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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NFI5
number of stems of young forest plants
size classe (young forest; 5 classes) · main tree species in young forest (counting)
regional demarcation: canton
unit: n/ha
evaluation area: stockable and accessible forest without shrub forest NFI2-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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
size classe (young forest; 5 classes)	main tree species in young forest (counting)	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.
	species not assessed in all inventories	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	.
height 10-39 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	843	63	607	96	349	98	1325	23	81	99	283	55	0	.	484	58	500	26	99	100	921	33	1316	64	877	97	838	46	806	49	676	73	194	60	1631	46	627	67	29	100	449	51	777	24	585	25	869	71	338	36	681	11
	fir	1358	32	1776	66	913	48	964	16	167	99	955	30	0	.	266	72	17	71	1830	42	1400	31	939	29	186	98	548	48	630	33	113	99	789	32	3975	51	1817	38	44	58	77	99	1452	21	90	85	6434	40	1954	33	839	9
	pine	0	.	0	.	0	.	0	.	0	.	41	99	0	.	0	.	36	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	14	100	0	.	0	.	9	38
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	62
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	9	78
	other conifers	0	.	0	.	0	.	75	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	226	79	0	.	0	.	0	.	0	.	0	.	0	.	18	40
	beech	6512	28	5162	72	620	75	2330	35	8635	35	5085	58	0	.	2367	53	360	37	2618	28	2033	69	3156	47	438	97	340	78	8031	36	6754	40	10892	38	1308	34	2347	37	1319	39	920	60	4666	24	224	53	0	.	3534	26	2825	10
	maple	10495	30	5116	52	1631	98	2397	20	18732	45	2483	49	4978	90	1354	61	298	39	6967	46	2186	45	4193	34	3967	47	144	70	6194	34	9026	80	10840	35	2161	38	9702	33	396	30	803	55	4086	28	233	38	2523	59	14770	31	3721	10
	ash	3621	35	0	.	6598	64	1997	33	14693	32	2436	41	4388	92	3557	44	845	36	6209	33	1966	41	2154	63	3107	69	920	61	4611	31	1917	37	5480	36	1209	49	9858	26	4108	32	2649	49	1688	25	4000	58	5610	40	5208	23	3163	10
	oak	742	75	0	.	0	.	76	50	2156	95	209	62	21313	42	0	.	0	.	49	100	30	100	239	83	255	96	0	.	205	42	0	.	1070	67	0	.	686	70	493	58	0	.	2067	45	461	29	0	.	1285	52	506	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	47	0	.	14	100	0	.	0	.	0	.	15	38
	other broadleaves	741	41	304	96	148	98	1013	20	2505	58	1746	35	13164	87	1338	60	1065	17	687	48	923	33	1664	35	405	68	1665	49	433	33	225	99	387	49	1049	33	934	36	324	33	1355	35	1091	27	2311	43	572	66	1395	65	1121	11
	species not assessed in all inventories	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	24313	20	12966	36	10260	60	10177	15	46969	26	13238	27	43844	30	9365	30	3192	14	18459	25	9460	24	13662	23	9235	48	4455	26	20938	22	18710	46	29652	32	11334	23	26197	22	6839	22	6253	30	15859	13	7991	32	16008	28	28484	19	12912	5
height 40-129 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	964	51	328	96	63	98	601	21	158	66	246	38	0	.	102	56	561	25	18	100	1462	54	183	50	550	84	430	37	457	26	81	69	139	52	249	45	82	60	36	47	414	35	611	32	366	31	522	78	290	50	445	12
	fir	1005	43	93	97	0	.	554	23	120	78	118	39	0	.	34	99	3	100	710	45	379	40	49	73	0	.	59	99	222	46	0	.	227	42	188	42	461	47	15	74	32	99	240	36	31	53	500	78	513	42	250	13
