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 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 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
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	1245	48	2080	48	672	79	1248	22	414	82	419	40	0	.	0	.	854	44	49	99	2560	61	1605	65	1096	97	1325	31	1073	42	112	99	240	52	919	43	455	55	44	57	528	38	1228	36	481	24	297	96	367	57	847	13
	fir	1608	39	304	96	1398	98	1511	20	1333	87	220	43	0	.	590	84	26	58	1138	37	876	31	562	32	0	.	144	70	573	28	0	.	476	50	1231	62	1545	35	43	57	0	.	749	31	103	52	3317	88	965	29	671	10
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	80	43	100	0	.	0	.	22	41
	larch	115	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	77	0	.	0	.	14	46
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	173	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	143	55	0	.	0	.	38	47
	other conifers	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	74
	beech	7990	28	7408	70	2070	53	5058	32	17646	32	14621	69	0	.	332	78	98	38	7826	31	978	62	9114	60	1753	97	902	47	4222	29	10365	49	14338	47	2469	80	8485	37	200	37	3113	55	7225	28	2293	71	275	97	5309	24	4564	13
	maple	10477	36	5116	57	1816	44	2132	21	16792	59	4731	58	2845	90	625	54	139	42	7260	39	1157	34	3373	45	3002	71	638	51	7375	34	3386	53	8641	28	2039	55	16351	36	256	60	1708	63	1499	37	163	42	16182	82	15751	34	3601	11
	ash	7476	32	304	96	2594	56	3024	23	13056	38	4713	36	627	92	6446	33	544	39	5677	27	5733	45	2550	47	3155	58	1762	82	7105	28	3037	40	6710	20	2732	56	14542	28	3668	29	2807	74	2319	24	2024	44	19842	62	9766	24	4055	8
	oak	975	86	0	.	0	.	136	40	0	.	175	52	84084	83	0	.	45	61	0	.	61	100	3444	93	0	.	0	.	57	70	0	.	0	.	0	.	65	99	334	63	0	.	2173	78	406	53	0	.	204	37	608	39
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	197	37	0	.	0	.	0	.	0	.	0	.	21	34
	other broadleaves	1108	37	1776	82	821	40	914	23	1025	45	688	33	5642	81	659	40	702	25	542	44	320	36	1446	50	439	67	553	48	370	30	110	99	245	65	557	41	1158	33	573	28	1463	41	1128	25	775	32	2224	84	601	42	784	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	30994	20	16988	32	9372	40	14030	15	50267	35	25567	43	93197	79	8652	27	2722	21	22493	21	11685	27	22094	31	9444	48	5324	33	20829	20	17009	42	30650	30	9947	35	42601	24	5314	22	9619	37	16388	17	6483	33	42137	72	32963	21	15230	6
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	478	46	202	66	521	53	578	18	33	99	232	33	0	.	236	60	571	26	0	.	326	36	213	39	153	97	697	31	304	26	161	59	155	47	230	59	170	48	70	36	573	32	486	31	381	31	519	97	345	52	377	9
	fir	337	43	0	.	0	.	401	25	930	93	58	49	0	.	0	.	10	74	375	36	305	36	52	57	0	.	67	70	95	68	0	.	159	53	187	77	238	46	20	79	65	69	288	46	65	54	249	95	236	55	181	15
	pine	28	100	0	.	0	.	3	100	0	.	0	.	0	.	0	.	69	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	99	0	.	0	.	0	.	0	.	6	100	15	57	0	.	0	.	14	35
	larch	55	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	54	0	.	0	.	0	.	0	.	38	99	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	21	79	0	.	0	.	26	45
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	60	0	.	0	.	9	34
	other conifers	14	100	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	73
