Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 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	272	29	73	37	76	33	1444	8	62	47	410	19	0	.	106	44	2204	8	214	31	412	16	256	33	79	43	354	22	464	15	82	37	101	34	253	23	249	37	394	14	168	21	739	13	947	10	50	39	337	21	9748	4
	fir	125	48	7	189	33	53	762	11	39	34	183	24	0	.	34	82	58	43	258	18	387	20	166	21	17	41	31	59	108	27	5	61	107	28	149	27	136	38	64	34	91	36	310	13	118	28	49	38	65	28	3303	6
	pine	7	68	0	.	0	.	37	73	6	76	0	0	0	.	0	.	308	16	9	100	11	102	1	100	0	.	17	100	7	71	4	100	-7	102	8	66	2	72	7	66	9	50	19	100	272	23	0	.	5	71	723	13
	larch	3	81	0	.	0	.	9	47	0	.	10	90	0	.	0	.	307	17	1	100	2	100	0	.	0	.	8	100	18	44	0	.	2	100	1	100	5	100	213	18	0	.	43	46	394	14	0	.	3	74	1018	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	24	0	.	0	.	0	.	0	.	1	100	1	100	0	.	0	.	0	.	0	.	1	79	3	100	0	.	82	31	0	.	0	.	229	19
	other conifers	7	57	4	100	1	100	27	42	0	.	0	.	0	.	1	100	41	31	0	2109	9	87	0	.	3	100	0	.	-10	205	0	.	0	3743	3	73	12	64	1	100	11	57	20	40	9	50	3	100	15	46	157	22
	beech	235	17	8	51	27	36	928	11	127	25	237	26	0	.	57	35	172	23	246	19	185	19	120	22	30	59	54	29	315	16	146	25	253	20	123	27	146	33	950	12	40	38	381	15	145	34	6	221	264	22	5192	4
	maple	126	29	26	115	18	83	357	14	51	29	85	25	2	100	46	32	78	31	88	25	48	26	58	40	14	44	51	50	78	24	15	46	70	32	109	26	100	33	129	22	21	49	290	15	126	19	8	61	126	36	2120	6
	ash	106	34	3	71	8	62	132	21	35	39	102	50	1	100	34	47	118	25	91	33	57	28	23	43	11	69	14	73	108	27	22	76	54	55	81	40	42	42	203	21	21	42	132	19	119	39	7	69	133	35	1657	8
	oak	32	41	0	.	0	.	5	52	15	60	1	100	21	64	0	.	14	43	2	100	3	100	16	57	0	.	0	.	5	72	1	100	23	47	1	100	2	73	117	26	1	100	119	29	72	26	0	.	21	52	468	12
	sweet chestnut	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	30	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	637	16	0	.	4	79	15	76	0	.	0	.	687	15
	other broadleaves	183	24	3	78	21	46	361	12	34	47	75	40	21	51	42	39	654	16	78	24	41	42	39	27	14	50	47	37	131	23	19	40	75	28	65	48	97	35	1158	9	99	40	279	16	502	13	0	0	89	26	4127	5
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	1096	13	124	37	185	26	4063	5	369	16	1104	13	45	49	320	22	4122	6	987	13	1156	11	679	15	167	28	577	16	1225	10	294	21	678	14	792	14	792	17	3873	5	463	17	2336	7	2803	7	123	28	1057	12	29431	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	510	24	213	30	223	35	3087	8	57	44	1251	16	0	.	410	32	4562	7	339	25	701	16	504	20	223	34	737	20	1019	15	296	36	198	30	561	19	577	26	671	15	445	21	1707	12	2418	10	147	35	944	19	21798	3
	fir	119	34	30	62	81	47	1487	12	106	31	277	26	0	.	65	64	134	23	322	18	543	18	331	23	14	62	89	40	208	32	7	100	217	24	152	26	133	34	135	32	137	34	656	16	296	27	99	35	150	29	5787	5
	pine	36	54	2	100	0	.	113	42	25	45	3	397	12	71	0	.	439	18	28	68	23	79	8	100	0	.	30	86	41	57	52	72	41	53	19	62	38	71	39	52	61	48	44	56	578	18	0	.	29	53	1662	10
	larch	39	49	3	100	0	.	29	69	0	.	6	100	0	.	0	.	760	13	0	.	3	100	0	.	0	.	0	.	41	51	12	76	0	.	0	.	33	59	526	16	0	.	63	47	853	12	0	.	4	100	2370	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	273	20	0	.	0	.	0	.	0	.	10	100	7	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	273	19	0	.	0	.	566	13