	pine	0	.	0	.	0	.	6	71	0	.	25	99	0	.	0	.	55	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	71	66	86	0	.	0	.	17	46
	larch	14	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	10	71	0	.	0	.	13	33
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	21	50	0	.	0	.	7	32
	other conifers	23	70	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	58
	beech	3051	27	8209	72	717	45	1514	20	9335	45	3345	34	0	.	775	32	173	44	2804	28	880	76	2434	41	1183	91	277	53	1350	25	4661	36	3804	24	312	45	1859	31	287	61	866	71	2388	20	46	58	427	74	2443	19	1510	8
	maple	2627	46	311	51	789	59	480	20	3689	64	1034	57	0	.	101	73	20	53	1069	34	192	40	240	41	79	97	78	73	704	47	527	52	1218	61	81	60	2398	45	51	40	254	56	801	48	52	49	2478	97	2215	25	644	13
	ash	709	34	219	96	179	74	388	26	1201	40	481	46	0	.	453	57	118	45	444	39	506	54	50	57	159	67	252	85	468	38	162	59	461	27	218	61	2038	36	554	48	375	77	417	32	125	36	619	52	1089	28	430	10
	oak	0	.	0	.	0	.	16	45	0	.	12	100	5867	92	0	.	10	58	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	73	20	71	0	.	23	50	73	39	0	.	20	70	30	43
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	49	0	.	0	.	0	.	0	.	0	.	2	44
	other broadleaves	753	39	0	.	223	75	541	20	631	51	826	57	3385	44	189	55	370	30	497	36	382	29	588	38	79	97	321	45	224	27	324	56	71	60	405	35	903	30	145	24	434	42	453	25	339	26	912	65	383	25	425	9
	species not assessed in all inventories	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	9146	23	9160	65	1971	37	4104	11	15134	39	6087	24	9253	67	1653	27	1423	17	5542	20	3813	30	3544	29	2050	56	1418	23	3425	19	5756	31	5921	21	1454	23	7800	22	1144	28	2374	36	4944	14	1129	16	5458	64	6954	15	3775	5
DBH 0-3.9 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	110	36	112	71	111	79	218	15	71	99	187	26	0	.	157	85	241	23	13	99	179	26	69	43	138	97	301	35	145	26	73	64	51	57	107	41	125	33	36	46	124	48	235	25	159	25	498	51	81	36	159	8
	fir	266	53	0	.	28	97	229	26	464	97	41	47	0	.	12	99	2	71	171	28	110	29	31	51	0	.	32	73	56	53	0	.	77	48	182	45	104	49	20	41	35	56	79	38	32	44	163	53	143	60	96	15
	pine	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	34	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	99	0	.	9	81	0	.	4	71	26	48	0	.	0	.	9	28
	larch	10	100	0	.	0	.	0	.	11	99	0	.	0	.	0	.	43	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	6	73	25	59	0	.	0	.	10	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	52	0	.	0	.	3	29
	other conifers	0	.	0	.	0	.	0	.	0	.	5	99	0	.	0	.	0	.	6	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	18	66	2	42
	beech	1885	15	763	45	888	68	733	18	4436	32	1359	48	0	.	273	33	153	46	1723	43	546	39	829	20	3011	91	384	42	763	25	1924	31	3023	21	346	40	2861	35	113	41	1926	90	1026	19	23	62	1672	59	2365	20	909	8
	maple	807	30	39	96	578	62	296	26	258	44	143	30	81	92	23	69	12	67	458	36	126	28	71	32	28	97	9	99	170	47	219	62	411	42	83	41	550	31	13	55	174	82	145	24	33	46	1943	85	1047	28	223	11
	ash	121	62	0	.	0	.	99	46	130	53	24	66	406	92	169	52	27	48	19	57	219	94	11	70	85	71	64	57	20	44	15	99	141	36	16	70	198	58	23	38	73	70	38	28	28	44	37	97	151	31	67	19
	oak	13	74	0	.	0	.	6	41	12	99	0	.	1778	65	0	.	1	100	0	.	5	100	0	.	0	.	11	99	0	.	0	.	0	.	0	.	24	75	0	.	0	.	9	62	22	69	0	.	0	.	9	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	32	0	.	2	100	2	100	0	.	0	.	3	29
