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
growing stock (stemwood)
diameter at breast height (main diameter classes) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/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
diameter at breast height (main diameter classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	174	40	9	71	14	52	531	13	21	48	136	47	0	.	44	33	775	12	39	38	187	22	67	33	20	67	121	31	99	23	45	46	16	73	86	46	94	42	112	22	61	30	261	18	376	19	12	51	133	30	3434	6
	fir	26	41	-17	114	20	61	271	24	13	44	66	36	0	.	4	77	11	46	66	28	111	28	86	35	3	71	17	47	35	44	2	71	40	38	39	42	51	46	30	56	33	45	110	23	18	38	19	57	29	44	1083	9
	pine	0	.	0	.	0	.	17	66	3	100	0	0	0	.	0	.	84	26	0	.	0	.	0	0	0	.	6	100	2	100	-4	100	2	100	9	78	0	.	4	100	0	.	0	0	117	41	0	.	5	100	245	23
	larch	1	100	0	.	0	.	2	71	0	.	0	.	0	.	0	.	77	24	0	.	1	100	0	.	0	.	3	100	11	57	0	.	0	.	0	.	0	.	64	38	0	.	31	79	106	26	0	.	2	100	298	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	27	65	0	.	0	.	108	46
	other conifers	2	100	0	.	2	100	13	35	0	.	0	.	0	.	0	.	20	36	4	72	1	100	0	.	0	.	0	.	4	59	0	.	6	71	0	.	1	100	0	.	6	100	14	67	4	62	0	.	5	96	82	20
	beech	35	82	3	71	6	68	293	22	79	26	46	36	0	.	16	45	56	32	58	33	79	24	50	27	8	60	15	47	127	27	63	41	98	25	19	29	45	45	272	17	7	50	98	32	85	73	1	94	63	22	1623	8
	maple	12	171	0	.	1	1310	86	27	26	35	17	37	0	.	17	57	35	35	17	44	22	39	16	154	6	67	14	89	21	37	7	79	20	86	37	60	69	69	45	32	9	71	102	23	53	28	3	100	28	170	663	14
	ash	49	68	0	.	0	.	42	39	2	314	78	69	2	100	5	61	47	33	25	43	32	38	16	47	-2	100	3	72	26	37	11	70	58	85	46	53	10	44	92	27	22	43	34	35	23	36	2	100	20	40	643	15
	oak	0	.	0	.	0	.	2	72	1	100	1	100	1	100	0	.	0	0	0	.	0	.	15	80	0	.	0	.	3	79	0	0	6	87	0	.	2	100	46	32	1	100	45	49	31	36	0	.	3	73	158	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	26	0	.	2	100	8	100	0	.	0	.	247	25
	other broadleaves	43	31	3	100	6	72	189	17	16	43	43	60	11	77	16	47	323	31	31	38	33	49	19	50	17	65	16	46	68	27	6	82	15	37	-9	346	38	53	422	14	60	51	119	23	202	17	0	.	34	25	1721	8
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	342	28	-1	1697	49	44	1447	10	161	21	387	25	14	61	101	23	1514	10	240	17	466	14	270	19	52	34	196	23	396	15	130	29	262	25	227	31	310	25	1318	9	199	23	815	11	1052	12	37	40	322	21	10305	3
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	239	38	63	44	122	39	1549	12	36	84	635	24	0	.	107	57	2349	10	137	39	484	20	226	33	181	49	339	25	511	20	178	45	96	49	287	28	336	31	455	22	261	30	865	19	1166	16	79	59	499	29	11199	5
	fir	79	53	13	74	41	47	848	15	57	45	208	31	0	.	5	100	63	34	267	27	440	21	231	30	17	53	26	117	46	56	19	70	110	39	80	41	216	48	87	39	136	40	343	19	105	49	57	50	30	51	3524	7
	pine	14	71	0	.	0	.	68	73	8	100	0	.	0	.	0	.	286	25	7	100	0	.	6	100	0	.	27	100	14	100	22	76	8	100	8	78	0	0	7	100	15	81	0	0	447	26	0	.	16	77	954	16
