Difference between revisions of "Version 1.9.5"
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==New features == | ==New features == | ||
| − | *If vegetation type has been missing, the program has set it to the most common vegetation type, which is | + | *If vegetation type has been missing, the program has set it to the most common vegetation type in Sweden, which is bilberry (blåbärsttyp). A more refined lookup table has now been implemented, using site index and soil moisture class as lookup variables if vegetation type is missing. |
*The number of tree objects (i.e. number of caliper trees) in a stand has a great impact on computation times and memory usage when running large scale simulations in PlanWise (and in RegWise depending on what data is used). For example, the time to generate treatment programs for a stand may vary from a few seconds to half a minute or even more, depending on the number of sample plots and the number of caliper trees on each plot. In some situations, execution time can be unacceptably long, and to overcome this a tree data aggregation algorithm has been implemented. If activated, this algorithm aggregates tree records stored in the database into fewer "runtime" tree objects by grouping the trees into diameter classes. The aggregation is activated under menu Settings > Performance. For each sample plot, species group, diameter class, and tree type (tree, sapling, overstorey tree, retention tree, etc.), a new "class tree" is thus created which substitutes the trees in the class. The diameter class width is 1 cm by default, and tests show that growth projections are affected only marginally by the aggregation. The diameter of the class tree is the quadratic mean diameter of the trees in the class. | *The number of tree objects (i.e. number of caliper trees) in a stand has a great impact on computation times and memory usage when running large scale simulations in PlanWise (and in RegWise depending on what data is used). For example, the time to generate treatment programs for a stand may vary from a few seconds to half a minute or even more, depending on the number of sample plots and the number of caliper trees on each plot. In some situations, execution time can be unacceptably long, and to overcome this a tree data aggregation algorithm has been implemented. If activated, this algorithm aggregates tree records stored in the database into fewer "runtime" tree objects by grouping the trees into diameter classes. The aggregation is activated under menu Settings > Performance. For each sample plot, species group, diameter class, and tree type (tree, sapling, overstorey tree, retention tree, etc.), a new "class tree" is thus created which substitutes the trees in the class. The diameter class width is 1 cm by default, and tests show that growth projections are affected only marginally by the aggregation. The diameter of the class tree is the quadratic mean diameter of the trees in the class. | ||