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: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		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	±%
n/a	n/a	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	.
	fir	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	.
	larch	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	.
	other conifers	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	.
	maple	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	.
	oak	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	.
	other broadleaves	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	.
	total	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	.
	spruce	898	15	208	29	301	40	663	19	874	19	411	18	949	14	579	20	501	17	247	24	170	38	907	16	1591	11	432	18	8732	5
	fir	777	12	90	39	104	30	462	24	313	30	195	28	568	16	181	33	100	37	149	37	32	52	96	52	33	36	56	41	3155	7
	pine	8	62	26	52	0	0	39	106	9	71	0	.	12	263	0	.	5	72	11	59	0	.	356	29	213	27	7	100	688	19
	larch	0	0	0	.	33	89	4	100	12	79	10	100	14	100	10	100	22	50	0	.	6	83	317	19	81	23	168	29	676	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	47	132	41	2	100	224	30
	other conifers	3	759	10	58	1	100	12	43	42	33	0	.	2	71	-1	2340	0	.	6	100	6	73	11	56	29	42	0	.	122	30
	beech	1076	11	261	28	219	29	342	19	455	12	137	41	435	20	367	20	228	29	104	38	152	33	193	45	67	38	884	16	4920	6
	maple	337	19	106	25	87	38	95	32	290	32	91	28	139	32	143	32	164	29	23	63	51	38	105	30	48	40	69	30	1747	9
	ash	150	23	137	36	125	48	101	53	173	32	28	49	97	28	113	27	43	60	70	49	24	50	71	39	79	47	195	33	1407	11
	oak	94	39	7	71	91	49	11	60	32	47	1	100	1	100	8	78	17	86	1	100	0	.	58	34	2	100	140	36	464	18
	sweet chestnut	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	813	19	814	19
	other broadleaves	256	16	70	26	100	35	155	25	250	19	75	28	118	24	110	31	176	26	98	44	106	36	405	18	420	26	1150	16	3488	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
	total	3599	6	915	15	1061	17	1884	12	2451	10	949	14	2333	9	1511	13	1256	13	709	19	545	21	2610	10	2696	9	3915	8	26435	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2032	13	536	34	706	26	1080	20	1990	17	1282	22	2003	13	1574	16	1048	18	516	26	712	32	2117	15	3104	11	1081	17	19780	4
	fir	1490	15	177	34	216	42	630	20	320	28	382	35	924	15	371	25	201	32	251	34	27	45	212	36	74	41	148	43	5423	7
	pine	193	47	111	62	47	50	45	53	81	42	0	.	62	74	0	.	17	100	85	56	0	.	589	23	369	28	45	61	1642	13
	larch	0	0	6	100	53	82	5	100	48	63	0	.	0	.	21	100	48	67	0	.	28	81	602	19	283	27	602	21	1695	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	213	31	223	26	3	100	440	20
	other conifers	12	72	38	56	22	100	48	58	43	66	0	.	4	100	6	100	8	100	0	.	0	.	4	100	10	59	0	.	195	27
	beech	2591	11	806	19	643	20	837	17	1357	17	310	40	1306	14	899	18	284	32	307	33	273	27	320	33	162	37	1580	18	11674	5
	maple	568	16	164	29	39	57	191	30	683	20	248	30	495	28	360	29	157	36	80	47	237	32	101	36	68	55	120	41	3509	8
	ash	302	24	169	35	332	30	177	32	390	25	205	40	242	42	497	25	64	47	131	40	75	44	116	35	56	62	210	33	2966	9
	oak	379	35	105	45	220	34	72	44	77	36	19	71	16	75	0	0	18	65	10	100	0	.	59	49	6	100	235	27	1216	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	5	100	1435	16	1455	15
	other broadleaves	225	24	112	35	84	38	232	35	282	25	60	45	125	34	177	45	221	35	35	73	69	40	281	22	239	32	1326	14	3468	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
	total	7791	6	2223	14	2363	14	3317	11	5270	9	2506	17	5176	8	3905	10	2066	13	1413	20	1421	20	4629	10	4597	9	6785	7	53463	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3293	12	349	28	875	25	1381	19	2780	14	1787	17	3225	12	2875	13	2174	15	1095	24	1043	27	2941	13	5303	9	2033	17	31155	4
	fir	2558	12	201	39	267	38	1085	19	627	23	482	27	1710	14	949	20	257	33	265	41	193	43	205	39	110	39	245	36	9155	6
