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 NFI4/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	907	15	208	29	313	38	663	19	874	19	535	18	985	14	634	18	589	15	296	21	205	33	980	16	1860	10	532	15	9582	4
	fir	783	12	90	39	104	30	462	24	313	30	195	28	568	16	181	33	100	37	157	35	32	52	96	52	33	36	56	41	3169	7
	pine	8	62	26	52	0	0	39	106	9	71	0	.	12	263	0	.	5	72	21	52	6	100	362	29	257	24	7	100	751	18
	larch	0	0	0	.	33	89	4	100	12	79	10	100	14	100	10	100	22	50	0	.	16	57	364	18	162	23	265	22	911	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	91	46	185	31	2	100	277	26
	other conifers	3	759	10	58	1	100	12	43	42	33	0	.	2	71	-1	2340	10	94	6	100	6	73	11	56	35	36	0	.	138	27
	beech	1076	11	279	26	219	29	342	19	472	12	138	41	435	20	367	20	233	29	110	37	155	33	197	44	67	38	929	15	5019	6
	maple	341	19	113	25	87	38	95	32	296	31	94	27	165	31	171	30	170	28	31	53	57	36	140	26	54	37	126	28	1941	8
	ash	150	23	142	35	126	47	101	53	183	30	28	49	97	28	142	30	59	48	85	42	47	43	173	48	85	44	214	31	1633	11
	oak	94	39	7	71	91	49	11	60	32	47	1	100	1	100	8	78	17	86	1	100	1	100	62	32	2	100	150	34	479	17
	sweet chestnut	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	836	19	857	18
	other broadleaves	269	16	79	26	107	34	159	25	253	18	137	34	139	22	123	45	219	24	180	40	116	34	447	17	527	23	1298	14	4054	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	3631	6	954	14	1081	17	1888	12	2487	10	1139	13	2417	9	1637	13	1424	12	887	17	640	19	2943	9	3270	8	4416	7	28812	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2069	13	536	34	750	25	1080	20	2000	16	1455	20	2174	13	1792	15	1206	17	618	23	740	31	2233	15	3497	10	1229	16	21379	4
	fir	1498	15	177	34	216	42	630	20	320	28	382	35	944	15	371	25	201	32	269	33	27	45	212	36	74	41	158	41	5480	7
	pine	193	47	111	62	47	50	45	53	81	42	0	.	62	74	0	.	20	84	85	56	40	80	595	23	369	28	45	61	1692	13
	larch	0	0	6	100	53	82	5	100	48	63	0	.	0	.	21	100	54	61	0	.	31	72	751	17	401	23	818	17	2188	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	233	29	294	24	3	100	531	19
	other conifers	12	72	38	56	22	100	48	58	43	66	0	.	4	100	6	100	29	78	0	.	0	0	4	100	10	59	0	.	217	26
	beech	2607	11	818	18	643	20	837	17	1364	17	310	40	1343	14	899	18	297	31	337	31	316	27	320	33	175	35	1641	17	11907	5
	maple	574	16	202	30	39	57	191	30	683	20	248	30	532	26	366	29	178	33	126	42	250	31	106	34	133	56	127	39	3755	8
	ash	302	24	169	35	332	30	177	32	395	25	205	40	242	42	566	26	106	49	144	37	81	42	175	34	79	49	251	29	3224	9
	oak	379	35	105	45	220	34	91	41	77	36	19	71	28	60	0	0	18	65	10	100	0	.	78	40	6	100	259	26	1289	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	75	5	100	1454	16	1481	16
	other broadleaves	231	23	124	33	95	36	247	34	287	24	65	42	125	34	235	36	232	33	56	51	74	38	349	23	263	30	1592	14	3975	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	7865	6	2286	13	2417	14	3351	11	5298	9	2684	16	5454	8	4256	10	2343	12	1645	18	1560	19	5078	9	5305	8	7576	6	57120	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3379	12	349	28	875	25	1381	19	2780	14	1965	16	3552	11	3102	12	2456	14	1173	22	1050	27	3103	12	5605	9	2126	16	32896	4
	fir	2571	12	201	39	267	38	1085	19	627	23	482	27	1710	14	972	19	257	33	288	38	193	43	205	39	110	39	254	35	9224	6
	pine	470	27	194	37	163	49	104	63	358	26	0	.	116	53	21	73	14	62	72	52	34	100	659	21	688	28	39	67	2932	11
	larch	22	71	42	64	46	80	39	80	118	44	0	.	0	.	22	71	41	71	0	.	63	80	1300	16	879	19	1160	17	3732	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	178	31	364	23	0	.	542	19
	other conifers	0	.	66	61	40	100	16	101	28	71	0	.	0	.	0	.	6	100	0	.	-19	100	0	.	0	.	0	.	137	48
	beech	4184	9	1355	18	858	23	1298	20	1580	15	413	38	1661	17	1582	17	441	25	261	34	349	29	148	36	265	31	996	16	15389	5
	maple	659	17	134	36	28	60	92	53	486	23	63	41	417	25	523	22	297	28	111	38	217	32	27	95	89	67	64	53	3207	8
	ash	398	22	309	30	462	33	380	34	794	17	189	54	262	50	499	25	259	41	87	57	75	49	35	77	42	63	95	40	3885	9
	oak	234	29	174	49	250	32	155	40	211	27	23	73	0	.	10	100	9	100	27	75	0	.	26	63	6	100	231	29	1355	13
