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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NFI2
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 1993/95
		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	1063	34	0	.	1919	91	1433	19	1882	99	667	46	0	.	189	99	440	22	0	.	654	26	162	44	0	.	741	31	451	51	269	55	537	59	808	36	787	43	69	39	436	51	903	46	649	22	280	95	1023	32	711	10
	fir	641	32	246	96	831	47	1208	16	202	50	291	39	0	.	38	99	15	61	774	26	832	27	604	33	0	.	301	50	373	35	45	99	563	31	369	39	467	30	65	50	75	99	583	26	48	50	360	96	1610	35	499	9
	pine	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	25	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	99	0	.	0	.	0	.	13	71	29	82	0	.	0	.	9	35
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	37	0	.	0	.	0	.	0	.	0	.	23	70	0	.	0	.	0	.	0	.	49	78	0	.	0	.	152	48	0	.	0	.	25	31
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	98	60	0	.	0	.	16	38
	other conifers	13	100	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	58	100	0	.	0	.	0	.	7	71
	beech	6760	19	1476	71	2747	81	2194	14	21256	30	2044	44	0	.	1592	47	359	44	4969	25	1190	42	2316	27	9484	95	1104	54	2735	28	16162	36	21369	27	1058	70	3645	40	73	41	1356	87	3484	18	207	86	2521	54	8995	23	3176	8
	maple	10613	25	5896	44	1975	33	2037	20	14003	43	2095	48	0	.	1327	40	113	39	2811	27	905	27	2562	25	342	58	811	50	3595	26	4958	62	11675	26	1383	45	9581	35	137	30	200	54	1322	28	239	44	12750	56	17805	28	3083	10
	ash	4176	18	805	39	1901	33	2661	23	9781	25	6418	53	6093	92	2293	31	208	34	4027	21	2158	34	1798	46	3088	39	496	44	4621	23	7092	30	8503	38	1578	41	7754	23	1901	32	1473	36	2539	42	502	35	5404	73	11085	18	3038	8
	oak	152	40	0	.	0	.	30	40	880	87	84	84	3134	81	0	.	18	60	139	65	38	57	78	61	0	.	0	.	12	100	364	87	19	99	0	.	480	64	263	73	37	99	420	73	156	46	0	.	255	39	145	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	856	27	0	.	0	.	0	.	0	.	0	.	83	26
	other broadleaves	1214	31	861	59	1208	58	528	17	1416	29	682	47	0	.	840	35	655	24	681	53	902	38	1007	32	89	97	339	42	499	24	450	48	591	22	1026	29	887	45	875	20	1614	46	591	26	702	20	396	70	4331	63	888	15
	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	24632	15	9284	41	10582	39	10102	10	49420	26	12282	36	9227	61	6280	29	1951	14	13403	17	6679	18	8527	19	13002	73	3791	28	12337	16	29340	28	43257	23	6243	28	23600	20	4288	20	5190	31	9913	15	2783	16	21711	51	45104	16	11682	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	242	31	0	.	729	90	642	12	69	49	333	25	113	92	338	53	569	15	32	45	545	23	147	29	260	73	497	28	362	28	189	54	248	41	634	49	376	41	161	26	601	26	547	29	547	20	163	50	394	41	439	6
	fir	117	51	0	.	16	98	161	22	181	54	122	29	56	92	0	.	12	39	777	27	358	32	177	43	0	.	96	57	31	41	0	.	168	36	217	31	214	76	79	43	88	44	271	23	87	40	623	69	98	47	143	9
	pine	6	100	0	.	0	.	7	73	0	.	3	99	0	.	0	.	48	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	99	0	.	5	70	0	.	6	100	31	40	0	.	3	100	13	26
	larch	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	80	43	0	.	0	.	0	.	0	.	0	.	51	85	0	.	0	.	0	.	0	.	22	43	0	.	2	100	74	29	0	.	0	.	23	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	28	35	0	.	0	.	8	26
