scholarly journals Crown projection area of Licania tomentosa (Benth.) Fritsch (Chrysobalanaceae), estimated by linear regression / Área de projeção da copa de Licania tomentosa (Benth.) Fritsch (Chrysobalanaceae), estimada por regressão linear

2021 ◽  
Vol 7 (4) ◽  
pp. 40150-40159
Author(s):  
Luilla Lemes Alves ◽  
Eliseu Mendes Monteiro ◽  
Júnia Laura Pêgo Ribeiro ◽  
Nívea Fransuelli da Silva Madureira ◽  
Tamires Mousslech Andrade Penido ◽  
...  
Keyword(s):  
Linear Regression ◽  
Linear Model ◽  
Urban Area ◽  
Regression Models ◽  
Projection Area ◽  
Abscissa Axis ◽  
Simple Linear Model ◽  
Crown Diameter ◽  
Breast Height ◽  
Crown Projection Area

Knowledge of the Crown Projection Area (CPA) allows to make inferences about the shading and to know space occupied by a tree. However, crown measurements are more time-consuming and laborious when compared to those of Circumference Breast Height (CBH). Thus, this work aimed to evaluate regression models and present the most suitable to CPA estimate of Licania tomentosa, in an urban area of São João Evangelista municipality, Brazil. Fifty trees distributed over 7 public roads were sampled. CBH and Crown Diameter (CD, m) were measured for later calculation of its projection area (CPA, m2). Four regression models were tested in order to estimate CPA as a function of CBH alone. The equation derived from of the model “” showed a homoscedastic distribution of the percentage residues, with closer deviations around the abscissa axis. It is concluded that the equation obtained with the adjustment of the simple linear model was the most efficient to estimate of the crown projection area of L. tomentosa. This projection area increased as the stem of the trees thickened.

scholarly journals Tree crown size as a measure of tree biosocial position in 135-year-old oak (Quercus L.) stand

2016 ◽  
Vol 58 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Katarzyna Kaźmierczak ◽  
Bogna Zawieja
Keyword(s):  
Principal Component ◽  
Tree Height ◽  
Projection Area ◽  
Tree Crown ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Crown Length ◽  
Pca Method ◽  
Crown Projection Area

AbstractThe paper presents an attempt to apply measurable traits of a tree – crown projection area, crown length, diameter at breast height and tree height for classification of 135-year-old oak (QuercusL.) trees into Kraft classes. Statistical multivariate analysis was applied to reach the aim. Empirical material was collected on sample plot area of 0.75 ha, located in 135-year-old oak stand. Analysis of dimensional traits of oaks from 135-year-old stand allows quite certain classification of trees into three groups: pre-dominant, dominant and co-dominant and dominated ones. This seems to be quite promising, providing a tool for the approximation of the biosocial position of tree with no need for assessment in forest. Applied analyses do not allow distinguishing trees belonging to II and III Kraft classes. Unless the eye-estimation-based classification is completed, principal component analysis (PCA) method provided simple, provisional solution for grouping trees from 135-year-old stand into three over-mentioned groups. Discriminant analysis gives more precise results compared with PCA. In the analysed stand, the most important traits for the evaluation of biosocial position were diameter at breast height, crown projection area and height.

scholarly journals Tree form morphometrics of Agathis dammara and Acacia mangium in the IPB’s Dramaga Landscape Campus, Bogor

2021 ◽  
Vol 918 (1) ◽  
pp. 012015
Author(s):  
L Karlinasari ◽  
U Adzkia ◽  
Y Fredisa ◽  
M M Rahman ◽  
N Nugroho ◽  
...  
Keyword(s):  
Morphometric Analysis ◽  
Slenderness Ratio ◽  
Acacia Mangium ◽  
Projection Area ◽  
Complex Structures ◽  
Crown Height ◽  
Tree Form ◽  
Crown Diameter ◽  
Biomechanical Responses ◽  
Crown Projection Area

