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
grid: 1.4-km grid
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
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	144	31	17	41	12	40	493	9	24	48	132	29	0	.	44	23	810	12	89	52	142	17	71	27	21	43	129	21	131	16	35	40	48	34	100	39	98	44	138	17	46	25	237	13	348	12	23	37	121	22	3452	5
	fir	41	50	-6	185	12	61	263	18	13	31	60	27	0	.	30	85	10	36	88	17	127	23	61	28	7	55	15	42	28	47	1	71	34	29	58	27	30	43	38	38	20	41	118	16	23	27	16	41	24	35	1112	7
	pine	2	71	0	.	0	.	20	46	2	100	0	0	0	.	0	.	104	18	5	100	1	100	1	100	0	.	4	100	1	100	-2	100	3	72	6	69	1	100	3	78	3	63	4	100	92	31	0	.	3	100	252	15
	larch	3	81	0	.	0	.	2	59	0	.	5	83	0	.	0	.	119	26	1	100	1	100	0	.	0	.	2	100	8	50	0	.	2	100	1	100	0	.	66	25	0	.	18	74	111	19	0	.	1	100	339	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	44	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	1	79	1	100	0	.	23	44	0	.	0	.	87	33
	other conifers	3	58	0	0	1	100	12	29	0	.	0	.	0	.	0	.	23	27	3	59	3	68	0	.	0	.	0	.	9	51	0	.	7	62	1	100	2	71	1	100	9	63	9	62	5	64	1	100	3	92	92	15
	beech	79	27	4	50	8	49	362	17	67	22	76	49	0	.	19	32	70	26	73	22	70	21	41	31	5	47	19	33	102	22	52	31	115	23	24	23	64	38	337	13	4	47	143	24	58	59	12	74	120	35	1923	6
	maple	44	45	13	55	2	267	110	19	24	28	19	26	2	100	16	41	32	28	31	29	18	30	14	107	5	56	13	61	31	30	5	67	15	67	37	44	49	56	44	24	7	58	107	15	47	26	2	100	33	89	720	9
	ash	50	48	1	100	0	0	47	26	4	90	56	70	1	100	11	76	39	30	31	38	25	31	9	47	-1	100	6	47	27	28	6	71	37	76	31	47	5	44	93	21	13	40	43	26	22	28	1	80	66	51	623	12
	oak	5	47	0	.	0	.	3	55	1	100	1	100	1	100	0	.	1	75	0	.	0	0	10	67	0	.	0	.	2	78	1	100	7	62	1	100	1	100	34	25	1	100	36	38	35	27	0	.	4	66	143	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	173	24	0	.	2	72	5	87	0	.	0	.	188	22
	other broadleaves	56	23	3	78	7	61	173	14	18	32	40	42	11	49	17	33	285	20	34	26	24	41	17	35	10	60	25	38	45	37	8	50	21	32	8	211	41	41	434	11	41	42	128	16	226	13	0	.	27	22	1700	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
	total	426	17	32	55	42	33	1485	7	153	16	388	19	16	44	137	25	1561	8	354	16	412	11	225	16	47	26	212	16	384	12	106	23	289	17	266	21	292	22	1364	6	144	19	845	8	993	8	56	28	402	18	10631	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	263	25	79	35	119	34	1623	9	68	44	539	18	0	.	138	39	2574	8	189	23	411	16	289	33	123	39	420	22	548	16	121	37	97	32	285	18	344	33	452	16	205	23	860	14	1240	10	84	40	466	20	11537	3
	fir	112	44	18	49	31	40	936	12	47	35	181	26	0	.	20	63	84	34	290	20	417	18	213	21	18	41	31	65	105	35	11	69	137	32	113	29	142	41	83	32	97	34	347	14	175	28	70	39	66	31	3747	5
	pine	11	58	2	100	0	.	47	58	9	71	0	.	0	.	0	.	340	17	15	77	18	101	3	100	0	.	16	100	17	63	13	77	-2	543	5	79	1	100	9	71	10	71	17	100	352	20	0	.	13	62	896	11