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	43	64	0	.	997	20	1045	19
	other broadleaves	265	27	118	40	162	47	192	32	460	33	6	100	74	54	134	33	134	40	50	46	32	50	126	36	99	36	1050	16	2903	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	12182	5	2941	12	3151	13	4742	11	7449	8	3140	13	7792	8	6865	9	3913	11	2070	16	1995	18	5850	8	8145	7	7011	7	77247	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4154	10	674	26	1161	22	1972	18	2570	12	2530	15	4606	11	3548	12	3273	15	1374	22	1457	19	2948	12	6745	9	2389	15	39400	3
	fir	3555	11	296	36	388	31	1489	18	1154	17	614	26	1827	14	1549	18	370	32	497	33	229	35	405	36	242	32	296	30	12911	5
	pine	449	27	233	40	233	45	170	37	513	24	0	.	265	73	17	100	15	72	87	58	25	75	233	31	565	24	45	53	2851	12
	larch	0	.	15	100	39	75	77	60	114	56	0	.	16	100	0	.	72	70	0	.	19	100	1827	17	1515	15	955	16	4649	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	145	36	312	23	0	.	451	20
	other conifers	0	.	64	72	41	100	100	78	34	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	259	40
	beech	4135	9	1858	16	1149	21	1877	14	2067	13	346	38	1839	16	1780	17	634	31	329	30	466	31	120	52	337	45	788	18	17726	4
	maple	537	18	184	42	2	399	30	94	206	31	91	50	165	40	347	24	114	37	80	44	83	48	48	82	47	71	9	100	1944	10
	ash	457	28	103	40	307	33	485	35	1063	17	31	103	135	40	422	29	113	64	61	89	147	46	40	71	9	100	109	50	3480	10
	oak	329	30	172	45	339	41	171	34	394	24	0	.	57	69	15	100	7	100	31	72	0	.	19	76	54	79	231	40	1819	13
	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	207	48	147	56	330	28	0	.	55	47	70	60	80	63	14	100	57	68	22	231	57	59	380	23	1587	13
	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	13730	5	3651	13	3867	13	6518	9	8444	7	3613	13	8965	8	7748	9	4679	12	2472	17	2484	16	5816	9	9890	7	5867	7	87743	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3629	10	738	31	906	32	2202	17	2758	16	2304	18	4392	13	4329	13	2662	15	1144	22	1302	22	2615	14	5419	11	2140	16	36538	4
	fir	2934	13	164	41	642	29	1853	16	1142	20	699	32	2063	15	1648	19	677	26	181	43	233	53	240	41	370	38	518	30	13361	6
	pine	265	30	21	100	158	53	253	35	443	26	0	.	94	64	43	100	9	100	20	100	0	.	172	48	420	29	38	100	1935	13
	larch	20	100	5	100	48	71	23	100	248	43	0	.	0	.	79	74	73	60	0	.	62	82	2136	14	1818	16	1085	14	5598	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	50	166	32	0	.	210	27
	other conifers	0	.	70	100	0	.	74	76	103	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	278	47
	beech	2834	12	1480	22	761	27	1697	19	2402	14	228	39	1489	21	971	23	457	30	214	41	372	36	91	72	242	39	568	24	13806	6
	maple	191	34	87	50	40	75	0	.	244	39	110	59	45	71	64	63	106	73	29	70	31	71	0	.	83	53	20	100	1049	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	.	33	100	2534	14
	oak	104	58	65	74	104	62	374	38	427	27	0	.	0	.	0	.	0	.	16	100	0	.	21	100	50	71	39	71	1201	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	455	28	463	28
	other broadleaves	149	87	91	61	219	54	36	71	20	100	0	.	20	100	46	73	416	56	32	76	33	72	43	73	18	145	103	52	1225	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	10334	6	2892	16	3243	14	7037	10	8612	8	3340	16	8218	9	7311	9	4437	12	1685	18	2090	17	5377	10	8593	8	5030	9	78198	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2110	15	438	38	359	34	1290	23	1104	22	1519	25	2520	14	2594	17	1279	20	863	25	808	27	1268	21	2797	13	1422	18	20372	5
	fir	1737	18	276	35	282	48	1399	20	1289	23	578	44	1739	20	711	28	625	32	302	47	34	100	46	100	179	41	99	59	9296	8
	pine	53	72	0	.	55	71	45	100	99	58	0	.	83	75	31	100	0	.	0	.	0	.	0	.	199	39	18	100	584	24
	larch	0	.	0	.	0	0	37	100	89	58	0	.	0	.	0	.	57	76	0	.	26	100	1150	23	842	21	734	24	2935	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	72	86	46	0	.	119	38
	other conifers	0	.	0	.	48	100	202	53	46	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	296	42
	beech	678	26	867	26	291	34	907	26	1011	21	143	52	676	33	401	33	198	44	77	59	217	69	24	100	101	58	406	31	5997	9
