Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI5
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
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
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	58	35	4	78	10	58	643	18	33	53	78	25	4	75	30	41	793	14	18	37	220	29	54	28	15	67	146	36	114	28	12	46	56	56	65	33	36	30	152	27	76	36	233	26	354	16	3	100	100	31	3308	6
	fir	30	38	4	71	17	69	339	14	18	36	59	33	4	100	3	100	7	44	98	39	147	32	78	48	6	80	29	49	18	38	3	71	24	37	36	42	-9	343	20	35	18	31	113	20	56	53	18	53	23	43	1159	9
	pine	0	.	0	.	0	.	5	71	0	.	2	100	0	.	0	.	100	29	0	0	1	100	0	0	4	100	6	100	1	100	0	.	5	59	6	82	0	.	7	65	0	.	0	0	54	39	0	.	3	100	194	19
	larch	7	88	0	.	0	.	3	74	0	.	3	100	0	.	0	.	71	24	0	.	3	72	0	.	0	.	0	0	10	49	0	.	0	.	0	.	4	100	76	45	0	.	26	55	120	32	0	.	4	71	325	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	46	69	0	.	0	.	104	40
	other conifers	0	.	0	.	0	.	15	34	2	100	0	.	0	.	0	.	17	35	0	.	2	71	0	.	0	.	1	100	5	52	0	.	5	75	0	.	2	100	1	100	5	100	10	91	6	79	0	.	16	54	87	20
	beech	91	23	1	100	5	53	278	16	90	32	87	40	0	.	10	35	38	28	63	27	55	26	51	36	10	60	11	39	126	35	67	40	122	29	35	48	38	33	263	16	6	51	137	27	34	30	1	100	98	28	1719	7
	maple	45	33	0	.	14	53	112	18	31	47	16	48	3	100	13	42	28	42	-21	236	27	36	48	42	-1	401	11	100	19	58	12	51	38	98	23	37	29	38	65	33	14	37	127	23	84	33	1	100	52	28	792	11
	ash	23	46	0	.	0	.	38	86	7	79	38	60	1	100	1	100	28	38	22	68	35	72	8	46	0	0	8	71	21	47	7	100	3	71	44	94	9	46	83	37	16	48	34	30	37	39	1	100	70	63	535	16
	oak	25	86	0	.	0	.	6	72	4	73	4	58	0	0	0	.	0	0	0	0	0	.	10	100	0	.	0	.	2	100	3	71	5	100	0	.	2	100	35	35	2	100	34	62	29	38	0	.	6	83	167	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	205	29	0	.	0	.	2	100	0	.	0	.	211	28
	other broadleaves	47	32	0	.	3	100	232	17	14	41	60	28	11	67	7	61	250	21	34	36	61	36	5	146	21	45	17	41	51	31	2	100	29	50	34	31	51	42	371	15	29	41	88	21	236	17	7	63	55	23	1716	6
	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	325	17	9	49	50	36	1672	10	199	23	349	17	23	57	64	26	1393	10	214	32	551	18	254	21	55	35	229	26	367	18	107	31	288	24	243	27	162	29	1279	10	165	22	803	12	1058	10	31	37	427	18	10318	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	316	32	74	67	57	73	1672	11	56	56	567	37	0	.	150	34	2583	10	74	36	417	22	215	26	175	55	461	29	545	22	76	52	81	44	232	25	203	29	459	19	370	25	747	17	1291	16	22	60	397	34	11240	5
	fir	67	58	12	75	39	53	1051	13	33	38	57	241	0	.	2	100	38	41	307	20	562	23	319	31	7	72	23	113	95	31	4	100	135	41	81	49	115	49	67	38	119	53	336	22	90	36	41	53	69	38	3670	8
	pine	-29	100	0	.	0	.	53	44	30	64	0	0	0	.	0	.	260	23	65	100	0	.	0	.	0	.	34	100	-11	100	0	.	6	100	15	64	0	.	7	100	10	72	0	.	404	29	0	.	32	80	877	18