	other conifers	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	6	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	71	0	.	0	.	0	.	1	56
	beech	2582	23	246	57	0	.	725	19	3935	63	261	46	0	.	808	64	52	46	1413	32	479	46	1062	25	908	97	256	37	483	26	7829	38	2927	24	432	72	976	27	95	31	78	72	1743	17	18	38	133	46	2055	25	872	9
	maple	2730	24	209	67	229	53	366	26	1483	38	91	45	0	.	193	47	29	37	787	37	309	70	518	29	0	.	109	42	383	40	504	39	1228	33	141	47	1820	33	26	33	219	67	266	19	69	41	881	69	1506	25	454	9
	ash	1218	31	124	59	447	58	686	38	1616	32	375	36	1130	92	532	42	60	50	986	39	571	38	514	33	454	50	81	43	1020	32	1394	54	1280	43	224	40	2207	25	312	38	404	67	558	29	105	36	984	56	1736	35	609	10
	oak	27	70	0	.	0	.	5	41	7	99	6	70	0	.	0	.	2	100	9	99	0	.	17	79	0	.	0	.	0	.	90	70	26	83	0	.	45	80	12	71	0	.	23	59	45	33	0	.	18	65	14	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	49	0	.	5	75	0	.	0	.	3	100	11	47
	other broadleaves	579	27	411	71	409	62	244	19	361	33	167	24	339	76	614	44	225	19	103	54	258	25	731	29	20	97	272	33	228	28	387	80	303	29	554	44	762	29	527	24	482	46	398	18	406	17	675	55	1479	67	407	12
	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	7502	16	991	49	1830	61	2839	13	7653	37	1358	19	1638	71	2485	32	1114	11	4106	20	2525	21	3168	16	1642	62	1310	19	2557	19	10394	32	6179	18	2206	27	6400	16	1354	15	1872	27	3821	11	1411	10	3458	42	7292	19	2993	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	87	39	0	.	31	98	256	13	82	60	168	25	169	64	177	44	287	15	9	70	305	26	138	44	68	72	251	29	244	27	180	54	26	39	182	32	187	29	96	23	256	25	155	20	193	17	81	67	94	38	187	6
	fir	84	71	55	65	0	.	135	19	97	59	150	55	0	.	0	.	8	80	319	26	235	33	92	35	0	.	50	41	53	58	0	.	161	34	117	48	183	85	45	59	54	45	111	22	42	45	672	52	8	57	89	10
	pine	12	99	0	.	0	.	7	90	0	.	0	.	0	.	0	.	47	42	0	.	0	.	0	.	0	.	7	99	0	.	0	.	0	.	0	.	5	99	13	72	0	.	3	100	54	55	0	.	9	100	15	27
	larch	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	45	0	.	6	100	0	.	0	.	22	99	5	100	0	.	4	99	0	.	0	.	18	43	0	.	1	100	122	27	0	.	0	.	20	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	45	0	.	0	.	4	49
	other conifers	3	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	4	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	2	70
	beech	560	19	0	.	16	98	253	19	727	31	127	34	0	.	346	53	23	43	904	31	175	30	806	27	39	67	216	63	301	29	1059	35	1160	34	271	57	543	30	161	43	48	45	652	17	31	43	253	41	454	40	313	8
	maple	1223	28	0	.	13	98	111	39	585	48	91	43	0	.	68	47	17	52	212	33	96	55	241	36	0	.	7	99	142	47	20	68	463	39	21	60	702	45	19	40	75	88	164	27	23	43	293	55	405	30	170	12
	ash	727	35	0	.	44	73	119	39	777	38	95	56	565	92	298	53	16	42	233	41	156	35	100	44	352	71	9	99	253	34	377	93	300	38	55	83	614	32	89	36	133	57	242	30	26	40	267	87	474	40	182	11
	oak	3	100	0	.	0	.	2	58	0	.	0	.	0	.	0	.	1	100	4	100	0	.	0	.	0	.	0	.	0	.	10	99	13	74	0	.	5	99	7	52	0	.	10	47	25	35	0	.	3	100	5	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	33	0	.	0	.	1	100	0	.	0	.	5	32
	other broadleaves	155	33	0	.	383	94	103	19	112	43	117	32	234	45	263	57	114	24	50	41	205	25	315	29	78	56	138	31	158	59	40	59	194	37	105	35	214	26	300	25	207	34	266	21	254	19	547	82	164	36	179	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	2860	20	55	65	487	86	994	10	2380	23	748	17	968	50	1152	29	571	11	1736	20	1183	15	1692	16	537	48	701	27	1156	17	1685	37	2320	20	750	25	2453	20	796	16	772	20	1606	11	788	11	2112	38	1610	21	1172	4
