Impacts of size and competition on tree form and distribution of aboveground biomass in Scots pine

1998 ◽  
Vol 28 (2) ◽  
pp. 216-227 ◽  
Author(s):  
Annikki Mäkelä ◽  
Petteri Vanninen
Keyword(s):  
Scots Pine ◽  
Data Sets ◽  
Allometric Relationships ◽  
Cross Sectional ◽  
Tree Form ◽  
Pipe Model ◽  
Crown Volume ◽  
Crown Structure ◽  
The Cross ◽  
Age Series

Studies on tree allometry have often focused on the average tree of a representative stand across an age gradient. Another dimension of change in tree form is the variation caused by differences in competitive status, evident between trees of one stand or between stands of comparable age but different stocking densities. This study compares the structural relationships of dominant Scots pine (Pinus sylvestris L.) trees over a wide age range with those in young trees of similar age but different competitive status. Allometric relationships are developed between biomass components and diameter, and crown structure is analysed in terms of crown allometry, pipe model relationships, and foliage density. The differences in allometry seem to be largely due to the rise of the crown base, which is positively correlated with size in the age series and negatively correlated with size in the cross-sectional data. The allometric relationships of the crown are less variable, but differences are found in the crown size to foliage biomass ratios between the two data sets. In the age series, foliage biomass is proportional to crown surface area, while in the cross-sectional data, it is proportional to crown volume. It is concluded that the reaction to competition for light is twofold: (1) to allocate new foliage higher up and, consequently, to lift the crown base, and (2) to grow sparser crowns.

The effect of crown position and tree age on resin-canal density in Scots pine (Pinus sylvestris L.) needles

2001 ◽  
Vol 79 (11) ◽  
pp. 1257-1261 ◽  
Author(s):  
Jinxing Lin ◽  
D A Sampson ◽  
R Ceulemans
Keyword(s):  
Pinus Sylvestris ◽  
Scots Pine ◽  
Cross Sections ◽  
Resin Canal ◽  
Tree Age ◽  
Needle Length ◽  
Cross Sectional ◽  
Crown Position ◽  
The Cross ◽  
Resin Canals

Resin canals are an important taxonomic characteristic in conifers. In this paper we examined within- and between-needle variation of the cross-sectional number of resin canals in Scots pine (Pinus sylvestris L.). Variation within needles was determined from 12 free-hand sections taken along the whole length of foliage collected from a common crown position. The effect of crown location and tree age on resin-canal density was also examined from the midpoint cross sections of 450 Scots pine needles collected from interior and exterior locations from the top, middle, and bottom of 25 crowns of trees ranging in age from 8 to 70 years. Within-needle resin-canal density varied with needle length. Two resin canals were typical for the basal and the terminal needle cross sections. There were 3.2 and 8.6 resin canals for cross sections taken from 10 and 30% of the needle length from the basal sheath, respectively. Resin-canal density was largest, and relatively constant, between 30 and 80% of the needle length. We found significant differences in the cross-sectional number of needle resin canals, as influenced by crown positions and tree age. Resin-canal density increased with foliage height. Foliage from the top one-third of crowns had significantly more resin canals than foliage from the bottom. Foliage collected from the crown interior (proximal to the stem) had fewer resin canals than samples from the crown edge. Resin-canal density increased from 7.1 to 10.3 as tree age increased from 8 to 70 years. These results suggest that crown position and tree age need to be incorporated into the sampling protocols used to establish species standards in resin-canal density, at least for Scots pine, if meaningful comparisons are to be made.Key words: resin canal, needle age, crown position, needle anatomy, Pinus sylvestris.

scholarly journals Geostatistical modeling of porosity and evaluating the local and global distribution

2021 ◽  
Author(s):  
MohammadHossein GhojehBeyglou
Keyword(s):  
Reservoir Characterization ◽  
Correlation Coefficients ◽  
Global Distribution ◽  
Geostatistical Simulation ◽  
Sequential Gaussian Simulation ◽  
Estimation Methods ◽  
Data Sets ◽  
Cross Sectional ◽  
Simple Kriging ◽  
The Cross

