vegetation parameter retrieval is of special relevance in order to extend our knowledge about the vegetation dynamics and terrestrial carbon stocks at regional and global scales.</p><p>In future in addition to multispectral satellites, hyperspectral satellite missions will start to provide remote sensing data to support the needs of forestry and other natural resource management practices. We investigated the influence of spectral and spatial resolution of remote sensing data on retrieval of biomass and other forest properties. The study contributed to better information productivity on forest variables in boreal forest ecosystem.</p><p>We used the remote sensing data by Sentinel-2 (10 bands, resolution 10 m) and hyperspectral AISA imager (128 bands, 400–1000 nm, resolution 0.7 m). As reference data, we used new forest resource dataset provided by the Finnish Forest Centre and additional independent in situ measurements. We applied kernel-based regression methods to relate the forest variables of interest with the remotely sensed data. Based on recent studies, we selected Gaussian process regression (GPR) and support vector regression (SVR), which have proven to work well with hyperspectral and multispectral remote sensing data. Regression estimations were performed for stem biomass, basal area, mean height, leaf area index (LAI) and main tree species. The estimation accuracies were examined with absolute and relative root-mean-square errors.</p><p>Successful forest variable estimations showed that kernel-based regression algorithms are suitable tools for quantification of forest structure and assessment of its change. The estimation accuracies between the two algorithms were similar. However, the faster SVR algorithm was found to be more practical, especially considering large scale mapping and future near real-time applications. Based on the study results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as basal area or stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.</p>

Floristic Variation of Tree Communities and Their Association with Soil Properties in Pulau Jerejak, Penang, Peninsular Malaysia

2020 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Faezah Pardi
Keyword(s):  
Tree Species ◽  
Diversity Index ◽  
Basal Area ◽  
Peninsular Malaysia ◽  
Importance Value ◽  
Conservation Action ◽  
Endangered Tree ◽  
Endangered Tree Species ◽  
Tree Communities ◽  
Floristic Variation

This study was conducted at Pulau Jerejak, Penang to determine the floristic variation of its tree communities. A 0.5-hectare study plot was established and divided into 11 subplots. A total of 587 trees with diameter at breast height (DBH) of 5 cm and above were measured, identified and recorded. The tree communities comprised of 84 species, 63 genera and 32 families. The Myrtaceae was the most speciose family with 10 recorded species while Syzgium glaucum (Myrtaceae) was the most frequent species. The Myrtaceae recorded the highest density of 306 individuals while Syzgium glaucum (Myrtaceae) had the highest species density of 182 individuals. Total tree basal area (BA) was 21.47 m2/ha and family with the highest BA was Myrtaceae with 5.81 m2/ha while at species level, Syzgium glaucum (Myrtaceae) was the species with the highest total BA in the plot with value of 4.95 m2/ha. The Shannon˗Weiner Diversity Index of tree communities showed a value of 3.60 (H'max = 4.43) and Evenness Index of 0.81 which indicates high uniformity of tree species. The Margalef Richness Index (R') revealed that the tree species richness was 13.02. Myrtaceae had the highest Importance Value of 20.4%. The Canonical Correspondence Analysis (CCA) showed that Diospyros buxifolia (Ebenaceae) and Pouteria malaccensis (Sapotaceae) were strongly correlated to low pH. Dysoxylum cauliflorum (Meliaceae) and Eriobotrya bengalensis (Rosaceae) were correlated to phosphorus (P) and calcium ion (Ca2+), respectively. Therefore, the trees species composition at Pulau Jerejak showed that the biodiversity is high and conservation action should be implemented to protect endangered tree species. Keywords: Floristic variation; Tree communities; Trees composition; Pulau Jerejak; Species diversity

Live tree species basal area of the contiguous United States (2000-2009)

2013 ◽  
Author(s):  
Barry T. Wilson ◽  
Andrew J. Lister ◽  
Rachel I. Riemann ◽  
Douglas M. Griffith
Keyword(s):  
United States ◽  
Tree Species ◽  
Basal Area ◽  
Live Tree
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