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
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: canton
unit: 1000 n
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 (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
12-20 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	1227	25	433	38	416	40	12545	9	576	45	3118	23	52	74	1126	30	18559	8	544	38	4236	19	1297	20	713	66	2932	27	3822	18	445	47	1059	48	1814	25	1140	26	4045	19	2028	23	5599	14	10011	11	116	49	2026	27	79881	4
	fir	489	35	63	60	332	54	5971	11	453	32	1068	24	34	100	83	61	351	32	1889	19	2703	23	1606	28	92	53	603	35	547	24	52	58	775	37	668	32	406	37	538	32	851	30	2422	16	629	27	300	55	366	34	23291	6
	pine	18	100	0	.	0	.	284	48	76	70	39	100	0	.	0	.	3330	24	39	71	20	100	34	71	66	100	282	72	65	100	0	.	142	57	226	79	0	.	181	60	0	.	20	100	2652	26	0	.	93	68	7569	15
	larch	153	64	0	.	0	.	76	50	0	.	66	100	0	.	0	.	3038	15	0	.	49	76	0	.	0	.	52	100	281	50	0	.	0	.	0	.	151	81	2722	19	0	.	793	54	4057	15	0	.	59	74	11495	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1761	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	795	29	0	.	0	.	2588	20
	other conifers	51	74	0	.	36	100	462	42	17	100	0	.	0	.	18	100	927	36	51	74	52	58	0	.	0	.	18	100	129	47	0	.	86	72	76	80	34	71	20	100	121	100	300	56	173	56	0	.	346	61	2917	18
	beech	1868	18	66	60	120	49	6442	11	1494	25	1336	25	0	.	684	42	1325	22	1495	22	1670	18	972	26	417	49	622	40	2901	19	1115	30	2096	22	745	23	578	27	5946	15	314	38	3584	17	1469	27	36	71	2205	22	39500	5
	maple	650	30	15	100	277	56	2400	16	554	33	338	36	20	100	422	46	427	31	522	29	500	29	730	43	117	47	264	81	433	28	124	61	842	38	544	38	543	27	1317	29	239	38	2293	20	1561	26	59	76	876	23	16065	7
	ash	618	34	0	.	18	100	927	26	207	45	676	34	34	100	86	53	652	28	373	38	455	31	178	35	135	63	96	59	689	35	131	63	266	38	473	47	286	34	1475	24	270	45	929	24	1008	29	17	100	510	42	10509	8
	oak	161	48	0	.	0	.	126	44	101	71	97	53	71	61	0	.	33	71	17	100	19	100	246	93	0	.	0	.	35	100	54	58	166	73	0	.	17	100	1166	38	18	100	1156	41	805	35	0	.	113	63	4403	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	355	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7619	16	0	.	0	.	131	77	0	.	0	.	8105	15
	other broadleaves	1012	23	0	.	121	62	4413	16	370	36	1286	26	222	56	381	47	5799	18	836	29	1053	29	415	36	295	47	364	35	1077	27	98	61	475	28	929	33	800	40	10919	11	1087	45	2405	17	4752	14	105	62	1122	21	40339	5
	indeterminable	0	.	0	.	15	100	65	50	38	100	18	100	0	.	0	.	285	35	17	100	52	58	0	.	0	.	18	100	89	45	0	.	34	100	0	.	17	100	75	50	0	.	111	41	90	45	0	.	17	100	940	16
	total	6247	14	577	36	1335	30	33710	6	3886	19	8042	13	433	43	2801	21	36844	6	5784	14	10807	12	5478	15	1836	35	5251	20	10067	12	2019	26	5941	17	5474	17	3972	17	36056	6	4928	19	19612	9	28134	7	633	35	7732	13	247602	2
21-30 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	877	29	332	38	520	31	7453	9	138	65	2247	22	0	.	733	31	11799	8	123	43	2114	18	831	25	687	42	1664	24	2118	18	311	38	262	36	1425	27	832	29	2355	18	1592	21	3261	15	5736	13	193	46	1410	26	49013	4