	beech	3725	29	421	76	758	47	1272	18	7480	26	3004	51	0	.	481	50	135	44	3885	36	687	45	1582	35	2700	91	435	50	3250	34	4256	39	5128	27	469	45	7448	42	27	44	1517	95	2114	19	38	47	1833	54	3034	19	1665	9
	maple	1842	28	109	96	726	61	297	19	359	38	768	56	0	.	97	74	33	49	768	35	497	45	171	42	79	97	139	82	595	36	568	54	1016	36	128	45	2076	35	71	68	197	84	432	45	68	41	1510	76	2983	31	513	11
	ash	1549	26	0	.	902	35	497	24	1082	31	660	31	0	.	1145	44	68	50	584	32	250	39	517	52	1171	60	607	49	1643	38	1583	71	1799	41	785	48	4222	69	335	40	330	63	409	29	221	31	2173	38	2964	28	757	12
	oak	24	70	0	.	0	.	28	36	60	99	13	100	0	.	0	.	0	.	0	.	24	70	48	99	0	.	0	.	0	.	41	99	0	.	0	.	147	50	0	.	33	99	69	52	67	55	0	.	31	57	26	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	45	0	.	13	100	0	.	0	.	0	.	5	41
	other broadleaves	871	29	109	96	214	77	352	18	568	60	128	37	0	.	369	45	268	30	249	40	140	35	341	53	0	.	366	48	211	32	121	73	167	44	609	35	938	35	200	24	617	44	361	24	225	25	826	73	771	39	344	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	8922	16	841	56	3121	32	3435	10	10511	23	4862	36	0	.	2327	30	1343	16	5861	27	2230	25	2923	22	4104	60	2349	18	6099	22	6730	32	8443	21	2409	23	15238	31	777	22	3333	46	4178	13	1126	17	7109	29	10364	15	3920	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	101	45	0	.	355	85	215	16	213	99	221	28	0	.	73	50	224	27	6	99	152	27	94	35	83	97	217	31	154	29	116	71	50	57	113	67	195	45	38	27	170	44	151	26	164	22	47	94	75	30	152	9
	fir	99	62	0	.	0	.	162	24	173	82	44	44	0	.	0	.	6	45	174	31	88	35	53	41	0	.	11	99	26	71	0	.	80	45	159	50	0	.	17	43	24	69	67	29	55	42	77	96	9	100	64	12
	pine	20	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	54	56	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	99	0	.	0	.	0	.	5	100	33	57	0	.	4	100	13	37
	larch	5	99	0	.	0	.	0	.	0	.	6	99	0	.	0	.	75	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	57	0	.	4	70	43	35	0	.	0	.	16	27
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	4	71	0	.	0	.	3	32
	other conifers	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	6	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	1	50
	beech	2995	26	590	66	118	61	788	21	3884	39	583	41	0	.	127	46	49	35	852	34	568	35	635	23	3067	95	438	46	881	39	1351	36	3406	29	157	46	1036	40	293	70	134	56	948	19	12	58	384	60	1367	23	769	9
	maple	854	33	0	.	286	55	139	19	162	44	52	39	0	.	37	56	21	50	376	29	256	35	165	59	57	97	25	69	51	47	160	63	575	35	64	51	442	42	15	42	142	83	202	32	20	39	192	96	859	40	183	12
	ash	669	33	0	.	110	55	358	38	783	51	240	36	975	83	276	46	10	51	445	33	202	35	101	33	876	67	180	45	237	48	423	66	722	40	268	68	804	32	51	36	179	65	213	29	100	33	1345	67	847	27	280	11
	oak	22	51	0	.	0	.	2	70	12	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	4	70	0	.	4	100	2	29
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	43	0	.	2	100	2	100	0	.	0	.	2	38
	other broadleaves	311	28	0	.	83	57	178	16	226	55	339	32	162	92	156	37	96	51	157	42	108	27	154	44	28	97	310	42	172	53	116	75	155	48	157	33	180	33	171	22	252	41	284	24	252	46	1490	79	411	33	203	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	5076	19	590	66	953	44	1845	13	5451	31	1486	21	1137	72	668	27	550	17	2017	22	1380	22	1201	17	4112	71	1181	27	1532	26	2167	29	4988	24	926	30	2656	22	615	36	902	30	1879	15	689	21	3536	46	3579	17	1688	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	50	37	0	.	69	98	96	16	52	62	89	32	0	.	47	72	130	17	7	57	101	26	50	69	50	60	191	30	95	23	51	80	32	65	61	30	82	38	42	23	143	28	64	18	94	15	0	.	49	49	81	6
