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 NFI1-NFI5
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	361	41	416	40	11604	9	576	45	2413	24	52	74	955	34	15220	9	544	38	4115	20	1297	20	685	69	2663	29	3600	19	445	47	1059	48	1633	27	1140	26	3010	22	1718	26	4989	15	9069	11	71	50	2009	28	70873	4
	fir	489	35	63	60	332	54	5875	11	435	33	1068	24	34	100	83	61	351	32	1889	19	2686	23	1606	28	92	53	603	35	501	26	52	58	775	37	549	36	406	37	504	34	851	30	2400	16	609	28	300	55	366	34	22920	6
	pine	18	100	0	.	0	.	284	48	76	70	39	100	0	.	0	.	2700	27	39	71	20	100	34	71	66	100	282	72	0	.	0	.	142	57	35	100	0	.	81	100	0	.	20	100	2635	26	0	.	93	68	6564	16
	larch	153	64	0	.	0	.	76	50	0	.	66	100	0	.	0	.	1279	20	0	.	49	76	0	.	0	.	52	100	188	63	0	.	0	.	0	.	151	81	1190	27	0	.	738	58	2571	16	0	.	59	74	6571	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	931	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	699	33	0	.	0	.	1662	27
	other conifers	51	74	0	.	36	100	157	37	17	100	0	.	0	.	18	100	766	43	51	74	52	58	0	.	0	.	0	.	129	47	0	.	86	72	18	100	34	71	20	100	121	100	300	56	173	56	0	.	346	61	2376	20
	beech	1774	18	66	60	120	49	6425	11	1443	26	1336	25	0	.	684	42	1183	23	1495	22	1636	19	972	26	417	49	622	40	2666	20	1115	30	2065	22	619	25	578	27	5608	15	314	38	3566	17	1364	29	36	71	2168	22	38272	5
	maple	650	30	15	100	277	56	2106	14	529	34	338	36	0	.	283	54	387	34	522	29	500	29	730	43	117	47	228	93	380	30	124	61	825	39	431	44	517	28	738	33	204	43	2275	20	915	27	59	76	821	23	13970	7
	ash	618	34	0	.	18	100	827	28	207	45	676	34	34	100	68	62	536	30	373	38	455	31	178	35	135	63	96	59	374	39	131	63	266	38	379	56	286	34	1332	26	217	50	876	25	563	30	17	100	437	46	9100	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	.	0	.	54	58	166	73	0	.	17	100	1134	39	18	100	1156	41	788	36	0	.	113	63	4318	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	355	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7550	16	0	.	0	.	37	100	0	.	0	.	7942	15
	other broadleaves	1012	23	0	.	121	62	3205	16	370	36	1028	27	69	79	381	47	4674	21	836	29	809	32	415	36	200	50	295	37	855	31	98	61	457	29	548	36	722	43	8805	13	513	40	1839	17	4006	15	105	62	1049	21	32413	6
	indeterminable	0	.	0	.	15	100	65	50	38	100	18	100	0	.	0	.	124	43	17	100	52	58	0	.	0	.	18	100	54	58	0	.	34	100	0	.	17	100	59	57	0	.	111	41	54	58	0	.	17	100	692	17
	total	6154	14	506	39	1335	30	30750	6	3792	20	7079	14	261	52	2473	23	28540	7	5784	14	10392	13	5478	15	1713	38	4859	21	8746	14	2019	26	5875	17	4211	20	3867	17	30063	7	3957	21	18270	9	23483	8	588	36	7478	14	217675	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	278	40	520	31	6747	10	138	65	1827	23	0	.	664	33	9577	9	123	43	2025	19	831	25	602	46	1538	25	1985	19	311	38	262	36	1268	29	832	29	1875	20	1339	23	2815	16	5149	13	148	52	1410	26	43140	4
	fir	255	48	51	76	176	56	3274	12	209	42	456	32	0	.	0	.	244	35	1048	23	1571	20	1015	23	65	61	262	39	323	26	35	100	553	32	300	36	437	36	249	40	413	30	1364	18	341	30	196	49	244	31	13080	6
	pine	36	71	0	.	0	.	417	41	69	61	34	100	0	.	0	.	1246	22	78	71	17	100	0	.	0	.	208	100	0	.	68	79	88	53	17	100	17	100	34	71	147	56	98	69	2185	20	0	.	110	53	4871	12