	fir	255	48	51	76	176	56	3329	11	226	40	456	32	0	.	0	.	262	33	1048	23	1587	19	1015	23	65	61	262	39	323	26	35	100	553	32	300	36	437	36	267	38	432	29	1400	18	341	30	196	49	244	31	13259	6
	pine	36	71	0	.	0	.	469	38	69	61	34	100	0	.	0	.	1302	22	78	71	17	100	0	.	0	.	208	100	101	74	68	79	88	53	17	100	17	100	34	71	147	56	98	69	2241	20	0	.	110	53	5135	12
	larch	97	83	0	.	0	.	35	71	0	.	17	100	0	.	0	.	2690	17	0	.	17	100	0	.	0	.	17	100	120	62	17	100	0	.	0	.	71	61	1964	18	0	.	209	53	2539	15	0	.	20	100	7812	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1284	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	614	26	0	.	0	.	1914	17
	other conifers	69	50	0	.	18	100	89	81	0	.	0	.	0	.	0	.	52	58	0	.	0	.	17	100	0	.	17	100	56	58	0	.	103	52	0	.	0	.	0	.	0	.	37	71	17	100	0	.	68	50	542	22
	beech	915	24	54	74	71	61	3998	11	698	32	1065	20	0	.	240	33	775	28	847	26	964	23	551	23	378	49	518	39	1618	20	490	34	1160	23	593	26	481	27	3861	16	481	34	1701	16	674	36	38	71	955	18	23126	5
	maple	213	31	0	.	218	49	1383	17	300	32	502	26	0	.	330	38	290	34	228	31	204	35	381	30	144	43	111	87	812	21	94	61	341	29	245	39	293	38	211	40	178	49	910	21	478	27	38	71	861	29	8765	7
	ash	171	45	0	.	160	53	514	25	107	53	356	31	17	100	166	50	351	55	177	46	161	47	34	71	198	70	52	58	418	28	70	50	176	40	254	50	105	47	637	34	105	52	456	28	458	36	52	100	252	33	5449	9
	oak	35	71	0	.	0	.	216	36	104	74	92	54	121	65	0	.	78	83	69	61	17	100	38	100	22	100	0	.	74	77	52	74	143	48	0	.	37	71	809	22	18	100	675	34	288	38	0	.	34	71	2923	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	293	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4219	15	0	.	0	.	37	71	0	.	0	.	4549	14
	other broadleaves	551	22	0	.	50	58	1077	17	156	39	262	32	0	.	113	48	2489	19	232	45	184	34	72	50	50	73	18	100	588	37	17	100	294	43	435	43	164	44	4237	13	331	46	903	24	1442	19	0	.	441	31	14106	6
	indeterminable	0	.	0	.	0	.	30	71	0	.	22	100	0	.	0	.	93	54	17	100	37	72	19	100	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	15	100	36	71	0	.	17	100	305	26
	total	3219	14	436	34	1214	26	18592	6	1798	19	5054	13	138	61	1582	22	21757	6	2820	15	5303	12	2959	14	1544	31	2868	20	6229	12	1154	24	3137	15	3270	18	2436	17	18609	6	3282	17	9666	10	14901	8	518	34	4413	13	136898	2
31-40 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	324	34	514	30	367	31	5271	9	87	58	1595	19	0	.	494	31	7069	8	235	45	1075	17	737	25	423	42	1057	28	1557	17	222	48	271	46	954	23	483	36	1274	20	882	23	2194	13	3722	11	227	37	942	21	31976	3
	fir	142	48	45	74	223	41	1881	13	111	47	378	37	0	.	76	68	248	30	532	26	830	19	304	26	37	52	212	44	299	33	14	71	289	32	185	32	198	43	179	37	250	42	1014	17	312	35	59	67	138	41	7956	6
	pine	26	75	0	.	0	.	416	36	84	50	44	54	45	100	0	.	714	23	68	77	47	63	110	63	0	.	145	72	53	100	131	40	115	44	93	86	16	100	129	57	89	59	63	68	832	20	0	.	109	33	3329	11
	larch	31	81	0	.	0	.	57	69	0	.	38	84	0	.	0	.	1696	13	0	.	0	.	0	.	0	.	0	.	74	73	7	100	0	.	0	.	0	.	1198	18	0	.	85	58	1883	18	0	.	20	58	5089	9
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	656	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	342	31	0	.	0	.	1007	19
	other conifers	34	100	0	.	0	.	59	51	24	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	15	100	0	.	7	100	0	.	0	.	0	.	0	.	7	100	17	100	0	.	7	100	177	33
