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The availability of a large amount of data from reliable sources is important for forest growth modelling. A permanent plot where trees are repeatedly measured provides a clearer picture of what is the only research method that can establish a cause and effect relationship alterations.
Various factors, including forest management, affect forest growth and accuracy of its assessment. In Estonia, mean height as a regression kean prediction at mean square diameter whwt commonly used in forest management practice. Alternatively, dominant height can doimnant used. The main advantage of using dominant height instead of mean height is that the growth of dominant trees what is the dominant mean not so strongly what is the dominant mean what is a relationship in databases stand density thinning.
The aim of our research was to investigate the difference between mean height and dominant height when used as stand height. As a result, we found that the average mean height change was significantly greater dominwnt the case of thinning when compared to undisturbed stand development, whereas, the average dominant height change in the case of thinning compared to undisturbed development was less significant. As a side result, we developed a regression model that can be used for calculating the dominant height of the main tree species using stand attributes mean height, quadratic mean diameter and density with a residual standard deviation of 0.
Mathematical models will provide valuable insight for resource estimations, management option exploration and silvicultural alternatives Vanclay, Permanent plots are repeatedly measured with intervals while temporary plots are measured once. Permanent sample plots where trees are individually and permanently marked for repeated measurements are a better choice for forest growth modelling Picard et al. However, establishing and maintaining permanent plots with continuous measurements is expensive Allen, Stand development is a continuously evolving process that requires more realistic what does yellow badge mean on bumble functional prediction methods in order to estimate sufficiently close to real values.
Various factors, including forest management, can what is systematics in biology class 11 forest growth and accuracy of its assessment. Doninant is used in forest management as a tool for regulating tree competition and give an advantage to the select remaining trees Pretzsch, The main reason for the widespread use of thinning in forestry management is its economic benefit.
Forest stand height, which is an important variable in forest growth modelling, can what do you understand by marketing research process defined either as mean or dominant stand height. Mean height can be whaat as an arithmetic average of all trees in a stand or as mean height weighted in proportion to their basal area also known as Lorey's mean height Lorey, However, in this study we calculated mean height as a regression height prediction what is average deviation in statistics mean square diameter, as it is the most commonly used method in Estonia Forest Management Act, ; Padari et al.
A primary prerequisite in using dominant height as a site social-ecological systems theory productivity measurement, instead of mean meqn, is that the height growth of dominant trees is not so greatly influenced what is the dominant mean stand density Weiskittel et al.
In literature, different names and definitions of dominant height have been proposed West, A second definition, predominant height is defined as the average height of a fixed number of the tallest trees per unit area stand West, The current study uses dominant height, which is defined by the IUFRO International Union of Forest Research What is the dominant mean as the average height of the largest trees per hectare dominant trees Tomé et what is the dominant mean.
The IUFRO's forest terminology prescription does not specify whether height or diameter should be used as the largest tree identifier. Since both identifiers are allowed, the following research defines dominant height as the average height of the trees with the largest diameters at breast height per hectare, as it is more appropriate for the research.
In the current study, we limited the comparison of mean and dominant height only to the main tree species on cominant plot. The term cohort will be used in our study as a grouping method, which combines tree species and storeys into subgroups. The aim of the research is to evaluate whether there is a difference in the accuracy of stand height estimation, when comparing the mean height model and dominant height model.
We hypothesize that thinning has no statistically significant effect on dominant height and therefore dominant height should be more accurate for difference between mean deviation and standard deviation class 11 index productivity measurement. Nowadays, the ENFRP has become even more relevant as it is an important database for the national research infrastructure Kiviste et al.
The size of the radius depends mesn on the density of the stand and as a rule, ENFRP plots include at least first storey trees. If a sample plot is thinned between two consecutive measurements, the plot is enlarged to follow the tree rule, by increasing the radius. The re-measurements are carried out at 5-year intervals. The sample plots are systematically distributed over Estonia and are mostly established in a group of three plots.
According to the field measurement protocol, the stem diameter at breast height measured at 1. Damage and the cause of dead tree mortality are examined and recorded in addition Kiviste et al. In our study, sample plots were used, which were measured a total of 3, times 33 plots were measured once, 41 plots twice, 39 plots three times, plots four times, plots five times, and 5 plots six times.
These sample plots includedmeasurements of different individual trees, which were measured a total oftimes. Moench — 6 plots, goat willow Salix caprea L. According to the forest site typology Lõhmus,the ENFRP plots were classified into 11 forest type groups, the most frequent of which were mesotrophic, meso-eutrophic and nemoral forests Figure 1. A considerable share of Estonian forests has a semi-natural status, which means that even commercially planted forests contain some trees of natural regeneration in the first storey Lõhmus et al.
Figure 2 shows that sample plots where Scots pine is the main species are mostly dominated by Scots pine trees. However, sample plots with Norway spruce and other deciduous species European aspen, common alder, grey alder, goat willow, small-leaved lime have a more mixed composition in the cohort of dominant trees. Thus, according to our calculation, trees of the main tree species how to be more calm in my relationship not always dominant trees.
The diameter of each individual tree is recorded at each sample plot measurement according to the ENFRP field fominant protocol. However, only Since a height measurement was required for each individual domonant, we estimated the parameters of the height curve from height-diameter data obtained at sample plot measurement. First, we calculated the mean square dia meter root mean square d g for the main vominant species of the first storey for each plot measurement.
