Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
number of stems
tree age (in 40-year classes) · main tree species
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
tree age (in 40-year classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	42	61	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	18	100	0	0	0	0	0	0	0	0	0	0	60	52
	fir	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	pine	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	17	100	0	.	0	0	17	100
	larch	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	164	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	17	100	0	.	0	.	181	91
	beech	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	80	100	0	0	17	100	0	0	0	0	0	0	97	84
	maple	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	22	100	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	22	100
	ash	0	0	0	.	0	0	0	0	17	100	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	17	100
	oak	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	other broadleaves	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	18	100	0	0	0	0	0	0	0	0	15	100	0	0	0	0	0	0	0	0	37	100	59	74	17	100	0	0	0	0	0	0	0	0	145	44
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	0	0	0	0	0	0	0	0	17	100	0	0	0	0	0	0	224	75	0	0	22	100	0	0	0	0	15	100	0	0	0	0	0	0	0	0	37	100	156	59	17	100	17	100	35	71	0	0	0	0	539	37
=40 years	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1857	30	55	58	468	58	6641	15	240	73	1828	34	0	.	533	40	5587	12	678	40	3043	25	1174	34	33	71	767	47	2193	28	1005	45	243	38	980	42	1616	34	771	28	564	38	3112	24	1811	18	286	52	3076	30	38562	6
	fir	235	39	0	.	368	73	3217	17	121	62	691	47	0	.	15	100	328	40	1016	25	1525	29	625	28	38	71	53	58	99	45	69	100	893	45	387	55	564	52	128	51	237	43	1410	20	160	40	216	70	382	37	12777	8
	pine	56	74	0	.	0	.	447	68	38	100	17	100	17	100	0	.	52	74	0	.	0	.	0	.	0	.	0	.	0	.	223	71	0	.	0	.	17	100	0	.	18	100	17	100	727	73	0	.	76	81	1706	38
	larch	38	71	0	.	0	.	58	58	0	.	52	100	0	.	0	.	459	31	0	.	58	66	0	.	0	.	87	100	188	83	17	100	0	.	0	.	50	100	383	55	0	.	804	49	936	29	0	.	37	71	3167	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	194	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	71	0	.	0	.	229	51
	other conifers	126	60	0	.	0	.	242	36	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	16	100	0	.	109	84	35	100	0	.	52	100	625	26
	beech	1162	21	33	71	120	53	3435	13	584	33	572	30	0	.	449	54	600	30	893	40	598	26	854	26	379	68	70	50	2090	24	287	43	1046	25	243	39	703	30	1733	24	190	55	1604	20	53	58	98	69	1353	20	19149	6
	maple	461	31	0	.	141	66	1254	19	399	32	552	32	0	.	395	49	594	33	280	43	280	38	674	49	126	47	156	91	594	35	75	71	547	37	252	55	464	35	261	45	169	62	1273	26	375	47	80	81	1378	34	10783	9
	ash	686	39	0	.	17	100	713	25	177	40	588	40	34	100	225	50	838	42	158	48	403	32	228	45	71	79	61	58	713	39	280	83	225	38	239	63	486	29	637	35	239	56	850	29	550	39	69	79	597	33	9086	9
	oak	128	100	0	.	0	.	34	71	24	100	59	58	0	.	0	.	15	100	17	100	0	.	34	100	0	.	0	.	40	71	0	.	36	71	0	.	71	61	455	48	18	100	120	87	35	100	0	.	86	66	1174	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3202	27	0	.	17	100	0	.	0	.	0	.	3219	27
	other broadleaves	846	28	30	100	124	65	2295	18	190	47	451	33	0	.	292	48	3915	26	323	41	446	40	330	38	111	74	95	56	838	35	137	63	479	52	484	56	600	48	4954	18	377	48	1276	24	2835	21	17	100	597	30	22043	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5595	17	118	47	1238	43	18336	9	1775	26	4810	19	52	100	1910	29	12582	11	3365	19	6360	18	3919	17	758	50	1289	40	6753	18	2132	33	3469	21	2585	28	4571	21	12540	12	1813	31	10594	13	7553	15	767	45	7635	16	122518	3
