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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NFI1
growing stock (stemwood)
diameter at breast height (main diameter classes) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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
diameter at breast height (main diameter classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.
	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	.
	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	.
	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	.
	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	.	0	.	0	.
	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	.
	maple	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	.
	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	.	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	.
	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	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	.
12-15 cm	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	137	26	27	42	36	33	587	11	18	61	198	18	1	100	60	38	547	8	91	28	182	17	76	30	31	38	83	27	200	19	48	43	62	34	99	26	95	31	59	19	53	24	230	14	294	11	18	54	161	21	3391	4
	fir	37	31	11	40	3	75	294	11	15	37	49	27	0	.	17	78	18	26	60	22	116	21	70	19	5	53	18	41	48	22	1	100	40	29	32	27	14	52	16	36	24	45	153	17	53	33	19	36	25	38	1140	6
	pine	6	44	0	.	0	.	14	39	13	100	1	100	8	100	0	.	121	20	0	.	7	79	5	80	0	.	8	67	5	80	7	85	10	53	1	100	0	.	2	100	8	47	1	100	136	19	0	.	3	80	357	12
	larch	14	78	0	.	0	.	2	100	0	.	0	.	0	.	0	0	41	19	0	.	0	.	0	.	0	.	0	.	8	66	3	100	1	100	0	.	0	.	31	31	0	.	2	71	98	19	0	.	4	75	204	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	26	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	18	35	0	.	0	.	49	20
	other conifers	1	100	1	100	0	.	9	41	3	100	0	.	0	.	1	100	8	52	1	100	2	78	0	.	2	100	0	.	3	60	1	100	3	65	1	100	0	.	0	.	3	63	8	66	2	100	0	.	8	62	57	20
	beech	190	14	7	55	15	44	438	11	61	23	140	19	0	.	33	31	57	22	95	24	97	19	48	24	13	51	58	43	148	18	107	23	127	20	56	33	64	30	300	14	17	47	162	14	71	34	5	42	122	18	2432	4
	maple	35	26	4	61	6	74	92	19	3	83	9	43	0	.	18	51	13	34	16	32	13	32	10	43	1	100	8	36	33	29	14	57	31	27	24	27	17	64	8	43	8	52	87	23	27	28	3	71	28	28	505	8
	ash	17	37	2	100	8	54	64	27	10	56	38	36	0	.	12	39	21	39	35	29	23	41	7	73	14	70	9	87	57	29	4	60	19	36	12	55	21	38	57	20	7	53	59	21	25	33	13	67	42	39	576	8
	oak	21	43	0	.	0	.	15	61	10	70	6	69	43	51	0	.	2	61	3	78	3	71	9	59	0	.	0	.	2	100	4	52	9	51	1	100	3	51	60	34	0	100	50	34	14	28	0	.	4	62	259	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	50	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	304	13	0	.	0	.	1	100	0	.	0	.	338	13
	other broadleaves	45	27	3	80	9	57	119	16	18	31	31	27	2	77	8	52	145	16	38	24	40	43	31	48	5	41	28	42	64	30	4	51	25	32	40	38	41	34	363	12	34	51	113	20	176	16	1	100	53	21	1437	5