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	80	35	23	97	0	.	114	15	74	63	110	26	0	.	192	56	98	16	18	49	121	30	33	45	23	96	199	29	84	28	41	77	48	44	122	35	169	25	27	33	136	33	50	21	90	17	138	54	96	31	87	6
	fir	16	53	27	96	31	68	60	17	30	60	41	56	0	.	7	99	3	74	71	32	123	21	33	38	37	68	21	56	19	43	10	99	74	36	52	71	65	49	10	39	46	61	41	24	21	35	132	61	0	.	34	9
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	99	5	70	16	99	1	100	42	39	0	.	3	100	7	25
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	48	0	.	0	.	0	.	0	.	7	99	5	100	0	.	0	.	0	.	0	.	19	40	0	.	4	73	55	28	0	.	0	.	9	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	50	0	.	0	.	2	35
	other conifers	12	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	99	0	.	0	.	0	.	0	.	4	73	0	.	0	.	0	.	1	43
	beech	64	28	0	.	0	.	48	17	76	40	48	37	0	.	48	69	17	36	102	40	47	47	69	35	59	71	51	50	76	31	51	41	98	36	43	53	56	38	27	32	23	56	42	25	14	73	31	95	58	34	43	8
	maple	18	47	0	.	0	.	6	50	30	59	19	57	0	.	26	73	0	.	31	61	2	100	20	44	0	.	7	99	7	57	10	99	53	35	11	70	61	47	7	52	16	69	11	43	15	56	0	.	31	38	13	13
	ash	48	64	0	.	0	.	5	52	89	64	13	70	0	.	9	99	2	71	5	99	9	57	4	99	59	97	0	.	2	100	0	.	30	64	0	.	66	36	8	47	8	99	17	44	5	62	53	66	13	65	12	17
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	99	0	.	4	99	0	.	0	.	0	.	0	.	4	99	0	.	0	.	8	57	8	99	10	74	27	52	0	.	3	100	5	32
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	35	0	.	4	100	0	.	0	.	0	.	2	33
	other broadleaves	15	44	0	.	31	98	32	20	15	69	23	44	56	92	135	42	24	28	13	57	35	37	29	41	38	97	44	57	21	47	20	69	31	42	42	49	40	57	79	19	56	45	43	24	57	22	0	.	10	61	37	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	253	19	51	66	62	57	265	9	313	27	255	18	56	92	417	32	174	11	252	23	337	16	193	17	216	49	329	24	215	17	132	31	343	23	269	24	467	18	211	11	311	22	229	12	340	11	353	28	214	18	253	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	90	42	0	.	0	.	58	15	7	99	77	30	56	92	25	73	54	18	32	46	46	31	21	53	92	96	33	48	46	27	91	57	31	41	84	35	143	33	8	47	47	45	47	25	55	17	169	58	57	33	51	7
	fir	9	57	23	97	31	98	25	22	15	69	3	100	0	.	0	.	3	75	58	28	28	36	16	60	0	.	0	.	8	58	0	.	39	44	9	70	31	57	0	.	32	59	20	36	9	47	27	96	3	100	14	11
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	62	0	.	0	.	0	.	0	.	0	.	0	.	20	99	4	99	0	.	5	99	0	.	0	.	0	.	31	29	0	.	0	.	5	26
	larch	3	100	0	.	0	.	0	.	0	.	3	100	0	.	0	.	7	48	0	.	0	.	0	.	0	.	7	99	3	100	10	99	0	.	0	.	5	99	5	61	0	.	4	75	18	35	0	.	0	.	4	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	53	0	.	0	.	1	37
	other conifers	3	100	0	.	0	.	2	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	99	0	.	4	100	0	.	0	.	0	.	0	.	1	100	0	.	0	.	3	100	1	38
	beech	24	38	0	.	0	.	29	20	7	99	26	38	0	.	26	72	12	36	31	41	23	46	20	64	39	97	22	56	17	43	51	50	30	41	4	100	26	43	19	28	28	78	20	30	5	61	124	95	20	34	20	9
	maple	3	100	0	.	0	.	4	41	15	69	32	39	0	.	17	99	0	.	9	99	6	70	4	100	40	97	0	.	0	.	0	.	8	99	6	99	0	.	0	.	0	.	9	40	4	58	0	.	10	49	5	17
