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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NFI2
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
state 1993/95
		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	2577	22	518	30	839	32	12828	8	452	42	4673	15	11	100	1399	22	14312	7	1938	21	4065	15	1317	21	831	39	2218	20	4332	14	667	35	1633	28	2538	20	1911	27	2127	15	1680	21	5492	11	7891	9	566	43	4014	18	80827	3
	fir	718	26	126	48	166	37	5659	8	355	26	876	21	0	.	248	43	509	20	1629	15	2020	13	1411	19	151	41	374	32	992	16	58	62	939	25	749	20	505	35	559	26	608	26	2710	13	965	25	342	35	361	27	23029	4
	pine	243	47	0	.	0	.	333	36	75	77	41	62	77	100	0	.	3102	18	50	59	117	68	94	67	0	.	190	71	143	53	161	78	116	61	42	71	135	65	115	55	112	54	79	59	3725	17	0	.	58	47	9008	10
	larch	108	67	0	.	0	.	137	64	0	.	50	72	0	.	0	.	2048	14	0	.	0	.	0	.	0	.	0	.	188	51	38	78	10	100	0	.	73	55	1576	16	0	.	100	38	3353	12	0	.	29	58	7711	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	907	22	0	.	0	.	0	.	0	.	32	100	0	.	0	.	0	.	0	.	0	.	10	100	11	100	0	.	552	24	0	.	0	.	1511	16
	other conifers	57	86	20	100	10	100	222	47	65	100	9	100	0	.	10	100	581	35	10	100	53	65	0	.	21	100	10	100	77	48	0	.	109	47	10	100	88	64	0	.	92	58	329	43	165	82	0	.	165	52	2105	16
	beech	2612	12	145	43	285	32	7367	9	1052	20	1939	16	0	.	785	30	1438	18	1529	19	1853	17	833	17	458	58	834	28	2593	14	1225	21	1894	16	967	21	731	19	6562	12	415	32	3189	10	1266	25	152	33	1651	14	41774	3
	maple	639	24	205	78	78	63	1662	14	153	27	429	23	10	100	261	37	453	29	496	22	251	27	233	29	73	43	273	51	577	22	153	49	581	20	469	22	384	40	350	26	158	40	1714	16	587	22	43	50	621	28	10854	6
	ash	381	26	35	100	162	50	751	20	398	38	410	29	29	74	202	60	800	31	528	26	447	31	92	49	104	55	165	50	802	21	67	47	247	32	445	46	317	28	1167	18	129	38	1032	22	561	24	95	48	544	28	9909	6
	oak	484	39	0	.	0	.	168	41	191	61	146	45	529	49	0	.	146	43	53	53	14	100	186	65	11	100	0	.	21	71	85	37	189	58	20	70	126	53	1337	24	20	70	1186	26	586	25	0	.	30	58	5528	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	586	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7759	12	0	.	49	66	96	67	0	.	0	.	8490	11
	other broadleaves	1098	19	106	57	109	58	2542	14	546	28	926	21	80	57	397	37	5010	14	932	21	857	29	449	29	70	43	487	37	1421	21	81	43	765	23	660	32	631	28	9190	9	618	41	2658	14	4102	13	63	58	807	20	34606	4
	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	8917	10	1155	28	1649	23	31668	5	3287	16	9499	10	738	41	3303	17	29892	5	7165	10	9677	10	4614	11	1719	28	4583	16	11146	9	2535	17	6483	12	5900	14	4901	15	30752	5	3843	15	18537	7	23848	6	1260	27	8280	11	235353	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	879	23	576	32	747	26	8534	7	154	34	2867	14	0	.	1142	32	10006	7	955	24	2271	16	948	20	546	30	1330	19	2594	13	382	34	552	25	1510	17	945	25	1267	16	1051	20	3575	10	5271	9	259	37	1548	16	49909	3
	fir	373	24	100	45	179	35	3083	10	117	37	638	22	0	.	95	49	336	24	835	17	1125	19	798	18	54	52	250	30	704	17	0	.	400	22	304	25	211	38	238	29	286	34	1428	14	461	25	103	56	279	27	12395	4