	other conifers	45	46	0	.	4	100	74	45	1	100	0	.	0	.	0	.	23	40	0	.	3	100	4	100	3	100	2	100	16	84	0	.	13	64	0	.	18	77	0	.	0	.	32	52	2	100	0	.	32	51	271	19
	beech	674	20	32	79	110	41	2199	9	314	22	498	21	0	.	166	30	342	23	524	17	410	18	225	21	110	42	268	30	713	18	311	25	543	19	245	23	222	20	1410	13	172	35	1055	16	348	28	45	39	490	16	11425	4
	maple	145	24	37	72	66	47	577	14	114	30	211	26	7	100	127	38	121	40	164	27	211	25	95	40	44	42	151	50	318	23	39	48	85	32	121	30	166	41	111	30	62	43	411	18	133	28	4	100	309	24	3830	6
	ash	93	32	13	71	87	50	296	24	82	43	263	28	6	100	45	44	195	45	137	26	138	31	15	71	39	94	39	50	270	25	38	43	114	36	152	35	122	35	280	20	62	48	282	22	151	27	17	62	195	27	3132	7
	oak	128	34	0	.	0	.	68	34	48	63	25	46	57	57	0	.	29	55	21	74	11	72	25	65	3	100	0	.	15	71	51	45	43	43	0	.	27	46	220	24	5	100	379	26	68	29	0	.	25	51	1250	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1109	13	0	.	7	100	15	64	0	.	0	.	1278	12
	other broadleaves	234	26	1	459	9	74	339	19	82	29	96	42	11	71	42	47	445	17	81	29	37	42	23	46	21	77	55	40	128	33	5	100	119	34	97	35	101	39	1293	11	40	42	269	22	465	16	5	100	136	23	4133	6
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	2023	11	331	27	580	21	8267	5	829	15	2630	11	93	42	854	20	7470	5	1616	11	2082	11	1231	12	458	25	1381	16	2775	10	810	19	1373	13	1347	13	1438	15	5795	5	983	17	4904	8	5600	6	318	24	2314	10	57502	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	695	21	458	25	537	26	5484	7	63	53	1827	15	0	.	556	29	6977	6	583	24	1215	16	754	20	270	36	737	17	1619	14	244	28	384	25	1058	17	601	25	904	16	772	21	2400	10	3293	9	112	48	1437	15	32979	3
	fir	245	27	105	54	167	35	2393	10	117	38	376	22	0	.	50	48	214	27	725	19	944	16	511	19	38	72	149	37	646	18	26	82	214	26	252	29	204	36	175	39	140	42	929	14	236	26	69	42	254	27	9180	5
	pine	97	34	0	.	0	.	227	31	59	43	60	50	34	100	0	.	693	19	152	46	62	49	46	53	0	.	49	66	65	57	92	40	143	36	94	62	82	45	157	62	36	51	62	43	571	16	0	.	261	23	3041	8
	larch	82	56	6	100	5	100	92	45	5	100	20	100	0	.	6	100	1316	12	0	.	5	100	0	.	0	.	0	.	107	39	25	63	14	71	0	.	7	83	734	14	0	.	71	54	1452	11	0	.	50	48	3997	7
	Arolla pine	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	368	22	0	.	0	.	0	.	0	.	23	100	3	100	0	.	0	.	0	.	0	.	5	100	13	100	0	.	255	19	0	.	0	.	671	14
	other conifers	36	45	0	.	0	.	18	62	43	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-12	100	17	100	0	.	0	.	24	100	0	.	0	.	79	60	0	.	0	.	3	774	208	35
	beech	915	19	95	34	128	34	2731	9	668	24	981	18	0	.	245	30	323	21	1190	16	672	23	527	21	159	37	299	32	1072	15	561	21	1256	14	335	25	302	25	1124	12	103	39	1573	12	180	24	75	47	771	15	16286	4
	maple	177	24	11	61	64	52	481	17	113	41	109	45	0	.	97	29	69	53	186	30	134	31	134	31	49	43	97	39	330	21	21	72	246	22	99	31	99	46	87	38	25	54	405	21	39	51	27	52	180	28	3279	7
	ash	251	26	18	60	100	42	351	21	109	38	325	31	9	100	22	77	58	38	242	21	176	39	15	76	104	73	6	100	416	23	28	61	152	35	65	56	182	27	267	24	31	69	438	22	81	37	31	59	265	22	3741	7
	oak	119	39	0	.	0	.	157	30	69	48	35	42	93	43	0	.	37	58	36	56	27	60	17	60	0	.	0	.	37	75	40	53	46	45	-17	100	94	32	272	20	15	74	317	27	42	52	6	100	39	42	1481	10
	sweet chestnut	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	881	15	0	.	13	100	23	64	0	.	0	.	988	14
