NFI Results

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 NFI1-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	4	100	14	52	494	13	21	48	127	50	0	.	30	32	647	14	39	38	185	22	67	33	19	71	107	34	84	25	45	46	16	73	75	52	94	42	92	24	57	32	220	17	356	20	7	53	133	30	3107	6
	fir	26	41	-17	114	20	61	267	25	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	36	46	51	46	30	56	33	45	110	23	18	38	19	57	29	44	1076	9
	pine	0	.	0	.	0	.	17	66	3	100	0	0	0	.	0	.	64	30	0	.	0	.	0	0	0	.	6	100	0	.	-4	100	2	100	1	100	0	.	4	100	0	.	0	0	116	42	0	.	5	100	214	25
	larch	1	100	0	.	0	.	2	71	0	.	0	.	0	.	0	.	42	32	0	.	1	100	0	.	0	.	3	100	6	85	0	.	0	.	0	.	0	.	40	54	0	.	31	79	89	30	0	.	2	100	216	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	27	67	0	.	0	.	97	52
	other conifers	2	100	0	.	2	100	10	38	0	.	0	.	0	.	0	.	16	43	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	75	21
	beech	33	87	3	71	6	68	293	22	70	28	46	36	0	.	16	45	55	33	58	33	79	24	50	27	8	60	15	47	124	27	63	41	98	25	16	31	45	45	264	17	7	50	97	32	83	75	1	94	62	22	1592	8
	maple	12	171	0	.	1	1310	77	28	26	35	17	37	0	.	9	58	29	37	17	44	22	39	16	154	6	67	14	89	17	40	7	79	20	86	15	58	69	69	24	40	9	71	100	23	31	30	3	100	22	218	562	16
	ash	49	68	0	.	0	.	32	41	2	314	78	69	2	100	4	74	42	36	25	43	32	38	16	47	-2	100	3	72	23	42	11	70	58	85	40	60	10	44	80	30	20	45	32	37	12	37	2	100	19	42	589	16
	oak	0	.	0	.	0	.	2	72	1	100	1	100	1	100	0	.	0	0	0	.	0	.	15	80	0	.	0	.	2	100	0	0	6	87	0	.	2	100	46	32	1	100	45	49	30	37	0	.	3	73	156	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	0	.	0	.	0	.	239	25
	other broadleaves	43	31	3	100	6	72	157	17	15	46	41	63	3	100	16	47	271	36	31	38	21	59	19	50	11	86	12	52	65	27	6	82	15	37	10	176	38	53	353	17	25	52	94	24	179	18	0	.	34	25	1469	9
	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
	total	340	29	-6	333	49	44	1351	10	151	22	376	25	6	61	78	23	1251	12	240	17	452	14	270	19	44	38	178	25	360	16	130	29	262	25	192	31	310	25	1164	10	158	23	745	11	944	13	33	43	313	22	9391	4
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	53	48	122	39	1451	12	36	84	513	26	0	.	97	62	2022	10	137	39	460	21	226	33	164	53	313	27	481	22	178	45	96	49	269	30	336	31	368	26	248	30	791	20	1061	17	40	63	499	29	10199	5
	fir	79	53	13	74	41	47	848	15	55	47	208	31	0	.	5	100	63	34	267	27	440	21	231	30	17	53	26	117	46	56	19	70	110	39	75	43	216	48	77	42	129	41	343	19	105	49	57	50	30	51	3500	7
	pine	14	71	0	.	0	.	65	77	8	100	0	.	0	.	0	.	262	27	7	100	0	.	6	100	0	.	27	100	0	.	22	76	8	100	6	100	0	0	7	100	15	81	0	0	437	26	0	.	16	77	901	17
	larch	6	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	174	24	0	.	8	77	0	.	0	.	11	100	41	68	6	100	0	.	0	.	17	71	163	40	0	.	42	50	381	21	0	.	0	.	853	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	91	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	121	46	0	.	0	.	216	29
	other conifers	21	58	0	.	6	100	18	77	0	.	0	.	0	.	2	100	19	41	-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	122	34
	beech	287	41	13	72	22	59	1195	13	153	43	439	29	0	.	60	38	105	33	305	27	239	24	161	26	57	69	134	37	473	23	140	35	213	26	173	35	123	26	1007	18	90	41	439	19	146	37	13	100	239	19	6225	6
	maple	78	38	3	100	44	83	284	24	63	37	113	34	0	.	56	41	48	34	59	35	56	30	88	33	14	59	33	81	134	35	25	80	82	33	33	47	84	37	53	43	32	57	197	25	111	31	14	85	338	33	2042	9