	pine	167	39	0	.	0	.	347	34	95	42	50	53	48	82	0	.	1518	17	140	43	68	62	29	75	0	.	115	84	129	54	19	71	166	40	133	52	41	62	135	55	231	45	62	41	1769	16	0	.	129	31	5392	9
	larch	164	50	0	.	0	.	69	52	0	.	61	63	0	.	0	.	1757	12	0	.	10	100	0	.	0	.	0	.	86	51	19	71	10	100	0	.	29	100	1167	17	0	.	91	40	2455	12	0	.	20	71	5938	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	814	20	0	.	0	.	0	.	0	.	43	100	20	100	0	.	0	.	0	.	0	.	0	.	23	100	0	.	667	23	0	.	0	.	1565	14
	other conifers	58	58	0	.	0	.	69	56	59	86	0	.	0	.	0	.	60	52	0	.	10	100	9	100	10	100	0	.	22	72	19	100	20	71	0	.	0	.	0	.	11	100	91	89	0	.	0	.	40	50	479	25
	beech	1662	15	100	42	260	34	5144	9	882	20	1201	19	0	.	469	29	661	22	1221	16	1078	17	719	18	212	35	491	30	1712	15	773	22	1688	15	481	23	488	21	2547	12	184	41	2548	11	536	25	62	47	1137	15	26256	3
	maple	329	20	57	62	117	41	991	12	263	28	240	32	10	100	263	37	156	29	293	24	288	26	252	36	115	33	89	46	770	18	29	58	262	23	196	36	164	45	239	32	79	44	831	17	151	32	54	71	523	21	6761	5
	ash	279	25	25	72	195	39	620	18	132	45	402	31	10	100	181	58	228	35	435	26	155	31	73	66	115	83	26	72	693	20	47	60	235	40	167	36	243	28	349	25	77	76	675	21	226	31	114	58	283	22	5982	7
	oak	199	29	0	.	0	.	220	28	155	57	31	75	279	46	0	.	116	56	69	56	57	53	90	71	0	.	0	.	125	50	85	43	127	41	13	100	85	43	538	21	10	100	994	22	117	37	0	.	79	35	3388	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	353	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2688	12	0	.	20	71	83	53	0	.	0	.	3144	12
	other broadleaves	468	20	20	71	43	62	940	16	193	29	139	35	29	100	77	50	999	17	302	25	192	35	41	50	62	74	74	48	610	22	0	.	196	30	209	36	316	31	2915	11	129	34	639	20	705	18	0	.	354	26	9650	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4578	9	877	25	1541	20	20018	5	2048	14	5629	10	376	40	2227	21	17004	5	4250	10	5254	10	2960	11	1115	22	2417	15	7466	8	1373	19	3655	11	3013	12	2523	14	12084	5	2080	16	10953	7	12440	6	592	27	4391	9	130861	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	769	17	357	29	572	27	6358	7	99	40	1913	13	0	.	660	28	6524	6	586	25	1618	13	793	18	231	29	935	17	1938	13	257	31	437	20	1154	17	531	22	964	16	699	19	2923	10	3370	9	201	37	1315	15	35204	3
	fir	354	22	153	39	165	31	2278	9	102	35	419	26	0	.	42	45	166	26	668	20	866	16	570	17	86	45	115	32	500	18	20	71	311	22	329	23	78	38	118	49	121	40	913	13	270	28	41	57	370	19	9053	4
	pine	138	29	4	100	0	.	231	27	185	30	86	51	0	.	0	.	839	17	209	45	71	55	49	75	0	.	37	59	70	54	105	42	208	24	126	47	116	31	157	47	69	40	136	49	777	15	0	.	374	21	3988	7
	larch	47	51	4	100	9	100	80	56	9	100	35	75	0	.	0	.	1344	12	0	.	8	71	0	.	0	.	4	100	86	46	43	60	0	.	0	.	4	100	585	17	0	.	20	52	1525	11	0	.	28	54	3831	7
	Arolla pine	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	475	20	0	.	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	346	21	0	.	0	.	853	14