	larch	97	83	0	.	0	.	17	100	0	.	17	100	0	.	0	.	1374	19	0	.	17	100	0	.	0	.	17	100	102	71	17	100	0	.	0	.	71	61	1280	22	0	.	209	53	1727	17	0	.	20	100	4965	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	811	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	561	28	0	.	0	.	1388	19
	other conifers	69	50	0	.	18	100	20	100	0	.	0	.	0	.	0	.	35	71	0	.	0	.	17	100	0	.	17	100	37	71	0	.	103	52	0	.	0	.	0	.	0	.	37	71	17	100	0	.	68	50	437	21
	beech	892	24	54	74	71	61	3851	11	672	33	1065	20	0	.	240	33	584	33	847	26	947	23	551	23	378	49	518	39	1546	21	490	34	1142	23	459	28	481	27	3656	17	439	36	1701	16	674	36	38	71	937	18	22234	5
	maple	213	31	0	.	218	49	1156	18	249	33	502	26	0	.	276	43	270	35	228	31	204	35	381	30	116	48	111	87	795	22	94	61	341	29	165	53	293	38	162	43	159	54	872	21	321	32	38	71	824	30	7990	7
	ash	171	45	0	.	160	53	487	26	107	53	356	31	17	100	166	50	299	62	177	46	161	47	34	71	198	70	52	58	374	30	70	50	176	40	128	61	105	47	577	37	105	52	456	28	232	40	52	100	219	35	4881	10
	oak	35	71	0	.	0	.	155	39	104	74	92	54	121	65	0	.	15	100	69	61	17	100	38	100	22	100	0	.	74	77	52	74	143	48	0	.	37	71	750	24	18	100	647	35	252	42	0	.	34	71	2674	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	293	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4126	15	0	.	0	.	18	100	0	.	0	.	4438	14
	other broadleaves	551	22	0	.	50	58	874	18	131	42	262	32	0	.	113	48	2164	21	232	45	184	34	72	50	50	73	18	100	443	47	17	100	294	43	247	62	164	44	3412	14	136	54	706	24	1180	19	0	.	441	31	11742	7
	indeterminable	0	.	0	.	0	.	30	71	0	.	22	100	0	.	0	.	76	62	17	100	37	72	19	100	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	15	100	36	71	0	.	17	100	287	27
	total	3196	14	382	37	1214	26	17030	7	1679	20	4634	13	138	61	1460	23	16988	7	2820	15	5182	12	2959	14	1430	32	2742	20	5680	13	1154	24	3120	15	2584	20	2436	17	16137	7	2756	19	8920	10	12693	9	472	36	4325	13	122129	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	481	31	367	31	4799	9	87	58	1298	20	0	.	400	31	6399	8	235	45	1015	18	720	26	409	43	952	30	1439	18	222	48	271	46	875	24	483	36	1163	21	780	24	2033	13	3392	11	186	42	916	21	29246	4
	fir	142	48	45	74	223	41	1787	13	111	47	378	37	0	.	32	83	248	30	532	26	815	19	304	26	37	52	212	44	252	34	14	71	282	33	178	33	198	43	164	40	242	43	1014	17	312	35	59	67	138	41	7720	6
	pine	26	75	0	.	0	.	409	37	84	50	44	54	45	100	0	.	670	24	68	77	47	63	110	63	0	.	145	72	0	.	131	40	115	44	86	93	16	100	129	57	89	59	63	68	832	20	0	.	109	33	3218	11
	larch	31	81	0	.	0	.	57	69	0	.	38	84	0	.	0	.	1128	16	0	.	0	.	0	.	0	.	0	.	28	100	7	100	0	.	0	.	0	.	847	21	0	.	85	58	1422	17	0	.	20	58	3663	10
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	482	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	293	34	0	.	0	.	785	20
	other conifers	34	100	0	.	0	.	52	56	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	170	34
	beech	562	22	66	48	135	53	2543	11	523	27	781	23	0	.	148	37	533	25	700	24	726	22	233	30	71	58	277	37	1069	20	413	29	944	22	300	26	217	33	1489	17	272	43	1279	15	291	32	77	46	639	20	14287	5
	maple	118	32	13	71	112	49	706	19	94	49	103	42	0	.	99	50	132	39	125	34	270	32	182	55	78	43	79	44	528	24	24	77	163	29	69	62	81	51	62	80	28	63	380	27	40	62	0	.	253	26	3741	8