	larch	52	89	0	.	0	.	4	100	0	.	3	100	0	.	0	.	326	18	0	.	3	100	0	.	0	.	9	100	10	100	0	.	0	.	0	.	29	81	319	23	0	.	88	69	600	17	0	.	4	100	1446	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	161	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	127	50	0	.	0	.	290	26
	other conifers	15	71	0	.	5	100	29	55	0	.	0	.	0	.	3	100	25	34	11	74	2	100	0	.	0	.	0	.	19	52	0	.	28	52	9	72	3	100	0	.	2	100	28	45	8	82	0	.	40	58	226	18
	beech	249	23	8	100	9	75	1088	15	251	30	350	26	0	.	24	41	170	30	272	27	227	23	181	26	75	68	99	35	450	20	129	31	266	21	192	33	95	33	1004	16	111	46	378	18	179	41	9	77	280	21	6098	6
	maple	72	36	4	100	41	61	429	20	74	42	109	34	0	.	78	42	62	34	81	43	83	31	116	35	26	70	46	79	55	71	12	100	104	35	96	42	80	55	139	35	9	158	335	21	150	25	9	100	175	31	2386	8
	ash	69	44	0	.	16	196	76	43	65	43	158	49	0	.	24	48	102	45	25	53	27	52	31	50	42	63	12	100	142	32	23	100	51	44	72	54	64	51	193	31	44	54	149	28	139	36	7	100	47	44	1576	10
	oak	14	74	0	.	0	.	42	70	6	70	15	100	58	89	0	.	9	74	0	0	0	0	12	100	0	.	0	.	7	100	5	100	13	100	0	.	6	100	129	35	0	.	146	42	70	40	0	.	3	100	535	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	854	18	0	.	0	.	9	100	0	.	0	.	881	17
	other broadleaves	137	24	0	.	17	51	342	18	24	47	104	36	3	100	47	68	650	20	55	41	98	35	34	42	10	58	18	48	136	37	15	64	121	56	86	51	91	43	1008	14	116	54	260	27	411	17	0	0	109	33	3892	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
	total	963	16	98	53	184	42	4786	7	540	21	1362	22	61	85	327	23	4404	7	890	18	1419	13	908	16	334	39	703	22	1448	15	264	27	805	17	783	19	688	20	4181	7	781	20	2467	10	3477	9	88	39	1157	16	33117	3
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	534	30	441	36	407	38	4409	10	39	57	1555	23	0	.	312	49	6674	9	107	67	1092	20	777	25	484	41	962	25	1307	20	310	45	261	48	795	26	680	33	1376	27	839	25	2266	15	3334	12	172	43	1176	24	30310	4
	fir	122	53	95	67	189	55	1996	14	190	37	350	38	0	.	11	100	167	39	496	26	1163	21	522	30	18	145	229	60	339	66	21	100	386	29	180	38	369	43	208	48	262	41	1121	18	310	35	140	54	178	34	9063	7
	pine	9	100	0	.	0	.	309	43	36	77	15	100	18	100	0	.	547	29	0	0	31	100	74	59	0	.	99	80	30	80	102	67	2	446	20	100	12	100	41	86	103	62	27	71	675	23	0	.	26	73	2177	14
	larch	19	100	0	.	0	.	17	72	0	.	39	100	0	.	0	.	1251	16	0	.	9	100	0	.	0	.	9	100	90	58	14	100	0	.	0	.	21	74	1059	19	0	.	136	56	1393	20	0	.	12	100	4067	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	531	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	231	27	0	.	0	.	768	17
	other conifers	84	71	0	.	9	100	38	63	-5	100	0	.	0	.	0	.	5	100	0	.	0	.	8	100	0	.	8	100	-18	146	0	.	16	100	0	.	0	.	0	.	0	.	10	100	0	0	0	.	9	100	164	45
	beech	757	26	52	71	112	78	3003	11	458	29	686	26	0	.	61	69	522	30	598	27	794	23	240	28	181	42	364	38	1149	22	433	37	844	24	330	28	365	29	1930	17	373	37	1222	16	310	36	68	50	666	20	15520	5