AbstractPorosity is one of the main variables needed for reservoir characterization. For this volumetric variable, there are many methods to simulate the spatial distribution. In this article, porosity was analyzed and modeled in the local and global distribution. For simulation, Sequential Gaussian simulation (SGS) and Gaussian Random Function (GRFS) were applied. Also, kriging was used to estimate the porosity at specific locations. The main purpose of this work was to investigate the porosity to compare geostatistical simulation and estimation methods in a sandstone reservoir as a real case study. First, the data sets were normalized by the Normal Scores Transformation (NST) and stratigraphic coordinate. The model of experimental variograms was fitted in the vertical and horizontal directions. For the simulation methods, 10 realizations were generated by each method. The Q-Q plots were calculated, and both sets of quintiles (Target Porosity Distribution versus Porosity realization) came from normal distributions with the following correlation coefficients: 0.93, 0.94 and 0.97 related to GRFS, SGS and Kriging, respectively. The extracted variograms from realizations showed that the kriging couldn’t reproduce the variograms with global distribution. For local validation, the cross-validation was evaluated and three wells were omitted. The re-estimation of porosity was considered at located well logs through the well sections window where the kriging had a better performance with minimum error to estimate porosity locally. Finally, the cross-sectional models were generated by each algorithm which showed that the simple kriging tries to produce smoother distribution, whereas conditional simulations (SGS and GRFS) try to represent more global-detailed sections.

scholarly journals Effects of tree size and position on pipe model ratios in Scots pine

2005 ◽  
Vol 35 (6) ◽  
pp. 1294-1304 ◽  
Author(s):  
Frank Berninger ◽  
Lluis Coll ◽  
Petteri Vanninen ◽  
Annikki Mäkelä ◽  
Sari Palmroth ◽  
...  
Keyword(s):  
Scots Pine ◽  
Area Ratio ◽  
Cross Sectional Area ◽  
Sectional Area ◽  
Relative Height ◽  
Cross Sectional ◽  
Tree Diameter ◽  
Pipe Model ◽  
Absolute Height ◽  
The Relationship

We investigate how the foliage mass to wood area ratios depend on tree and stand characteristics of previously collected data from Scots pine (Pinus sylvestris L.). Our analysis allowed a separation of the relationship between stem and branch cross-sectional areas and the relationship between the branch cross-sectional area and foliage mass. We studied how these relationships varied within and between stands. The lowest site fertility class had a higher foliage mass to stem area ratio than better sites. The relative height of a tree in the stand (Φ) was the major factor that determined the variation in the relationship between the branch cross-sectional area and the stem cross-sectional area. Models based on absolute height or tree diameter were usually weaker. Models based on Φ were simpler, since no other variables were able to explain between-stand variation in the presence of Φ. We were able to predict changes in the branchiness of the tree but not in the foliage mass supported per unit of branch area.

scholarly journals SimpleForest - a comprehensive tool for 3d reconstruction of trees from forest plot point clouds

2021 ◽  
Author(s):  
Jan Hackenberg ◽  
Kim Calders ◽  
Miro Demol ◽  
Pasi Raumonen ◽  
Alexandre Piboule ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Forest Plot ◽  
Data Sets ◽  
Cross Sectional ◽  
Data Set ◽  
Terrain Model ◽  
Digital Terrain ◽  
Pipe Model ◽  
The One

The here-on presented SimpleForest is written in C++ and published under GPL v3. As input data SimpleForest utilizes forestry scenes recorded as terrestrial laser scan clouds. SimpleForest provides a fully automated pipeline to model the ground as a digital terrain model, then segment the vegetation and finally build quantitative structure models of trees (QSMs) consisting of up to thousands of topologically ordered cylinders. These QSMs allow us to calculate traditional forestry metrics such as diameter at breast height, but also volume and other structural metrics that are hard to measure in the field. Our volume evaluation on three data sets with destructive volumes show high prediction qualities with concordance correlation coefficient CCC (r2 adj.) of 0.91 (0.87), 0.94 (0.92) and 0.97 (0.93) for each data set respectively. We combine two common assumptions in plant modeling The sum of cross sectional areas after a branch junction equals the one before the branch junction (Pipe Model Theory) and Twigs are self-similar (West, Brown and Enquist model). As even sized twigs correspond to even sized cross sectional areas for twigs we define the Reverse Pipe Radius Branchorder (RPRB) as the square root of the number of supported twigs. The prediction model radius = B 0 ∗ RP RB relies only on correct topological information and can be used to detect and correct overestimated cylinders. In QSM building the necessity to handle overestimated cylinders is well known. The RPRB correction performs better with a CCC (r2 adj.) of 0.97 (0.93) than former published ones 0.80 (0.88) and 0.86 (0.85) in our validation. We encourage forest ecologists to analyze output parameters such as the GrowthVolume published in earlier works, but also other parameters such as the GrowthLength, VesselVolume and RPRB which we define in this manuscript.