	beech	573	22	66	48	135	53	2630	10	548	26	781	23	0	.	148	37	597	25	700	24	756	21	233	30	71	58	277	37	1155	19	413	29	944	22	340	26	217	33	1506	16	313	40	1285	15	291	32	77	46	646	20	14703	4
	maple	118	32	13	71	112	49	706	19	120	44	103	42	0	.	120	43	160	36	125	34	283	31	182	55	78	43	150	53	535	23	24	77	163	29	76	57	81	51	163	47	28	63	380	27	64	48	16	100	253	26	4055	8
	ash	78	54	0	.	123	47	341	27	104	49	315	42	0	.	95	52	141	42	285	42	55	49	0	.	202	75	26	78	284	29	28	71	95	43	65	55	190	37	151	34	65	100	364	29	190	38	8	100	166	34	3372	10
	oak	49	63	0	.	0	.	92	63	45	62	27	79	66	65	0	.	7	100	28	50	0	.	8	100	0	.	0	.	16	71	52	58	82	65	0	.	75	45	313	27	7	100	244	30	114	43	0	.	71	49	1295	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	294	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1351	19	0	.	0	.	87	62	0	.	0	.	1733	17
	other broadleaves	104	42	0	.	15	71	225	25	59	47	51	53	0	.	31	67	453	22	156	39	61	67	27	100	36	100	18	100	207	35	0	.	52	58	44	54	113	43	1551	19	23	75	217	39	159	29	0	.	130	38	3732	10
	indeterminable	0	.	0	.	7	100	78	41	0	.	0	.	0	.	0	.	59	52	0	.	0	.	17	100	0	.	7	100	14	71	0	.	17	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	17	100	224	25
	total	1480	16	638	28	981	25	11766	6	1182	18	3332	13	110	60	963	24	12095	6	2130	16	3113	12	1617	17	846	31	1891	21	4210	11	891	23	2034	17	1757	17	1373	20	7817	8	1658	19	5852	9	8021	8	388	30	2499	12	78647	2
41-50 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	255	26	276	32	218	36	3819	8	37	52	1205	16	0	.	462	28	4708	7	240	27	705	17	533	19	225	40	573	26	951	16	160	39	183	29	638	21	202	26	903	20	537	28	1616	13	2053	11	140	43	662	17	21301	3
	fir	138	37	82	41	125	32	1319	11	63	53	180	27	0	.	65	48	162	29	405	26	579	16	352	22	29	61	103	36	292	22	28	79	126	27	278	27	135	35	118	36	164	43	619	15	181	34	26	58	183	28	5751	5
	pine	0	.	0	.	0	.	223	37	64	40	20	75	21	74	0	.	408	22	61	56	39	74	14	100	0	.	14	100	21	100	51	51	89	38	7	100	62	46	34	60	31	62	61	43	310	27	0	.	183	27	1713	10
	larch	34	72	0	.	0	.	36	73	14	71	0	.	0	.	0	.	1241	12	0	.	7	100	0	.	0	.	0	.	43	70	7	100	0	.	0	.	7	100	666	17	0	.	43	47	1299	15	0	.	7	100	3402	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	262	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	160	30	0	.	0	.	422	18
	other conifers	14	71	0	.	0	.	27	50	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	8	100	0	.	21	100	0	.	0	.	0	.	90	35
	beech	491	20	69	55	83	46	1620	12	305	28	330	24	0	.	142	37	254	28	408	23	423	21	225	26	71	58	175	33	844	18	260	28	623	19	235	29	119	30	657	16	89	40	984	15	77	55	51	54	345	19	8879	4
	maple	84	35	0	.	7	100	260	20	28	61	36	45	0	.	79	38	55	45	72	34	35	45	64	41	14	71	18	70	189	23	7	100	80	37	42	46	43	52	29	61	20	58	126	28	56	60	23	73	46	47	1414	8
	ash	54	56	0	.	41	71	113	26	33	65	121	39	0	.	45	52	28	79	131	37	92	60	29	60	43	52	7	100	174	30	28	50	89	37	53	57	74	30	44	45	20	58	149	32	27	71	17	100	162	26	1574	9
	oak	94	42	0	.	0	.	74	32	7	100	53	56	43	52	0	.	27	71	59	70	56	52	21	100	0	.	0	.	0	.	42	75	30	50	0	.	97	40	158	31	25	72	137	31	21	75	7	100	34	45	985	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	658	20	0	.	7	100	16	71	0	.	0	.	750	18