Abstract Tree growth comprises diverse tree forms and crown shapes that are influenced by the growing space and are related to biomechanical responses. Due to the complex structures of tree forms and crown architecture, more understanding of their functions is necessary. The study aimed to evaluate the morphometrics of two tree species of contrasting tree forms. Each represents excurrent and decurrent crown architectures located in the Dramaga Campus Landscape, Bogor. Morphometric analysis was conducted on those two species, namely excurrent agathis (n=23 trees) and decurrent mangium (n= 15 trees). The morphometric analysis was carried out for several basic growth variables such as diameter at breast height, total height, and crown height. In addition, other variables and parameters were also assessed, namely live crown ratio, slenderness ratio, crown diameter, crown projection area, crown index, and coefficient of space for growth. The results showed that the average diameters of agathis and mangium trees in this study were 0.49 and 0.48 m, respectively, while the average heights were 24.63 m and 18.23 m, respectively. The live crown ratio of both trees was more than 80%. The average slenderness ratio for agathis trees was 50.66 higher than that of mangium trees (40.64). The crown projection areas for agathis and mangium were 37.60 m2 and 69.69 m2, respectively. On the other hand, the crown index of agathis was 3.20, and mangium was 1.84. The coefficient of space for the growth of agathis and mangium was 0.14. and 0.19. The information related to tree morphometry is important for tree management, especially in evaluating healthy and steady tree stands.

scholarly journals Biomass equations for Brazilian semiarid caatinga plants

2005 ◽  
Vol 19 (4) ◽  
pp. 935-943 ◽  
Author(s):  
Everardo V.S.B. Sampaio ◽  
Grécia C. Silva
Keyword(s):  
Ground Level ◽  
Projection Area ◽  
Mixed Species ◽  
Crown Area ◽  
Breast Height ◽  
Tropical Vegetation ◽  
Power Equation ◽  
Brazilian Semiarid ◽  
The Common ◽  
Crown Projection Area

Allometric equations to estimate total aboveground alive biomass (B) or crown projection area (C) of ten caatinga species based on plant height (H) and/or stem diameter at ground level (DGL) or at breast height (DBH) were developed. Thirty plants of each species, covering the common range of stem diameters (3 to 50 cm), were measured (C, H, DGL, DBH), cut at the base, separated into parts, weighted and subsampled to determine dry biomass. Wood density (p) of the stem and the largest branches was determined. B, C, H and p ranged from 1 to 500 kg, 0.2 to 112 m², 1.3 to 11.8 m, and 0.45 to 1.03 g cm-3. Biomass of all 10 species, separately or together (excluding one cactus species), could be estimated with high coefficients of determination (R²) using the power equation (B = aDGLb) and DGL, DBH, H or combinations of diameter, height and density. Improvement by multiplying H and/or p to DGL or DBH was small. The mixed-species equation based only on DBH (valid up to 30 cm) had a = 0.173 and b = 2.295, similar to averages of these parameters found in the literature but slightly lower than most of those for humid tropical vegetation. Crown area was significantly related to diameter, height and biomass.

scholarly journals Allocation of oaks to Kraft classes based on linear and nonlinear kernel discriminant variables

2016 ◽  
Vol 53 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Bogna Zawieja ◽  
Katarzyna Kaźmierczak
Keyword(s):  
Gaussian Kernel ◽  
Projection Area ◽  
Breast Height ◽  
Oak Trees ◽  
Standing Trees ◽  
Discriminant Variable ◽  
Linear And Nonlinear ◽  
Crown Projection Area

SummaryA method of discriminant variable determination was used to visualize the division of oak trees into Kraft classes. Usual discriminant variables and several types of kernel discriminant variables were studied. For this purpose the traits of oak (Quercus L.) trees, measured on standing trees, were used. These traits included height of tree, breast height diameter and crown projection area. The use of the Gaussian kernel and modified Gaussian kernel enabled the clearest division into Kraft classes. In particular, the latter method proved to be the most effective.

scholarly journals The use of measurable traits of trunk and crown to assess the biosocial classes of oak trees (Quercus robur L.)