	other broadleaves	236	27	33	97	68	54	258	17	328	51	217	26	3071	65	121	42	79	29	168	32	150	27	154	45	170	82	238	49	159	28	0	.	391	47	87	33	229	26	94	25	322	34	209	22	152	26	929	66	302	40	190	8
	species not assessed in all inventories	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	3448	13	948	44	1673	56	1840	10	5710	27	1975	34	5337	48	756	36	605	16	2559	31	1340	23	1165	17	3432	79	1039	22	1312	17	2230	28	4093	18	844	21	4090	26	339	17	2655	66	1753	14	511	15	5242	36	4107	15	1679	5
DBH 4-7.9 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	44	32	39	97	69	98	101	14	35	61	120	27	0	.	114	70	113	19	5	70	89	23	69	50	67	47	106	32	95	25	23	99	26	51	67	29	83	38	33	25	124	29	74	18	76	18	81	65	20	41	77	7
	fir	44	55	0	.	0	.	50	18	76	73	36	32	0	.	4	99	2	50	103	23	51	28	14	56	11	97	29	49	9	40	6	99	41	37	71	51	26	44	11	50	18	59	39	22	16	43	139	44	5	76	29	9
	pine	4	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	46	0	.	0	.	0	.	0	.	8	99	0	.	0	.	0	.	0	.	0	.	3	100	0	.	1	100	16	39	0	.	0	.	5	30
	larch	8	99	0	.	0	.	0	100	0	.	0	.	0	.	0	.	11	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	47	0	.	1	100	13	32	0	.	1	100	4	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	7	39	0	.	0	.	2	32
	other conifers	4	69	0	.	0	.	0	100	0	.	4	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	47
	beech	253	24	72	53	20	67	58	17	145	33	26	35	0	.	23	52	8	38	50	52	71	28	90	30	11	97	57	39	84	24	96	37	208	25	48	38	90	24	42	36	22	67	56	19	2	58	180	80	102	23	60	7
	maple	84	27	0	.	57	48	23	22	42	37	5	57	0	.	14	73	5	37	68	30	10	47	27	49	0	.	10	69	8	51	23	78	44	44	11	78	55	47	3	50	9	69	22	27	8	34	0	.	44	29	20	9
	ash	13	52	0	.	11	97	11	43	13	72	11	84	63	92	19	59	1	71	11	60	7	49	4	99	89	97	5	99	8	71	6	99	7	57	4	99	37	59	7	40	0	.	6	37	6	66	15	96	17	40	9	15
	oak	0	.	0	.	0	.	0	100	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	70	2	74	0	.	1	100	3	61	0	.	3	70	1	30
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	36	0	.	0	.	0	.	0	.	0	.	1	36
	other broadleaves	41	28	0	.	9	98	40	16	30	49	59	39	68	57	29	55	22	38	53	42	35	32	35	35	33	71	20	59	27	31	34	44	22	44	28	41	27	44	62	22	100	40	43	19	54	35	132	85	42	29	40	8
	species not assessed in all inventories	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	495	15	111	61	166	52	285	8	341	25	262	17	131	45	202	44	190	13	290	16	263	14	238	21	211	45	236	19	231	15	187	28	348	18	229	22	322	18	179	14	273	27	242	10	201	14	546	37	235	15	248	4
DBH 8-11.9 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	28	36	0	.	37	73	52	14	12	99	50	30	0	.	24	58	68	15	0	.	47	25	16	46	23	67	64	34	63	24	17	55	19	65	40	32	23	45	22	28	75	38	31	21	35	18	51	71	11	42	40	6
	fir	13	55	0	.	9	98	24	21	34	48	7	50	0	.	4	99	1	58	63	27	34	31	33	36	9	98	17	48	14	53	0	.	17	36	39	53	6	99	5	55	26	38	18	35	9	42	15	96	8	68	15	10
	pine	4	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	40	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	14	99	0	.	10	39	0	.	0	.	3	26
	larch	4	100	0	.	0	.	0	100	0	.	5	99	0	.	0	.	6	35	0	.	0	.	0	.	0	.	0	.	1	100	0	.	2	99	0	.	0	.	6	54	0	.	1	100	13	27	0	.	0	.	3	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	1	35
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	71
	beech	69	23	31	96	0	.	31	15	71	34	16	46	0	.	5	99	2	60	41	33	28	33	23	33	0	.	13	56	35	32	79	40	51	48	17	46	29	36	32	25	13	56	29	22	0	.	32	65	40	26	25	7
	maple	28	31	0	.	0	.	10	26	25	45	0	.	0	.	24	50	3	87	24	45	5	57	23	43	0	.	0	.	6	60	0	.	2	99	0	.	21	45	4	52	4	99	15	32	6	35	0	.	12	39	9	11
