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
grid: 1.4-km grid
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	1012	34	1662	42	730	48	1439	21	210	82	403	30	0	.	48	99	952	25	22	100	2084	51	1202	52	1501	58	939	25	725	31	129	70	308	39	1713	47	303	50	49	35	381	34	1482	34	523	16	128	98	751	44	858	10
	fir	2319	31	1433	67	2284	48	1493	14	1118	54	735	39	0	.	332	74	65	57	1170	27	771	26	765	38	678	98	221	47	465	23	197	73	698	45	1291	37	1095	32	37	47	45	99	825	19	97	32	1991	68	2337	33	757	8
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	33	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	6	100	0	.	34	80	32	66	0	.	16	100	22	25
	larch	54	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	71	0	.	0	.	248	67	0	.	0	.	35	50
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	174	36	0	.	0	.	37	29
	other conifers	0	.	0	.	0	.	9	71	0	.	0	.	0	.	44	100	0	.	0	.	0	.	26	100	0	.	0	.	29	100	0	.	49	70	28	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	7	35
	beech	7201	20	3789	73	4760	55	3983	24	22034	24	9323	58	0	.	364	48	143	36	5194	28	1305	41	6825	45	3160	67	1189	50	3138	25	9854	35	11334	31	1655	62	6904	30	635	49	1887	53	8752	30	1539	50	634	64	6492	25	3960	9
	maple	14427	25	3667	46	3576	36	2247	17	14139	38	3947	42	2078	77	935	37	255	43	4237	33	1023	30	3812	31	3495	39	933	35	5513	29	5481	33	7554	22	2312	34	13400	29	610	28	1240	50	4492	47	660	50	7268	84	12468	26	3527	9
	ash	9094	18	466	72	6241	56	2616	18	9944	27	3393	31	8436	81	4022	30	678	34	4159	21	6001	40	3471	37	3585	44	4751	61	4646	24	5377	45	8302	34	2444	35	14045	23	3996	28	2104	54	2159	17	1549	30	9752	59	9640	17	3796	7
	oak	549	80	0	.	0	.	124	29	164	77	118	47	61392	73	0	.	68	59	0	.	76	70	2041	91	0	.	39	100	29	71	64	99	92	100	27	100	37	100	234	45	0	.	1504	58	333	37	0	.	113	37	407	30
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	182	24	0	.	102	92	7	100	0	.	0	.	32	29
	other broadleaves	1299	23	1173	68	638	35	898	15	835	33	662	25	3451	84	789	33	877	14	582	35	720	37	1500	33	486	59	1790	39	257	25	545	66	1458	48	1399	27	857	30	654	19	1453	28	1097	18	1078	21	1503	65	868	31	929	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	35955	15	12189	27	18229	29	12809	10	48444	21	18580	32	75357	62	6534	20	3312	14	15363	17	11999	23	19641	21	12905	35	9862	32	14801	18	21648	29	29794	21	10897	20	36640	19	6424	20	7110	28	20447	17	6239	19	21276	65	32685	15	14365	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	312	37	543	63	351	48	650	15	16	99	245	30	0	.	411	40	556	16	108	52	322	25	199	31	267	73	580	27	277	20	168	54	131	36	623	44	177	37	64	31	394	29	593	22	331	19	650	50	260	42	386	7
	fir	226	33	0	.	176	70	356	20	657	70	134	35	0	.	0	.	44	79	420	24	280	28	60	57	90	68	42	57	82	45	0	.	192	34	226	43	165	41	13	57	52	57	252	32	42	38	102	98	338	49	167	11
	pine	13	100	0	.	0	.	8	54	0	.	0	.	0	.	0	.	67	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	41	61	0	.	22	91	0	.	3	100	29	62	0	.	0	.	19	23
	larch	26	100	0	.	0	.	5	58	0	.	0	.	0	.	0	.	110	36	0	.	0	.	0	.	44	98	14	100	0	.	0	.	0	.	0	.	0	.	31	52	0	.	3	100	68	33	0	.	0	.	29	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	40	0	.	0	.	9	23
	other conifers	6	100	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	6	100	0	.	0	.	0	.	1	53