	ash	6	100	0	.	0	.	2	71	0	.	3	100	0	.	0	.	0	.	9	70	3	100	4	99	0	.	0	.	8	58	0	.	4	99	15	73	36	50	13	51	8	99	6	50	8	74	0	.	8	57	6	20
	oak	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	99	0	.	4	99	0	.	0	.	0	.	0	.	0	.	0	.	10	99	5	50	8	99	5	61	15	69	0	.	0	.	3	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	50	0	.	1	100	1	100	0	.	0	.	1	42
	other broadleaves	6	70	23	97	0	.	15	28	7	99	16	52	0	.	34	77	17	33	14	57	3	100	4	99	0	.	34	70	7	75	0	.	26	46	20	99	5	99	70	20	40	52	17	32	26	33	26	97	13	44	21	10
	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	147	27	46	66	31	98	135	10	52	40	161	20	56	92	102	44	106	13	156	20	108	18	74	28	171	57	97	33	92	18	172	36	146	23	140	33	261	23	130	15	163	26	131	13	179	12	346	39	113	18	132	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	1562	26	23	97	2679	91	2503	13	2114	89	1356	26	339	43	922	45	1448	12	90	29	1671	17	501	27	443	50	1720	20	1186	29	770	37	889	38	1830	29	1661	26	360	22	1476	26	1703	30	1533	14	830	57	1665	25	1474	6
	fir	868	30	351	67	910	44	1589	13	525	43	607	32	56	92	45	84	40	40	1999	19	1575	22	923	30	37	68	467	45	485	30	56	82	1004	25	764	29	960	41	199	38	296	40	1027	19	208	34	1814	59	1718	34	779	7
	pine	18	100	0	.	0	.	21	55	0	.	3	100	0	.	0	.	146	32	0	.	0	.	0	.	0	.	7	99	0	.	20	99	4	99	27	99	20	78	23	71	16	99	24	71	188	35	0	.	14	83	49	19
	larch	9	74	0	.	0	.	2	100	0	.	3	100	0	.	0	.	174	31	0	.	6	100	0	.	0	.	37	99	88	71	10	99	4	99	0	.	5	99	114	39	0	.	12	43	421	26	0	.	0	.	81	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	163	43	0	.	0	.	32	26
	other conifers	31	59	0	.	0	.	16	90	0	.	0	.	0	.	0	.	0	.	4	100	12	61	0	.	0	.	0	.	30	92	0	.	9	70	0	.	0	.	0	.	0	.	68	85	0	.	0	.	3	100	12	47
	beech	9990	18	1722	64	2763	80	3249	12	26001	29	2507	38	0	.	2819	50	464	38	7419	21	1914	37	4274	21	10530	94	1650	43	3612	24	25150	28	25584	25	1808	59	5245	30	374	28	1533	77	5940	15	276	67	3062	48	11584	21	4425	7
	maple	14587	20	6105	42	2217	30	2524	18	16117	38	2327	44	0	.	1632	37	159	33	3849	26	1318	34	3346	22	381	52	933	46	4127	25	5492	57	13427	24	1562	43	12165	32	189	26	510	47	1772	23	350	33	13923	54	19758	26	3726	9
	ash	6175	16	929	35	2392	35	3472	22	12262	23	6903	51	7788	92	3132	28	285	33	5260	21	2896	29	2420	39	3953	35	586	40	5904	21	8863	30	10117	36	1871	36	10677	20	2323	30	2025	37	3362	35	646	33	6707	66	13315	18	3846	7
	oak	186	37	0	.	0	.	37	33	887	86	91	82	3134	81	0	.	21	53	170	55	38	57	104	64	0	.	0	.	12	100	464	81	62	57	0	.	540	58	295	65	54	74	468	68	269	34	0	.	278	36	172	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1048	26	0	.	10	64	3	70	0	.	3	100	103	25
	other broadleaves	1969	26	1295	46	2031	45	922	15	1911	26	1005	34	629	57	1886	32	1035	18	861	48	1403	28	2087	23	225	56	826	23	913	23	898	49	1145	19	1746	28	1908	27	1851	17	2398	40	1315	16	1445	13	1644	50	5996	62	1532	12
	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	35394	12	10427	37	12992	39	14335	9	59818	23	14803	31	11946	59	10436	26	3915	9	19653	14	10832	15	13654	14	15569	67	6227	19	16356	15	41723	23	52245	21	9609	24	33180	17	6779	15	8308	23	15699	11	5501	10	27981	43	54334	15	16231	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. 1196526. Birmensdorf, Swiss Federal Research Institute WSL

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