	fir	12	42	0	.	9	98	52	22	51	48	26	35	0	.	4	99	2	53	113	24	65	25	32	37	11	97	46	40	10	54	0	.	29	35	94	49	9	72	17	43	49	34	49	29	20	38	30	65	14	55	31	10
	pine	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	48	0	.	0	.	0	.	0	.	8	99	1	100	0	.	0	.	3	99	0	.	5	100	0	.	1	100	19	38	0	.	3	70	6	29
	larch	4	70	0	.	0	.	0	.	4	99	0	.	0	.	0	.	19	33	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	41	0	.	0	.	34	27	0	.	1	100	7	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	34	0	.	0	.	2	27
	other conifers	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	2	100	0	69
	beech	141	31	31	96	18	68	74	15	136	29	58	69	0	.	24	65	21	46	79	34	51	26	71	29	0	.	33	45	90	23	152	41	183	32	52	40	57	39	40	28	9	70	79	21	4	84	30	66	102	25	60	7
	maple	80	36	0	.	8	98	19	19	71	44	7	62	0	.	9	69	5	64	39	35	6	49	65	60	0	.	5	99	7	52	6	99	18	63	7	99	20	50	10	33	13	56	33	29	9	31	0	.	29	32	19	11
	ash	66	33	0	.	0	.	15	42	4	99	42	53	95	92	18	79	2	53	24	57	12	38	13	70	78	97	13	56	20	55	6	99	34	66	30	76	100	43	11	35	9	99	18	40	4	62	44	51	66	43	21	13
	oak	3	100	0	.	0	.	1	74	0	.	6	75	0	.	0	.	0	.	5	70	0	.	0	.	0	.	0	.	0	.	0	.	11	78	0	.	0	.	2	74	0	.	1	100	4	60	0	.	17	90	2	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	40	0	.	0	.	1	100	0	.	0	.	1	37
	other broadleaves	44	35	0	.	0	.	32	17	13	56	37	51	0	.	47	56	22	46	38	40	44	28	43	40	11	97	31	48	19	34	11	68	27	52	19	41	40	34	81	19	62	47	53	20	48	21	33	64	38	49	39	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	403	17	31	96	103	66	292	8	331	21	264	23	95	92	151	35	230	12	306	17	280	13	274	26	150	53	327	19	242	15	225	38	332	21	266	22	309	19	225	11	285	24	299	10	248	9	137	29	321	17	270	3
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	29	32	0	.	17	98	64	14	12	99	31	32	63	92	42	60	73	12	0	.	61	28	39	41	46	59	58	44	50	23	34	60	15	56	44	29	51	37	23	25	62	36	34	26	52	15	14	97	23	38	46	6
	fir	9	51	0	.	0	.	26	17	25	61	19	39	0	.	4	99	1	71	55	26	40	31	51	41	9	98	12	56	14	55	0	.	25	38	30	58	21	59	3	61	13	75	21	27	10	55	0	.	4	57	16	10
	pine	2	100	0	.	0	.	1	71	5	99	0	.	0	.	0	.	10	37	0	.	0	.	0	.	0	.	13	99	0	.	0	.	0	.	0	.	0	.	5	75	9	99	0	.	11	31	0	.	7	100	4	21
	larch	0	.	0	.	0	.	1	58	0	.	0	.	0	.	0	.	12	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	99	0	.	0	.	7	39	0	.	4	80	17	29	0	.	0	.	4	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	74	0	.	0	.	1	36
	other conifers	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	97	0	.	1	100	0	.	0	.	6	99	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	47
	beech	44	28	0	.	0	.	39	15	58	40	14	42	0	.	8	69	7	43	38	31	24	35	27	37	0	.	33	41	28	37	84	60	64	40	17	46	49	37	27	28	12	56	22	29	3	50	50	70	17	39	25	8
	maple	9	52	0	.	0	.	5	29	38	54	3	70	0	.	5	99	0	.	5	70	6	61	13	56	11	97	0	.	9	70	6	99	7	74	7	69	6	70	4	47	5	99	13	29	11	31	0	.	9	52	7	12
	ash	22	41	0	.	16	68	3	38	4	99	10	60	63	92	5	99	1	73	2	99	5	57	2	99	0	.	0	.	8	51	6	99	2	99	12	59	37	33	8	40	5	99	7	53	3	50	45	70	17	38	7	13