	other broadleaves	59	44	0	.	0	.	126	37	21	58	13	71	0	.	18	74	162	34	134	54	26	58	0	.	7	100	0	.	18	74	7	100	44	63	6	100	61	43	418	21	0	.	87	31	67	37	0	.	117	35	1393	11
	indeterminable	0	.	0	.	0	.	34	60	7	100	0	.	0	.	0	.	33	52	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	14	100	15	71	7	100	0	.	0	.	117	29
	total	1223	14	427	28	475	26	7651	6	592	21	1958	12	64	57	811	21	7408	6	1510	15	1961	12	1238	13	389	30	890	20	2539	11	597	21	1264	14	1260	16	800	17	3694	7	899	22	3865	8	4274	8	264	33	1739	11	47791	2
51-60 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	113	30	264	32	203	32	2149	9	63	51	591	19	0	.	249	23	2621	8	174	33	550	18	359	20	40	51	249	25	618	16	70	39	108	36	519	20	100	34	634	30	300	25	827	14	1156	12	98	45	310	21	12365	4
	fir	80	44	21	58	158	49	1125	12	28	50	219	27	0	.	37	70	150	28	267	25	454	20	305	21	21	74	72	42	347	19	0	.	41	41	122	32	27	61	152	33	26	60	457	17	167	37	14	71	114	29	4404	6
	pine	35	64	0	.	0	.	97	37	15	71	47	62	0	.	0	.	220	27	41	47	22	58	14	100	0	.	7	100	24	76	22	58	34	45	0	.	34	67	21	74	22	74	0	.	78	46	0	.	74	32	808	13
	larch	36	45	0	.	0	.	21	58	0	.	0	.	0	.	0	.	852	13	7	100	0	.	0	.	0	.	0	.	12	100	0	.	7	100	14	100	0	.	385	18	0	.	29	61	850	14	0	.	33	53	2247	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	149	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	47	0	.	0	.	219	23
	other conifers	14	71	0	.	0	.	47	62	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	14	100	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	124	36
	beech	357	23	0	.	30	50	958	13	142	27	208	27	0	.	108	46	96	38	182	25	272	21	83	35	14	71	114	50	325	21	98	36	338	24	156	35	133	29	293	21	57	52	464	16	34	63	23	58	284	19	4769	5
	maple	35	45	0	.	0	.	122	26	28	50	39	45	0	.	6	100	52	41	21	57	19	71	0	.	0	.	0	.	103	34	0	.	45	47	12	70	22	76	0	.	0	.	18	72	0	.	14	71	43	41	578	12
	ash	74	37	0	.	0	.	72	39	32	64	67	47	0	.	31	52	0	.	7	100	69	58	0	.	21	100	0	.	64	36	7	100	14	71	40	53	61	42	40	63	0	.	48	38	7	100	0	.	96	31	751	12
	oak	32	59	0	.	0	.	73	32	14	100	0	.	22	100	0	.	27	81	42	47	35	62	7	100	0	.	0	.	9	100	0	.	8	100	6	100	41	47	57	41	0	.	48	51	8	100	0	.	89	32	518	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	314	25	0	.	0	.	8	100	0	.	0	.	352	23
	other broadleaves	14	71	0	.	8	100	60	40	7	100	27	80	0	.	12	100	25	71	7	100	17	100	0	.	0	.	0	.	37	53	0	.	14	100	6	100	24	58	57	42	0	.	102	45	23	57	0	.	14	71	456	17
	indeterminable	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	34	45
	total	790	15	285	31	400	29	4730	6	349	21	1199	14	22	100	444	23	4220	6	749	16	1438	13	768	14	110	36	442	23	1559	11	210	24	609	17	876	15	443	19	1962	13	406	22	1991	9	2417	9	149	34	1057	12	27626	2
61-70 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	74	35	79	37	62	50	922	11	21	57	192	24	0	.	131	37	992	11	88	36	295	24	151	31	64	51	95	38	484	19	56	39	68	48	176	31	69	34	252	21	178	30	488	17	444	17	14	100	142	28	5535	5
	fir	61	46	29	61	21	74	553	15	7	100	67	42	0	.	0	.	73	34	129	33	205	25	130	30	27	62	22	58	132	28	14	71	21	58	72	35	20	58	22	58	14	71	315	23	26	60	0	.	101	36	2061	7