	ash	83	41	0	.	49	67	102	34	46	47	135	44	10	100	40	52	91	50	45	45	48	48	36	51	33	65	11	71	139	33	59	52	40	47	53	55	70	47	163	38	37	51	157	25	97	42	5	100	84	45	1632	10
	oak	30	75	0	.	0	.	13	59	10	74	0	0	32	61	0	.	2	100	3	100	13	71	7	100	5	100	0	.	6	100	5	100	26	65	0	.	9	78	138	33	0	.	177	35	56	44	0	.	21	65	553	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1065	17	0	.	0	.	0	0	0	.	0	.	1087	17
	other broadleaves	120	26	0	.	15	63	281	20	23	45	53	42	0	.	63	51	511	24	65	38	53	37	26	42	9	76	10	71	111	44	11	71	87	48	33	55	30	49	910	17	80	47	186	25	357	19	0	0	78	34	3111	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	957	19	81	37	298	29	4261	8	394	23	1460	17	43	52	324	28	3409	8	879	16	1317	13	780	16	300	37	568	20	1427	15	464	26	661	18	645	21	888	20	3954	8	631	21	2350	11	2884	10	128	41	1336	16	30439	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	452	35	398	36	491	37	4340	10	43	106	1606	24	0	.	643	43	5675	9	442	36	1334	21	839	29	457	43	743	25	1313	21	459	37	324	37	768	27	919	29	889	24	841	27	2385	16	2963	13	83	52	1487	20	29893	4
	fir	151	54	90	63	92	47	2166	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	239	31	349	37	122	56	254	51	996	19	265	36	96	48	182	38	8421	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	0	.	96	63	8	287	19	100	23	72	37	79	88	58	64	68	706	23	0	.	119	44	2384	13
	larch	50	84	0	.	0	.	20	100	0	.	46	100	0	.	0	.	873	19	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	24	100	706	22	0	.	102	48	1090	16	0	.	56	58	2982	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	371	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	229	34	0	.	0	.	601	19
	other conifers	43	60	0	.	0	.	50	52	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	193	38
	beech	878	25	55	74	121	52	3106	12	557	28	756	26	0	.	134	35	427	29	649	28	778	25	306	23	191	43	361	40	930	23	561	30	1036	22	291	28	352	29	1476	18	290	39	1287	16	377	32	57	54	804	20	15781	5
	maple	167	30	0	.	159	51	717	17	91	48	153	42	0	.	109	53	95	49	116	40	274	32	221	38	76	43	155	66	528	26	45	60	101	45	104	34	86	42	89	50	68	46	417	29	64	43	0	.	445	25	4280	8
	ash	110	53	0	.	139	54	494	26	168	46	472	30	16	100	51	70	66	51	196	38	135	43	15	100	144	96	18	72	446	26	14	100	112	42	136	46	260	40	147	37	77	70	406	32	71	47	27	80	206	35	3927	9
	oak	92	76	0	.	0	.	148	44	66	53	52	55	110	68	0	.	6	100	37	74	0	.	10	100	0	.	0	.	11	100	63	52	74	50	0	.	101	39	334	28	10	100	390	33	40	74	0	.	55	52	1600	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1219	17	0	.	0	.	26	100	0	.	0	.	1462	17
	other broadleaves	135	39	0	.	25	100	373	26	72	51	46	47	0	.	17	71	517	23	81	40	65	61	60	62	20	100	13	100	148	40	9	100	115	40	103	45	199	49	1112	17	22	71	322	39	161	31	7	100	165	31	3788	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	542	34	1028	27	11707	7	1114	21	3503	15	193	57	976	32	9050	7	2008	15	3593	14	1958	16	933	32	1623	20	3674	13	1282	22	2040	17	1660	18	2312	18	6132	8	1650	22	6431	10	5992	9	270	36	3535	12	75312	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	859	24	939	30	1040	31	10943	9	146	60	2801	18	0	.	1188	26	11773	8	1032	29	2615	17	1570	20	363	41	1047	24	2495	17	589	32	742	32	1819	21	813	24	1750	19	1393	27	4062	12	4671	11	178	55	2507	16	57335	3