	other broadleaves	201	34	0	0	9	100	231	23	65	34	39	44	4	100	5	100	350	20	140	28	45	42	26	73	33	71	6	100	87	31	10	72	42	47	37	42	186	43	920	13	21	52	224	27	216	29	16	78	148	33	3060	7
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0
	total	2820	11	692	24	1011	20	12171	5	1311	16	3771	10	141	45	980	21	10471	5	3253	10	3279	11	2031	12	652	24	1365	14	4372	9	1063	16	2496	11	1923	13	1780	14	5527	6	1156	17	6511	7	6389	6	336	27	3409	9	78911	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	710	18	456	27	728	26	7119	7	120	38	1962	14	0	.	876	19	8131	6	744	21	1737	14	1218	16	186	33	818	17	2057	13	455	31	666	23	1309	16	595	25	1084	16	787	21	3435	10	2966	9	206	40	1336	14	39700	2
	fir	386	25	174	35	359	33	3250	9	169	34	542	24	0	.	130	40	375	20	884	17	1260	16	838	15	61	65	159	34	960	17	28	58	327	27	427	23	161	35	225	27	147	44	1257	13	344	27	105	40	490	22	13058	4
	pine	101	38	20	100	10	100	358	36	181	30	97	61	15	71	0	.	648	16	107	53	37	60	49	74	0	.	22	71	68	39	91	53	183	25	17	80	140	37	97	53	64	50	96	35	320	21	0	.	319	23	3038	8
	larch	68	65	0	.	24	100	89	43	8	100	0	.	0	.	0	.	1987	11	0	.	9	100	0	.	0	.	0	.	182	56	16	100	23	72	0	.	8	100	694	15	0	.	79	38	1938	11	0	.	60	56	5187	7
	Arolla pine	0	.	0	.	0	.	4	218	0	.	0	.	0	.	0	.	337	18	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	164	26	0	.	0	.	523	14
	other conifers	19	71	0	.	0	.	60	79	57	73	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	14	100	0	.	0	.	0	.	11	100	0	.	72	60	0	.	0	.	0	.	256	32
	beech	1285	14	78	44	321	38	3078	9	845	18	1018	18	0	.	325	27	295	34	1339	15	714	18	562	17	72	50	246	28	1209	16	530	22	1294	13	415	23	337	24	902	13	106	39	1897	12	116	35	126	54	911	14	18022	3
	maple	144	31	0	.	9	88	377	17	67	53	61	43	0	.	98	37	36	54	148	29	61	47	82	33	6	100	13	75	207	22	6	100	145	31	62	46	84	43	26	92	32	50	124	28	45	53	47	41	124	29	2004	8
	ash	382	22	30	80	48	55	298	24	101	38	220	29	0	.	90	47	36	52	270	30	171	50	43	47	33	64	40	62	325	25	36	51	60	43	136	48	315	25	126	31	4	100	315	25	47	52	50	51	391	21	3568	7
	oak	139	31	0	.	0	.	180	37	30	88	35	61	141	49	0	.	71	50	122	40	58	56	41	76	0	.	0	.	76	50	75	50	66	51	0	.	96	45	180	33	17	73	293	24	27	47	0	.	146	29	1794	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	565	16	0	.	13	76	9	73	0	.	0	.	620	14
	other broadleaves	99	51	0	.	0	.	145	31	19	61	24	78	0	.	33	70	132	29	81	37	42	85	15	72	14	100	0	.	77	39	0	.	47	41	13	74	91	47	414	19	10	100	205	34	24	125	0	.	132	33	1619	10
	indeterminable	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	3333	9	758	23	1500	20	14958	5	1598	14	3970	11	156	46	1552	16	12083	5	3695	10	4089	10	2848	11	374	26	1312	16	5171	9	1252	19	2809	11	2379	12	1827	15	4325	6	1171	18	7785	7	5998	7	535	26	3909	9	89388	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	803	24	385	33	855	25	7373	8	208	39	1534	16	0	.	638	21	6874	7	639	20	1548	18	1078	16	185	38	963	24	2254	14	313	27	563	30	1453	15	528	28	998	17	553	21	3371	10	2425	11	345	36	1341	17	37227	3
	fir	359	28	123	37	410	36	3950	9	147	49	601	24	0	.	145	53	589	23	612	20	1102	16	956	19	65	100	116	38	1147	18	29	71	353	28	331	27	176	48	308	32	59	62	1630	14	294	31	124	41	509	25	14134	4