	ash	78	54	0	.	123	47	293	29	104	49	315	42	0	.	95	52	121	46	285	42	55	49	0	.	202	75	26	78	276	29	28	71	95	43	65	55	190	37	126	38	65	100	357	30	160	43	8	100	166	34	3233	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	288	28	7	100	215	32	81	51	0	.	71	49	1208	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	294	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1342	19	0	.	0	.	49	79	0	.	0	.	1686	18
	other broadleaves	104	42	0	.	15	71	197	27	59	47	51	53	0	.	31	67	411	23	156	39	61	67	27	100	36	100	18	100	170	36	0	.	52	58	44	54	106	46	1299	21	23	75	181	43	86	38	0	.	123	39	3249	10
	indeterminable	0	.	0	.	7	100	78	41	0	.	0	.	0	.	0	.	34	71	0	.	0	.	17	100	0	.	7	100	14	71	0	.	17	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	17	100	198	26
	total	1469	16	605	29	981	25	11022	6	1130	19	3035	14	110	60	805	23	10460	6	2130	16	2996	12	1601	17	832	31	1715	22	3809	12	891	23	2027	17	1617	18	1366	21	6910	9	1506	20	5613	9	6983	8	331	33	2458	12	72404	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	268	33	218	36	3413	9	28	61	1051	17	0	.	324	28	4292	8	240	27	616	17	494	20	204	43	566	27	801	17	160	39	183	29	572	22	202	26	827	21	500	29	1481	13	1937	11	86	54	655	17	19373	3
	fir	138	37	82	41	125	32	1303	12	63	53	180	27	0	.	65	48	162	29	405	26	566	16	352	22	29	61	103	36	247	24	28	79	126	27	235	30	135	35	118	36	164	43	619	15	181	34	15	71	183	28	5624	5
	pine	0	.	0	.	0	.	216	38	64	40	20	75	21	74	0	.	385	23	61	56	39	74	14	100	0	.	14	100	0	.	51	51	89	38	7	100	62	46	27	71	15	71	61	43	300	27	0	.	183	27	1629	11
	larch	34	72	0	.	0	.	36	73	14	71	0	.	0	.	0	.	937	14	0	.	7	100	0	.	0	.	0	.	14	71	7	100	0	.	0	.	7	100	576	19	0	.	43	47	1113	16	0	.	7	100	2795	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	34	0	.	0	.	338	20
	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	1514	11	305	28	330	24	0	.	142	37	245	29	408	23	384	21	225	26	71	58	175	33	777	19	260	28	604	19	222	31	119	30	621	17	89	40	927	16	77	55	51	54	345	19	8532	4
	maple	84	35	0	.	7	100	260	20	28	61	36	45	0	.	72	41	37	45	72	34	35	45	64	41	14	71	18	70	183	23	7	100	66	39	42	46	43	52	29	61	20	58	126	28	56	60	23	73	46	47	1369	8
	ash	54	56	0	.	41	71	113	26	14	71	121	39	0	.	45	52	28	79	131	37	92	60	29	60	43	52	7	100	150	32	28	50	66	37	53	57	74	30	44	45	20	58	149	32	27	71	17	100	162	26	1509	10
	oak	84	46	0	.	0	.	67	34	7	100	53	56	43	52	0	.	12	100	59	70	56	52	21	100	0	.	0	.	0	.	42	75	30	50	0	.	97	40	151	32	25	72	137	31	21	75	0	.	34	45	938	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	7	100	0	.	0	.	742	18
	other broadleaves	59	44	0	.	0	.	93	41	21	58	13	71	0	.	18	74	133	35	134	54	26	58	0	.	7	100	0	.	18	74	7	100	22	74	6	100	55	47	350	23	0	.	81	33	59	39	0	.	117	35	1219	12
	indeterminable	0	.	0	.	0	.	22	75	7	100	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	14	100	15	71	7	100	0	.	0	.	86	33
	total	1212	14	419	29	475	26	7064	6	564	22	1804	13	64	57	666	21	6540	6	1510	15	1819	12	1200	13	368	31	883	20	2198	12	597	21	1185	15	1138	17	794	17	3409	8	847	23	3666	9	3899	9	191	37	1731	11	44244	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	102	32	234	33	203	32	1967	9	63	51	556	20	0	.	229	25	2409	9	174	33	515	18	335	21	40	51	242	25	553	17	70	39	108	36	476	21	100	34	612	31	293	25	752	14	1136	12	44	49	295	22	11511	4