	fir	407	35	252	42	766	37	4504	11	141	44	641	27	0	.	194	53	552	26	1552	23	1944	18	1334	19	87	77	315	37	1182	20	49	71	409	29	628	29	277	38	325	34	358	42	1906	15	530	34	32	100	630	24	19013	5
	pine	104	55	0	.	17	100	592	40	150	43	302	56	43	74	0	.	883	24	192	49	118	59	89	86	0	.	38	87	17	100	225	47	277	32	19	72	222	41	51	54	60	63	143	49	599	24	0	.	566	24	4707	10
	larch	76	75	0	.	0	.	153	56	25	72	0	0	0	.	9	100	2318	14	20	100	9	100	0	.	0	.	0	.	42	54	33	77	17	100	0	.	15	100	984	18	0	.	106	43	2389	15	0	.	54	84	6250	8
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	457	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	214	30	0	.	0	.	665	18
	other conifers	42	77	0	.	0	.	100	79	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	.	362	41
	beech	1802	18	141	48	321	42	4792	10	1177	25	1573	22	0	.	478	33	599	33	1554	19	1276	20	817	20	180	63	532	37	1985	19	906	26	1930	17	566	31	432	26	1214	15	251	42	2930	14	143	51	134	57	1508	16	27242	4
	maple	247	32	15	71	40	58	722	21	135	47	149	47	0	.	98	46	84	55	229	30	150	45	101	39	34	58	47	53	474	21	9	100	264	27	159	39	104	56	32	77	29	52	171	38	57	86	66	51	181	30	3596	8
	ash	419	31	0	.	123	53	349	30	52	59	391	32	0	.	131	56	56	47	425	34	526	64	60	66	137	48	34	100	568	25	79	51	229	33	223	48	422	29	150	43	47	66	522	30	67	71	52	71	657	20	5719	9
	oak	118	56	0	.	0	.	218	27	84	71	53	70	242	60	0	.	60	75	226	42	201	53	40	85	0	.	0	.	35	72	86	78	49	74	0	.	258	35	290	36	38	81	443	28	29	73	0	.	208	28	2680	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	.	328	31	48	65	73	67	0	.	44	100	283	27	101	58	114	60	0	.	33	100	0	.	124	39	10	100	35	59	21	74	161	39	563	22	6	100	298	38	89	78	0	.	280	35	2751	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	4218	13	1347	30	2308	24	22695	6	2063	20	5983	13	285	56	2142	22	17173	6	5331	13	6953	13	4010	13	834	31	2013	21	6941	11	2023	21	3952	13	3435	16	2704	16	6433	8	2181	21	10629	9	8807	9	462	38	6592	11	131513	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	928	38	1209	35	13175	9	476	49	2688	20	0	.	1859	28	11418	10	1123	27	3573	21	1920	20	339	49	1107	28	5314	19	617	36	887	41	2529	22	1011	29	2783	20	1440	27	4489	14	4603	15	198	59	2273	22	67434	4
	fir	1225	29	219	49	623	40	7791	12	133	66	1187	32	0	.	301	53	1216	26	1197	29	2377	19	2022	22	180	68	458	46	1633	22	53	71	492	41	864	28	405	56	534	33	178	62	3062	18	369	38	0	0	1264	23	27782	5
	pine	65	76	0	.	0	.	430	35	55	73	162	54	0	.	0	.	698	27	169	47	41	100	17	100	0	.	11	100	57	71	21	100	36	71	0	.	197	41	38	100	20	100	0	.	138	54	0	.	291	41	2444	13
	larch	167	45	0	.	0	.	127	51	0	.	25	100	0	.	57	100	3875	14	0	.	0	.	43	100	0	.	0	.	104	71	0	.	0	.	52	100	0	.	1460	23	0	.	64	71	4213	14	55	100	175	52	10418	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	.	232	37	0	.	0	.	586	23
	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	3829	16	762	29	489	30	0	.	273	50	600	33	1027	26	1070	28	203	66	0	.	497	43	1449	25	439	38	1578	23	587	47	771	30	961	20	213	52	1881	18	173	56	44	71	1561	23	20553	6
	maple	79	59	0	.	0	.	92	48	127	56	53	60	0	.	46	71	82	54	25	100	24	100	0	.	0	.	0	.	214	45	0	.	56	58	0	.	96	81	0	.	14	100	56	61	0	.	22	100	165	54	1151	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	46	100	0	.	494	45	15	100	85	100	473	42	3809	13