	other conifers	28	75	0	.	0	.	10	100	34	100	10	100	0	.	0	.	0	.	0	.	16	79	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	4	100	0	.	10	100	0	.	0	.	10	100	126	37
	beech	1073	14	91	44	141	41	2509	9	747	17	852	16	0	.	219	31	249	26	984	15	548	17	430	19	172	37	237	28	1294	14	364	20	1276	13	356	25	274	25	1007	13	76	39	1806	11	172	39	58	45	717	15	15650	3
	maple	187	24	13	75	51	40	373	18	143	40	40	40	0	.	115	29	54	44	145	29	82	41	70	34	71	44	39	58	277	21	4	100	166	26	60	44	114	38	31	59	37	48	199	23	39	53	37	45	138	30	2487	7
	ash	286	23	16	79	78	37	224	21	37	52	154	33	0	.	67	39	34	48	256	29	208	68	33	65	54	56	24	62	252	25	44	58	108	33	63	58	95	34	104	36	0	.	317	22	37	54	59	48	302	21	2855	8
	oak	164	28	0	.	0	.	170	26	46	52	40	52	73	49	0	.	94	52	69	44	85	79	34	58	0	.	0	.	71	81	50	54	69	38	0	.	116	42	173	29	4	100	312	22	11	100	0	.	81	36	1662	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	773	15	0	.	17	100	32	58	0	.	0	.	874	14
	other broadleaves	238	32	10	100	0	.	267	32	94	39	38	60	10	100	19	52	282	24	121	29	31	55	35	67	37	100	4	100	126	33	13	77	82	42	24	61	169	33	730	15	37	53	151	30	160	31	0	.	157	28	2834	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3282	9	648	23	1016	20	12509	5	1497	13	3586	10	83	50	1122	19	10112	5	3037	10	3534	10	2013	11	652	23	1419	15	4618	8	899	18	2656	10	2112	13	1496	14	4647	6	1042	16	6805	6	6740	6	397	24	3493	9	79416	1
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	612	16	165	30	333	22	4632	6	118	31	1265	12	0	.	363	18	4099	6	432	16	1199	13	571	14	101	33	606	16	1310	11	174	25	408	20	832	15	471	21	562	14	331	20	2074	9	1627	8	159	33	915	12	23359	2
	fir	262	20	50	35	176	32	1660	8	55	51	270	20	0	.	42	48	185	21	376	17	584	14	420	15	52	53	78	36	460	16	16	61	177	20	156	25	78	37	139	30	44	46	695	12	118	30	33	43	355	17	6481	4
	pine	121	33	4	100	5	100	251	27	125	31	87	47	4	100	0	.	426	16	62	36	22	52	35	71	0	.	13	74	55	36	42	42	125	27	8	100	91	29	46	51	22	67	48	35	235	23	0	.	230	18	2058	7
	larch	43	39	0	.	9	100	40	42	4	100	16	62	0	.	0	.	1077	9	4	100	8	71	0	.	0	.	0	.	35	54	4	100	4	100	0	.	4	100	370	15	0	.	68	40	1164	10	0	.	43	35	2894	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	247	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	150	27	0	.	0	.	402	15
	other conifers	0	.	0	.	0	.	21	45	4	100	0	.	0	.	0	.	0	.	0	.	12	74	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	54	35
	beech	648	13	34	43	122	30	1456	10	426	21	311	20	0	.	99	27	158	25	509	17	310	20	186	21	58	43	155	36	587	16	140	28	647	14	224	25	135	22	407	14	44	46	831	11	36	54	42	37	501	13	8066	4
	maple	59	30	8	71	0	.	112	22	35	37	21	45	0	.	20	53	12	58	39	37	32	50	9	71	13	100	16	62	137	24	0	.	36	36	32	42	29	58	9	100	8	71	34	39	4	100	8	71	65	32	739	9
	ash	131	25	0	.	20	60	100	25	55	42	75	35	0	.	22	52	14	59	53	31	75	58	4	100	12	74	6	100	80	25	4	100	35	40	32	46	78	31	27	41	4	100	92	27	12	58	29	61	151	22	1111	8
	oak	47	37	0	.	0	.	95	22	20	52	27	50	51	60	0	.	48	52	56	36	54	54	8	71	0	.	0	.	14	100	31	47	46	50	0	.	44	33	73	42	8	70	156	30	8	71	0	.	76	27	863	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	367	18	0	.	8	100	4	100	0	.	0	.	400	17