	pine	197	41	0	.	0	.	228	30	188	55	88	52	0	.	0	.	468	20	94	39	33	78	28	74	0	.	7	100	125	49	11	100	67	38	11	100	79	42	60	61	21	71	0	.	302	31	0	.	315	25	2321	10
	larch	112	39	0	.	17	100	48	52	0	.	15	100	0	.	0	.	2214	13	26	71	26	100	0	.	0	.	17	100	71	55	16	100	15	100	30	100	0	.	823	13	0	.	182	40	2290	11	16	100	115	47	6034	7
	Arolla pine	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	191	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	31	0	.	0	.	310	19
	other conifers	8	100	0	.	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	44	77	0	.	0	.	0	.	54	100	0	.	0	.	0	.	0	.	16	100	0	.	51	82	0	.	0	.	0	.	214	41
	beech	1138	17	37	76	185	43	2652	12	683	23	717	21	0	.	231	33	274	29	638	19	586	21	286	31	56	50	316	39	980	16	349	29	1229	16	347	30	401	26	611	17	65	69	1337	14	76	59	41	58	1148	15	14382	4
	maple	48	51	0	.	0	.	136	41	64	46	66	58	0	.	31	82	58	48	62	45	38	59	24	65	0	.	7	100	124	36	0	.	39	50	9	100	76	66	11	100	8	100	88	38	0	.	13	100	92	43	992	13
	ash	155	32	21	100	25	71	201	29	89	52	225	32	0	.	35	65	7	100	156	33	189	65	0	.	54	80	0	.	102	46	9	100	73	42	53	71	201	35	58	61	0	.	167	31	7	123	55	51	304	29	2186	11
	oak	81	48	0	.	13	100	238	28	87	51	64	60	65	71	0	.	63	53	50	66	116	59	8	100	0	.	19	100	0	.	26	73	65	67	10	100	116	41	66	41	0	.	237	40	17	74	0	.	160	32	1501	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	489	20	0	.	39	100	0	.	0	.	0	.	570	19
	other broadleaves	79	69	0	.	20	71	161	55	10	100	90	71	0	.	8	100	34	59	11	100	11	100	0	.	10	100	0	.	36	60	0	0	39	72	6	100	21	71	95	38	12	100	273	50	79	46	0	.	51	62	1048	19
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	2980	11	565	28	1526	22	14986	5	1515	17	3400	11	65	71	1088	18	10813	6	2288	11	3694	12	2380	12	370	32	1444	19	4893	10	753	22	2445	13	2250	13	1598	17	3534	8	719	20	7375	8	5609	7	594	27	4034	10	80919	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	304	34	192	42	557	34	3722	10	91	62	723	24	0	.	411	27	3793	10	336	31	1056	19	699	23	78	53	394	25	1738	15	204	44	227	37	786	21	171	48	709	22	322	28	2094	13	1297	16	98	56	632	26	20632	4
	fir	543	25	19	100	218	41	3025	12	96	53	472	31	0	.	17	100	234	32	374	28	955	19	738	21	40	71	153	54	493	26	19	100	245	34	306	29	147	65	129	38	51	59	885	22	104	59	36	100	564	23	9864	6
	pine	94	60	0	.	0	.	132	43	0	.	47	58	0	.	0	.	209	33	80	59	0	.	17	100	0	.	11	100	39	71	0	.	25	72	0	.	52	58	10	100	0	.	0	.	39	58	0	.	101	52	856	16
	larch	20	100	0	.	0	.	46	71	0	.	0	0	0	.	0	.	1042	14	0	.	58	100	0	.	0	.	0	.	16	100	17	100	14	100	26	100	17	100	486	21	0	.	85	72	1417	14	0	.	56	59	3298	9
	Arolla pine	0	.	0	.	0	.	27	73	0	.	0	.	0	.	0	.	152	30	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	46	45	0	.	0	.	236	23
	other conifers	21	100	0	.	0	.	41	71	0	.	0	.	0	.	0	.	0	.	0	.	81	71	0	.	0	.	0	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	18	100	216	37
	beech	669	20	29	71	152	42	893	19	535	26	391	30	0	.	70	66	145	39	234	32	446	30	54	100	0	.	156	55	364	32	138	51	494	28	177	45	128	39	304	25	32	73	468	22	28	71	62	63	644	21	6612	7
	maple	20	100	0	.	0	.	56	54	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	58	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	46	243	30
	ash	55	58	0	.	0	.	85	51	69	58	21	100	0	.	18	100	0	.	54	52	22	100	0	.	36	71	0	.	21	100	21	100	0	.	48	71	62	58	44	71	0	.	209	53	0	.	40	100	130	41	936	18