	oak	350	45	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	.	360	35	2348	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1269	25	0	.	0	.	46	75	0	.	0	.	1410	23
	other broadleaves	170	52	0	.	17	100	295	54	17	100	317	62	0	.	47	76	60	52	21	100	0	.	0	.	0	.	0	.	120	86	0	.	41	100	9	100	0	.	77	58	0	.	292	77	43	73	0	.	31	100	1555	24
	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	5915	14	1193	36	1959	28	26677	7	1860	24	5186	15	87	100	2639	23	18491	7	3827	15	7627	14	4219	16	645	39	2074	24	9450	13	1269	26	3199	20	4197	18	3308	21	7284	10	1890	24	10666	10	9851	10	404	44	6593	14	140509	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	3200	21	2322	31	2875	27	30403	7	722	40	7736	15	0	.	3817	24	31534	7	2773	22	8167	15	4622	15	1342	35	3317	20	9686	14	1888	25	2066	27	5459	17	3174	19	5883	16	3979	22	11947	10	13654	10	506	43	6900	14	167969	2
	fir	1887	25	557	38	1541	36	15576	9	455	40	2442	24	0	.	525	49	1991	23	3568	20	5854	15	4123	17	331	54	1068	34	3196	18	139	57	1316	24	1841	23	1299	30	1088	30	952	32	6417	13	1287	28	204	44	2135	19	59792	4
	pine	212	44	0	.	17	100	1397	30	217	41	495	46	110	79	0	.	2555	18	369	37	186	52	159	71	0	.	163	91	74	72	360	39	330	31	45	85	441	35	137	57	184	62	207	51	1995	19	0	.	997	22	10650	8
	larch	300	43	0	.	0	.	306	41	25	72	71	74	0	.	67	87	7281	11	20	100	18	62	43	100	0	.	14	100	210	47	39	66	17	100	52	100	55	85	3352	16	0	.	345	29	8161	12	55	100	287	38	20718	7
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	1342	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	823	24	0	.	0	.	2164	15
	other conifers	121	49	0	.	8	78	365	46	177	100	0	.	0	.	2	100	39	39	-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	1770	27
	beech	5014	16	258	46	557	33	13216	9	2719	19	3303	19	0	.	961	30	1786	23	3594	16	3441	18	1538	19	436	42	1539	30	4962	16	2108	21	4855	15	1633	27	1723	19	4923	13	850	35	6635	12	922	30	249	46	4174	14	71394	3
	maple	583	23	18	71	245	52	1893	14	442	30	484	26	0	.	317	31	339	29	445	22	526	22	426	29	129	34	249	57	1367	19	86	52	523	21	311	28	439	37	197	35	152	42	941	18	262	31	105	53	1151	20	11631	5
	ash	1017	24	0	.	335	48	1445	22	452	39	1288	25	28	100	282	44	285	36	922	27	929	50	128	42	439	57	67	70	1434	21	215	42	503	29	592	40	1065	24	585	30	180	51	1611	22	263	34	171	70	1438	20	15676	7
	oak	589	37	0	.	0	.	663	21	197	52	160	48	473	55	0	.	130	71	299	48	308	49	86	59	5	100	0	.	82	55	241	52	201	38	16	100	895	41	894	21	75	63	1197	24	176	42	0	.	648	25	7336	10
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4838	13	0	.	9	84	90	53	0	.	0	.	5391	12
	other broadleaves	606	26	3	100	63	75	1433	19	175	33	529	52	3	100	187	47	1641	19	299	38	253	34	105	40	72	59	35	49	570	29	37	47	292	35	176	44	427	37	3014	13	133	44	1193	28	829	19	7	100	589	27	12674	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	0	0
	total	13536	11	3158	29	5641	22	66691	5	5581	16	16508	11	614	53	6159	19	49373	5	12284	12	19942	11	11237	11	2756	27	6455	18	21853	10	5169	18	10114	12	10129	14	9523	14	24967	5	6511	17	30820	7	28478	7	1297	33	18369	9	387165	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. 1280544. Birmensdorf, Swiss Federal Research Institute WSL

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