	pine	0	.	0	.	0	.	45	41	7	100	14	71	0	.	0	.	65	37	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	20	75	7	100	0	.	0	.	7	100	0	.	0	.	179	21
	larch	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	423	17	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	157	26	0	.	7	100	452	18	0	.	20	58	1078	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	36	45
	other conifers	7	100	0	.	0	.	7	100	14	100	0	.	0	.	0	.	0	.	0	.	33	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	69	42
	beech	113	31	14	71	14	71	286	21	59	42	54	35	0	.	21	74	57	35	91	36	73	35	0	.	0	.	35	59	124	38	51	42	74	35	13	71	55	39	135	30	26	80	120	27	20	73	7	100	141	28	1582	8
	maple	0	.	0	.	0	.	35	52	0	.	0	.	0	.	0	.	0	.	7	100	0	.	8	100	0	.	0	.	34	64	0	.	0	.	0	.	8	100	0	.	8	100	13	71	0	.	0	.	27	61	140	27
	ash	20	74	0	.	0	.	39	45	0	.	0	.	0	.	12	100	0	.	32	61	0	.	0	.	7	100	0	.	23	72	7	100	14	71	7	100	15	71	10	100	0	.	21	58	0	.	7	100	34	45	247	19
	oak	7	100	0	.	0	.	28	50	7	100	7	100	7	100	0	.	6	100	7	100	11	100	0	.	0	.	0	.	0	.	0	.	12	100	0	.	32	80	15	71	0	.	16	72	0	.	0	.	14	71	170	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	31	0	.	0	.	16	71	0	.	0	.	114	25
	other broadleaves	14	100	0	.	0	.	20	58	0	.	7	100	0	.	6	100	26	59	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	9	100	7	100	8	100	45	60	7	100	0	.	0	.	158	26
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	total	295	19	121	34	98	40	1941	8	115	30	346	17	7	100	171	31	1686	9	354	19	625	17	289	22	98	39	152	31	818	16	127	29	188	25	268	25	227	23	688	12	234	26	1025	13	993	12	28	71	480	16	11375	3
71-80 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	33	53	14	71	6	100	390	15	28	61	117	30	0	.	98	41	382	16	50	51	101	31	46	40	26	50	73	35	229	24	28	61	19	73	58	42	20	74	174	29	81	42	159	23	200	25	7	100	69	39	2407	6
	fir	47	38	8	100	23	58	305	17	7	100	36	45	0	.	7	100	49	38	36	60	74	35	82	30	0	.	38	86	81	36	0	.	14	71	50	38	20	100	15	71	0	.	99	31	37	60	0	.	54	35	1080	9
	pine	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	48	38
	larch	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	140	24	0	.	0	.	7	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	70	31	0	.	7	100	180	26	7	100	0	.	433	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	0	.	0	.	50	38
	other conifers	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	13	71	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	58
	beech	54	46	0	.	7	100	90	32	31	51	7	100	0	.	0	.	32	65	7	100	42	52	7	100	0	.	7	100	30	58	14	71	61	46	28	80	7	100	37	45	0	.	48	38	6	100	0	.	47	37	560	13
	maple	7	100	0	.	0	.	0	.	9	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	36	45
	ash	7	100	0	.	0	.	10	100	0	.	8	100	0	.	0	.	6	100	15	71	0	.	0	.	0	.	0	.	7	100	0	.	0	.	6	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	65	34
	oak	39	46	0	.	0	.	0	.	0	.	7	100	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	76	32
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	33	0	.	0	.	8	100	0	.	0	.	112	31
	other broadleaves	16	71	0	.	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	13	71	0	.	0	.	7	100	56	35
	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	202	21	22	58	36	45	821	11	75	37	195	23	7	100	112	39	651	12	115	33	230	21	141	23	26	50	118	36	375	19	51	42	94	35	142	30	60	45	414	16	81	42	334	17	460	17	14	71	190	23	4965	4