	maple	148	35	0	.	174	43	773	17	152	39	229	33	0	.	148	66	179	30	141	39	285	34	246	37	93	42	161	57	758	23	56	61	120	40	108	38	144	40	143	45	118	43	390	25	152	34	32	58	568	26	5317	7
	ash	66	50	0	.	127	58	280	31	87	55	444	35	8	100	42	67	133	41	155	53	50	62	8	100	158	93	22	71	326	35	32	59	104	48	76	47	174	43	241	40	85	78	349	35	180	36	18	100	120	53	3288	11
	oak	78	73	0	.	0	.	115	46	44	58	49	51	45	60	0	.	17	73	27	72	8	100	5	100	5	100	0	.	0	0	69	51	81	46	0	.	64	54	367	29	0	0	398	33	98	40	0	.	75	45	1546	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	234	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1365	17	0	.	0	.	57	71	0	.	0	.	1657	16
	other broadleaves	161	30	0	.	15	100	405	23	61	41	87	44	0	.	33	66	528	24	89	44	76	54	11	84	48	85	15	100	180	36	11	100	106	47	147	45	83	47	1740	17	60	47	260	33	282	30	0	.	202	35	4601	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	1978	16	588	33	1034	30	11347	7	1063	20	3454	14	71	54	607	35	10788	7	1610	17	3510	13	1891	16	987	31	1869	20	4162	14	1047	25	1920	17	1657	17	1914	18	8475	8	1840	21	6179	10	7022	9	431	32	3033	13	78478	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	657	26	946	29	805	36	9845	9	125	53	3437	17	0	.	1087	31	12468	7	671	28	1952	16	1502	21	606	41	1272	24	3104	16	385	35	586	30	1778	19	691	28	2300	22	1445	28	4094	13	4965	11	415	40	2178	16	57314	3
	fir	321	40	250	46	476	31	4120	11	123	48	474	31	0	.	211	43	441	29	1328	26	1798	17	1118	21	85	54	349	34	1126	21	89	74	440	24	676	28	386	38	312	41	391	40	2014	15	530	32	79	54	501	30	17638	5
	pine	39	75	0	.	0	.	500	40	159	41	107	67	48	100	0	.	1079	24	198	52	130	68	15	100	0	.	67	77	25	75	169	45	225	33	36	81	213	46	55	75	129	49	154	52	624	21	0	.	517	25	4490	10
	larch	82	74	0	.	0	.	107	68	27	71	18	100	0	.	0	.	2799	12	0	.	13	100	0	.	0	.	0	.	93	67	23	73	0	.	0	.	16	100	1439	16	0	.	113	45	2788	15	0	.	47	51	7565	8
	Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	549	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	288	27	0	.	0	.	849	16
	other conifers	37	71	0	.	0	.	114	39	38	100	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	16	100	9	100	0	.	0	.	16	100	0	.	56	100	0	.	0	.	9	100	306	29
	beech	1566	20	222	48	195	43	5158	11	1019	26	1430	23	0	.	480	36	648	27	1476	20	1362	22	675	23	219	61	436	36	2243	17	950	27	1725	17	768	30	359	27	1605	15	234	37	2810	14	215	50	148	50	1196	17	27138	4
	maple	241	32	21	73	37	58	855	16	117	53	103	34	0	.	167	38	153	45	262	28	104	36	156	36	35	110	51	58	600	20	21	73	332	26	124	36	111	46	53	63	47	53	298	23	97	67	29	86	241	28	4255	7
	ash	218	41	0	.	111	53	435	23	78	55	410	33	0	.	96	53	60	50	334	36	287	50	52	60	217	57	75	100	568	29	106	52	234	33	220	53	264	34	165	42	74	70	595	28	114	54	52	100	453	25	5215	9