	beech	3582	19	437	47	1372	33	1405	15	7057	19	2442	39	0	.	295	43	91	32	2694	27	626	33	2581	25	1824	76	260	41	3084	26	5053	27	5318	21	369	32	5491	35	34	27	873	95	2410	17	64	49	1343	46	2646	15	1531	6
	maple	2939	28	56	98	602	44	281	16	1817	40	705	38	828	95	170	43	33	31	564	30	381	37	261	31	666	67	272	38	617	26	1365	42	1522	27	139	29	1986	29	53	43	174	55	469	25	78	36	761	69	1930	29	528	9
	ash	1377	20	57	98	788	32	422	18	1214	27	486	26	414	68	642	42	80	41	463	23	270	30	731	50	785	52	943	35	884	37	1160	60	1583	27	512	37	3108	56	457	25	294	45	450	25	133	26	936	43	2052	23	599	9
	oak	17	58	0	.	0	.	22	35	46	73	14	71	0	.	0	.	3	100	0	.	14	70	36	78	0	.	0	.	0	.	25	99	9	100	0	.	99	45	2	100	17	99	40	46	46	44	0	.	17	58	18	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	32	0	.	6	100	0	.	0	.	0	.	4	30
	other broadleaves	645	26	157	55	374	47	411	13	488	37	338	37	143	95	596	44	321	18	313	34	227	24	307	38	89	69	433	32	196	24	120	59	607	46	700	25	705	29	194	20	542	31	562	26	281	18	368	75	512	32	380	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	9143	13	1249	39	3663	23	3563	8	11296	15	4365	25	1386	75	2114	23	1351	10	4562	18	2121	18	4175	20	3764	40	2544	18	5140	18	7890	24	9371	15	2610	17	11743	25	908	15	2347	38	4794	11	1095	11	4161	26	7755	13	3673	4
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	51	71	58	206	82	240	12	117	90	186	22	0	.	110	38	197	17	54	42	156	21	86	27	48	98	300	30	164	19	84	58	47	42	140	34	133	43	53	23	131	35	152	17	135	15	65	60	85	25	148	6
	fir	58	55	0	.	121	99	130	18	107	68	85	45	0	.	0	.	3	41	207	23	120	24	80	30	48	72	14	57	32	42	8	99	78	32	152	33	0	.	13	36	12	70	75	23	43	32	82	58	40	41	61	9
	pine	11	84	0	.	0	.	2	58	0	.	0	.	0	.	0	.	52	33	0	.	15	71	0	.	0	.	0	.	4	100	0	.	0	.	11	57	4	100	8	100	13	99	3	75	31	40	0	.	2	100	14	22
	larch	4	71	0	.	0	.	2	100	0	.	3	100	0	.	0	.	78	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	20	29	0	.	2	71	43	24	0	.	0	.	19	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	12	32	0	.	0	.	4	20
	other conifers	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	3	100	3	100	0	.	0	.	0	.	2	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	6	75	1	40
	beech	2624	18	356	58	337	40	835	16	3252	27	742	31	50	95	207	42	45	25	1147	21	497	27	1082	18	2010	82	347	35	737	26	1601	25	2596	21	304	36	951	32	145	61	82	53	1093	15	28	36	299	43	1600	17	730	6
	maple	992	24	0	.	167	52	118	15	549	28	98	32	99	95	50	55	15	37	289	24	214	27	113	50	351	53	82	27	136	34	472	48	631	24	64	35	585	31	17	31	88	72	254	29	23	25	375	61	736	31	193	9
	ash	385	28	62	98	215	51	227	33	415	48	146	32	745	70	142	47	13	38	260	29	196	31	131	34	631	54	269	43	169	37	350	52	478	33	131	60	655	28	142	49	170	48	202	23	66	26	579	70	632	21	202	9
	oak	20	54	0	.	0	.	2	60	6	99	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	2	100	0	.	0	.	0	.	48	67	2	58	0	.	6	57	4	53	0	.	2	100	4	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	99	0	.	26	43	0	.	1	100	1	100	0	.	0	.	3	39
	other broadleaves	246	21	0	.	163	59	239	14	330	44	342	26	151	67	253	26	104	26	194	35	162	28	187	30	160	46	305	22	170	35	76	68	269	29	214	30	170	27	138	15	309	25	279	16	192	29	697	78	310	26	206	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	4441	14	489	44	1209	31	1795	9	4775	22	1603	17	1046	51	762	21	525	10	2154	15	1362	16	1682	13	3249	51	1316	17	1416	16	2592	21	4099	16	1019	18	2556	18	567	21	805	21	2067	11	577	12	2097	37	3413	13	1585	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	44	28	92	53	57	73	109	10	49	53	73	25	0	.	65	33	126	10	13	32	102	17	47	46	40	47	162	19	107	16	40	64	31	37	70	25	52	36	39	18	115	25	61	14	80	10	26	77	40	35	79	4