	larch	6	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	277	24	0	.	8	77	0	.	0	.	11	100	50	59	6	100	0	.	0	.	17	71	253	30	0	.	42	50	466	20	0	.	0	.	1139	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	146	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	125	45	0	.	0	.	275	24
	other conifers	21	58	0	.	6	100	28	61	0	.	0	.	0	.	2	100	21	38	-9	100	0	.	0	.	0	.	4	100	-3	512	0	.	-1	2023	3	100	3	100	0	.	0	.	18	57	11	67	0	.	31	58	135	32
	beech	298	39	13	72	22	59	1200	13	175	39	439	29	0	.	60	38	128	30	305	27	239	24	161	26	57	69	134	37	475	22	140	35	229	25	189	33	123	26	1050	17	90	41	439	19	147	36	13	100	239	19	6364	6
	maple	78	38	3	100	44	83	309	23	79	33	113	34	0	.	59	40	48	34	59	35	56	30	88	33	20	51	33	81	134	35	25	80	82	33	44	39	84	37	89	33	32	57	209	24	129	27	14	85	338	33	2167	8
	ash	83	41	0	.	49	67	102	34	51	44	135	44	10	100	54	46	97	47	45	45	48	48	36	51	33	65	17	58	150	31	59	52	40	47	117	49	70	47	181	35	37	51	160	25	220	42	5	100	97	40	1895	10
	oak	30	75	0	.	0	.	25	56	10	74	0	0	32	61	0	.	10	79	3	100	13	71	7	100	5	100	0	.	6	100	5	100	26	65	0	.	9	78	148	31	0	.	177	35	70	38	0	.	21	65	594	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1142	17	0	.	0	.	11	100	0	.	0	.	1174	17
	other broadleaves	120	26	0	.	15	63	311	19	45	41	56	40	0	0	63	51	590	21	65	38	53	37	26	42	9	76	10	71	127	39	11	71	87	48	105	62	30	49	1098	15	134	41	242	25	398	18	0	0	78	34	3671	7
	indeterminable	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	968	19	91	35	298	29	4444	8	460	21	1585	16	43	52	350	27	4035	7	879	16	1340	13	780	16	322	35	600	20	1510	14	464	26	677	18	832	19	888	20	4513	8	705	20	2495	10	3294	10	168	39	1350	16	33091	2
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	452	35	436	34	491	37	4728	10	43	106	1732	22	0	.	646	43	6062	9	442	36	1370	20	839	29	469	42	812	23	1409	20	459	37	324	37	915	25	919	29	1003	22	883	26	2514	15	3103	12	146	69	1497	19	31694	4
	fir	151	54	90	63	92	47	2195	16	114	40	341	41	0	.	21	75	150	39	486	28	981	20	451	27	44	100	252	45	301	37	17	100	264	33	255	30	349	37	131	52	273	48	996	19	265	36	96	48	182	38	8493	7
	pine	28	77	0	.	0	.	294	47	1	3696	31	71	67	87	0	.	649	27	0	0	27	100	47	59	0	.	81	88	64	87	96	63	8	287	19	100	23	72	37	79	88	58	64	68	706	23	0	.	119	44	2448	13
	larch	50	84	0	.	0	.	26	80	0	.	46	100	0	.	0	.	1135	16	0	.	0	.	0	.	0	.	0	.	43	72	0	.	0	.	0	.	24	100	824	20	0	.	102	48	1234	15	0	.	56	58	3539	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	447	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	254	31	0	.	0	.	700	18
	other conifers	43	60	0	.	0	.	69	47	2	100	0	.	0	.	0	.	4	100	0	.	0	.	7	100	0	.	0	.	-19	100	20	100	7	100	0	.	0	.	0	.	0	.	62	88	0	0	0	.	17	100	211	36
