Difference between revisions of "Import of stand register"
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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. E.g., 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. | 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. E.g., 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. | + | 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. |
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. . .. Weibull continuous probability density distribution... single trees with sizes (i.e. diameters at breast height) according to... The Weibull distribution share properties with many other distributions, usually skewed with a right-side tail. Explained by the often occuring ... in a stand, there can be some, not many but much larger trees. On the other side, it is impossible that a stand show trees with negative diameters (zero is ok, indicating the occurrence of trees (saplings) with a height below 1.3 m). Properties common with the exponential and the normal distribution. Important for a diameter distribution is scale parameter (correponding to the tree species' DG) and the shape parameter, usually with a value of 1.5 - 5. | 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. . .. Weibull continuous probability density distribution... single trees with sizes (i.e. diameters at breast height) according to... The Weibull distribution share properties with many other distributions, usually skewed with a right-side tail. Explained by the often occuring ... in a stand, there can be some, not many but much larger trees. On the other side, it is impossible that a stand show trees with negative diameters (zero is ok, indicating the occurrence of trees (saplings) with a height below 1.3 m). Properties common with the exponential and the normal distribution. Important for a diameter distribution is scale parameter (correponding to the tree species' DG) and the shape parameter, usually with a value of 1.5 - 5. | ||