	oak	211	42	0	.	0	.	212	32	57	90	101	59	168	64	0	.	48	63	159	54	120	52	42	85	0	.	0	.	45	69	80	75	30	59	0	.	256	39	330	30	46	78	387	29	57	53	12	100	102	40	2463	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	151	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1066	21	0	.	6	100	21	70	0	.	0	.	1244	19
	other broadleaves	169	38	0	.	20	71	298	28	88	53	43	51	0	.	20	75	346	43	202	46	38	55	9	100	11	100	0	.	92	46	12	100	63	65	27	65	175	44	1007	16	8	100	181	36	201	39	0	.	286	37	3296	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
	total	3540	13	1439	28	1643	26	21656	6	1831	21	6122	13	217	61	2061	24	18741	6	4629	14	5816	12	3569	13	1173	30	2251	20	7896	11	1850	21	3643	13	3630	15	2473	17	8348	8	2373	21	10708	8	9899	8	734	33	5530	11	131773	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	1053	36	1331	32	816	34	12881	9	515	45	3035	18	0	.	2045	27	13869	9	1370	29	3348	17	2107	20	441	46	1332	26	5769	16	605	44	845	42	2650	21	961	33	3150	22	1730	24	5189	14	5515	13	427	44	1937	21	72919	3
	fir	858	32	248	50	829	45	8953	11	184	60	1319	27	0	.	329	53	1452	25	1660	27	2673	18	2554	21	253	65	628	45	2663	19	66	71	443	40	1190	28	516	54	753	29	98	62	3463	16	656	35	121	83	1282	24	33194	5
	pine	124	67	0	.	0	.	483	34	72	73	207	57	0	.	0	.	790	25	121	52	84	62	34	100	0	.	12	100	107	64	59	57	81	51	0	.	179	50	41	74	23	100	0	.	162	41	0	.	262	33	2838	12
	larch	126	45	0	.	0	.	140	47	0	.	34	100	0	.	50	100	4957	12	22	100	0	.	41	100	0	.	0	.	116	73	0	.	20	100	57	100	0	.	2056	18	0	.	134	62	4994	13	40	100	218	46	13006	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	475	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	389	30	0	.	0	.	864	20
	other conifers	62	85	0	.	0	.	373	54	218	100	0	.	0	.	0	.	0	.	0	.	338	71	0	.	0	.	0	.	337	100	49	100	0	.	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	1442	36
	beech	1940	20	39	75	173	48	4528	13	706	34	813	26	0	.	360	45	651	32	1007	27	1320	25	157	39	0	.	455	40	1756	23	580	34	1856	24	704	39	877	28	1267	19	338	46	2276	17	182	56	88	50	2070	21	24144	5
	maple	129	42	0	.	0	.	312	28	110	52	119	43	0	.	25	100	101	46	46	71	63	72	34	100	0	.	0	.	372	35	0	.	136	48	42	74	110	81	0	.	30	100	86	52	0	.	40	71	262	38	2016	13
	ash	367	34	0	.	0	.	543	41	107	62	293	44	0	.	99	53	33	100	233	48	175	80	0	.	95	100	0	.	442	43	63	73	112	53	210	66	267	39	148	63	0	.	243	33	15	100	37	100	588	38	4071	12
	oak	492	41	0	.	0	.	328	30	78	100	60	100	113	100	0	.	112	71	147	52	177	64	18	100	0	.	0	.	45	100	53	100	83	75	19	100	634	62	120	46	37	100	224	44	22	100	0	.	399	31	3161	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1430	25	0	.	0	.	75	60	0	.	0	.	1612	23
	other broadleaves	170	54	0	.	20	100	275	37	21	100	227	53	0	.	48	77	134	62	14	100	40	100	0	.	0	.	0	.	78	58	0	.	60	100	22	100	189	61	133	42	32	100	726	50	79	51	0	.	62	75	2332	19
	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