	oak	162	38	0	.	0	.	69	51	42	71	27	71	20	100	0	.	0	.	0	.	16	100	0	.	0	.	0	.	40	100	125	71	72	58	0	.	40	71	28	65	0	.	63	50	17	100	22	100	128	45	870	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	192	26	0	.	0	.	27	76	0	.	0	.	236	23
	other broadleaves	102	45	0	.	0	.	19	100	10	100	39	72	0	.	20	100	20	72	0	.	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	0	20	100	69	62	0	.	63	78	0	.	0	.	34	71	436	24
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	1988	14	239	39	927	27	8116	7	843	21	1721	16	20	100	550	23	5613	8	1077	17	2633	14	1508	15	154	37	713	22	2847	13	524	31	1077	18	1343	17	636	24	1972	11	405	25	3892	10	2973	10	258	39	2403	13	44435	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	188	48	58	100	108	63	2231	13	46	71	409	28	19	100	405	32	2380	13	258	35	366	33	263	35	15	100	256	32	959	22	51	71	109	50	547	31	173	49	460	24	483	37	937	20	1080	24	71	60	296	30	12168	6
	fir	183	68	64	71	104	60	1346	18	20	100	172	47	0	.	50	71	250	34	153	42	365	30	413	29	0	.	119	55	534	29	0	.	108	60	230	36	56	71	182	45	28	100	452	26	94	46	67	72	226	34	5217	8
	pine	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	55	59	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	46
	larch	0	.	0	.	20	100	0	.	0	.	0	.	0	.	34	100	553	20	0	.	35	100	24	100	0	.	0	.	26	100	0	.	0	.	0	.	0	.	271	29	0	.	20	100	867	19	16	100	0	.	1868	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	43	0	.	0	.	133	31
	other conifers	21	100	0	.	0	.	29	100	25	100	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	0	.	84	100	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	226	47
	beech	352	31	0	.	28	100	491	31	234	41	152	42	0	.	0	.	81	63	85	59	114	54	65	58	0	.	0	.	205	36	21	100	211	41	108	74	49	74	161	38	20	100	285	37	12	100	0	.	278	32	2955	11
	maple	0	.	0	.	0	.	22	100	34	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	99	49
	ash	86	59	0	.	0	.	117	66	0	.	36	100	0	.	0	.	18	100	37	71	0	.	0	.	0	.	0	.	115	50	0	.	0	.	0	.	30	100	0	.	0	.	79	58	0	.	0	.	27	100	544	24
	oak	216	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	73	16	100	0	.	0	.	0	.	0	.	16	100	47	71	0	.	0	.	58	71	16	100	16	100	110	77	0	.	0	.	0	.	512	27
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	250	25	0	.	0	.	10	100	0	.	0	.	260	24
	other broadleaves	25	100	0	.	0	.	0	.	0	.	64	71	0	.	0	.	0	.	0	.	31	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	82	0	.	33	73	22	100	0	.	0	.	228	36
	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	1072	21	122	60	260	38	4259	10	360	31	841	21	19	100	489	29	3413	10	567	24	941	21	766	22	15	100	375	29	1958	16	119	45	428	29	885	26	365	32	1392	13	547	33	1969	14	2162	15	154	43	826	18	24303	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	124	51	0	.	0	.	916	21	65	71	128	51	0	.	49	58	1025	18	85	58	24	100	76	100	22	100	91	71	386	48	0	.	0	.	132	52	83	74	320	30	49	100	392	29	351	32	0	.	187	47	4505	9
	fir	130	50	0	.	51	100	549	28	0	.	67	72	0	.	99	58	123	58	58	71	34	100	100	58	0	.	0	.	155	51	0	.	66	71	66	71	63	100	31	100	0	.	276	36	36	71	0	.	72	71	1976	14