41-80 years	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	941	41	565	58	621	50	9893	11	184	59	3077	23	0	.	1193	33	13240	11	659	37	2690	20	1662	27	1502	57	1953	31	2424	22	431	45	459	40	1317	26	1671	33	2530	20	1597	31	5186	19	8290	15	103	100	1981	21	64170	5
	fir	531	53	126	66	93	50	5459	10	438	36	1020	38	0	.	93	51	440	29	1301	26	2486	19	1733	21	108	47	646	37	879	25	31	71	440	40	521	34	998	47	582	43	915	37	2540	16	751	31	289	56	272	36	22693	6
	pine	74	60	0	.	0	.	475	49	74	57	23	77	90	100	0	.	1494	28	35	83	96	66	52	100	0	.	278	100	0	.	14	71	93	56	35	100	94	46	81	68	149	78	169	72	2793	28	0	.	184	39	6301	15
	larch	90	76	0	.	0	.	28	100	7	100	0	.	0	.	0	.	1766	23	0	.	0	.	0	.	0	.	0	.	32	64	14	100	7	100	0	.	59	100	2026	23	0	.	96	45	2568	18	0	.	44	71	6735	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	773	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	649	34	0	.	0	.	1438	22
	other conifers	72	61	0	.	18	100	112	46	52	100	0	.	0	.	18	100	316	47	51	74	15	100	0	.	0	.	0	.	125	49	0	.	137	58	18	100	17	100	0	.	87	100	196	56	70	61	0	.	291	68	1596	20
	beech	1954	20	146	63	104	46	8180	12	1449	29	2393	23	0	.	395	33	1315	28	1393	27	2786	21	787	19	314	43	673	43	2658	20	1140	32	1925	22	871	28	667	21	7851	17	849	36	3030	14	1904	31	72	62	1452	17	44309	5
	maple	338	30	21	100	267	69	1884	18	195	38	408	31	0	.	138	37	284	36	286	27	499	25	425	39	134	38	313	72	1009	21	87	100	405	25	283	36	220	45	419	38	177	43	1514	21	647	31	30	61	685	31	10668	7
	ash	320	36	0	.	243	48	831	22	211	50	827	31	52	100	115	65	204	54	564	36	542	39	92	55	282	84	61	59	770	28	94	53	256	38	373	50	353	39	1085	37	207	62	915	23	410	56	64	72	471	43	9342	9
	oak	92	49	0	.	0	.	279	39	170	59	53	56	325	58	0	.	15	100	55	66	83	54	272	91	0	.	0	.	21	100	52	57	246	59	0	.	124	34	1369	32	26	76	1902	32	903	35	0	.	84	48	6071	15
	sweet chestnut	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	730	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7421	17	0	.	7	100	70	81	0	.	0	.	8236	16
	other broadleaves	385	31	0	.	41	73	1408	20	172	38	189	37	34	100	210	56	2459	29	564	35	285	34	216	37	61	89	101	69	765	39	17	100	163	49	198	40	244	37	6465	17	139	51	1159	23	1462	25	0	.	592	29	17331	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4803	17	859	46	1387	33	28549	7	2952	20	7990	15	501	53	2162	25	23037	8	4908	16	9483	13	5240	14	2401	40	4025	22	8685	14	1881	26	4130	16	3616	20	4447	20	29845	8	4146	23	16715	11	20517	10	558	43	6056	13	198890	2
81-120 years	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	396	29	709	34	634	36	5488	10	111	56	1855	22	0	.	400	37	7739	10	465	34	1358	18	731	25	173	43	1083	29	1875	24	220	39	481	42	1211	23	293	30	1702	23	976	31	2120	17	3291	14	120	60	970	20	34403	4
	fir	279	37	162	48	348	44	2867	19	105	54	385	30	0	.	57	68	299	36	805	29	999	21	613	23	21	100	171	56	429	26	42	61	224	38	288	36	78	44	132	39	150	52	1102	21	330	33	0	.	399	24	10287	7
	pine	28	77	0	.	9	100	312	43	103	49	155	54	7	100	0	.	1862	28	148	46	14	71	113	72	0	.	184	100	17	100	127	51	190	49	66	100	84	49	54	67	84	83	21	58	839	28	0	.	320	28	4735	14
	larch	49	60	0	.	0	.	87	51	7	100	7	100	0	.	0	.	1298	17	7	100	0	.	0	.	0	.	0	.	85	81	7	100	0	.	14	100	0	.	571	24	0	.	13	100	1044	22	0	.	57	50	3246	11
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	725	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	363	37	0	.	0	.	1114	23
	other conifers	0	.	0	.	18	100	34	100	21	100	0	.	0	.	0	.	106	62	0	.	46	77	17	100	0	.	17	100	73	79	0	.	52	58	0	.	17	100	0	.	0	.	91	60	17	100	0	.	0	.	509	25
	beech	968	20	97	50	230	40	3509	15	713	34	811	25	0	.	173	48	450	32	1119	29	636	25	486	26	104	62	622	54	1743	26	773	36	1504	25	391	34	318	31	1093	27	128	44	2133	16	222	43	74	66	905	21	19204	6