	total	5322	14	1618	32	1840	29	28818	6	2011	24	6107	13	113	100	2956	22	22681	7	4619	16	8217	13	4946	15	789	39	2427	24	11686	12	1475	26	3635	19	4894	17	3734	20	9162	10	2288	22	12340	10	12092	9	752	37	7080	12	161599	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	2617	21	2795	30	2096	30	29451	7	769	35	8671	14	4	75	3623	23	36387	6	2239	23	7030	14	4655	15	1721	34	4173	20	10839	13	1388	28	1829	26	5520	16	2572	21	7436	19	4459	20	12529	10	15460	9	1039	37	5788	14	175091	2
	fir	1398	28	609	41	1551	36	16459	9	549	37	2259	24	4	100	555	44	2105	23	3889	20	6344	14	4592	17	368	56	1259	34	4242	17	184	59	1427	22	2163	24	1377	32	1360	27	888	32	7047	13	1642	26	398	44	2054	20	64723	4
	pine	143	58	0	.	0	.	1350	28	297	35	331	55	67	100	0	.	2776	17	383	37	246	54	123	59	4	100	217	83	152	50	330	39	320	32	78	75	404	42	151	52	265	53	181	46	1918	18	0	.	840	22	10577	8
	larch	286	41	0	.	0	.	271	45	27	71	97	69	0	.	50	100	9404	9	22	100	28	59	41	100	0	.	18	100	319	45	36	59	20	100	57	100	70	60	4948	13	0	.	497	31	9895	12	40	100	285	37	26409	6
	Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	1773	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	1082	21	0	.	0	.	2875	13
	other conifers	198	50	0	.	14	74	568	40	252	100	0	.	0	.	3	100	47	32	11	74	352	71	8	100	0	.	9	91	344	98	66	100	57	58	9	72	5	72	82	98	7	100	105	57	13	58	0	.	75	49	2224	25
	beech	4603	16	323	48	494	36	14057	9	2525	19	3366	19	0	.	935	31	2029	21	3416	17	3758	17	1304	18	486	42	1366	30	5724	15	2159	21	4813	15	2029	23	1734	20	6069	12	1062	31	6824	12	920	30	314	43	4310	14	74620	3
	maple	636	22	25	75	266	42	2481	12	484	27	576	24	3	100	431	34	522	26	508	24	561	21	600	27	153	42	269	49	1805	17	101	53	730	22	392	26	476	39	401	29	218	39	1237	15	483	26	110	46	1299	18	14767	5
	ash	743	29	0	.	254	55	1372	23	343	42	1343	28	9	100	262	44	356	35	770	29	575	49	99	46	511	59	116	76	1498	23	231	46	505	26	622	37	779	27	830	32	218	56	1369	22	485	30	115	60	1279	22	14686	7
	oak	819	34	0	.	0	.	703	20	189	52	231	43	384	58	0	.	185	61	332	45	305	48	87	60	5	100	0	.	99	56	210	47	212	42	19	100	963	45	981	20	86	72	1188	24	276	33	12	100	585	25	7872	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	515	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4920	13	0	.	6	100	165	51	0	.	0	.	5605	12
	other broadleaves	684	24	0	.	75	69	1553	15	208	32	521	31	14	65	155	46	1908	19	393	34	313	27	59	37	91	51	50	39	537	24	39	48	378	35	317	33	590	33	4259	12	245	37	1514	28	1209	18	7	63	715	25	15836	6
	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
	total	12128	11	3751	28	4750	24	68278	5	5643	16	17394	11	485	54	6015	18	58007	5	11963	12	19513	10	11568	11	3339	26	7478	17	25559	9	4744	18	10291	12	11207	13	8970	13	31445	6	7447	17	32496	7	33548	6	2036	30	17228	9	415285	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. 1280622. Birmensdorf, Swiss Federal Research Institute WSL

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