	fir	11	39	0	.	10	69	41	16	33	41	36	31	0	.	2	100	3	40	109	17	74	18	27	29	49	54	24	36	20	37	0	.	34	26	86	33	7	59	11	33	34	35	46	19	21	30	31	41	13	38	28	7
	pine	3	63	0	.	0	.	0	100	0	.	0	.	0	.	0	.	25	27	2	100	1	100	0	.	0	.	4	100	1	100	0	.	0	.	7	52	0	.	6	58	8	99	6	93	17	24	0	.	2	70	7	17
	larch	3	60	0	.	0	.	1	83	2	99	0	.	0	.	0	.	23	20	0	.	1	100	0	.	0	.	0	.	1	100	0	.	9	100	0	.	0	.	20	24	0	.	0	.	30	17	0	.	1	100	9	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	99	0	.	9	27	0	.	0	.	2	19
	other conifers	2	100	0	.	0	.	2	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	2	100	0	.	0	.	1	71	0	.	0	.	1	100	1	37
	beech	112	21	38	50	47	37	65	12	166	31	66	40	0	.	64	40	13	35	80	31	53	21	73	22	44	69	27	34	70	17	133	31	125	25	41	30	59	26	33	20	10	49	75	15	7	35	33	41	108	19	54	5
	maple	95	24	15	68	4	99	19	19	98	30	33	39	58	95	17	41	4	43	37	34	22	29	37	60	13	68	10	52	11	30	13	77	25	40	16	50	26	33	6	28	15	39	36	20	12	18	58	69	43	21	22	8
	ash	48	26	18	97	24	82	18	25	20	51	27	43	58	95	10	79	3	42	23	34	11	30	23	45	56	77	14	51	21	36	7	70	36	48	18	54	78	39	12	27	10	61	21	30	5	36	19	55	58	36	19	9
	oak	3	71	0	.	0	.	2	55	5	99	3	75	0	.	0	.	0	.	2	70	0	.	0	.	0	.	0	.	0	.	0	.	11	64	0	.	15	88	3	49	0	.	2	41	5	36	0	.	9	90	3	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	32	0	.	0	100	0	100	0	.	0	.	1	29
	other broadleaves	33	27	0	.	20	58	39	12	42	43	33	36	39	64	47	38	24	24	36	25	43	23	30	35	25	59	38	25	23	22	14	48	36	33	43	27	43	31	68	12	71	29	47	15	57	14	33	64	28	38	41	5
	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	355	12	163	40	161	42	296	6	416	19	271	15	155	56	205	20	228	7	302	13	308	10	236	18	227	31	279	12	254	11	206	29	307	15	282	15	282	16	202	8	264	18	296	8	244	6	201	29	304	13	265	2
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	31	26	113	45	28	50	55	11	16	71	30	24	39	95	39	39	71	9	14	48	49	22	31	33	49	41	74	25	60	16	33	53	19	44	35	24	31	36	25	18	76	40	29	17	48	11	20	55	19	28	44	4
	fir	13	40	8	98	5	99	23	13	19	45	17	29	0	.	2	100	1	45	45	20	51	24	41	32	12	69	5	57	11	37	3	99	21	31	36	30	13	59	4	45	14	51	21	18	10	43	11	99	7	46	15	7
	pine	2	70	0	.	0	.	1	45	2	99	0	.	0	.	0	.	12	26	1	100	3	100	0	.	0	.	6	100	1	100	0	.	0	.	1	100	0	.	3	61	5	99	2	100	7	25	0	.	4	100	4	16
	larch	1	100	0	.	0	.	1	45	0	.	0	.	0	.	0	.	14	21	0	.	0	.	0	.	0	.	0	.	1	100	0	.	1	100	1	100	0	.	12	30	0	.	2	79	15	19	0	.	0	.	5	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	99	0	.	3	48	0	.	0	.	1	24
	other conifers	2	100	0	.	0	.	1	66	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	68	0	.	1	100	0	.	0	.	3	100	0	.	0	.	0	.	0	100	0	.	13	98	1	100	1	32
	beech	39	20	0	.	14	56	32	13	52	26	22	31	0	.	9	49	4	34	25	30	24	26	29	26	13	68	35	36	31	25	72	48	47	29	18	29	51	26	22	20	7	57	22	20	4	33	21	73	25	32	22	6
	maple	12	30	40	78	0	.	7	20	20	50	4	62	19	95	10	60	0	71	8	42	7	42	8	57	12	69	4	70	9	43	3	99	5	61	9	40	7	49	5	36	5	70	19	20	8	24	0	.	13	32	7	9