	beech	878	25	55	74	121	52	3256	12	556	28	756	26	0	.	134	35	466	28	649	28	778	25	306	23	191	43	361	40	1008	22	561	30	1036	22	308	27	352	29	1483	18	290	39	1287	16	377	32	57	54	827	19	16093	5
	maple	167	30	0	.	159	51	760	16	120	48	153	42	0	.	117	50	160	50	116	40	274	32	221	38	86	40	190	57	528	26	45	60	101	45	110	33	86	42	96	47	80	42	417	29	83	37	17	100	445	25	4530	8
	ash	110	53	0	.	139	54	558	25	192	42	472	30	16	100	51	70	85	43	196	38	135	43	15	100	144	96	18	72	446	26	14	100	112	42	186	43	260	40	177	32	77	70	406	32	112	40	27	80	206	35	4154	9
	oak	92	76	0	.	0	.	167	40	66	53	52	55	110	68	0	.	14	72	37	74	0	.	10	100	0	.	0	.	11	100	63	52	74	50	0	.	101	39	361	27	10	100	390	33	48	63	0	.	55	52	1662	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1185	18	0	.	0	.	57	71	0	.	0	.	1459	17
	other broadleaves	135	39	0	.	25	100	383	26	72	51	46	47	11	100	17	71	523	23	81	40	65	61	60	62	20	100	13	100	164	37	9	100	115	40	117	42	199	49	1347	17	31	58	335	38	217	29	7	100	165	31	4157	8
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0
	total	2107	16	580	32	1028	27	12435	7	1165	20	3630	14	204	54	987	32	9911	7	2008	15	3629	13	1958	16	954	31	1726	19	3955	12	1282	22	2040	17	1909	17	2312	18	6644	7	1730	21	6572	10	6455	8	350	42	3569	12	79141	2
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	888	23	1009	29	1040	31	11663	8	168	54	2950	17	0	.	1339	24	12329	8	1032	29	2903	16	1570	20	397	38	1085	23	2815	16	589	32	742	32	1916	20	813	24	1756	19	1453	26	4294	12	4799	11	314	44	2507	16	60372	3
	fir	407	35	252	42	766	37	4541	11	141	44	641	27	0	.	194	53	552	26	1552	23	1944	18	1334	19	87	77	315	37	1312	19	49	71	409	29	695	27	277	38	339	33	358	42	1906	15	530	34	62	71	630	24	19291	5
	pine	104	55	0	.	17	100	613	39	150	43	302	56	43	74	0	.	900	24	192	49	118	59	89	86	0	.	38	87	42	60	225	47	277	32	19	72	222	41	61	48	114	58	143	49	612	24	0	.	566	24	4848	10
	larch	76	75	0	.	0	.	153	56	25	72	0	0	0	.	9	100	2667	13	20	100	9	100	0	.	0	.	0	.	85	57	33	77	17	100	0	.	15	100	1137	16	0	.	106	43	2652	15	0	.	54	84	7057	8
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	507	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	262	28	0	.	0	.	763	17
	other conifers	42	77	0	.	0	.	105	75	106	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	37	100	0	.	0	.	0	.	20	100	0	.	41	100	0	.	0	.	0	.	367	40
	beech	1802	18	141	48	321	42	4936	10	1201	25	1573	22	0	.	478	33	599	33	1554	19	1276	20	817	20	180	63	532	37	2089	18	906	26	1946	17	596	29	432	26	1239	15	276	39	2939	14	175	46	134	57	1508	16	27649	4
	maple	247	32	15	71	40	58	735	20	135	47	149	47	0	.	126	42	164	56	229	30	150	45	101	39	34	58	47	53	528	21	9	100	296	26	159	39	104	56	32	77	50	52	171	38	57	86	66	51	181	30	3824	8
	ash	419	31	0	.	123	53	349	30	83	53	391	32	0	.	131	56	56	47	425	34	526	64	60	66	137	48	34	100	635	24	79	51	229	33	223	48	422	29	150	43	47	66	534	29	67	71	52	71	657	20	5829	9