	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	504	11	55	35	77	26	1634	6	150	21	471	12	54	46	148	25	1035	6	338	13	484	12	256	14	71	27	213	21	569	11	195	19	326	13	267	16	255	17	1200	7	155	21	865	8	914	8	59	30	450	12	10746	2
16-23 cm	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	326	23	146	32	269	27	2099	8	39	39	771	15	0	.	248	29	2097	8	257	32	556	17	256	20	125	32	338	21	581	14	167	36	144	36	377	20	260	28	211	18	249	27	892	12	1202	11	52	45	363	16	12024	3
	fir	167	25	44	49	82	39	1069	10	35	39	165	25	0	.	47	58	85	23	271	22	423	20	253	18	27	55	72	32	208	20	0	.	126	26	152	24	75	35	40	36	60	48	411	15	117	27	50	60	123	24	4103	5
	pine	21	50	0	.	0	.	47	34	1	206	4	100	9	73	0	.	341	18	18	68	15	65	1	167	0	.	8	100	12	62	4	100	35	40	6	80	8	100	14	50	40	49	23	67	413	17	1	100	23	44	1044	10
	larch	25	48	0	.	0	.	30	56	0	.	28	100	0	.	0	.	285	15	0	.	2	100	0	.	0	.	0	.	7	67	4	100	2	100	0	.	11	100	175	35	0	.	15	53	374	15	0	.	0	.	957	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	88	21	0	.	0	.	0	.	0	.	8	100	4	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	102	26	0	.	0	.	202	16
	other conifers	13	54	0	.	2	100	28	56	14	100	7	100	0	.	0	.	14	52	0	.	2	100	1	100	0	.	0	.	7	52	0	.	7	65	0	.	4	81	0	.	0	.	1	100	0	.	0	.	10	58	110	24
	beech	570	16	28	51	85	37	1627	9	260	23	357	20	3	100	155	36	166	23	358	16	317	20	171	21	88	40	219	38	435	17	299	22	541	16	124	24	171	23	680	14	59	38	607	12	144	26	14	42	385	15	7862	4
	maple	77	23	10	59	11	60	258	15	74	34	68	36	0	.	56	37	29	34	82	27	82	30	59	41	26	44	33	40	238	20	24	41	122	25	59	32	42	42	39	42	19	52	243	17	33	36	14	71	164	26	1863	6
	ash	91	23	18	55	92	42	231	20	34	47	149	30	4	80	38	68	53	49	119	26	46	37	24	68	49	76	17	49	195	22	33	49	100	42	49	33	71	29	112	26	24	57	260	22	23	46	47	54	147	28	2026	7
	oak	94	27	0	.	0	.	61	28	32	53	18	54	91	51	0	.	22	61	12	52	21	43	27	66	1	100	0	.	18	64	27	35	15	48	3	100	43	52	142	22	0	.	219	24	38	35	0	.	34	41	917	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	45	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	825	13	0	.	18	93	7	71	0	.	0	.	952	12
	other broadleaves	175	20	15	71	8	71	276	16	67	26	55	26	4	100	33	42	250	17	112	24	47	34	21	37	14	54	34	37	200	25	5	59	57	29	82	38	120	26	751	10	18	43	220	19	222	17	9	64	139	23	2935	5
	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	1559	10	260	29	549	21	5726	5	554	16	1621	11	112	44	578	20	3531	6	1227	12	1511	11	814	12	331	25	729	17	1906	9	563	19	1149	12	852	13	806	15	2987	6	470	20	2909	7	2675	7	187	31	1389	10	34995	2
24-35 cm	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	1093	18	542	32	705	28	7424	7	108	38	2503	14	0	.	721	27	6728	7	669	24	2094	15	907	19	268	35	1045	18	2316	14	452	36	517	24	1295	18	899	24	673	19	848	21	3217	12	3346	10	142	38	2012	14	40522	3
	fir	579	22	270	42	207	37	3198	10	115	36	589	25	0	.	78	39	207	27	954	19	1329	15	752	17	69	47	113	36	728	17	14	72	461	24	300	23	114	37	139	64	134	36	1013	14	210	28	88	43	698	19	12361	4
	pine	115	33	0	.	0	.	164	33	156	29	89	60	0	.	0	.	858	17	168	44	69	58	33	69	0	.	39	62	59	58	87	59	172	30	122	51	66	51	161	52	83	45	181	43	654	19	0	.	406	28	3681	8