	maple	59	47	7	100	87	55	362	29	84	52	77	46	0	.	59	45	33	64	107	35	41	55	51	54	45	72	23	58	318	30	42	100	150	35	40	46	106	49	0	.	28	71	98	40	12	100	14	100	169	31	2012	10
	ash	148	35	0	.	111	67	58	49	50	63	89	47	0	.	100	64	0	.	84	49	30	51	0	.	104	64	7	100	228	30	21	74	117	40	88	48	121	42	23	71	6	100	150	38	59	63	7	100	224	25	1826	11
	oak	69	59	0	.	0	.	130	36	38	71	51	77	103	74	0	.	32	81	104	63	53	75	28	61	22	100	0	.	18	71	111	64	69	62	0	.	138	38	235	38	23	74	140	35	36	59	0	.	146	33	1547	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	385	27	0	.	0	.	10	100	0	.	0	.	407	26
	other broadleaves	27	50	0	.	0	.	124	46	24	77	8	100	0	.	18	74	23	75	18	100	0	.	0	.	0	.	0	.	40	55	7	100	147	62	27	71	23	77	33	63	0	.	124	95	29	62	0	.	62	54	735	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2024	15	976	33	1437	28	12982	8	1256	28	3438	16	110	73	807	29	12579	9	2856	17	3177	13	2040	15	469	30	2108	25	4826	15	1349	30	2935	19	2126	18	1177	20	4245	13	1395	27	5992	12	6255	11	216	55	3253	13	80026	3
121-160 years	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	103	62	200	60	179	65	3165	13	48	62	161	33	0	.	636	53	5092	13	163	35	764	35	487	22	135	65	564	29	840	24	91	61	201	51	576	26	80	50	615	21	530	50	1452	19	2436	18	7	100	264	38	18791	6
	fir	96	56	7	100	96	71	1101	17	14	70	141	44	0	.	56	88	145	37	379	36	316	32	419	27	49	88	217	36	323	31	21	100	132	42	217	39	66	68	157	50	78	77	730	23	101	42	0	.	100	42	4958	8
	pine	7	100	0	.	0	.	206	68	28	79	7	100	0	.	0	.	595	34	14	100	13	71	0	.	0	.	21	74	7	100	14	100	36	59	13	71	0	.	56	88	0	.	0	.	400	37	0	.	20	74	1436	20
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	6	100	978	17	0	.	0	.	7	100	0	.	0	.	9	100	0	.	0	.	0	.	0	.	677	31	0	.	0	.	1410	20	14	100	7	100	3114	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	314	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	148	50	0	.	0	.	478	27
	other conifers	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	24	77
	beech	107	36	19	73	14	71	619	26	215	54	14	71	0	.	99	66	214	44	175	40	139	43	142	40	0	.	219	51	69	43	106	47	256	42	142	49	60	51	192	29	34	63	406	32	6	100	0	.	148	39	3394	10
	maple	7	100	0	.	0	.	66	45	9	100	0	.	0	.	12	70	18	100	0	.	0	.	48	83	0	.	15	100	7	100	0	.	0	.	7	100	0	.	0	.	0	.	53	67	0	.	0	.	14	71	257	27
	ash	0	.	0	.	0	.	37	81	9	100	0	.	0	.	0	.	6	100	15	71	7	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	15	71	0	.	0	.	28	80	125	34
	oak	29	50	0	.	0	.	34	45	0	.	0	.	7	100	0	.	28	100	7	100	23	100	0	.	0	.	0	.	0	.	32	71	41	76	6	100	69	73	25	71	0	.	52	52	0	.	0	.	41	47	394	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	55	0	.	0	.	0	.	0	.	0	.	94	52
	other broadleaves	14	70	0	.	0	.	21	58	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	41
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	368	29	226	57	290	57	5256	10	329	42	323	28	7	100	810	44	7429	10	753	26	1262	25	1103	20	184	55	1036	25	1255	21	263	47	674	29	962	23	275	37	1793	17	642	43	2708	14	4518	13	21	74	621	26	33108	4
>160 years	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	36	100	19	73	1744	18	0	.	141	48	0	.	204	46	2960	14	36	60	75	48	108	55	6	100	213	43	568	32	14	100	0	.	470	39	0	.	533	25	624	45	654	22	1302	21	0	.	0	.	9706	7
	fir	38	84	21	100	19	73	324	29	0	.	24	79	0	.	21	58	62	48	7	100	7	100	52	47	0	.	42	63	14	71	0	.	7	100	22	58	0	.	53	83	39	73	117	34	74	66	0	.	0	.	942	15
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	161	39	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	412	57	0	.	0	.	610	40