	oak	144	49	0	.	0	.	218	27	84	71	53	70	242	60	0	.	65	70	226	42	201	53	40	85	0	.	0	.	35	72	86	78	49	74	0	.	258	35	296	35	58	63	443	28	36	63	10	100	208	28	2753	12
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1054	21	0	.	7	100	18	70	0	.	0	.	1194	18
	other broadleaves	139	50	0	.	0	.	386	31	59	56	73	67	0	.	44	100	319	25	101	58	114	60	0	.	33	100	0	.	124	39	10	100	35	59	21	74	198	37	679	20	6	100	387	34	128	58	0	.	280	35	3138	10
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	4274	13	1417	29	2308	24	23693	6	2152	19	6131	13	285	56	2321	21	18266	6	5331	13	7241	12	4010	13	868	30	2050	20	7683	11	2023	21	4000	13	3629	15	2742	16	6763	8	2361	20	10971	8	9336	8	638	34	6592	11	137086	2
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1475	31	1009	36	1209	35	13913	9	476	49	2736	20	0	.	1911	28	12393	9	1123	27	3685	20	1920	20	387	45	1181	26	5754	18	617	36	914	40	2560	22	1011	29	2861	20	1440	27	5010	13	4803	15	265	47	2273	22	70927	4
	fir	1225	29	219	49	623	40	7906	12	133	66	1187	32	0	.	301	53	1216	26	1197	29	2377	19	2022	22	180	68	458	46	1926	21	53	71	492	41	884	27	405	56	534	33	178	62	3062	18	393	36	137	100	1264	23	28372	5
	pine	65	76	0	.	0	.	430	35	55	73	162	54	0	.	0	.	714	26	169	47	41	100	17	100	0	.	11	100	57	71	21	100	36	71	0	.	197	41	57	75	20	100	0	.	138	54	0	.	291	41	2479	13
	larch	167	45	0	.	0	.	126	52	0	.	25	100	0	.	57	100	4238	14	0	.	0	.	43	100	0	.	0	.	117	64	0	.	0	.	52	100	0	.	1860	20	0	.	64	71	4461	14	55	100	175	52	11440	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	354	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	290	33	0	.	0	.	643	22
	other conifers	14	100	0	.	0	.	187	61	70	100	0	.	0	.	0	.	0	.	0	.	258	72	0	.	0	.	0	.	274	100	0	.	0	.	0	.	0	.	30	100	0	.	185	100	0	.	0	.	0	.	1018	40
	beech	2014	22	46	75	87	61	4057	15	1284	33	489	30	0	.	273	50	620	32	1027	26	1070	28	203	66	0	.	497	43	1564	24	439	38	1627	22	633	44	771	30	961	20	213	52	1881	18	173	56	68	58	1561	23	21557	6
	maple	79	59	0	.	0	.	135	45	127	56	53	60	0	.	46	71	82	54	25	100	24	100	4	100	0	.	0	.	216	45	0	.	56	58	0	.	96	81	0	.	14	100	87	53	0	.	22	100	165	54	1232	17
	ash	355	32	0	.	24	100	469	41	185	46	212	39	0	.	57	65	30	100	231	42	189	65	0	.	126	85	0	.	258	53	53	76	63	59	140	72	303	43	79	72	0	.	537	42	15	100	85	100	473	42	3885	13
	oak	430	41	0	.	0	.	282	37	36	100	54	100	87	100	0	.	62	73	33	100	94	83	14	100	0	.	0	.	27	100	87	100	46	100	16	100	525	63	85	60	26	100	143	62	21	100	0	.	380	33	2448	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1301	25	0	.	0	.	99	64	0	.	0	.	1496	22
	other broadleaves	170	52	0	.	17	100	438	49	17	100	317	62	0	.	47	76	68	47	21	100	0	.	0	.	0	.	0	.	120	86	0	.	41	100	9	100	400	100	77	58	22	100	504	55	43	73	0	.	31	100	2342	25