	pine	470	27	194	37	163	49	104	63	358	26	0	.	116	53	21	73	8	79	60	59	0	.	659	21	688	28	39	67	2879	11
	larch	22	71	42	64	46	80	39	80	118	44	0	.	0	.	22	71	41	71	0	.	14	75	1169	17	791	20	962	20	3265	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	32	315	25	0	.	474	20
	other conifers	0	.	66	61	40	100	16	101	28	71	0	.	0	.	0	.	0	.	0	.	-19	100	0	.	0	.	0	.	131	50
	beech	4184	9	1389	18	858	23	1298	20	1563	15	413	38	1512	16	1549	17	441	25	261	34	318	30	148	36	265	31	996	16	15193	5
	maple	648	17	134	36	28	60	92	53	486	23	63	41	417	25	453	22	284	28	111	38	189	34	8	263	35	75	64	53	3011	8
	ash	398	22	254	33	462	33	380	34	782	18	189	54	262	50	499	25	224	46	87	57	75	49	21	116	42	63	95	40	3770	9
	oak	234	29	174	49	250	32	155	40	211	27	23	73	0	.	0	.	9	100	27	75	0	.	19	78	6	100	208	31	1316	13
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	19	71	0	.	997	20	1022	20
	other broadleaves	252	28	107	43	152	50	192	32	460	33	6	100	74	54	134	33	122	43	50	46	32	50	77	46	78	41	895	17	2632	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
	total	12060	5	2910	12	3141	13	4742	11	7420	8	2962	13	7316	8	6501	9	3559	11	1955	17	1845	19	5427	9	7632	8	6533	7	74003	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4078	10	651	27	1161	22	1972	18	2541	12	2474	15	4231	12	3118	13	2951	16	1322	23	1285	20	2907	12	6510	9	2322	16	37523	3
	fir	3544	11	296	36	388	31	1489	18	1133	17	600	26	1827	14	1447	19	370	32	429	35	217	37	405	36	242	32	282	31	12669	6
	pine	449	27	233	40	233	45	170	37	513	24	0	.	265	73	17	100	9	100	46	64	0	.	219	32	548	25	36	61	2737	12
	larch	0	.	15	100	39	75	77	60	114	56	0	.	16	100	0	.	72	70	0	.	0	0	1651	18	1345	15	825	17	4154	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	118	41	294	24	0	.	406	21
	other conifers	0	.	64	72	41	100	100	78	34	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	259	40
	beech	4102	9	1801	16	1149	21	1877	14	2067	13	337	39	1764	16	1722	18	634	31	274	35	449	31	88	61	337	45	764	18	17365	4
	maple	515	18	184	42	2	399	30	94	206	31	91	50	165	40	347	24	114	37	59	48	83	48	48	82	22	100	9	100	1876	10
	ash	457	28	103	40	307	33	485	35	1009	17	31	103	135	40	422	29	113	64	61	89	147	46	40	71	9	100	109	50	3426	10
	oak	329	30	172	45	339	41	171	34	367	25	0	.	57	69	15	100	7	100	11	100	0	.	19	76	54	79	226	41	1768	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	10	100	5	100	644	21	666	20
	other broadleaves	114	40	53	52	191	51	147	56	292	29	0	.	55	47	70	60	34	62	14	100	57	68	22	231	57	59	353	24	1461	14
	indeterminable	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	0
	total	13589	5	3571	13	3851	13	6518	9	8276	7	3535	14	8515	8	7157	9	4305	12	2216	18	2238	16	5528	10	9424	7	5589	8	84310	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3556	10	738	31	906	32	2202	17	2758	16	2155	19	4151	13	3988	13	2541	15	1144	22	1302	22	2576	14	5165	11	2077	17	35258	4
	fir	2878	13	164	41	642	29	1853	16	1077	20	699	32	2063	15	1648	19	656	27	161	47	233	53	216	44	370	38	518	30	13176	6
	pine	265	30	21	100	158	53	253	35	443	26	0	.	94	64	43	100	0	.	20	100	0	.	172	48	403	30	38	100	1910	13
	larch	20	100	5	100	48	71	23	100	248	43	0	.	0	.	79	74	75	59	0	.	49	100	1992	15	1681	17	919	15	5138	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	58	166	32	0	.	200	28
	other conifers	0	.	70	100	0	.	74	76	103	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	278	47
	beech	2683	12	1341	23	761	27	1697	19	2406	14	228	39	1431	22	916	23	457	30	193	45	372	36	91	72	242	39	549	24	13366	6
	maple	187	34	87	50	40	75	0	.	244	39	110	59	45	71	61	66	106	73	29	70	31	71	0	.	83	53	20	100	1042	17
	ash	209	37	171	44	366	31	525	44	825	23	0	.	116	67	132	46	38	71	49	100	57	65	15	100	0	.	0	.	2501	14
	oak	104	58	65	74	104	62	374	38	376	29	0	.	0	.	0	.	0	.	16	100	0	.	21	100	50	71	39	71	1150	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	455	28	463	28
