scholarly journals Estimates of Tree Canopy Closure and Basal Area as Proxies for Tree Crown Volume at a Stand Scale

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1180
Author(s):  
Guntis Brūmelis ◽  
Iluta Dauškane ◽  
Didzis Elferts ◽  
Linda Strode ◽  
Tatjana Krama ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Basal Area ◽  
Tree Canopy ◽  
Tree Level ◽  
Canopy Closure ◽  
Tree Crown ◽  
Mixed Stands ◽  
Crown Volume ◽  
Canopy Volume

Research Highlights: Studies on tree canopy dwelling species often require simple proxies of tree canopy volume estimated at a stand level. These include allometrically related tree crown parameters such as crown area and basal area, and canopy cover. Background and Objectives: In monoculture Scot’s pine and mixed pine/Norway spruce forest, we aimed to test the relationships between tree diameter at breast height (DBH) and tree crown volume at a tree level and between densitometer canopy closure estimates and tree crown volume at a stand level. Materials and Methods: The study was carried out in eastern Latvia (hemiboreal zone) in monoculture pine and mixed coniferous stands. On a subset of trees in 22 forest stands (88 100 m2 plots), we determined the best regression model that described the relationship between tree DBH and crown volume for spruce and pine. Tree crown volume at a stand level was determined from the individual tree volume estimates calculated from these regression models. On a stand level, we also calculated regression models for densitometer closure estimates versus total crown volume for pine and mixed stands. Results: Linear mixed effects models showed significant relationships between DBH and crown volume for pine (R2 = 0.63) and spruce (R2 = 0.40), indicating that basal area could be used as a predictor of crown volume at a stand level. Variance explained by a regression model of canopy closure versus tree crown volume at a stand level was R2 = 0.52. Conclusions: Tree basal area and crown closure can be used as proxies of tree crown volume at a stand scale in monoculture stands. In mixed stands estimates of crown volume based on basal area need to be calculated separately for each tree species, while canopy closure will provide an estimate of total crown volume.

scholarly journals Species Mixing Effects on Forest Productivity: A Case Study at Stand-, Species- and Tree-Level in the Netherlands

Forests ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 713 ◽  
Author(s):  
Huicui Lu ◽  
Godefridus Mohren ◽  
Miren del Río ◽  
Mart-Jan Schelhaas ◽  
Meike Bouwman ◽  
...  
Keyword(s):  
Forest Management ◽  
Soil Fertility ◽  
Basal Area ◽  
Tree Level ◽  
Stand Age ◽  
Forest Plot ◽  
Mixed Species ◽  
Mixed Stands ◽  
Individual Tree ◽  
Species Mixtures

Many monoculture forests have been converted to mixed-species forests in Europe over the last decades. The main reasons for this conversion were probably to increase productivity, including timber production, and enhance other ecosystem services, such as conservation of biodiversity and other nature values. This study was done by synthesizing results from studies carried out in Dutch mixed forests compared with monoculture stands and evaluating them in the perspective of the current theory. Then we explored possible mechanisms of higher productivity in mixed stands, in relation to the combination of species, stand age and soil fertility, and discussed possible consequences of forest management. The study covered five two-species mixtures and their corresponding monoculture stands from using long-term permanent forest plots over multiple decades as well as two inventories (around 2003 and 2013) across the entire Netherlands. These forest plot data were used together with empirical models at total stand level, species level and tree level. Overyielding in Douglas-fir–beech and pine–oak mixtures was maintained over time, probably owing to the intensive thinning and was achieved on the poorer soils. However, this overyielding was not always driven by fast-growing light-demanding species. On individual tree level, intra-specific competition was not necessarily stronger than inter-specific competition and this competitive reduction was less seen at lower soil fertility and dependent on species mixtures. Moreover, size-asymmetric competition for light was more associated with tree basal area growth than size-symmetric competition for soil resources. Overall, this study suggests a substantial potential of species mixing for increasing productivity and implies developing forest management strategies to convert monospecific forests to mixed-species forests that consider the complementarity in resource acquisition of tree species.