	other broadleaves	52	36	0	.	5	100	56	30	4	100	12	74	0	.	7	100	52	33	20	45	33	69	13	77	12	58	0	.	44	42	0	.	46	43	4	100	17	50	124	20	0	.	67	50	52	46	9	71	52	34	680	11
	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	1974	9	261	25	671	19	8422	5	846	15	2084	10	54	57	553	16	6340	5	1551	10	2327	10	1246	10	249	27	874	14	2736	8	410	19	1525	11	1288	12	947	14	2123	7	467	18	4073	6	3411	6	279	26	2393	8	47105	1
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	299	19	74	39	228	26	2542	7	45	61	493	15	0	.	171	25	2090	7	224	18	643	13	309	16	49	45	271	18	824	12	84	26	170	24	393	17	183	22	342	15	207	21	1024	10	833	12	83	36	466	14	12047	3
	fir	143	27	10	72	50	33	990	11	43	39	163	22	0	.	23	52	92	24	183	18	309	15	215	18	20	54	43	42	247	19	8	71	90	26	81	25	90	40	66	32	31	55	325	16	80	37	17	50	179	20	3497	5
	pine	88	29	0	.	0	.	103	33	39	44	31	42	4	100	0	.	156	23	45	35	4	100	18	51	0	.	0	.	38	58	7	100	12	58	4	100	31	43	23	47	4	100	8	100	113	37	0	.	80	29	809	10
	larch	20	45	0	.	4	100	24	47	0	.	8	71	0	.	0	.	587	11	4	100	4	100	0	.	0	.	4	100	19	53	4	100	0	.	15	72	4	100	249	16	4	100	48	41	700	11	4	100	29	51	1732	7
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	107	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	36	0	.	0	.	146	20
	other conifers	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	12	74	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	4	100	45	48
	beech	339	15	16	50	55	33	582	13	215	20	157	24	0	.	67	41	85	31	152	20	155	28	66	37	24	57	89	46	219	18	54	30	219	20	105	40	71	33	142	22	21	61	233	18	12	58	12	74	269	17	3360	5
	maple	8	71	0	.	0	.	25	41	0	.	5	100	0	.	10	71	8	71	4	100	6	100	8	71	4	100	0	.	30	42	0	.	10	72	0	.	14	76	0	.	0	.	20	45	0	.	4	100	41	37	196	16
	ash	29	38	0	.	0	.	37	33	12	74	21	52	0	.	11	74	0	.	20	51	10	71	0	.	12	74	0	.	16	59	4	100	0	.	8	71	26	49	6	100	0	.	56	42	0	.	9	71	52	31	330	13
	oak	64	33	0	.	4	100	46	30	39	45	17	62	16	100	0	.	12	74	17	63	19	59	0	.	0	.	5	100	7	100	24	57	21	52	4	100	36	36	11	71	4	100	93	32	10	72	0	.	39	54	488	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	234	20	0	.	0	.	25	85	0	.	0	.	271	19
	other broadleaves	34	40	0	.	0	.	19	72	0	.	8	71	0	.	9	100	12	58	0	.	12	74	5	100	0	.	5	100	8	71	4	100	5	100	4	100	4	100	23	57	4	100	21	60	12	74	0	.	17	62	206	17
	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	1025	10	100	34	341	22	4376	5	393	16	902	11	20	83	291	20	3162	5	649	11	1175	11	622	12	111	30	416	17	1424	9	189	19	527	15	614	14	458	16	1096	8	276	19	1837	8	1819	8	130	29	1177	10	23127	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	95	27	18	65	38	36	829	9	32	50	179	18	4	100	86	35	731	10	66	35	205	19	109	21	11	58	118	25	305	18	20	60	56	37	177	22	81	33	170	23	78	37	323	15	282	18	37	41	108	22	4156	4
	fir	32	40	4	100	8	71	314	15	8	71	33	48	0	.	19	53	45	30	47	31	85	26	59	28	0	.	29	42	83	28	0	.	53	32	55	31	16	79	31	46	4	100	145	19	32	35	4	100	24	41	1130	7