	ash	14	34	0	.	14	56	3	30	7	75	8	43	39	95	2	99	1	53	1	100	8	48	12	79	0	.	0	.	8	39	13	59	6	83	7	52	26	30	7	31	7	74	6	37	3	35	25	59	11	36	6	10
	oak	0	.	0	.	0	.	1	79	2	99	0	.	0	.	0	.	0	71	2	70	0	.	0	.	0	.	0	.	1	100	0	.	8	85	0	.	2	100	3	42	2	99	4	90	7	30	0	.	5	87	2	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	29	0	.	0	100	0	.	0	.	0	.	1	27
	other broadleaves	19	28	0	.	9	69	20	16	22	36	16	33	19	95	15	46	11	19	11	45	7	37	5	60	19	98	17	36	16	31	7	70	12	33	10	37	28	38	46	12	34	49	16	21	26	16	0	.	11	31	19	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	131	13	161	38	70	34	145	6	142	18	97	13	116	43	77	24	121	7	108	14	148	12	126	18	117	29	141	17	139	11	132	31	118	20	121	14	157	14	131	8	156	24	121	9	131	8	90	28	96	15	128	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1500	26	2481	34	1373	38	2492	14	408	54	937	21	39	95	673	30	1902	16	212	37	2713	39	1564	41	1905	52	2054	19	1332	19	454	35	536	26	2581	41	696	27	230	17	1097	22	2317	26	1117	12	889	39	1156	31	1515	7
	fir	2626	28	1441	67	2596	45	2043	13	1935	58	1008	32	0	.	337	73	117	44	1951	20	1296	19	972	30	876	76	307	38	611	22	209	72	1023	34	1790	28	1279	30	78	29	157	36	1220	18	214	25	2218	61	2735	30	1028	7
	pine	29	82	0	.	0	.	11	40	2	99	0	.	0	.	0	.	243	21	4	100	39	85	0	.	0	.	10	100	5	87	0	.	9	100	88	49	4	100	46	82	25	82	48	70	115	34	0	.	24	72	66	16
	larch	88	90	0	.	0	.	9	57	2	99	3	100	0	.	0	.	265	24	0	.	1	100	0	.	44	98	14	100	1	70	0	.	10	88	1	100	5	100	95	27	0	.	7	54	404	45	0	.	1	100	98	22
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	8	99	0	.	221	32	0	.	0	.	53	22
	other conifers	10	69	0	.	0	.	17	46	2	99	0	.	0	.	44	100	0	.	3	100	3	100	26	100	12	68	0	.	32	88	0	.	49	70	31	91	17	100	6	100	0	.	7	83	0	.	13	98	8	60	10	25
	beech	13559	15	4620	59	6530	45	6320	17	32561	17	12595	44	50	95	939	29	297	24	9139	21	2504	28	10589	33	7051	61	1858	34	7060	22	16713	25	19420	20	2387	46	13456	26	870	38	2859	62	12351	22	1642	47	2330	39	10871	17	6297	7
	maple	18465	22	3779	46	4349	33	2672	15	16624	34	4787	40	3084	60	1182	33	307	37	5135	29	1647	25	4231	29	4537	33	1300	30	6285	26	7335	30	9736	19	2541	31	16004	26	692	26	1522	42	5269	41	780	44	8463	78	15190	24	4278	8
	ash	10918	17	602	57	7281	48	3286	16	11599	24	4060	27	9693	73	4818	28	776	33	4906	18	6487	37	4367	34	5056	36	5977	53	5728	23	6908	38	10405	29	3112	32	17911	25	4615	26	2585	48	2838	15	1756	27	11312	52	12394	15	4621	6
	oak	589	75	0	.	0	.	151	26	223	59	135	41	61392	73	0	.	71	57	5	61	90	60	2080	89	0	.	39	100	31	64	89	77	119	78	27	100	202	34	244	43	20	88	1557	57	395	34	0	.	146	31	433	28
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	99	0	.	255	20	0	.	110	86	8	86	0	.	0	.	40	24
	other broadleaves	2240	19	1329	61	1204	32	1607	10	1717	25	1392	21	3804	76	1699	29	1338	11	1135	25	1158	25	2029	28	779	42	2582	30	662	18	761	55	2382	42	2366	21	1803	21	1100	13	2409	21	2001	16	1635	16	2601	48	1729	23	1575	5
	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	50025	12	14252	24	23332	25	18608	8	65073	17	24916	25	78060	59	9692	17	5537	10	22488	14	15938	18	25860	17	20260	29	14142	25	21749	16	32469	23	43690	16	14928	17	51377	17	8233	16	10682	24	27724	13	8286	14	27825	52	44254	12	20015	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. 1196561. Birmensdorf, Swiss Federal Research Institute WSL

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