	larch	46	51	0	.	0	.	55	66	7	100	76	71	0	.	0	.	1014	16	0	.	0	.	0	.	0	.	0	.	122	69	47	55	0	.	0	.	2	100	465	17	0	.	15	71	1205	13	0	.	77	54	3129	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	318	21	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	215	25	0	.	0	.	542	16
	other conifers	8	100	0	.	0	.	14	77	0	.	16	72	0	.	0	.	3	100	0	.	29	100	0	.	0	.	0	.	25	110	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	12	71	113	40
	beech	1318	14	115	46	282	34	3038	9	901	19	1037	17	0	.	339	32	303	26	1279	17	723	18	490	21	200	38	380	31	1288	16	438	21	1570	15	369	25	340	24	750	13	90	42	2212	11	107	43	144	51	1074	15	18787	3
	maple	247	23	15	85	103	43	479	17	96	31	59	40	7	100	92	31	34	45	296	22	131	39	89	30	67	46	44	49	341	19	0	.	172	28	61	38	103	35	40	41	27	51	278	19	37	39	41	60	219	29	3077	6
	ash	348	23	80	100	81	44	307	23	62	47	179	32	0	.	48	66	34	52	268	25	139	37	29	63	64	52	32	53	391	26	75	47	88	36	82	43	132	35	49	48	28	71	427	22	80	36	57	54	282	23	3363	7
	oak	187	28	0	.	0	.	175	28	71	51	55	65	52	50	0	.	82	63	83	53	48	42	47	54	0	.	0	.	73	66	55	43	93	40	0	.	122	46	192	27	5	100	394	22	11	59	0	.	127	38	1875	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	700	14	0	.	19	83	24	58	0	.	0	.	766	13
	other broadleaves	330	27	3	100	6	100	235	21	93	34	81	46	15	75	39	43	201	25	176	28	34	43	47	52	57	88	17	100	186	27	12	72	165	26	60	49	106	35	789	15	32	57	291	27	171	30	0	.	197	27	3345	7
	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	4271	9	1025	26	1384	21	15090	5	1609	15	4684	10	74	46	1316	19	9805	6	3893	10	4597	10	2395	12	725	24	1675	15	5529	9	1180	20	3240	11	2289	13	1884	15	3957	7	1251	17	8046	7	6067	7	472	28	5104	9	91562	2
36-51 cm	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	2168	16	588	30	1228	21	15400	6	394	34	3897	12	0	.	1141	19	11210	6	1337	15	3989	13	1908	13	366	31	1695	17	4723	11	539	23	1335	20	2419	14	1629	19	1317	14	923	18	5762	8	4035	8	652	30	3882	11	72536	2
	fir	1047	19	209	38	490	29	5719	8	166	40	893	19	0	.	143	60	537	24	1120	17	1893	13	1203	15	188	51	226	39	1586	16	45	64	609	19	498	23	408	36	326	25	121	40	1753	12	360	27	196	38	1513	17	21250	4
	pine	574	28	17	100	5	100	732	22	385	32	276	41	8	100	0	.	1050	15	203	31	50	54	85	52	0	.	17	70	204	37	113	36	491	22	47	81	378	27	135	43	37	48	150	45	609	20	0	.	860	16	6426	7
	larch	177	33	7	100	30	100	114	44	32	77	71	58	0	.	0	.	2399	10	9	100	24	90	0	.	0	.	6	100	135	51	5	100	0	.	23	100	7	100	798	14	8	100	98	43	2290	10	12	100	147	36	6391	6
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	453	21	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	189	23	0	.	0	.	655	16
	other conifers	0	.	0	.	0	.	14	71	0	.	5	100	0	.	0	.	0	.	0	.	41	74	0	.	0	.	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	67	100	0	.	0	.	7	100	190	49
	beech	1736	13	62	41	298	28	4090	9	1130	18	855	19	0	.	323	28	405	33	1418	15	1168	18	544	23	172	46	411	36	1428	15	339	25	1991	13	579	26	345	26	717	14	98	52	1989	12	45	55	117	46	1801	12	22061	3