	pine	16	50	0	.	0	.	18	51	0	.	4	100	0	.	0	.	34	45	18	51	0	.	0	.	0	.	4	100	4	100	12	74	0	.	0	.	8	71	4	100	0	.	0	.	4	100	0	.	23	47	149	19
	larch	0	.	0	.	4	100	12	58	0	.	4	100	0	.	4	100	159	18	0	.	8	100	0	.	0	.	0	.	13	59	0	.	0	.	0	.	0	.	74	24	0	.	4	100	286	16	4	100	8	71	581	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	35	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	41	0	.	0	.	72	26
	other conifers	4	100	0	.	0	.	12	73	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	29	42
	beech	89	27	0	.	4	100	156	25	45	46	37	36	0	.	0	.	19	53	53	30	27	61	11	58	0	.	4	100	39	36	14	59	46	40	27	47	20	53	58	28	4	100	48	35	0	.	0	.	80	30	780	9
	maple	0	.	0	.	0	.	8	71	10	71	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	38
	ash	8	100	0	.	0	.	22	61	0	.	6	100	0	.	0	.	7	100	8	71	0	.	0	.	0	.	0	.	13	58	0	.	0	.	0	.	9	71	0	.	0	.	13	58	0	.	0	.	12	58	97	24
	oak	23	46	0	.	0	.	4	100	0	.	7	100	0	.	0	.	8	71	17	63	0	.	0	.	0	.	0	.	7	100	16	60	0	.	0	.	12	58	5	100	0	.	21	67	0	.	5	100	4	100	129	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	27	0	.	0	.	5	100	0	.	0	.	87	25
	other broadleaves	7	100	0	.	0	.	8	71	10	71	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	9	72	0	.	9	71	0	.	0	.	4	100	59	30
	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	273	15	23	56	55	33	1384	7	105	28	277	16	4	100	109	30	1052	8	209	20	329	16	179	17	11	58	155	21	473	14	69	29	155	21	258	18	154	22	426	12	86	34	566	11	635	11	50	34	262	15	7297	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	28	42	12	76	0	.	202	17	8	71	58	32	0	.	44	35	262	15	16	50	9	71	32	43	4	100	37	40	162	25	0	.	23	46	62	30	15	50	77	28	40	40	119	21	97	25	4	100	12	58	1322	7
	fir	12	58	0	.	0	.	156	18	0	.	8	71	0	.	7	71	28	38	12	58	14	58	31	43	0	.	4	100	15	50	0	.	8	71	8	71	4	100	8	71	0	.	44	33	4	100	0	.	16	50	381	11
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	12	58
	larch	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	88	24	0	.	0	.	4	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	31	35	0	.	0	.	131	21	0	.	8	100	274	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	45	0	.	0	.	47	29
	other conifers	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	12	58
	beech	24	41	0	.	0	.	60	31	20	54	12	58	0	.	0	.	0	.	15	50	0	.	0	.	0	.	0	.	16	59	0	.	20	68	0	.	13	75	37	43	0	.	8	71	0	.	0	.	39	40	265	15
	maple	0	.	0	.	0	.	6	100	0	.	0	.	0	.	4	100	4	100	0	.	0	.	0	.	4	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	46
	ash	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	12	58
	oak	7	100	0	.	0	.	7	71	0	.	6	100	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	4	100	0	.	0	.	6	100	8	71	0	.	5	100	17	60	0	.	0	.	0	.	66	29
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	26	0	.	0	.	4	100	0	.	0	.	80	26
	other broadleaves	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	18	51
	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	75	28	12	76	0	.	449	11	29	43	88	27	0	.	55	30	421	12	44	30	30	41	67	33	8	71	41	38	203	22	0	.	55	41	76	27	43	33	233	15	45	37	195	17	255	15	4	100	83	26	2510	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	4	100	0	.	0	.	94	24	4	100	8	71	0	.	24	47	48	29	4	100	4	100	4	100	0	.	8	71	50	31	0	.	0	.	4	100	0	.	35	36	0	.	20	52	40	32	0	.	11	75	361	11