	larch	4	100	0	.	0	.	7	59	0	.	5	100	0	.	0	.	343	17	0	.	4	77	0	.	0	.	6	100	33	51	3	100	0	.	0	.	9	71	244	21	0	.	32	44	526	13	0	.	5	100	1222	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	123	18	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	2	100	2	100	0	.	109	30	0	.	0	.	240	17
	other conifers	11	58	4	100	4	100	37	53	0	.	0	.	0	.	1	100	32	34	-3	190	9	73	0	.	6	100	2	100	-7	192	0	.	2	728	2	100	14	76	0	.	2	100	22	38	7	67	2	100	26	46	174	22
	beech	297	26	10	60	45	40	1064	10	140	30	344	25	0	.	79	36	174	26	299	19	227	18	126	20	49	53	130	36	383	17	151	24	271	20	188	25	144	26	1043	12	56	38	402	14	163	32	4	246	282	16	6070	4
	maple	151	24	32	111	24	83	345	15	56	28	104	25	0	.	55	32	55	38	99	27	66	28	48	44	13	47	44	44	129	26	23	57	78	30	108	25	85	29	115	23	25	47	290	18	122	20	11	68	230	29	2308	6
	ash	81	28	9	81	33	60	135	26	38	36	135	36	6	100	36	43	115	29	87	32	48	34	20	51	20	65	14	76	149	27	41	45	31	38	90	41	63	35	202	23	21	51	171	19	146	35	11	68	137	27	1837	7
	oak	57	38	0	.	0	.	14	56	21	72	3	100	29	53	0	.	28	45	2	100	8	71	14	59	3	100	0	.	3	100	3	100	32	47	0	.	5	78	124	25	0	.	157	28	73	27	0	.	17	54	590	12
	sweet chestnut	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	35	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	904	14	0	.	4	100	12	71	0	.	0	.	956	13
	other broadleaves	202	24	0	0	18	48	275	16	40	41	68	46	10	71	45	41	519	16	66	26	34	46	25	32	12	47	43	48	127	26	11	48	95	31	100	39	56	46	1115	10	87	37	235	19	436	13	0	0	91	25	3712	5
	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	1188	12	153	31	275	22	4484	6	420	16	1378	13	45	46	374	21	4423	6	1044	12	1243	11	737	16	244	27	711	16	1487	11	377	20	739	14	890	13	864	17	4292	5	506	16	2539	8	3361	7	181	28	1333	11	33289	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	706	25	385	27	438	30	4745	8	70	54	1866	16	0	.	607	30	6636	7	560	22	1138	16	810	20	318	36	881	18	1545	14	340	33	375	27	921	18	712	23	926	16	794	20	2451	11	3253	9	142	46	1315	17	31936	3
	fir	209	32	58	57	98	42	2102	11	148	31	418	25	0	.	56	60	192	27	545	19	918	18	407	21	33	81	162	43	499	20	9	100	238	27	271	24	251	33	167	40	189	39	932	15	291	26	96	40	280	28	8571	5
	pine	58	45	0	.	0	.	210	39	12	119	14	101	37	87	0	.	626	18	63	51	34	71	33	51	0	.	54	81	70	63	82	49	73	69	57	73	58	80	126	61	71	46	61	49	683	17	0	.	103	35	2526	10
	larch	87	61	3	100	0	.	87	47	0	.	26	100	0	.	0	.	1253	12	0	.	0	.	0	.	0	.	0	.	61	46	8	100	0	.	0	.	35	59	774	15	0	.	94	36	1349	12	0	.	33	59	3809	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	423	20	0	.	0	.	0	.	0	.	24	100	7	100	0	.	0	.	0	.	0	.	5	100	13	100	0	.	357	20	0	.	0	.	829	14
	other conifers	38	46	0	.	0	.	60	43	12	92	0	.	0	.	0	.	9	58	0	.	0	.	4	100	0	.	0	.	-8	166	11	100	4	100	0	.	24	82	0	.	0	.	60	60	0	0	0	.	17	118	230	26
	beech	908	19	39	63	162	37	3060	10	526	24	783	18	0	.	193	31	411	22	945	19	667	20	423	22	151	40	306	30	1007	16	536	23	972	16	316	24	300	23	1560	12	192	36	1570	16	348	25	71	43	749	15	16195	4