	maple	145	27	8	100	26	77	268	21	90	34	43	51	0	.	52	40	35	43	79	35	38	50	30	50	25	82	21	54	213	23	5	100	108	29	36	42	95	53	14	100	6	100	119	27	5	100	27	65	206	27	1695	8
	ash	320	21	0	.	27	61	289	24	85	39	188	32	0	.	48	64	6	100	163	29	87	35	57	92	23	59	9	100	192	25	23	72	58	46	90	48	200	33	32	65	0	.	232	29	7	100	103	57	365	20	2604	8
	oak	210	31	0	.	0	.	312	21	86	40	95	38	93	69	0	.	81	49	184	36	49	56	11	72	0	.	8	100	55	64	60	44	63	42	6	100	126	30	134	38	7	100	354	24	10	115	0	.	234	34	2178	9
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	554	18	0	.	4	100	4	100	0	.	0	.	596	17
	other broadleaves	220	27	6	100	0	.	175	27	37	45	64	52	0	.	23	73	41	38	47	35	52	52	42	78	19	60	16	71	80	33	4	100	71	48	22	59	54	58	192	23	18	100	97	39	79	49	10	71	186	31	1556	9
	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	6597	9	897	25	2103	19	27117	5	2406	14	6388	10	101	64	1730	17	16251	5	4559	10	7390	9	3878	10	793	26	2415	15	8672	8	1134	18	4726	10	3720	12	3242	14	4219	7	1222	16	10626	6	7632	6	1118	24	9200	8	138137	1
=52 cm	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	1391	20	234	54	563	30	9865	7	280	47	1990	15	1	100	939	29	8612	8	791	24	1899	15	1530	18	185	38	1092	19	3773	14	179	38	701	30	1911	18	802	24	1626	17	888	26	3800	10	3008	12	315	47	1619	16	47994	3
	fir	372	27	46	58	141	42	4701	10	47	51	439	32	0	.	216	65	645	24	563	24	844	21	886	19	27	71	329	39	580	24	14	100	418	31	478	28	144	43	443	33	134	58	1456	16	265	32	70	51	576	26	13833	5
	pine	291	29	0	.	0	.	312	35	53	64	126	46	0	.	0	.	370	29	124	43	0	.	18	71	0	.	10	100	42	72	73	58	48	50	0	.	78	41	20	71	0	.	10	100	164	56	0	.	306	36	2045	12
	larch	72	66	0	.	14	100	116	44	13	100	52	73	0	.	22	100	2089	13	13	100	53	100	19	100	0	.	0	.	125	51	12	100	9	100	16	100	12	100	737	22	29	100	79	48	2515	11	18	100	133	48	6152	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	367	22	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	146	32	0	.	0	.	520	18
	other conifers	16	100	0	.	0	.	99	64	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	29	71	0	.	0	.	10	100	199	39
	beech	1096	19	11	100	89	49	2134	14	633	30	591	31	0	.	118	57	307	37	546	24	552	31	141	48	21	71	165	46	399	27	76	55	463	24	259	38	362	37	419	23	69	51	455	23	53	65	14	100	962	21	9934	6
	maple	65	52	0	.	0	.	110	44	67	58	20	72	0	.	16	73	17	71	0	.	0	.	0	.	20	71	0	.	125	43	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	59	491	20
	ash	129	48	0	.	0	.	182	46	37	74	42	75	0	.	7	100	15	100	36	58	9	100	0	.	12	100	0	.	49	58	12	100	11	100	0	.	60	50	0	.	0	.	97	53	0	.	0	.	128	43	825	18
	oak	395	29	0	.	12	100	122	40	58	62	50	76	26	100	0	.	33	74	92	77	55	59	0	.	0	.	0	.	43	76	173	47	97	45	19	100	193	40	-7	100	11	100	274	38	0	.	21	100	177	33	1844	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1146	22	0	.	0	.	17	74	0	.	0	.	1230	21
	other broadleaves	79	48	0	.	0	.	115	43	90	66	37	58	0	.	0	.	28	67	0	.	13	100	0	.	0	.	0	.	19	100	0	.	16	100	0	.	52	80	32	55	0	.	22	72	43	90	0	.	25	100	571	20
