Difference between revisions of "Structural Diversity Results"

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| Even-aged Class||code||Even-aged type. <ol start="0"><li>Unknown: No information <li>EvenAged: If at least 80 % of the volume are within a 20-year age range <li>UnevenAged: Otherwise.</ol>Note that the definition differs from the one used in the Swedish NFI.
 
| Even-aged Class||code||Even-aged type. <ol start="0"><li>Unknown: No information <li>EvenAged: If at least 80 % of the volume are within a 20-year age range <li>UnevenAged: Otherwise.</ol>Note that the definition differs from the one used in the Swedish NFI.
 
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| Tree Size Diversity (Gini Coefficient)||||Tree size diversity (Gini coefficient).
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| Tree Size Diversity (Gini Coefficient)||0-1||The [http://en.wikipedia.org/wiki/Gini_coefficient Gini coefficient Gini coefficient] is an equality index between 0 (=maximum equality, i.e. all trees have the the same size) and 1 (=maximum inequality). The index has been proposed to be used for forestry planning by Lexerød & Eid (2006)<ref name="LexerodEid2006_GiniCoeff">.  {{:Bibliografi:LexerodEid2006_GiniCoeff}}</ref>.  
 
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| Tree Size Diversity Class (Hugin def.)||||Tree size diversity class according the Hugin system definition. Trees are grouped into four diameter classes, with class width = (dbh<sub>max</sub>-dbb<sub>min</sub>)/4. If the number of trees in class<sub>i</sub> > class<sub>i+1</sub>, the diameter class distribution is set as InverseJShaped, otherwise as Homogeneous.
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| Tree Size Diversity Class (Hugin def.)||code||Tree size diversity class according the Hugin system definition. Trees are grouped into four diameter classes, with class width = (dbh<sub>max</sub>-dbb<sub>min</sub>)/4. If the number of trees in class<sub>i</sub> > class<sub>i+1</sub>, the diameter class distribution is set as InverseJShaped, otherwise as Homogeneous.
 
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==Calculation of Gini coefficient in Heureka==
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The Gini-coefficent is defined primarily for population with discrete unit. In Heureka, a stand is represented by type trees, where each type tree represents a cohort of trees, where each type tree has a tree expansion factor (a wieght) for the number of trees it represents. This prevents any of the currently available formulas to be applied correctly. As an approximation, the Gini coefficient is calculated with the formula for a discrete probability distribution.
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==References==
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<references />

Revision as of 17:07, 21 March 2016

This result group contains results that describe the Structural diversity of the trees in a treatment unit.

Variable name Unit Description
Even-aged Class code Even-aged type.
  1. Unknown: No information
  2. EvenAged: If at least 80 % of the volume are within a 20-year age range
  3. UnevenAged: Otherwise.
Note that the definition differs from the one used in the Swedish NFI.
Tree Size Diversity (Gini Coefficient) 0-1 The Gini coefficient Gini coefficient is an equality index between 0 (=maximum equality, i.e. all trees have the the same size) and 1 (=maximum inequality). The index has been proposed to be used for forestry planning by Lexerød & Eid (2006)[1].
Tree Size Diversity Class (Hugin def.) code Tree size diversity class according the Hugin system definition. Trees are grouped into four diameter classes, with class width = (dbhmax-dbbmin)/4. If the number of trees in classi > classi+1, the diameter class distribution is set as InverseJShaped, otherwise as Homogeneous.


Calculation of Gini coefficient in Heureka

The Gini-coefficent is defined primarily for population with discrete unit. In Heureka, a stand is represented by type trees, where each type tree represents a cohort of trees, where each type tree has a tree expansion factor (a wieght) for the number of trees it represents. This prevents any of the currently available formulas to be applied correctly. As an approximation, the Gini coefficient is calculated with the formula for a discrete probability distribution.

References

  1. . Lexerød, N.L, Eid, T. 2006. An evaluation of different diameter diversity indices based on criteria related to forest management planning. Forest Ecology and Management 222 (2006) 17–28.