	maple	178	27	18	79	92	51	714	12	157	32	243	30	7	100	124	39	128	39	208	29	238	25	190	31	61	38	216	45	401	22	42	46	135	31	135	33	166	46	151	28	61	40	545	20	102	30	10	100	303	23	4624	6
	ash	185	31	6	100	120	43	468	21	157	35	372	27	9	100	37	60	191	42	217	25	163	29	19	71	88	95	32	57	379	24	21	59	147	43	161	34	195	33	341	20	57	60	417	23	143	26	35	53	210	26	4171	7
	oak	171	34	0	.	0	.	142	32	78	54	31	56	69	62	0	.	21	53	21	75	17	72	24	80	0	.	0	.	42	64	78	35	60	46	0	.	63	37	342	22	5	100	435	27	56	46	0	.	45	42	1701	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	170	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1141	13	0	.	11	100	31	71	0	.	0	.	1353	13
	other broadleaves	254	29	1	459	15	100	347	20	92	30	93	42	11	71	21	63	460	18	124	24	37	60	38	55	20	71	34	48	144	30	5	100	110	32	86	35	203	33	1428	12	24	47	306	28	411	20	10	71	181	27	4455	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	2793	11	511	25	924	21	11935	5	1251	15	3845	11	134	48	1039	22	10522	5	2684	11	3211	11	1950	12	671	27	1710	15	4147	9	1133	17	2114	12	1949	13	2009	14	6961	5	1406	17	6882	7	7023	6	364	28	3235	10	80400	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	1127	17	748	25	1093	25	11045	6	151	38	3094	13	0	.	1212	20	12559	6	1115	19	2448	13	1603	15	295	32	1295	16	3150	12	608	28	840	21	1989	15	958	24	1654	14	1200	20	5037	9	4983	8	324	33	2305	13	60831	2
	fir	534	26	247	35	577	30	4972	9	231	35	762	20	0	.	184	40	570	19	1353	17	1792	14	1247	15	73	57	231	32	1430	14	45	51	498	23	586	22	227	31	331	26	227	38	1904	12	513	24	152	34	625	20	19310	4
	pine	176	30	20	100	10	100	496	30	258	28	175	56	24	74	0	.	1055	16	215	41	89	48	76	61	0	.	44	60	114	36	145	42	314	23	70	56	202	31	161	50	86	46	138	35	634	18	0	.	553	21	5054	7
	larch	99	55	6	100	30	100	123	41	14	73	0	0	0	.	6	100	2854	10	11	100	14	73	0	.	0	.	8	100	258	44	41	55	37	63	0	.	10	87	1160	13	0	.	123	32	2894	10	0	.	77	47	7764	6
	Arolla pine	0	.	0	.	0	.	9	148	0	.	0	.	0	.	0	.	536	16	0	.	0	.	0	.	0	.	20	100	3	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	305	20	0	.	0	.	876	12
	other conifers	55	48	0	.	0	.	70	69	115	71	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	20	100	0	.	0	.	13	100	11	100	0	.	150	53	0	.	0	.	4	100	459	28
	beech	1903	13	161	35	382	37	4765	8	1290	17	1627	16	0	.	507	25	503	25	2105	14	1073	17	873	15	176	42	470	29	1934	13	872	19	2175	12	605	22	547	22	1550	11	187	37	3035	11	220	27	166	46	1467	13	28592	3
	maple	228	23	11	61	38	51	692	15	134	35	132	34	0	.	173	30	101	51	259	23	131	33	117	27	35	45	40	40	384	18	11	71	326	21	120	32	148	36	44	74	46	39	303	21	73	49	64	37	194	23	3805	6
	ash	540	19	48	71	91	47	426	21	127	36	376	24	0	.	109	44	61	36	428	25	373	52	48	50	81	48	46	62	576	19	55	45	164	28	170	43	423	23	240	25	28	66	536	21	88	42	70	42	609	18	5712	6