Using crown condition variables as indicators of forest health

2004 ◽  
Vol 34 (5) ◽  
pp. 1057-1070 ◽  
Author(s):  
S J Zarnoch ◽  
W A Bechtold ◽  
K W Stolte
Keyword(s):  
Surface Area ◽  
Production Efficiency ◽  
Three Dimensional ◽  
Forest Health ◽  
Tree Level ◽  
Permanent Plots ◽  
Tree Crown ◽  
Crown Condition ◽  
Distributional Properties ◽  
Crown Volume

Indicators of forest health used in previous studies have focused on crown variables analyzed individually at the tree level by summarizing over all species. This approach has the virtue of simplicity but does not account for the three-dimensional attributes of a tree crown, the multivariate nature of the crown variables, or variability among species. To alleviate these difficulties, we define composite crown indicators based on geometric principles to better quantify the entire tree crown. These include crown volume, crown surface area, and crown production efficiency. These indicators were then standardized to a mean of 0 and variance of 1 to enable direct comparison among species. Residualized indicators, which can also be standardized, were defined as the deviation from a regression model that adjusted for tree and plot conditions. Distributional properties were examined for the three composite crown indicators and their standardized-residualized counterparts for 6167 trees from 250 permanent plots distributed across Virginia, Georgia, and Alabama. Comparisons between the composite crown indicators and their associated standardized residual indicators revealed that only two or three plots were jointly classified as poor by both when thresholds were set at the lower 5 percentiles of statistical distributions. In contrast, 19-21 other plots were classified differently, emphasizing that different aspects of crown condition are being summarized when the raw values are adjusted and standardized. Generally, crown volume and crown surface area behaved similarly, while crown production efficiency was substantially different.

scholarly journals TREE STEM AND CANOPY BIOMASS ESTIMATES FROM TERRESTRIAL LASER SCANNING DATA

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 157-160 ◽  
Author(s):  
K. Olofsson ◽  
J. Holmgren
Keyword(s):  
Regression Models ◽  
Point Cloud ◽  
Laser Scanning ◽  
Tree Canopy ◽  
Model Fit ◽  
Extraction Techniques ◽  
Independent Variables ◽  
Canopy Volume ◽  
Tree Extraction ◽  
Tree Stem

In this study an automatic method for estimating both the tree stem and the tree canopy biomass is presented. The point cloud tree extraction techniques operate on TLS data and models the biomass using the estimated stem and canopy volume as independent variables. The regression model fit error is of the order of less than 5 kg, which gives a relative model error of about 5 % for the stem estimate and 10–15 % for the spruce and pine canopy biomass estimates. The canopy biomass estimate was improved by separating the models by tree species which indicates that the method is allometry dependent and that the regression models need to be recomputed for different areas with different climate and different vegetation.

Contribution to the automation of the calculations involving the forest inventory with the aid of an office computer

1970 ◽  
Vol 20 ◽  
Author(s):  
R. Goossens
Keyword(s):  
Tree Species ◽  
Forest Inventory ◽  
Basal Area ◽  
Annual Increment ◽  
Mixed Stands ◽  
Mean Values ◽  
Standing Trees ◽  
General Program ◽  
Magnetic Card ◽  
The Right

Contribution to the automation of the calculations involving  the forest inventory with the aid of an office computer - In this contribution an attempt was made to perform the  calculations involving the forest inventory by means of an office computer  Olivetti P203.     The general program (flowchart 1), identical for all tree species except  for the values of the different parameters, occupies the tracks A and B of a  magnetic card used with this computer. For each tree species one magnetic  card is required, while some supplementary cards are used for the  subroutines. The first subroutine (flowchart 1) enables us to preserve  temporarily the subtotals between two tree species (mixed stands) and so  called special or stand cards (SC). After the last tree species the totals  per ha are calculated and printed on the former, the average trees occuring  on the line below. Appendix 1 gives an example of a similar form resulting  from calculations involving a sampling in a mixed stand consisting of Oak  (code 11), Red oak (code 12), Japanese larch (code 24) and Beech (code 13).  On this form we find from the left to the right: the diameter class (m), the  number of trees per ha, the basal area (m2/ha), the current annual increment  of the basal area (m2/year/ha), current annual volume increment (m3/year/ha),  the volume (m3/ha) and the money value of the standing trees (Bfr/ha). On the  line before the last, the totals of the quantities mentioned above and of all  the tree species together are to be found. The last line gives a survey of  the average values dg, g, ig, ig, v and w.     Besides this form each stand or plot has a so-called 'stand card SC' on  wich the totals cited above as well as the area of the stand or the plot and  its code are stored. Similar 'stand card' may replace in many cases  completely the classical index cards; moreover they have the advantage that  the data can be entered directly into the computer so that further  calculations, classifications or tabling can be carried out by means of an  appropriate program or subroutine. The subroutine 2 (flowchart 2) illustrates  the use of similar cards for a series of stands or eventually a complete  forest, the real values of the different quantities above are calculated and  tabled (taking into account the area). At the same time the general totals  and the general mean values per ha, as well as the average trees are  calculated and printed. Appendix 2 represents a form resulting from such  calculations by means of subroutine 2.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Analytical approaches for modeling tree crown volume in black wattle (Acacia mearnsii De Wild.) stands