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	56	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	297	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	178	45	0	.	0	.	342	27	0	.	0	.	847	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	50	0	.	0	.	120	34
	other conifers	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	0	.	114	58
	beech	84	58	0	.	0	.	137	52	0	0	0	.	0	.	0	.	15	100	47	71	0	.	0	.	0	.	0	.	111	59	0	.	130	50	41	100	41	100	54	60	25	100	0	.	21	100	0	.	291	44	997	20
	maple	0	.	0	.	0	.	43	100	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	68
	ash	0	.	0	.	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	10	100	0	.	0	.	38	100	130	54
	oak	0	.	0	.	0	.	29	100	41	100	55	72	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	27	100	27	100	0	.	41	100	37	100	0	.	0	.	28	100	0	.	0	.	77	58	378	28
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	175	34	0	.	0	.	17	100	0	.	0	.	192	33
	other broadleaves	84	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	0	.	124	58
	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	422	30	0	.	51	100	1764	15	106	58	251	36	0	.	162	40	1567	14	206	36	58	72	176	54	34	100	91	71	678	32	61	71	227	38	280	36	258	46	758	20	74	74	776	21	819	19	0	.	692	25	9509	6
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	.	236	45	0	.	27	100	0	.	133	45	174	47	0	.	31	100	0	.	0	.	119	79	158	52	0	.	0	.	0	.	0	.	127	52	25	100	103	75	27	100	0	.	43	100	1202	19
	fir	0	.	0	.	0	.	195	51	0	.	0	.	0	.	0	.	118	58	72	71	0	.	172	62	0	.	0	.	0	.	0	.	0	.	35	100	0	.	41	100	37	100	0	0	0	.	0	.	28	100	698	26
	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	.	181	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	231	37	0	.	0	.	412	29
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	34	71
	other conifers	0	.	0	.	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	100
	beech	33	100	0	.	0	.	19	100	0	.	27	100	0	.	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	24	100	0	.	28	100	0	.	0	.	0	.	0	.	50	100	244	37
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	34	100
	oak	26	100	0	.	0	.	0	.	45	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	95	100	0	.	0	.	0	.	0	.	0	.	0	.	165	65
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	55	0	.	0	.	0	.	0	.	0	.	67	55
	other broadleaves	0	.	0	.	0	.	79	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	46	100	0	.	0	.	167	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	58	71	0	.	0	.	528	32	45	100	98	59	0	.	133	45	529	27	72	71	31	100	172	62	0	.	119	79	158	52	0	.	37	100	59	72	95	100	262	34	62	72	178	53	320	32	0	.	121	59	3078	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	104	60	0	.	38	100	0	.	92	76	593	42	39	100	0	.	0	.	0	.	43	100	22	100	0	.	0	.	58	71	0	.	216	47	43	100	21	100	62	71	0	.	0	.	1329	23
	fir	88	71	0	.	0	.	135	71	0	.	0	.	0	.	46	100	79	100	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	68	71	0	.	0	.	0	.	0	.	0	.	450	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	157	64	0	.	0	.	26	100	0	.	0	.	260	43
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	40	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	.	214	100	0	.	0	.	0	.	0	.	0	.	214	100
	maple	0	.	0	.	0	.	54	100	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	77