	oak	185	29	0	.	0	.	262	30	88	55	61	46	222	43	0	.	118	50	169	35	123	50	50	67	0	.	0	.	87	55	86	50	78	43	-17	100	162	34	290	25	32	62	511	20	45	38	6	100	176	26	2733	9
	sweet chestnut	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1033	14	0	.	36	90	13	59	0	.	0	.	1159	13
	other broadleaves	204	43	0	0	0	.	290	25	49	42	42	66	4	100	38	74	323	21	155	34	70	56	23	53	39	71	6	100	107	32	10	72	58	39	18	61	195	34	839	14	18	65	356	28	135	34	12	100	206	30	3198	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
	total	5055	9	1240	23	2221	19	23150	5	2456	13	6282	10	251	42	2228	16	18751	5	5810	9	6114	10	4037	10	699	24	2159	14	8053	8	1893	16	4490	10	3539	12	2884	14	7313	5	1827	16	12128	6	9902	6	795	23	6216	9	139493	1
=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	1366	23	605	35	1422	24	13811	7	398	36	2678	15	19	100	1676	21	14132	7	1284	19	2951	17	2076	15	300	40	1785	20	5302	13	541	29	887	28	2862	16	864	27	2712	14	1403	24	6613	10	5043	11	456	31	2346	16	73531	3
	fir	1281	23	205	41	706	32	8817	9	255	49	1272	24	0	.	345	41	1317	19	1181	21	2335	15	2297	16	105	68	377	36	2222	16	49	58	733	26	919	23	427	42	737	27	155	50	3097	13	521	30	214	49	1363	18	30929	4
	pine	284	37	0	.	0	.	345	29	178	57	105	48	0	.	0	.	724	21	189	35	25	100	36	60	0	.	18	73	143	47	11	100	65	41	11	100	131	37	70	54	21	71	0	.	323	34	0	.	386	28	3063	10
	larch	131	37	0	.	37	100	94	44	0	.	15	100	0	.	34	100	4164	11	15	100	120	100	24	100	0	.	9	100	103	49	33	100	59	62	56	71	17	100	1838	14	0	.	275	40	4929	10	32	100	171	37	12158	6
	Arolla pine	0	.	0	.	0	.	27	73	0	.	0	.	0	.	0	.	483	20	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	309	23	0	.	0	.	829	15
	other conifers	51	61	0	.	0	.	112	61	40	100	44	100	0	.	0	.	0	.	0	.	155	73	0	.	0	.	0	.	166	100	34	100	0	.	0	.	0	.	16	100	0	.	114	89	0	.	0	.	18	100	751	33
	beech	2199	15	66	53	356	34	3879	12	1383	24	1193	21	0	.	295	35	514	27	881	20	1089	22	377	32	45	58	415	40	1543	17	445	26	1914	16	683	31	570	27	1266	21	124	53	1844	14	137	46	103	45	2229	17	23549	4
	maple	59	51	0	.	0	.	241	35	71	57	41	52	0	.	79	48	58	48	51	50	38	59	24	65	12	100	7	100	187	33	0	.	31	58	0	.	76	66	11	100	8	100	90	38	0	.	13	100	167	38	1263	13
	ash	272	28	21	100	25	71	451	32	158	45	253	29	0	.	53	55	25	77	225	33	144	51	0	.	90	72	0	.	226	39	30	76	73	42	83	71	302	34	102	46	0	.	499	33	7	123	83	68	461	29	3584	10
	oak	518	28	0	.	13	100	324	25	192	53	147	43	75	74	0	.	64	48	71	53	84	61	8	100	0	.	19	100	83	61	224	53	138	44	51	83	333	57	110	37	16	100	446	35	34	62	22	100	386	26	3356	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1774	19	0	.	22	100	54	64	0	.	0	.	1904	18
	other broadleaves	291	51	0	.	20	71	249	48	80	77	183	61	0	.	28	77	75	45	11	100	43	78	126	100	10	100	0	.	77	76	0	0	39	72	6	100	262	93	186	41	12	100	394	47	148	52	0	.	86	47	2324	18