	fir	80	44	21	58	158	49	1094	12	28	50	219	27	0	.	37	70	150	28	267	25	448	20	305	21	21	74	72	42	303	21	0	.	41	41	116	33	27	61	145	35	26	60	457	17	157	39	14	71	114	29	4299	6
	pine	35	64	0	.	0	.	90	39	15	71	47	62	0	.	0	.	206	29	41	47	22	58	14	100	0	.	7	100	17	100	22	58	34	45	0	.	34	67	14	100	15	100	0	.	78	46	0	.	74	32	765	13
	larch	36	45	0	.	0	.	14	71	0	.	0	.	0	.	0	.	750	14	7	100	0	.	0	.	0	.	0	.	12	100	0	.	7	100	14	100	0	.	315	20	0	.	29	61	780	15	0	.	33	53	1998	9
	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	.	42	71	0	.	0	.	191	26
	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	897	13	132	29	208	27	0	.	108	46	96	38	182	25	266	21	83	35	14	71	114	50	307	21	98	36	306	25	133	39	133	29	279	22	49	58	464	16	26	77	23	58	284	19	4588	5
	maple	35	45	0	.	0	.	122	26	28	50	32	50	0	.	6	100	42	45	21	57	19	71	0	.	0	.	0	.	90	36	0	.	45	47	12	70	22	76	0	.	0	.	18	72	0	.	14	71	43	41	549	13
	ash	74	37	0	.	0	.	72	39	32	64	67	47	0	.	31	52	0	.	7	100	69	58	0	.	21	100	0	.	52	38	7	100	14	71	40	53	61	42	33	72	0	.	48	38	7	100	0	.	96	31	733	13
	oak	22	72	0	.	0	.	64	33	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	499	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	.	0	.	0	.	0	.	343	24
	other broadleaves	14	71	0	.	8	100	40	48	7	100	27	80	0	.	12	100	14	100	7	100	17	100	0	.	0	.	0	.	37	53	0	.	14	100	6	100	24	58	38	52	0	.	80	54	23	57	0	.	14	71	383	18
	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	769	15	255	32	400	29	4412	6	338	22	1157	14	22	100	424	24	3872	7	749	16	1390	13	744	15	110	36	435	23	1401	12	210	24	578	18	804	16	443	19	1816	14	383	22	1895	9	2272	9	95	34	1042	12	26016	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	64	40	62	50	826	12	21	57	192	24	0	.	131	37	883	12	88	36	286	25	151	31	57	56	88	40	428	21	56	39	61	53	176	31	69	34	245	21	161	32	455	17	414	18	14	100	142	28	5141	5
	fir	61	46	29	61	21	74	547	15	7	100	67	42	0	.	0	.	73	34	129	33	205	25	130	30	27	62	22	58	111	32	14	71	21	58	65	37	20	58	22	58	14	71	315	23	26	60	0	.	101	36	2027	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	0	.	0	.	0	.	7	100	0	.	0	.	171	22
	larch	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	415	17	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	135	29	0	.	7	100	423	19	0	.	20	58	1021	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	.	7	100	0	.	0	.	29	50
	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	277	22	42	52	54	35	0	.	21	74	48	38	91	36	73	35	0	.	0	.	35	59	89	36	51	42	62	37	13	71	55	39	135	30	26	80	120	27	20	73	7	100	141	28	1500	8
	maple	0	.	0	.	0	.	23	59	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	34	64	0	.	0	.	0	.	8	100	0	.	0	.	13	71	0	.	0	.	27	61	112	30
	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	.	14	71	0	.	7	100	34	45	240	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	.	7	100	163	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	.	7	100	0	.	0	.	105	26
	other broadleaves	14	100	0	.	0	.	20	58	0	.	7	100	0	.	6	100	15	71	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	9	100	7	100	0	.	38	69	7	100	0	.	0	.	132	29