Scots pine and Norway spruce foliage biomass in Finland and Sweden — testing traditional models vs. the pipe model theory

2020 ◽  
Vol 50 (2) ◽  
pp. 146-154 ◽  
Author(s):  
Aleksi Lehtonen ◽  
Juha Heikkinen ◽  
Hans Petersson ◽  
Boris Ťupek ◽  
Eero Liski ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Accurate Method ◽  
Cross Sectional Area ◽  
Sectional Area ◽  
Tree Crown ◽  
Cross Sectional ◽  
Pipe Model ◽  
Biomass Models ◽  
Model Approach

The pipe model approach was compared with foliage biomass models by using the cross-sectional area at the tree crown base for predicting foliage biomass of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.). We evaluated the impacts of site type, fertilization, and climate on the relationship between foliage biomass and cross-sectional area at the tree crown base, referred as to the pipe model ratio. Our hypotheses were that (i) the pipe model approach is a more precise and accurate method for foliage prediction than the traditional biomass models and (ii) the pipe model ratio for foliage does not explicitly depend on any single environmental driver. Data used here consisted of felled trees from Finnish and Swedish biomass studies. These data were analyzed by linear mixed models with different covariates, and the uncertainties of different modelling approaches were evaluated. The pipe model outperformed other models for Scots pine but not for Norway spruce. Results showed larger pipe model ratios for Scots pine in herb-rich forests compared with those of trees in subxeric heath forest. Results from fertilized trees indicated that the addition of nitrogen temporarily increased foliage biomass.

Cross-sectional area relationships in root systems of loblolly and shortleaf pine

1987 ◽  
Vol 17 (6) ◽  
pp. 556-558 ◽  
Author(s):  
William C. Carlson ◽  
Constance A. Harrington
Keyword(s):  
System Development ◽  
Lateral Roots ◽  
Root Systems ◽  
Shortleaf Pine ◽  
Cross Sectional ◽  
Tree Form ◽  
Root System Development ◽  
Root Area ◽  
Pipe Model ◽  
Highly Correlated

The relationship between cross-sectional root area at groundline and composite root area (the sum of the areas of the first-order lateral roots plus the area of the taproot subtending the most distal lateral root) was examined in 3- to 9-year-old loblolly and shortleaf pine (Pinustaeda L. and P. echinata Mill.). For both species, root area at groundline and composite root area were highly correlated, and the slopes in equations relating the two root areas were close to 1.0. These results imply that (i) the pipe model of tree form is appropriate for young root systems, and (ii) the development of basal stem diameter is directly related to root system development.

On the cross-sectional shape of the cellulose crystallites in the tunicate Halocynthia papillosa

1990 ◽  
Vol 48 (3) ◽  
pp. 566-567
Author(s):  
J.-F. Revol ◽  
Y. Van Daele ◽  
F. Gaill
Keyword(s):  
Contrast Imaging ◽  
Animal Kingdom ◽  
Bright Field ◽  
Field Mode ◽  
Cross Sectional ◽  
Ultrathin Sections ◽  
The Cross ◽  
Sectional Shape ◽  
Valonia Ventricosa ◽  
C Nmr

The only form of cellulose which could unequivocally be ascribed to the animal kingdom is the tunicin that occurs in the tests of the tunicates. Recently, high-resolution solid-state l3C NMR revealed that tunicin belongs to the Iβ form of cellulose as opposed to the Iα form found in Valonia and bacterial celluloses. The high perfection of the tunicin crystallites led us to study its crosssectional shape and to compare it with the shape of those in Valonia ventricosa (V.v.), the goal being to relate the cross-section of cellulose crystallites with the two allomorphs Iα and Iβ.In the present work the source of tunicin was the test of the ascidian Halocvnthia papillosa (H.p.). Diffraction contrast imaging in the bright field mode was applied on ultrathin sections of the V.v. cell wall and H.p. test with cellulose crystallites perpendicular to the plane of the sections. The electron microscope, a Philips 400T, was operated at 120 kV in a low intensity beam condition.

A Technique for Measuring the Cross-Sectional Axes of the Longissimus Dorsi Muscle and Fat Depth in Lambs1,2

1960 ◽  
Vol 19 (3) ◽  
pp. 803-809
Author(s):  
D. J. Matthews ◽  
R. A. Merkel ◽  
J. D. Wheat ◽  
R. F. Cox
Keyword(s):  
Longissimus Dorsi ◽  
Cross Sectional ◽  
Longissimus Dorsi Muscle ◽  
Dorsi Muscle ◽  
The Cross
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