	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	6452	10	896	30	2579	22	28349	5	2754	17	5931	11	94	62	2510	17	21609	6	3909	12	6983	11	4967	12	562	31	2629	17	10063	10	1367	21	3938	13	4672	14	2982	18	8822	7	1740	21	13394	7	11503	7	922	26	7612	10	157240	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	3606	15	1834	25	3084	20	31716	5	712	26	8308	11	19	100	3677	17	36712	5	3237	15	7090	12	4848	12	1057	27	4510	14	10676	10	1646	21	2247	17	6158	12	2976	18	5882	11	3648	17	15198	7	14867	7	1028	26	6552	11	181288	2
	fir	2177	19	521	30	1425	27	17090	7	694	31	2692	17	0	.	635	34	2173	16	3458	13	5590	12	4227	13	235	45	815	28	4285	13	114	47	1639	17	1947	18	1077	26	1356	21	689	27	6396	10	1523	20	550	29	2358	16	63668	3
	pine	531	26	22	90	10	100	1118	23	459	31	294	45	62	79	0	.	2849	13	486	31	167	41	150	48	0	.	135	70	345	37	249	33	452	24	149	53	393	28	369	45	191	42	221	33	2082	15	0	.	1056	18	11791	6
	larch	324	34	9	75	67	100	313	29	14	73	51	61	0	.	40	87	8734	8	27	69	139	93	24	100	0	.	26	75	462	36	86	69	98	53	57	70	70	50	4082	10	0	.	543	28	9809	8	32	100	287	28	25291	4
	Arolla pine	0	.	0	.	0	.	35	87	0	.	0	.	0	.	0	.	1626	14	0	.	0	.	0	.	0	.	48	100	22	100	0	.	0	.	0	.	0	.	8	70	20	100	0	.	1102	15	0	.	0	.	2861	10
	other conifers	157	32	4	100	5	79	291	37	166	72	56	100	0	.	1	100	64	30	0	2109	168	68	4	100	6	100	2	100	170	104	65	71	13	126	3	73	54	83	28	97	11	57	355	43	12	50	3	100	68	42	1705	19
	beech	5384	11	279	34	953	30	13130	7	3406	15	4022	14	0	.	1093	22	1672	18	4303	12	3126	14	1840	14	427	30	1339	26	4968	11	2057	16	5447	10	1815	18	1626	17	5757	10	563	33	6995	9	926	21	355	37	4848	11	76329	2
	maple	660	16	73	75	156	47	2102	10	442	23	540	20	9	78	447	24	374	29	649	18	491	17	393	24	125	27	319	36	1132	15	81	42	585	18	400	22	525	28	365	21	147	32	1336	13	344	19	99	37	927	16	12721	4
	ash	1128	16	85	65	269	38	1527	16	485	27	1192	19	16	100	244	34	431	28	988	19	753	36	96	37	277	54	99	42	1357	16	154	35	453	24	536	30	988	20	978	16	119	48	1665	17	405	23	201	43	1483	15	15928	5
	oak	937	20	0	.	13	100	745	19	379	39	242	32	396	42	0	.	233	40	263	34	232	41	106	58	3	100	19	100	217	52	391	36	315	26	34	75	564	37	900	16	54	54	1584	18	242	24	28	81	628	20	8524	7
	sweet chestnut	3	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	337	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5025	10	0	.	75	94	116	54	0	.	0	.	5559	9
	other broadleaves	1007	27	4	159	60	55	1334	15	280	27	426	40	37	49	150	37	1662	13	391	22	207	29	229	56	92	44	108	33	500	21	34	38	323	25	218	28	758	39	4002	8	182	32	1418	19	1355	13	22	64	590	19	15389	5
	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	0
	total	15915	8	2832	22	6041	17	69403	4	7035	12	17824	8	539	39	6288	14	56866	4	13802	8	17964	8	11917	9	2223	21	7420	12	24134	7	4876	14	11570	9	11316	10	9031	11	28752	4	5622	14	35787	5	32783	5	2317	21	18797	7	421054	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. 1280582. Birmensdorf, Swiss Federal Research Institute WSL

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