2016 ◽  
Vol 11 (49) ◽  
pp. 4979-4989
Author(s):  
C. Cadori Guilherme ◽  
R. Sanquetta Carlos ◽  
Pellico Netto Sylvio ◽  
Behling Alexandre ◽  
Costa Junior Sergio ◽  
...  
Keyword(s):  
Tree Crown ◽  
Acacia Mearnsii ◽  
Crown Volume ◽  
Black Wattle ◽  
Analytical Approaches

Structure and Regeneration of Sal (Shorea robusta Gaertn. f.) Forests in Shiwalik Region of Kumaun Himalaya, India

2017 ◽  
Vol 40 (1) ◽  
pp. 1-8
Author(s):  
Bhawna Adhikari ◽  
◽  
Bhawana Kapkoti ◽  
Neelu Lodhiyal ◽  
L.S. Lodhiyal ◽  
...  
Keyword(s):  
Tree Species ◽  
Basal Area ◽  
Tree Canopy ◽  
Canopy Density ◽  
Kumaun Himalaya ◽  
Shorea Robusta ◽  
Canopy Trees ◽  
Dense Forest ◽  
Open Forest ◽  
Sal Forest

Present study was carried out to assess the structure and regeneration of Sal forests in Shiwalik region of Kumaun Himalaya. Vegetation analysis and tree canopy density was determined by using quadrat and densitometer, respectively. Density of seedlings, saplings and trees was 490-14067, 37-1233, and 273-863 ind.ha-1 respectively. The basal area was 0.12-5.44 m2 ha-1 reported for saplings and 25.4-77.6 m2 ha-1 for trees. Regeneration of Sal was found good in Sal mixed dense forest followed by Sal open forest and Sal dense forest, respectively. Regeneration of Sal was assisted by the presence of associated tree species as well as the sufficient sunlight availability on ground due to adequate opening of canopy trees in Sal forest. Thus it is concluded that the density of tree canopy, sunlight availability and also associated tree species impacted the regeneration of Sal in the region.

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

A Unifying Classification for Functional Regression Modeling

2018 ◽  
Author(s):  
Frédéric Ferraty ◽  
Philippe Vieu
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Functional Model ◽  
Additive Functional ◽  
Regression Modeling ◽  
Functional Regression ◽  
Regression Problem ◽  
Nonparametric Models ◽  
Index Model ◽  
Functional Linear Regression

This article presents a unifying classification for functional regression modeling, and more specifically for modeling the link between two variables X and Y, when the explanatory variable (X) is of a functional nature. It first provides a background on the proposed classification of regression models, focusing on the regression problem and defining parametric, semiparametric, and nonparametric models, and explains how semiparametric modeling can be interpreted in terms of dimension reduction. It then gives four examples of functional regression models, namely: functional linear regression model, additive functional regression model, smooth nonparametric functional model, and single functional index model. It also considers a number of new models, directly adapted to functional variables from the existing standard multivariate literature.

scholarly journals Genotypic parameters for egg production in pure breed hens by using random regression model

2017 ◽  
Vol 47 (5) ◽  
Author(s):  
Priscila Becker Ferreira ◽  
Paulo Roberto Nogara Rorato ◽  
Fernanda Cristina Breda ◽  
Vanessa Tomazetti Michelotti ◽  
Alexandre Pires Rosa ◽  
...  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Egg Production ◽  
Random Regression ◽  
Broad Sense Heritability ◽  
Residual Matrix ◽  
Random Regression Model ◽  
Residual Covariance ◽  
Genotypic Correlations ◽  
Selection Of

ABSTRACT: This study aimed to test different genotypic and residual covariance matrix structures in random regression models to model the egg production of Barred Plymouth Rock and White Plymouth Rock hens aged between 5 and 12 months. In addition, we estimated broad-sense heritability, and environmental and genotypic correlations. Six random regression models were evaluated, and for each model, 12 genotypic and residual matrix structures were tested. The random regression model with linear intercept and unstructured covariance (UN) for a matrix of random effects and unstructured correlation (UNR) for residual matrix adequately model the egg production curve of hens of the two study breeds. Genotypic correlations ranged from 0.15 (between age of 5 and 12 months) to 0.99 (between age of 10 and 11 months) and increased based on the time elapsed. Egg production heritability between 5- and 12-month-old hens increased with age, varying from 0.15 to 0.51. From the age of 9 months onward, heritability was moderate with estimates of genotypic correlations higher than 90% at the age of 10, 11, and 12 months. Results suggested that selection of hens to improve egg production should commence at the ninth month of age.

Sign in / Sign up

Export Citation Format

Share Document