	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	106	36	98	40	1817	9	98	33	346	17	7	100	171	31	1551	9	354	19	616	17	281	23	91	42	145	32	706	17	127	29	169	27	262	25	227	23	652	12	200	28	978	13	919	13	28	71	472	17	10717	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	337	16	28	61	117	30	0	.	92	44	353	17	50	51	94	32	46	40	19	58	66	37	214	25	28	61	7	100	58	42	20	74	159	31	64	46	138	25	185	26	7	100	69	39	2204	7
	fir	47	38	8	100	23	58	297	18	7	100	36	45	0	.	7	100	49	38	36	60	74	35	82	30	0	.	38	86	69	39	0	.	14	71	50	38	20	100	15	71	0	.	99	31	37	60	0	.	54	35	1061	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	129	24	0	.	0	.	7	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	63	33	0	.	7	100	163	28	7	100	0	.	398	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	21	58	7	100	0	.	0	.	32	65	7	100	42	52	7	100	0	.	7	100	21	72	14	71	54	50	21	100	7	100	37	45	0	.	48	38	6	100	0	.	47	37	527	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	.	0	.	0	.	0	.	7	100	29	50
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	59	36
	oak	29	50	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	.	66	33
	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	.	0	.	0	.	0	.	104	33
	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	191	21	22	58	36	45	761	11	64	39	195	23	7	100	106	41	612	12	115	33	223	21	141	23	19	58	111	38	339	20	51	42	75	39	135	31	60	45	385	17	64	46	305	17	419	18	14	71	190	23	4641	5
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	.	21	58	149	26	15	71	0	.	16	71	0	.	0	.	48	42	0	.	7	100	35	51	20	58	19	72	7	100	31	51	43	41	0	.	20	58	594	12
	fir	13	71	0	.	0	.	78	35	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	.	0	.	24	59	342	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	.	21	58	0	.	0	.	72	40	0	.	0	.	199	24
	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	.	14	100	0	.	0	.	29	61
	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	.	216	20	7	100	62	39	0	.	27	50	293	20	15	71	28	50	60	39	0	.	21	77	140	26	0	.	14	71	35	51	55	43	130	27	32	51	67	34	136	27	0	.	65	34	1462	8
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	.	51	38	0	.	0	.	0	.	29	50	56	40	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	11	100	0	.	16	71	22	58	0	.	0	.	206	20
	fir	0	.	0	.	0	.	13	71	0	.	0	.	0	.	7	100	15	71	7	100	0	.	8	100	0	.	0	.	6	100	0	.	7	100	7	100	7	100	10	100	0	.	7	100	0	.	0	.	0	.	95	28
	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	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45	0	.	0	.	43	41
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	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	13	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	39	46
	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	.	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	20	58	0	.	0	.	81	30	0	.	0	.	0	.	42	41	85	31	7	100	0	.	8	100	0	.	0	.	26	60	0	.	7	100	14	71	21	58	67	42	0	.	23	58	80	30	0	.	14	71	494	13
>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	.	21	58	0	.	7	100	0	.	7	100	30	50	7	100	7	100	7	100	0	.	0	.	14	71	0	.	0	.	7	100	0	.	11	100	0	.	7	100	7	100	0	.	0	.	133	24
	fir	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58
	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	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	22	74