	ash	7	60	0	.	0	.	4	42	0	.	15	79	0	.	0	.	1	100	5	70	0	.	0	.	0	.	0	.	1	100	0	.	2	99	3	99	8	73	4	45	0	.	7	69	4	52	15	96	1	100	4	22
	oak	7	49	0	.	0	.	0	.	0	.	2	99	0	.	0	.	0	.	5	99	2	99	0	.	0	.	0	.	0	.	0	.	3	99	0	.	0	.	1	100	0	.	9	100	6	50	0	.	8	82	2	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	44	0	.	0	.	0	.	0	.	0	.	0	45
	other broadleaves	28	34	0	.	9	98	14	22	4	99	13	47	0	.	28	44	13	44	15	45	23	41	8	74	43	97	19	48	13	49	0	.	12	43	9	56	27	39	38	21	26	46	21	26	26	21	15	96	15	36	19	9
	species not assessed in all inventories	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	188	14	31	96	54	52	136	8	146	24	108	20	0	.	85	29	110	12	153	17	143	17	102	19	76	58	113	24	133	15	96	34	109	33	108	23	114	19	120	11	159	26	130	13	110	11	128	33	96	18	122	4
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	1989	49	1087	82	628	61	2298	17	357	60	885	24	0	.	880	42	1483	18	134	75	2699	38	1654	54	1655	81	1740	28	1566	30	870	57	427	34	2094	37	938	49	156	35	1187	28	1727	20	1221	19	2020	42	740	32	1402	8
	fir	2686	29	1869	63	950	45	1822	14	860	71	1157	26	0	.	320	60	26	50	2877	32	1974	26	1065	25	207	88	686	41	931	27	119	94	1150	26	4455	46	2413	36	96	37	187	47	1828	19	179	46	7251	40	2623	28	1229	8
	pine	8	78	0	.	0	.	7	62	0	.	66	72	0	.	0	.	154	38	0	.	2	100	0	.	0	.	8	99	0	.	0	.	0	.	21	99	0	.	17	89	14	99	34	80	132	60	0	.	0	.	44	27
	larch	35	100	0	.	0	.	1	71	11	99	5	99	0	.	0	.	175	25	0	.	0	.	0	.	0	.	0	.	1	100	0	.	2	99	0	.	0	.	21	39	0	.	8	67	61	35	0	.	1	100	36	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	111	66	0	.	0	.	22	35
	other conifers	27	61	0	.	0	.	78	49	0	.	9	99	0	.	0	.	0	.	6	100	6	76	0	.	0	.	0	.	29	100	0	.	0	.	0	.	226	79	2	100	0	.	1	100	0	.	0	.	20	61	22	34
	beech	11770	19	14238	67	2244	43	4666	21	22621	28	9830	41	0	.	3443	37	698	30	7235	25	3559	53	6532	37	4643	82	1071	37	10261	30	13514	30	17978	26	2032	29	7185	24	1794	34	3747	62	8164	17	294	49	2311	62	8483	15	5329	7
	maple	14042	28	5467	48	3056	59	3205	17	22747	45	3665	48	5059	88	1516	55	338	36	8587	39	2519	40	4554	32	4075	46	241	64	7083	30	9796	76	12516	32	2337	35	12726	31	467	27	1245	41	5068	28	331	30	6944	60	18088	26	4617	9
	ash	4471	30	219	96	6788	63	2500	28	16038	30	2967	37	4858	92	4198	38	992	35	6688	31	2698	35	2219	62	3440	63	1241	64	5108	28	2099	34	6092	34	1450	43	12140	26	4696	33	3098	50	2156	22	4163	57	6297	39	6467	21	3672	9
	oak	763	73	0	.	0	.	99	40	2168	95	226	57	28959	37	0	.	11	53	54	91	48	68	239	83	255	96	11	99	205	42	0	.	1073	67	0	.	776	62	516	56	0	.	2109	45	565	28	0	.	1317	51	548	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	185	33	0	.	16	87	2	100	0	.	0	.	22	28
	other broadleaves	1799	27	337	86	457	47	1865	16	3498	45	2861	36	19688	60	1705	48	1549	16	1421	31	1513	25	2448	31	730	50	2263	40	855	20	583	53	883	31	1579	29	2120	24	663	19	2238	29	1817	20	2883	35	2560	43	2137	44	1794	8
	species not assessed in all inventories	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	37591	17	23216	44	14124	48	16541	11	68300	24	21670	23	58564	24	12061	24	5520	11	27002	19	15019	19	18711	20	15004	38	7261	19	26039	18	26979	36	40123	26	13968	20	38524	18	8622	20	11715	28	22929	11	9942	27	27382	32	39877	15	18736	4

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. 1196577. Birmensdorf, Swiss Federal Research Institute WSL

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