Difference between revisions of "Import of stand register"
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==Simulating single tree-data== | ==Simulating single tree-data== | ||
| − | A stand register usually includes, in each stand, average values and totals for several variables. However, models applied in the simulations of growth, treatments, etc. need initial forest state descriptions at the single tree-level. If not obtained in a forest inventory, e.g. according to [[Ivent]], such data can be simulated. Other inventory methods, and other simulation techniques (e.g. sample plot imputation), are available. | + | A stand register usually includes, in each stand, average values and totals for several variables. However, models applied in the simulations of growth, treatments, etc. need initial forest state descriptions at the single tree-level. If not obtained in a forest inventory, e.g. according to [[Ivent]], such data can be simulated. Other inventory methods, and other simulation techniques (e.g. sample plot imputation), are available. Remote sensing like airborne laser sensors may soon return (a sample of) single-tree data for large areas without great costs, but of great interest. |
Single tree-data is randomly simulated using a set of mandatory parameters given by the imported stand register. Moreover, the user define the size (in m<sup>2</sup>) and the number of plots (i.e. reference (or prediction) units) per stand to be simulated. ''Why simulate a sample of trees and not all trees of the stand?'' Acually, all trees are simulated, but only in the 2D-/3D-vizualisations in StandWise. In any other situation, a sample of single tree-data is the expected format describing the forests (saving both space and computational time). By using the tree species-wise mean diameter, mean height, total basal area (in m<sup>2</sup>/ha), and total number of stems per hectare, it is quite straightforward to distribute a total volume over a certain number of single trees, of certain sizes. A Weibull probability density distribution is applied ([http://en.wikipedia.org/wiki/Weibull_distribution]), actually distributing the trees' diameters (at breast height). The Weibull distribution share properties with many other distributions, like the exponential and the normal (the Gaussian). However, by using the scale parameter (approx. correponding to the tree species' DG) and the shape parameter, usually with a value of 1.5 - 5, a skewed distribution with a right-side tail is obtained. This corresponds fairly well to real forest stands and reflects the occurrence of some, but not many large trees (larger than the average tree of the stand). On the other side (the left-side), it is prohibited that a stand include trees with negative diameters. | Single tree-data is randomly simulated using a set of mandatory parameters given by the imported stand register. Moreover, the user define the size (in m<sup>2</sup>) and the number of plots (i.e. reference (or prediction) units) per stand to be simulated. ''Why simulate a sample of trees and not all trees of the stand?'' Acually, all trees are simulated, but only in the 2D-/3D-vizualisations in StandWise. In any other situation, a sample of single tree-data is the expected format describing the forests (saving both space and computational time). By using the tree species-wise mean diameter, mean height, total basal area (in m<sup>2</sup>/ha), and total number of stems per hectare, it is quite straightforward to distribute a total volume over a certain number of single trees, of certain sizes. A Weibull probability density distribution is applied ([http://en.wikipedia.org/wiki/Weibull_distribution]), actually distributing the trees' diameters (at breast height). The Weibull distribution share properties with many other distributions, like the exponential and the normal (the Gaussian). However, by using the scale parameter (approx. correponding to the tree species' DG) and the shape parameter, usually with a value of 1.5 - 5, a skewed distribution with a right-side tail is obtained. This corresponds fairly well to real forest stands and reflects the occurrence of some, but not many large trees (larger than the average tree of the stand). On the other side (the left-side), it is prohibited that a stand include trees with negative diameters. | ||
The shape parameter is of importance, including both the actual shape of the distribution and its scale. A scale parameter is known for "stretching/shrinking the width of the distribution". A stand register, unfortunately, seldom includes any information about the distribution of tree sizes. Any notes like "homogeneous" or "heterogeneous" should be taken into consideration. For the moment, this is done using the mandatory field "EvenAgedCode" in the stand register import. With a heterogeneous stand the user should here select "UnevenAged" to obtain a relatively wide diameter distribution, and vice versa. As you notice, this is not fully consistent - a stand with trees of same age might very well include both large and small trees (both dominating and supressed trees). | The shape parameter is of importance, including both the actual shape of the distribution and its scale. A scale parameter is known for "stretching/shrinking the width of the distribution". A stand register, unfortunately, seldom includes any information about the distribution of tree sizes. Any notes like "homogeneous" or "heterogeneous" should be taken into consideration. For the moment, this is done using the mandatory field "EvenAgedCode" in the stand register import. With a heterogeneous stand the user should here select "UnevenAged" to obtain a relatively wide diameter distribution, and vice versa. As you notice, this is not fully consistent - a stand with trees of same age might very well include both large and small trees (both dominating and supressed trees). | ||