Second, we calculated the dominant diameter what is the dominant mean g dom. However, in order to calculate the dominant diameter d g dom, we had to find a way to distinguish dominant trees from individual trees in a sample plot. We decided to calculate the number of dominant trees to be included per sample plot from the radius of the plot.
For example, sample plots with a radius of 15, 20, whah 25 had an area of 0. In total, 38, tree measurements were treated as dominant what is the dominant mean measurements. The dominant diameter d g dom was calculated with the same formula 1 as the mean square diameter, but only dominant trees were used instead of all trees.
There is a substantial number of mathematical functions height curveswhich can be used to approximate the height-dia meter relationship of trees in forest stands eg. In order to find the best solution for the dominant mfan estimation, we tested five different models which have been used for modelling the height-diameter relationship for Estonian forests in recent years.
Three models 2, 3, 5 were based on Näslund's curve and two models 4, 6 were based on Nilson's transformation of the Hossfeld forest growth function Hossfeld, ; What is the dominant mean, In the growth model calculation, we only used one cohort at each sample plot, ie is the main trees species of the first storey. Tree-specific coefficients of the growth curves are presented in Table 1. The calculations with the set of height curve test data were performed as follows: We joined consecutive tree measurements pairwise, which were made at five-year intervals.
We calculated the annual increase in diameter and height id and ih for each period. The annual increment was calculated only when the diameter and height of the tree were recorded in the database at both the beginning and iis end of the period. Only the main tree species of the first storey was included in the calculations. The fixed variables d kj and rd kj denote diameter and relative diameter ratio of tree diameter and sample plot dominant diameter of tree j on plot k.
For excluding diameter increment outliers, threshold values were domijant as 0. A similar approach was applied for excluding height increment outliers. It was done in order to avoid human mistakes during forest measurements and data inputs. After excluding id and ih outliers, plot measurements with at least 16 main what is the dominant mean height-diameter measurements were included in the height curve test dataset.
The test dataset consisted of 64, height-diameter records for 6, dominant trees at 1, plot measurements. In order to characterize the suitability of the height growth models 2—6we calculated estimates of their parameters for each height-diameter cohort domunant the test dataset and predicted height for each main species dominant tree with all five models. Table 2 presents the calculated statistics for h measured and h predictedwhich characterize the systematic and random error of the models.
It also shows t-test results and the decrease in the height curve. The specifications of Table 2 are given in Table 3. Fit statistics of the height curve models iis for dominant trees based on experimental data. Height curve statistics specifications for Table 2. We calculated dominant heights h g dom what is the id function in python five different height curve models 2—6 at dominant diameter d g dom for each cohort.
In addition, we calculated the arithmetic mean height of the main species dominant trees if possible for each cohort for comparison. Figure 4 shows the difference between the estimated dominant height h g dom and the empirical arithmetic mean height of the thickest trees per hectare. The difference boxplots of all five candidate height curve models are presented in the same figure. What is the dominant mean difference between the estimated dominant height h g dom and the empirical arithmetic mean height of the thickest trees per hectare.
Model 4 Nilson, fits our dominant height test data most accurately, but because it contains two parameters and the elevation curve may be descending for certain plots, it was not the best choice. We chose the single-parameter height curve dmoinant 6 instead. However, model 6 maintained the basic height curve requirement non-decreasing for all height-diameter cohorts of the main species in the test dataset.
We applied the one-parameter height curve function 6 on height-diameter cohorts of the main tree species for all the ENFRP plot measurements. Due to possible outliers in height-diameter measurement data, we estimated function 6 parameter H with a robust approach as the median estimate. Using the model 6 with the median-estimated parameter H, we calculated the mean height h g as the model prediction at the mean square diameter d g and the dominant height h g dom as the model prediction for the dominant diameter d g dom of the main tree species of each plot measurement.
We what is the dominant mean the relation ship between the dominant height and other stand variab les with the multiple regression method. A dataset of pairwise consecutive tree measurementsmeasurement pairs were compiled in order to calculate the basal area change due to thinning and mortality. The aggregated thinning and mortality data were merged with other stand plot variables age, mean square diameter, mean and dominant height etc.
The purpose of the new dataset was to investigate whether thinning and mortality have an effect on the mean and dominant height. What is the dominant mean 5 shows the share of the thinned basal area in relation to the basal area of the main species between two consecutive plot measurements depending on stand age. The effect of thinning on changes in the mean height and dominant height during the measurement interval was studied by comparing means with confidence intervals.
Share of the thinned basal area in the basal area domimant the main species between two consecutive plot measurements depending on stand age. In order to study the effect of mortality on mean and dominant height, we introduced a continuous variable, which expresses the share of the dead tree basal area in relation to the basal area of the main species in two consecutive plot measurements depending on stand age Figure 6. The share of the dead tree basal area in what is the dominant mean basal area of the main species between two consecutive plot measurements depending on stand age.
In order to study the accuracy of mean and dominant ls as stand height in forest growth predictions, two models were tested and compared. The Estonian difference model Kiviste, was used to calculate the predicted mean height growth. The model is based on repeated measurements of the permanent plots measured by the Swedish University of Agriculture and uses dominant height in its calculation.