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Krzysztof Turczański ◽  
Bogna Zawieja ◽  
Tomasz Najgrakowski ◽  
Katarzyna Kaźmierczak
Keyword(s):  
Principal Component Analysis ◽  
Quercus Robur ◽  
Principal Component ◽  
Component Analysis ◽  
Projection Area ◽  
Diameter At Breast Height ◽  
Breast Height ◽  
Oak Trees ◽  
Crown Projection Area ◽  
Forestry Practice

The crown class assessment is a key element in forestry practice. It is a traditional method that finds application in thinning plans, assessment of site index, tree competition, or crown condition. Assigning trees into a given class is done during field surveys and requires precision and experience to avoid inaccuracy. Therefore, Kraft’s system has often been criticized and modified. Thus, in our study, we aimed to analyse whether the directly measured traits of trunk and crown of oak trees (Quercus robur L.) can be applied to crown class assessment. For this purpose, we used the principal component analysis (PCA) and nonlinear kernel principal component analysis (KPCA) based on measurable traits of trunk and crown, i.e., the height of the tree, the diameter at breast height, the length of the crown, and the field crown projection area. In total, we measured 286 mature trees in three oak stands located in western Poland. Results indicate that all chosen traits of trunk and crown allowed, though not always perfect, to assign the trees into given crown classes. The greatest contribution to crown class distinction had the diameter at breast height and the parameters of crown, i.e., and the field crown projection area. Furthermore, results show that the best method of assigning the trees into biosocial classes is the KPCA Gauss, considering the percentage explanation of the total variability, and KPCA Laplace, considering the visual division. In the latter, the multivariate analysis resulted in a similar crown class assignment as the field-assigned method. However, its application requires measurements that make it neither cheaper nor faster than a traditional crown class assessment. It indicates that a traditional field-assigned method, despite its subjectivity, should continue to be of great importance in forestry practice. Moreover, the alternative traits of trunk and crown can be a potentially useful statistical substitute for crown class assessment. Keywords: crown class, oak stand, trunk and crown traits, multivariate methods

scholarly journals MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA

2016 ◽  
Vol III-7 ◽  
pp. 187-194 ◽  
Author(s):  
N. A. M. Zaki ◽  
Z. A. Latif ◽  
M. N. Suratman ◽  
M. Z. Zainal
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Correlation Coefficient ◽  
Remotely Sensed ◽  
Carbon Stocks ◽  
Projection Area ◽  
Linear Regression Method ◽  
Remotely Sensed Data ◽  
Tropical Lowland ◽  
Crown Projection Area

Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland <i>Dipterocarp</i> forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland <i>Dipterocarp</i> forest.

scholarly journals MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA

2016 ◽  
Vol III-7 ◽  
pp. 187-194 ◽  
Author(s):  
N. A. M. Zaki ◽  
Z. A. Latif ◽  
M. N. Suratman ◽  
M. Z. Zainal
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Correlation Coefficient ◽  
Remotely Sensed ◽  
Carbon Stocks ◽  
Projection Area ◽  
Linear Regression Method ◽  
Remotely Sensed Data ◽  
Tropical Lowland ◽  
Crown Projection Area

Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland &lt;i&gt;Dipterocarp&lt;/i&gt; forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p &lt; 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland &lt;i&gt;Dipterocarp&lt;/i&gt; forest.

scholarly journals Model-based Informal Inference

2017 ◽  
Vol 6 (5) ◽  
pp. 140
Author(s):  
Theodosia Prodromou
Keyword(s):  
Linear Regression ◽  
Instructional Practices ◽  
Linear Model ◽  
Regression Models ◽  
Linear Models ◽  
Linear Regression Models ◽  
Journal Paper ◽  
Support Students ◽  
Best Fit ◽  
Informal Inference

Following recent scholarly interest in teaching informal linear regression models, this study looks at teachers’ reasoning about informal lines of best fit and their role in pedagogy. The case results presented in this journal paper provide insights into the reasoning used when developing a simple informal linear model to best fit the available data. This study also suggests potential in specific aspects of bidirectional modelling to help foster the development of robust knowledge of the logic of inference for those investigating and coordinating relations between models developed during modelling exercises and informal inferences based on these models. These insights can inform refinement of instructional practices using simple linear models to support students’ learning of statistical inference, both formal and informal.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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