	fir	8	71	0	.	6	100	75	25	0	.	0	.	0	.	0	.	8	71	8	71	0	.	4	100	0	.	0	.	7	100	0	.	4	100	4	100	0	.	10	71	4	100	10	72	4	100	0	.	4	100	156	17
	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	.	35	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	71	0	.	0	.	33	35	0	.	0	.	77	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	beech	4	100	0	.	0	.	13	58	8	71	9	100	0	.	0	.	8	71	0	.	8	100	0	.	0	.	0	.	0	.	0	.	8	71	8	71	0	.	4	100	4	100	0	.	4	100	0	.	11	72	88	24
	maple	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	76
	ash	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	10	72
	oak	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	9	100	0	.	0	.	4	100	0	.	0	.	12	58	36	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	36	0	.	0	.	0	.	0	.	0	.	65	36
	other broadleaves	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	18	60
	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	24	41	0	.	6	100	204	17	12	58	20	54	0	.	24	47	106	21	12	58	12	74	8	71	0	.	8	71	60	30	0	.	16	63	16	50	14	74	123	23	8	71	38	36	84	23	0	.	39	37	834	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	0	.	0	.	0	.	4	100	0	.	8	71	0	.	12	58	12	58	4	100	0	.	0	.	0	.	4	100	20	45	0	.	0	.	4	100	4	100	15	58	8	71	0	.	8	71	0	.	0	.	103	20
	fir	0	.	0	.	0	.	16	50	0	.	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	32	35
	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	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	72	0	.	0	.	16	50	0	.	0	.	29	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	12	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	4	100	0	.	0	.	10	71	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	50
	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	.	4	100	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	55	0	.	0	.	0	.	0	.	0	.	43	45
	other broadleaves	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	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	13	58	0	.	0	.	29	38	4	100	8	71	0	.	15	50	31	38	4	100	0	.	0	.	0	.	4	100	20	45	0	.	0	.	8	71	4	100	57	37	8	71	4	100	32	35	0	.	4	100	246	14
>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	.	13	58	0	.	0	.	0	.	4	100	26	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	8	100	0	.	4	100	0	.	0	.	0	.	58	29
	fir	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	72	0	.	0	.	0	.	0	.	0	.	18	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	.	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	58	0	.	0	.	4	100	0	.	0	.	28	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	12	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	23	84
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	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	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	35	0	.	0	.	0	.	0	.	0	.	79	35