	larch	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	1055	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	26	0	.	35	72	1424	17	0	.	0	.	2937	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	400	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	371	36	0	.	0	.	771	23
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	17	100
	beech	34	52	0	.	0	.	40	47	0	.	0	.	0	.	87	56	18	74	7	100	0	.	0	.	0	.	7	100	26	75	7	100	70	100	0	.	0	.	87	52	7	100	65	57	7	100	0	.	0	.	463	24
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	77
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	9	100	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	38	45
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	156	44	0	.	0	.	8	100	0	.	0	.	164	42
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	72	59	57	100	39	68	2129	16	0	.	164	45	0	.	318	39	4656	11	50	52	82	48	161	43	6	100	279	40	624	31	21	100	107	92	492	38	0	.	1257	16	679	42	871	20	3599	14	0	.	17	100	15677	6
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3298	22	1566	31	1922	28	26931	7	583	41	7061	16	0	.	2966	24	34661	7	2000	22	7930	14	4163	17	1849	48	4581	22	7900	14	1761	30	1384	28	4555	16	3660	21	6168	15	4292	21	12523	12	17131	10	517	40	6291	17	165693	3
	fir	1178	30	317	40	924	37	12968	9	678	33	2261	25	0	.	242	44	1273	22	3507	18	5333	14	3442	17	215	39	1128	31	1744	18	163	56	1696	28	1435	26	1707	33	1052	33	1419	29	5899	12	1416	25	506	47	1153	19	51657	4
	pine	165	48	0	.	9	100	1439	30	243	34	202	43	114	80	0	.	4164	20	197	40	123	58	165	58	0	.	500	95	24	76	378	46	320	36	113	95	194	33	210	73	251	58	206	60	5188	20	0	.	601	23	14807	10
	larch	184	43	0	.	0	.	179	39	14	71	58	89	0	.	13	71	5555	12	7	100	58	66	7	100	0	.	87	100	314	57	38	61	7	100	14	100	109	71	4066	17	0	.	948	43	7382	11	14	100	145	35	19199	7
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	2406	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	1566	24	0	.	0	.	4030	15
	other conifers	198	44	0	.	36	71	396	28	73	100	0	.	0	.	18	100	585	39	51	74	68	57	17	100	0	.	17	100	198	42	38	100	188	53	18	100	34	71	16	100	87	100	396	43	156	53	0	.	360	57	2952	15
	beech	4225	14	295	46	468	35	15783	8	2962	21	3791	19	0	.	1203	30	2596	21	3588	18	4160	17	2269	16	796	41	1591	30	6586	15	2313	23	4801	15	1647	21	1747	18	11036	14	1208	34	7255	11	2194	29	244	49	3858	12	86616	3
	maple	865	21	28	79	496	56	3566	13	686	24	1036	24	0	.	605	34	929	25	672	24	842	21	1198	32	305	33	508	72	1937	18	204	54	1124	22	582	30	790	27	679	34	374	44	2938	17	1035	27	124	63	2245	23	23771	5
	ash	1154	27	0	.	371	47	1640	17	464	33	1504	24	86	100	440	39	1049	37	820	29	982	28	320	39	458	57	128	48	1711	23	395	61	606	27	700	36	960	23	1746	29	453	41	1930	19	1019	38	140	53	1320	22	20396	6
	oak	318	45	0	.	0	.	491	25	233	48	162	41	435	52	0	.	91	72	184	45	159	48	334	77	22	100	0	.	86	49	195	45	400	59	6	100	402	28	2085	25	75	63	2215	29	975	34	0	.	357	26	9224	11
	sweet chestnut	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	781	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11219	14	0	.	24	76	89	66	0	.	0	.	12120	13
	other broadleaves	1271	22	30	100	165	58	3848	14	393	31	648	30	34	100	520	43	6415	20	905	32	732	28	546	30	172	57	212	46	1643	26	161	55	789	36	709	40	904	38	11511	12	532	37	2560	18	4325	16	17	100	1251	21	40295	6
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	12863	11	2236	29	4390	22	67251	5	6329	16	16724	11	670	50	6007	18	60505	6	11931	12	20387	10	12463	11	3818	30	8752	17	22143	10	5646	19	11316	12	9780	14	10506	14	49836	5	8691	17	36896	8	42475	7	1561	32	17582	10	450759	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1327229. Birmensdorf, Swiss Federal Research Institute WSL

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