	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	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	0	.	0	.	32	100	105	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	660	39	0	.	0	.	0	.	0	.	0	.	660	39
	other broadleaves	0	.	0	.	0	.	0	.	60	100	0	.	0	.	0	.	18	100	0	.	0	.	126	100	0	.	0	.	0	.	0	.	0	.	0	.	242	100	0	.	0	.	0	.	0	.	0	.	0	.	447	63
	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	122	58	0	.	0	.	292	43	60	100	38	100	0	.	158	54	786	34	39	100	0	.	126	100	0	.	43	100	57	73	0	.	0	.	58	71	242	100	1314	28	43	100	60	74	108	51	0	.	32	100	3579	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3606	15	1834	25	3084	20	31716	5	712	26	8308	11	19	100	3677	17	36712	5	3237	15	7090	12	4848	12	1057	27	4510	14	10676	10	1646	21	2247	17	6158	12	2976	18	5882	11	3648	17	15198	7	14867	7	1028	26	6552	11	181288	2
	fir	2177	19	521	30	1425	27	17090	7	694	31	2692	17	0	.	635	34	2173	16	3458	13	5590	12	4227	13	235	45	815	28	4285	13	114	47	1639	17	1947	18	1077	26	1356	21	689	27	6396	10	1523	20	550	29	2358	16	63668	3
	pine	531	26	22	90	10	100	1118	23	459	31	294	45	62	79	0	.	2849	13	486	31	167	41	150	48	0	.	135	70	345	37	249	33	452	24	149	53	393	28	369	45	191	42	221	33	2082	15	0	.	1056	18	11791	6
	larch	324	34	9	75	67	100	313	29	14	73	51	61	0	.	40	87	8734	8	27	69	139	93	24	100	0	.	26	75	462	36	86	69	98	53	57	70	70	50	4082	10	0	.	543	28	9809	8	32	100	287	28	25291	4
	Arolla pine	0	.	0	.	0	.	35	87	0	.	0	.	0	.	0	.	1626	14	0	.	0	.	0	.	0	.	48	100	22	100	0	.	0	.	0	.	0	.	8	70	20	100	0	.	1102	15	0	.	0	.	2861	10
	other conifers	157	32	4	100	5	79	291	37	166	72	56	100	0	.	1	100	64	30	0	2109	168	68	4	100	6	100	2	100	170	104	65	71	13	126	3	73	54	83	28	97	11	57	355	43	12	50	3	100	68	42	1705	19
	beech	5384	11	279	34	953	30	13130	7	3406	15	4022	14	0	.	1093	22	1672	18	4303	12	3126	14	1840	14	427	30	1339	26	4968	11	2057	16	5447	10	1815	18	1626	17	5757	10	563	33	6995	9	926	21	355	37	4848	11	76329	2
	maple	660	16	73	75	156	47	2102	10	442	23	540	20	9	78	447	24	374	29	649	18	491	17	393	24	125	27	319	36	1132	15	81	42	585	18	400	22	525	28	365	21	147	32	1336	13	344	19	99	37	927	16	12721	4
	ash	1128	16	85	65	269	38	1527	16	485	27	1192	19	16	100	244	34	431	28	988	19	753	36	96	37	277	54	99	42	1357	16	154	35	453	24	536	30	988	20	978	16	119	48	1665	17	405	23	201	43	1483	15	15928	5
	oak	937	20	0	.	13	100	745	19	379	39	242	32	396	42	0	.	233	40	263	34	232	41	106	58	3	100	19	100	217	52	391	36	315	26	34	75	564	37	900	16	54	54	1584	18	242	24	28	81	628	20	8524	7
	sweet chestnut	3	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	337	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5025	10	0	.	75	94	116	54	0	.	0	.	5559	9
	other broadleaves	1007	27	4	159	60	55	1334	15	280	27	426	40	37	49	150	37	1662	13	391	22	207	29	229	56	92	44	108	33	500	21	34	38	323	25	218	28	758	39	4002	8	182	32	1418	19	1355	13	22	64	590	19	15389	5
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	15915	8	2832	22	6041	17	69403	4	7035	12	17824	8	539	39	6288	14	56866	4	13802	8	17964	8	11917	9	2223	21	7420	12	24134	7	4876	14	11570	9	11316	10	9031	11	28752	4	5622	14	35787	5	32783	5	2317	21	18797	7	421054	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1218025. Birmensdorf, Swiss Federal Research Institute WSL

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