81-90 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	13	100	7	100	0	.	117	27	0	.	26	60	0	.	29	50	196	23	15	71	9	100	16	71	0	.	0	.	77	34	0	.	7	100	35	51	20	58	33	51	7	100	38	46	43	41	0	.	20	58	707	11
	fir	13	71	0	.	0	.	86	33	0	.	29	60	0	.	6	100	15	71	0	.	21	57	44	46	0	.	21	77	35	52	0	.	7	100	0	.	13	100	7	100	0	.	28	50	0	.	11	100	24	59	360	16
	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	.	7	100	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	40	0	.	0	.	81	37	0	.	0	.	245	22
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	71	0	.	0	.	43	52
	other conifers	0	.	0	.	0	.	14	71	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	47
	beech	13	71	0	.	0	.	7	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	7	100	30	50	15	71	8	100	7	100	0	.	0	.	102	27
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	ash	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	33	51
	oak	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	15	71	0	.	10	100	0	.	0	.	0	.	7	100	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	47	0	.	0	.	0	.	0	.	0	.	53	47
	other broadleaves	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	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	53	39	7	100	0	.	224	20	7	100	62	39	0	.	35	45	339	18	15	71	37	45	60	39	0	.	21	77	168	24	0	.	14	71	35	51	55	43	180	23	32	51	74	32	160	26	11	100	65	34	1654	7
91-100 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	7	100	0	.	0	.	60	36	0	.	9	100	0	.	29	50	62	37	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	11	100	0	.	24	58	22	58	0	.	0	.	238	18
	fir	0	.	0	.	0	.	13	71	0	.	0	.	0	.	7	100	15	71	7	100	0	.	8	100	0	.	0	.	14	71	0	.	7	100	7	100	7	100	10	100	0	.	7	100	0	.	0	.	0	.	102	27
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	36	45	0	.	0	.	61	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58	0	.	0	.	22	58
	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	13	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	12	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	51	42
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	17	71
	oak	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	14	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	53	0	.	0	.	7	100	0	.	0	.	53	48
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100
	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	20	58	0	.	0	.	90	29	0	.	9	100	0	.	42	41	102	29	7	100	0	.	8	100	0	.	0	.	33	52	0	.	19	73	14	71	21	58	74	39	0	.	37	45	87	29	0	.	14	71	577	12
>100 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	0	.	0	.	0	.	30	50	0	.	7	100	0	.	7	100	37	45	7	100	7	100	7	100	0	.	0	.	21	58	0	.	0	.	7	100	0	.	11	100	0	.	7	100	7	100	0	.	0	.	156	22
	fir	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	51
	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	.	15	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	29	61
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	14	71
	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	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	51	0	.	0	.	0	.	0	.	0	.	50	51