	other broadleaves	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	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	4	100	0	.	0	.	21	45	5	100	0	.	0	.	8	71	42	32	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	126	28	0	.	4	100	12	58	0	.	0	.	229	18
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	5263	15	1720	26	2757	21	36035	6	911	28	11464	10	15	78	3904	21	38110	5	4225	16	10014	11	4083	13	1773	29	5526	15	11536	10	1584	24	3278	18	6676	13	4140	18	5567	11	4094	17	15553	7	19418	7	1309	30	8390	11	207347	2
	fir	1901	17	443	33	750	27	14240	6	678	23	2408	17	0	.	480	35	1372	16	3757	13	5003	11	3508	14	364	32	893	26	3008	12	101	42	1982	17	1690	17	982	24	1177	23	1098	25	6274	10	1932	20	539	33	1589	15	56170	3
	pine	773	23	8	100	5	100	1283	20	520	27	298	36	134	87	0	.	6083	13	524	33	282	50	226	46	0	.	359	70	439	42	347	41	627	27	313	44	422	29	480	44	438	42	332	33	6623	14	0	.	899	16	21415	6
	larch	386	40	4	100	26	100	368	37	13	76	178	59	0	.	4	100	7108	8	8	71	38	63	4	100	0	.	7	100	428	34	108	47	28	74	15	72	114	51	4082	12	4	100	331	27	9667	8	8	100	164	29	23094	5
	Arolla pine	0	.	0	.	0	.	12	58	0	.	0	.	0	.	0	.	2633	16	0	.	0	.	0	.	0	.	98	100	24	100	0	.	0	.	0	.	0	.	10	100	38	100	0	.	1813	18	0	.	0	.	4628	12
	other conifers	148	58	20	100	10	100	347	38	162	71	22	72	0	.	10	100	642	35	10	100	107	50	9	100	31	100	10	100	133	46	22	85	129	48	10	100	88	64	4	100	104	53	447	43	165	82	0	.	223	45	2854	15
	beech	6454	10	386	34	867	28	17294	7	3398	14	4517	14	0	.	1639	25	2617	16	4464	12	3979	13	2247	13	924	34	1810	25	6465	12	2570	18	5798	11	2168	17	1730	15	10783	10	747	30	8663	8	2027	22	327	32	4405	11	96281	2
	maple	1222	16	283	59	245	38	3185	10	605	20	738	19	20	71	676	27	687	22	977	16	659	20	571	26	280	28	417	37	1804	15	190	46	1056	17	757	23	705	33	629	25	282	35	2799	14	781	21	147	45	1389	17	21103	4
	ash	1118	17	76	60	455	36	1759	14	635	32	1068	22	39	79	484	51	1083	29	1300	20	895	28	201	50	299	51	221	43	1858	17	166	46	625	28	715	35	768	20	1653	17	209	43	2193	16	836	21	305	41	1348	16	20306	5
	oak	1000	22	0	.	4	100	711	19	452	47	273	30	948	43	0	.	423	44	281	32	235	40	318	59	11	100	5	100	250	55	292	27	456	38	43	56	436	27	2136	19	52	56	2781	19	733	23	5	100	324	21	12168	8
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1040	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12149	10	0	.	94	72	249	50	0	.	0	.	13533	9
	other broadleaves	1901	17	135	50	157	50	3845	12	852	22	1127	20	120	56	510	32	6364	13	1375	19	1125	26	544	27	182	45	570	34	2208	18	97	39	1093	21	901	30	1154	23	12996	8	788	35	3545	13	5031	13	71	51	1390	18	48081	4
	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	20165	7	3075	22	5278	18	79080	4	8225	12	22093	8	1275	38	7707	16	68162	4	16921	8	22338	8	11710	9	3864	20	9916	12	28151	7	5477	14	15071	9	13288	10	10539	11	51668	4	7855	13	43013	5	49276	5	2712	21	20120	7	526978	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. 1297039. Birmensdorf, Swiss Federal Research Institute WSL

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