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	5994	14	1274	34	1959	28	27943	7	2382	24	5234	15	87	100	2692	23	19873	7	3827	15	7739	14	4223	15	693	37	2147	23	10314	13	1269	26	3276	19	4294	18	3707	21	7844	10	1913	24	11474	10	10435	10	632	42	6613	13	147839	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	3229	21	2526	29	2875	27	32385	7	744	39	8189	14	0	.	4047	22	33907	6	2773	22	8628	14	4622	15	1454	33	3537	20	10588	13	1888	25	2092	26	5764	16	3174	19	6186	15	4098	21	12945	10	14247	9	816	38	6910	14	177626	2
	fir	1887	25	557	38	1541	36	15761	9	457	40	2442	24	0	.	525	49	1991	23	3568	20	5854	15	4123	17	331	54	1068	34	3619	17	139	57	1316	24	1953	23	1299	30	1121	29	977	31	6417	13	1311	28	371	51	2135	19	60763	4
	pine	212	44	0	.	17	100	1422	29	217	41	495	46	110	79	0	.	2632	18	369	37	186	52	159	71	0	.	163	91	179	52	360	39	330	31	55	71	441	35	165	50	238	53	207	51	2020	19	0	.	997	22	10974	8
	larch	300	43	0	.	0	.	311	40	25	72	71	74	0	.	67	87	8395	10	20	100	18	62	43	100	0	.	14	100	306	44	39	66	17	100	52	100	55	85	4139	13	0	.	345	29	8917	11	55	100	287	38	23475	6
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	1532	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	958	22	0	.	0	.	2489	13
	other conifers	121	49	0	.	8	78	402	43	177	100	0	.	0	.	2	100	45	35	-5	145	259	72	7	100	0	.	4	100	273	107	57	100	12	226	3	100	4	74	50	100	6	100	320	66	15	58	0	.	52	52	1813	27
	beech	5027	16	258	46	557	33	13742	9	3295	20	3303	19	0	.	961	30	1869	22	3594	16	3441	18	1538	19	436	42	1539	30	5263	15	2108	21	4936	15	1745	25	1723	19	5004	13	875	34	6645	12	958	29	273	43	4199	14	73286	3
	maple	583	23	18	71	245	52	2024	13	487	28	484	26	0	.	365	28	490	37	445	22	526	22	430	29	145	33	284	51	1426	18	86	52	556	21	349	26	439	37	263	29	185	39	986	17	322	27	122	47	1157	20	12416	5
	ash	1017	24	0	.	335	48	1520	21	512	35	1288	25	28	100	297	42	314	34	922	27	929	50	128	42	439	57	73	65	1516	20	215	42	503	29	712	36	1065	24	678	27	182	51	1672	21	437	31	171	70	1453	19	16407	6
	oak	696	35	0	.	0	.	694	21	197	52	160	48	473	55	0	.	151	63	299	48	308	49	86	59	5	100	0	.	82	54	241	52	201	38	16	100	895	41	936	20	95	54	1197	24	205	37	10	100	668	25	7615	9
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4911	13	0	.	9	84	194	59	0	.	0	.	5569	12
	other broadleaves	606	26	3	100	63	75	1708	20	209	30	534	52	21	87	187	47	1822	17	299	38	265	32	105	40	79	55	39	45	605	28	37	47	292	35	244	41	864	54	3624	12	253	37	1586	25	987	18	7	100	589	27	15029	7
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	13685	11	3362	28	5641	22	69962	5	6320	16	16966	11	632	51	6452	18	53598	5	12284	12	20415	10	11241	11	2890	26	6720	17	23858	9	5169	18	10254	12	10892	13	9960	14	27082	5	6908	17	32328	7	30572	7	1826	31	18446	9	407462	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1280564. Birmensdorf, Swiss Federal Research Institute WSL

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