	other broadleaves	21	100	91	61	191	60	36	71	20	100	0	.	20	100	46	73	273	68	9	100	33	72	43	73	30	75	69	60	882	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	9922	6	2752	16	3215	14	7037	10	8500	8	3191	16	7919	9	6911	10	4145	13	1621	19	2076	18	5159	10	8198	9	4716	9	75363	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2066	15	438	38	359	34	1290	23	1104	22	1358	26	2335	14	2443	17	1208	21	863	25	780	28	1204	22	2589	14	1400	19	19437	5
	fir	1699	19	276	35	282	48	1399	20	1256	23	578	44	1739	20	513	26	625	32	302	47	34	100	46	100	179	41	99	59	9027	8
	pine	53	72	0	.	55	71	45	100	99	58	0	.	83	75	31	100	0	.	0	.	0	.	0	.	199	39	0	.	565	24
	larch	0	.	0	.	0	0	37	100	89	58	0	.	0	.	0	.	57	76	0	.	26	100	1132	24	842	21	685	25	2868	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	86	46	0	.	105	42
	other conifers	0	.	0	.	48	100	202	53	46	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	296	42
	beech	609	28	725	27	291	34	907	26	982	22	143	52	676	33	378	35	198	44	52	73	217	69	24	100	101	58	406	31	5708	9
	maple	0	.	35	100	0	.	0	.	133	51	0	.	24	100	69	100	35	71	0	.	23	100	0	.	0	.	0	.	318	35
	ash	99	52	109	58	166	86	47	100	194	47	36	100	72	71	74	71	0	.	39	100	0	.	0	.	0	.	46	100	882	25
	oak	46	100	77	71	110	51	57	73	244	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	71	580	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	143	35	158	33
	other broadleaves	0	.	96	58	70	72	0	.	31	100	0	.	0	.	72	100	58	71	0	0	33	100	0	.	0	.	16	100	376	32
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	4570	11	1756	19	1383	20	3983	13	4178	12	2114	21	4929	12	3581	14	2181	17	1257	23	1112	25	2440	16	3995	11	2841	12	40321	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	795	25	96	71	244	58	586	30	827	27	338	37	1562	20	1221	27	597	28	737	31	671	33	603	35	1155	24	946	26	10376	8
	fir	796	26	42	100	198	60	599	34	133	58	110	59	1044	24	692	31	157	50	204	51	137	76	78	71	248	46	178	51	4616	11
	pine	29	100	0	.	0	.	43	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	71	0	.	127	51
	larch	43	100	0	.	0	.	0	.	0	.	0	.	0	.	73	72	0	.	0	.	57	100	853	25	454	28	352	35	1832	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	45	43	71	0	.	143	38
	other conifers	0	.	44	100	65	100	98	71	139	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	346	50
	beech	240	50	164	50	89	71	340	55	576	31	36	100	207	55	238	62	86	72	37	100	39	100	20	100	116	71	158	41	2346	16
	maple	0	.	60	100	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	32	100	0	.	0	.	0	.	106	66
	ash	36	100	60	100	45	100	42	100	91	71	0	.	0	.	113	71	45	100	0	.	0	.	0	.	30	100	0	.	462	32
	oak	0	.	106	71	0	.	0	.	202	51	0	.	0	.	27	100	0	.	26	100	0	.	0	.	12	100	0	.	373	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	162	36	162	36
	other broadleaves	0	.	0	.	130	62	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	21	100	184	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1939	17	571	29	770	29	1708	20	1969	18	497	33	2814	16	2364	19	918	22	1004	27	937	30	1653	19	2113	16	1816	16	21073	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	217	62	58	100	71	100	160	58	405	39	243	46	300	41	431	69	536	39	66	100	106	59	296	51	686	26	433	36	4008	14
	fir	422	38	0	.	42	100	181	58	207	61	0	.	333	52	113	71	67	100	0	.	116	71	29	100	84	100	67	100	1662	20
	pine	0	.	0	.	0	.	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	0	.	102	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	289	42	226	48	142	63	657	28
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	48	72	0	.	70	58
	other conifers	0	.	0	.	72	100	0	.	0	.	0	.	71	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	71
	beech	0	.	0	0	0	.	0	.	368	42	0	.	43	100	67	100	81	100	46	100	0	.	39	100	0	.	82	58	726	28
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	.	0	.	23	100
	ash	0	.	0	.	0	.	86	100	56	100	0	.	0	.	65	100	0	.	0	.	0	.	0	.	0	.	0	.	207	59