	oak	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	3	99	0	.	0	.	2	58	5	99	7	100	8	52	0	.	8	100	2	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	35	0	.	0	.	0	.	0	.	0	.	1	35
	other broadleaves	17	41	0	.	0	.	19	20	13	73	26	39	0	.	14	56	10	27	18	54	9	40	4	99	32	97	21	51	15	53	11	69	14	46	6	70	36	47	46	19	22	59	14	32	28	22	0	.	9	56	19	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	132	19	0	.	33	78	159	8	155	26	103	16	127	59	79	37	118	9	121	19	145	16	136	24	110	39	136	23	125	15	141	38	132	26	122	21	200	16	133	11	132	25	122	14	145	10	108	40	93	22	131	3
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	1903	34	2282	45	1635	50	2201	16	724	59	993	24	63	92	398	46	1851	26	63	79	3200	49	2001	53	1429	76	2488	25	1676	28	474	43	491	34	1368	33	953	30	217	24	1475	27	1963	26	1172	19	877	63	859	38	1503	9
	fir	2065	36	304	96	1407	97	2152	18	2512	86	366	32	0	.	598	83	45	40	1855	28	1375	26	749	26	21	68	280	43	719	30	0	.	769	39	1701	47	1813	33	101	40	150	37	1174	29	253	41	3672	79	1228	26	964	9
	pine	53	96	0	.	0	.	5	65	5	99	0	.	0	.	0	.	237	35	0	.	0	.	0	.	0	.	21	99	9	100	0	.	20	99	10	99	0	.	9	86	9	99	79	83	122	47	0	.	14	71	58	25
	larch	178	95	0	.	0	.	1	58	4	99	6	99	0	.	0	.	284	43	0	.	2	100	0	.	0	.	38	99	0	.	0	.	2	99	0	.	0	.	29	35	0	.	8	57	168	30	0	.	1	100	68	29
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	248	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	186	48	0	.	0	.	53	37
	other conifers	14	100	0	.	0	.	18	60	0	.	0	.	0	.	0	.	0	.	6	100	5	100	0	.	11	97	0	.	55	97	0	.	0	.	6	99	0	.	0	.	0	.	2	71	0	.	0	.	5	75	7	45
	beech	14895	20	8450	61	2964	43	7231	23	29204	24	18279	57	0	.	973	44	310	33	12680	26	2308	39	11428	52	7521	94	1842	33	8470	26	16208	37	23118	30	3165	67	17074	32	587	39	4785	64	10389	22	2349	69	2572	47	9828	18	7083	9
	maple	13261	30	5226	58	2836	40	2592	18	17421	57	5562	57	2845	90	773	44	197	36	8449	35	1922	30	3787	41	3149	68	806	47	8037	32	4125	46	10258	26	2244	50	18894	32	356	55	2065	54	2179	31	270	32	17884	81	19631	30	4324	10
	ash	9783	26	304	96	3623	45	3898	21	14929	34	5665	31	1760	61	7889	30	626	36	6733	23	6203	42	3183	40	5280	46	2562	67	9013	27	5054	39	9266	21	3827	49	19705	32	4073	27	3330	68	2967	22	2353	38	23450	52	13660	20	5119	7
	oak	1025	82	0	.	0	.	169	34	72	84	194	47	84084	83	0	.	45	61	7	74	84	74	3492	92	0	.	0	.	61	66	41	99	13	71	0	.	212	45	338	62	38	87	2252	76	489	48	0	.	264	30	642	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	280	28	0	.	15	100	3	100	0	.	0	.	31	27
	other broadleaves	2352	28	1885	77	1117	35	1495	16	1844	40	1217	26	5804	78	1245	29	1098	20	1004	32	622	23	1988	40	511	57	1281	34	787	25	370	49	608	34	1349	25	2353	25	1070	18	2417	32	1840	19	1327	23	4573	55	1830	30	1388	6
	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	45528	15	18450	30	13582	35	19762	11	66715	27	32283	35	94557	78	11877	24	4963	15	30798	19	15720	21	26629	27	17921	46	9317	22	28828	18	26272	31	44546	22	13670	28	61004	21	7064	18	14271	32	22867	13	8690	25	53028	59	47321	17	21240	5

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. 1196551. Birmensdorf, Swiss Federal Research Institute WSL

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