	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	.	44	57	0	.	0	.	0	.	0	.	0	.	44	57
	other broadleaves	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	.	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	0	.	0	.	0	.	35	45	0	.	7	100	0	.	7	100	44	41	14	71	7	100	15	71	0	.	0	.	14	71	0	.	0	.	7	100	16	100	69	45	0	.	7	100	14	71	0	.	0	.	258	19
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	2912	21	1708	32	1793	26	29882	7	940	37	7488	17	52	74	2853	26	39368	7	1474	26	8674	15	3897	16	2016	46	6115	22	9089	14	1292	31	1957	31	5100	18	2873	21	7932	18	4863	21	12718	11	21354	10	557	40	5517	18	182423	3
	fir	1225	28	299	40	1058	38	14276	8	859	29	2431	23	34	100	238	46	1309	22	4322	17	6386	16	3854	19	271	37	1332	28	1847	17	142	60	1825	28	1501	25	1262	30	1234	30	1709	28	6302	12	1663	24	584	48	1224	19	57188	4
	pine	115	60	0	.	0	.	1475	28	315	33	199	52	66	90	0	.	5294	20	295	44	151	52	171	62	66	100	656	79	24	76	273	39	468	35	145	96	155	37	285	65	265	56	242	52	6037	19	0	.	583	26	17280	10
	larch	350	50	0	.	0	.	214	37	14	71	128	64	0	.	7	100	6132	12	7	100	73	57	7	100	0	.	69	100	359	55	31	64	7	100	14	100	228	66	4442	17	0	.	1117	45	8307	12	7	100	159	43	21675	7
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	2663	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	1778	24	0	.	0	.	4500	17
	other conifers	188	45	0	.	54	74	329	29	98	100	0	.	0	.	18	100	801	42	51	74	112	47	17	100	0	.	17	100	237	39	21	100	195	54	18	100	34	71	44	70	121	100	365	47	208	56	0	.	420	56	3349	16
	beech	4269	14	268	47	461	34	15603	8	3137	21	3781	18	0	.	1344	30	2727	21	3730	18	4074	16	2071	17	952	42	1748	30	6494	16	2440	23	5177	15	1774	20	1598	19	11854	14	1204	34	8113	12	2464	27	231	40	4568	14	90082	3
	maple	1107	21	28	79	614	43	4373	11	936	26	1018	25	0	.	742	36	869	26	976	21	1027	20	1357	32	325	33	435	75	2018	18	249	53	1440	28	719	35	964	27	992	31	411	39	3684	17	1332	26	134	59	2022	20	27773	5
	ash	1030	26	0	.	342	46	1851	18	462	32	1543	25	52	100	418	39	990	37	1021	31	832	26	242	36	606	60	182	52	1276	23	270	42	631	29	679	40	738	23	2123	26	406	46	1899	20	989	27	101	73	1121	25	19805	6
	oak	392	36	0	.	0	.	539	25	278	47	282	37	338	53	0	.	109	63	222	41	138	40	319	86	22	100	0	.	107	61	209	45	442	55	6	100	337	30	2394	26	79	61	2219	31	1150	33	0	.	361	27	9943	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1063	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14312	13	0	.	7	100	127	74	0	.	0	.	15510	12
	other broadleaves	1770	18	0	.	195	55	4436	14	589	29	1410	24	69	79	562	43	7411	19	1365	26	1097	27	514	34	293	46	331	35	1532	28	123	51	838	31	851	40	1079	37	13919	12	672	36	2937	16	5363	15	105	62	1750	20	49210	5
	indeterminable	0	.	0	.	22	75	201	30	45	86	40	71	0	.	0	.	248	31	34	71	89	45	36	71	13	71	25	77	75	46	0	.	69	71	0	.	17	100	59	57	14	100	141	35	126	37	0	.	51	58	1305	12
	total	13360	11	2302	29	4540	22	73189	5	7672	16	18320	11	610	52	6182	19	68984	6	13497	12	22652	10	12487	11	4564	29	10910	17	23058	10	5050	19	13050	13	10807	14	9285	14	59639	6	9746	17	39745	8	50898	7	1719	31	17775	10	500040	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. 1358374. Birmensdorf, Swiss Federal Research Institute WSL

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