	oak	0	.	0	.	51	100	51	100	199	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	301	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	134	46	164	42
	other broadleaves	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	639	32	99	71	235	51	477	36	1282	23	243	46	747	30	677	47	683	34	112	72	246	43	706	29	1098	21	859	23	8103	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	65	100	0	.	60	100	0	.	0	.	34	100	131	72	81	100	0	.	47	100	144	59	51	100	154	72	37	100	803	28
	fir	225	58	0	.	0	.	60	100	46	100	0	.	89	100	0	.	0	.	65	100	0	.	0	.	0	.	145	71	629	34
	pine	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	.	157	58	61	100	0	.	218	50
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	0	.	0	.	28	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	54	100	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	96	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	56	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	100
	oak	0	.	0	.	0	.	0	.	168	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	62	102	62
	other broadleaves	0	.	0	.	0	.	143	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	143	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	290	50	0	.	116	71	204	76	269	68	34	100	220	59	123	74	0	.	111	72	144	59	236	46	214	59	284	44	2244	18
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	67	100	0	.	67	100	74	100	0	.	0	.	68	100	43	100	38	100	0	.	87	72	53	100	195	58	204	71	897	28
	fir	0	.	0	.	0	.	0	.	153	71	0	.	126	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	279	60
	pine	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	.	52	100	163	100	215	80
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	35	100	0	.	73	71
	other conifers	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	.
	maple	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	.
	oak	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	.	385	52	385	52
	other broadleaves	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	.
	total	67	100	0	.	67	100	74	100	153	71	0	.	194	74	43	100	38	100	0	.	87	72	91	72	282	46	752	39	1849	22
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17067	7	3073	19	4750	17	9409	12	13279	9	10081	13	18956	8	16354	10	11593	12	6036	18	6301	17	13654	10	26451	7	10966	11	167969	2
	fir	14388	8	1246	25	2139	24	7756	13	5266	13	3046	21	10421	10	5914	13	2433	22	1827	27	989	33	1287	28	1341	24	1738	25	59792	4
	pine	1468	21	584	28	657	37	699	26	1550	18	0	.	632	55	113	51	39	59	221	54	0	.	1995	19	2528	19	164	50	10650	8
	larch	85	59	68	46	219	39	185	49	629	28	10	100	30	71	204	46	315	36	0	.	179	55	8161	12	5815	13	4818	13	20718	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	823	24	1342	19	5	100	2164	15
	other conifers	16	170	292	65	290	73	549	44	436	68	0	.	77	93	5	306	8	100	6	100	-13	118	15	58	39	39	50	100	1770	27
	beech	15485	6	6485	12	4010	17	7298	11	9829	9	1604	23	7372	12	6177	13	2407	20	1275	26	1820	21	922	30	1290	28	5418	12	71394	3
	maple	2255	10	770	22	196	29	408	22	2041	15	616	24	1284	18	1433	17	860	21	301	29	668	25	262	31	256	35	281	29	11631	5
	ash	1651	17	1003	23	1858	22	1841	29	3520	13	489	36	924	31	1915	19	527	35	437	38	378	35	263	34	215	41	655	28	15676	7
	oak	1187	23	707	33	1165	28	892	22	1876	23	43	63	74	61	50	63	51	57	91	54	0	.	176	42	130	71	894	21	7336	10
	sweet chestnut	0	.	0	.	2	100	0	.	6	100	0	.	0	.	0	.	7	100	0	.	0	.	90	53	18	100	5269	12	5391	12
	other broadleaves	867	18	570	25	917	35	905	26	1335	18	141	27	392	21	608	29	918	34	207	35	329	31	829	19	825	24	3831	11	12674	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
	total	54468	3	14798	9	16202	10	29944	7	39767	5	16030	11	40163	6	32773	7	19151	10	10401	14	10651	14	28478	7	40250	6	34090	4	387165	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. 1217986. Birmensdorf, Swiss Federal Research Institute WSL

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