	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	3905	11	292	48	821	26	17757	6	1279	20	3377	14	28	95	1317	24	12551	6	2166	14	3425	12	2594	14	264	30	1596	18	5162	12	566	23	1763	19	2684	16	1704	17	4416	10	1131	23	6221	9	6210	8	438	38	3975	11	85639	2
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	5116	13	1537	26	2801	19	35374	5	839	30	9359	10	2	75	3109	18	29193	5	3144	14	8720	11	4677	12	974	27	4252	15	11593	10	1384	22	2759	17	6101	12	3685	16	3886	12	2961	17	13900	7	11884	7	1179	28	8037	10	176466	2
	fir	2201	17	580	33	924	25	14982	7	378	32	2135	18	0	.	501	38	1492	17	2968	15	4605	12	3165	13	316	38	757	28	3151	14	74	61	1653	17	1461	17	756	27	964	24	474	34	4785	10	1005	21	424	28	2935	15	52687	3
	pine	1007	21	17	100	5	100	1269	20	608	28	496	35	25	65	0	.	2740	13	512	27	140	47	142	45	0	.	83	63	323	34	285	33	756	20	175	46	531	25	331	40	169	38	364	39	1977	15	1	100	1597	16	13553	6
	larch	334	27	7	100	44	100	317	29	52	73	227	52	0	.	22	100	5827	8	23	72	78	95	19	100	0	.	6	100	396	39	71	60	12	77	40	72	32	72	2206	13	37	100	210	32	6482	8	30	100	360	31	16833	5
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	1255	16	0	.	0	.	0	.	0	.	21	100	11	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	670	17	0	.	0	.	1969	12
	other conifers	37	52	1	100	2	100	164	47	17	100	58	75	0	.	1	100	26	47	1	100	75	80	1	100	2	100	0	.	91	92	17	93	10	55	1	100	4	81	0	.	3	63	105	79	9	77	0	.	47	41	670	24
	beech	4911	10	222	39	769	27	11327	7	2985	14	2980	15	3	100	967	26	1237	22	3696	12	2857	15	1394	16	494	33	1234	28	3698	13	1260	16	4693	11	1386	20	1281	18	2866	10	333	32	5424	9	420	24	294	42	4344	11	61077	3
	maple	569	18	36	52	146	37	1208	12	330	22	199	27	7	100	234	25	128	25	472	18	265	25	187	29	139	38	106	37	951	14	54	35	433	20	181	25	258	34	101	36	60	40	727	13	101	27	84	44	656	17	7630	4
	ash	905	17	100	86	208	34	1073	17	226	33	596	23	4	80	153	47	129	40	622	21	304	23	117	52	162	42	66	41	884	18	146	37	277	26	234	31	484	23	249	21	59	55	1075	17	134	29	221	40	965	15	9394	5
	oak	907	18	0	.	12	100	685	16	257	32	223	30	306	41	0	.	220	51	374	37	176	40	94	42	1	100	8	100	191	51	320	32	277	26	29	78	487	25	522	19	23	72	1292	17	74	31	21	100	575	22	7073	6
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	259	33	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	3528	11	0	.	41	89	52	46	0	.	0	.	3883	10
	other broadleaves	849	22	28	64	23	55	920	13	305	29	268	31	21	71	102	35	667	16	373	21	186	27	141	34	96	64	95	31	548	19	26	48	334	23	204	28	373	26	2127	10	103	40	743	18	691	19	20	46	601	19	9843	5
	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	16836	7	2528	23	4933	17	67324	4	5997	12	16541	8	369	39	5089	15	43173	4	12184	8	17407	8	9938	9	2184	21	6628	13	21838	7	3638	15	11203	9	9812	10	7891	11	16779	4	4229	15	28667	5	23499	5	2274	22	20118	7	361078	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. 1280448. Birmensdorf, Swiss Federal Research Institute WSL

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