	other broadleaves	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	7	100	0	.	0	.	0	.	27	61
	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	.	44	41	0	.	7	100	0	.	7	100	59	35	14	71	7	100	15	71	0	.	0	.	21	58	0	.	11	100	7	100	30	71	76	42	0	.	14	71	14	71	0	.	0	.	327	17
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	2923	21	1918	30	1793	26	32756	7	950	37	9108	16	52	74	3358	23	46426	6	1474	26	9092	14	3976	16	2178	43	6643	21	9884	14	1292	31	1976	31	5624	17	2873	21	9692	16	5606	19	14213	10	23396	9	797	36	5582	17	203580	3
	fir	1225	28	299	40	1058	38	14589	8	894	28	2431	23	34	100	281	42	1326	22	4322	17	6454	16	3854	19	271	37	1332	28	2070	16	142	60	1843	27	1682	23	1262	30	1308	28	1736	27	6360	12	1693	24	606	46	1224	19	58298	4
	pine	115	60	0	.	0	.	1548	26	315	33	199	52	66	90	0	.	6061	18	295	44	151	52	171	62	66	100	656	79	271	71	273	39	468	35	343	65	155	37	407	50	289	52	242	52	6120	19	0	.	583	26	18794	10
	larch	350	50	0	.	0	.	239	35	14	71	128	64	0	.	7	100	10217	10	7	100	73	57	7	100	0	.	69	100	543	47	31	64	7	100	14	100	228	66	7242	13	0	.	1172	43	11378	11	7	100	159	43	31892	6
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	4176	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	2079	22	0	.	0	.	6313	15
	other conifers	188	45	0	.	54	74	710	37	98	100	0	.	0	.	18	100	979	35	51	74	112	47	17	100	0	.	35	71	256	37	21	100	195	54	76	80	34	71	44	70	121	100	365	47	208	56	0	.	420	56	4002	15
	beech	4397	14	268	47	461	34	16030	8	3277	20	3781	18	0	.	1344	30	3142	20	3730	18	4200	16	2071	17	952	42	1748	30	7016	15	2440	23	5307	15	2118	19	1598	19	12465	13	1296	32	8194	12	2579	27	231	40	4630	14	93274	3
	maple	1107	21	28	79	614	43	4906	12	1038	25	1025	25	20	100	963	32	983	25	976	21	1040	20	1365	32	354	31	542	64	2115	17	249	53	1471	28	920	30	990	27	1719	27	472	34	3747	16	2158	24	149	53	2113	19	31067	5
	ash	1030	26	0	.	342	46	2025	18	481	31	1543	25	52	100	435	37	1179	33	1021	31	832	26	242	36	606	60	182	52	1678	22	270	42	654	28	898	35	738	23	2364	24	459	43	1967	20	1690	26	101	73	1227	24	22019	6
	oak	424	34	0	.	0	.	615	24	278	47	282	37	338	53	0	.	186	55	222	41	138	40	319	86	22	100	0	.	142	52	209	45	442	55	6	100	337	30	2519	25	79	61	2277	30	1236	31	7	100	369	27	10446	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1063	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14488	13	0	.	7	100	311	67	0	.	0	.	15870	12
	other broadleaves	1770	18	0	.	195	55	5929	14	614	28	1668	23	222	56	562	43	8953	16	1365	26	1342	25	514	34	388	42	400	33	1936	24	123	51	879	29	1421	33	1184	35	17197	10	1448	43	3786	17	6451	13	105	62	1830	19	60281	5
	indeterminable	0	.	0	.	22	75	214	29	45	86	40	71	0	.	0	.	470	26	34	71	89	45	36	71	13	71	25	77	110	39	0	.	69	71	0	.	17	100	75	50	14	100	141	35	162	33	0	.	51	58	1627	12
	total	13529	11	2513	28	4540	22	79570	5	8003	16	20205	11	782	44	6970	17	85161	5	13497	12	23522	10	12574	11	4850	28	11633	16	26021	10	5050	19	13311	13	13102	13	9415	14	69570	5	11521	16	42470	7	59462	6	2003	29	18188	10	557462	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. 1358391. Birmensdorf, Swiss Federal Research Institute WSL

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