	maple	0	.	35	100	0	.	0	.	133	51	0	.	24	100	69	100	77	64	0	.	23	100	0	.	0	.	0	.	360	33
	ash	99	52	109	58	166	86	47	100	194	47	36	100	72	71	74	71	43	100	39	100	0	.	0	.	0	.	46	100	926	24
	oak	46	100	77	71	110	51	57	73	293	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	71	628	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	76	0	.	143	35	193	32
	other broadleaves	0	.	96	58	70	72	0	.	31	100	0	.	0	.	72	100	58	71	0	0	33	100	0	.	21	100	16	100	397	31
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	4722	11	1898	18	1383	20	3983	13	4288	12	2275	20	5113	12	3954	14	2337	16	1282	22	1140	24	2571	16	4226	10	2930	12	42102	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	838	24	96	71	244	58	586	30	827	27	338	37	1685	19	1221	27	713	25	737	31	671	33	700	32	1319	22	990	25	10964	8
	fir	796	26	42	100	198	60	599	34	133	58	110	59	1044	24	764	29	157	50	204	51	137	76	78	71	248	46	178	51	4689	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	890	24	512	27	448	32	2023	15
	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	281	45	363	45	89	71	340	55	576	31	36	100	207	55	273	55	86	72	37	100	39	100	20	100	116	71	158	41	2621	16
	maple	0	.	60	100	0	.	0	.	0	.	13	100	0	.	0	.	32	100	0	.	32	100	0	.	0	.	0	.	137	56
	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	.	19	100	0	.	162	36	181	34
	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	2024	16	771	28	770	29	1708	20	1969	18	497	33	2936	15	2471	18	1066	20	1004	27	937	30	1808	18	2334	15	1957	15	22251	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	342	38	520	59	709	34	66	100	131	51	296	51	761	26	553	31	4531	13
	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	.	342	38	307	44	302	44	951	24
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	58	48	72	0	.	114	45
	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	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	639	32	99	71	286	45	477	36	1282	23	243	46	788	29	766	43	857	31	112	72	270	40	803	26	1254	21	1138	20	9013	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	.	125	58	131	72	136	72	0	.	47	100	144	59	51	100	154	72	84	71	996	24
	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	.	68	100	143	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	212	75
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	290	50	0	.	184	58	204	76	269	68	125	58	220	59	177	60	0	.	111	72	144	59	236	46	214	59	331	40	2506	17
>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	267	50	204	71	970	27
	fir	0	.	0	.	0	.	0	.	215	58	0	.	126	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	341	52
	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	.	107	71	163	100	270	67
	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	.	417	49	417	49
	other broadleaves	0	.	0	.	0	.	0	.	400	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	400	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	67	100	0	.	67	100	74	100	615	68	0	.	194	74	43	100	38	100	0	.	87	72	91	72	409	39	784	38	2470	23
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17435	6	3096	19	4805	17	9409	12	13319	9	11013	12	20455	8	17918	10	12925	12	6318	17	6593	17	14247	9	28423	7	11670	10	177626	2
	fir	14521	8	1246	25	2139	24	7756	13	5447	13	3060	21	10441	10	6310	13	2453	21	1963	26	1002	33	1311	28	1341	24	1771	25	60763	4
	pine	1468	21	584	28	657	37	699	26	1550	18	0	.	632	55	113	51	63	47	285	46	105	72	2020	19	2606	18	192	45	10974	8
	larch	85	59	68	46	219	39	185	49	629	28	10	100	30	71	204	46	320	36	0	.	275	49	8917	11	6604	12	5930	11	23475	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	958	22	1532	18	5	100	2489	13
	other conifers	16	170	292	65	290	73	549	44	436	68	0	.	77	93	5	306	45	83	6	100	-13	118	15	58	45	35	50	100	1813	27
	beech	15794	6	7020	12	4010	17	7298	11	9894	9	1614	23	7692	12	6382	13	2426	20	1412	25	1914	21	958	29	1304	27	5569	12	73286	3
	maple	2301	10	815	21	196	29	408	22	2047	15	620	24	1349	17	1541	16	974	20	376	27	716	24	322	27	406	43	347	25	12416	5
	ash	1651	17	1063	22	1859	22	1841	29	3602	12	489	36	924	31	2014	19	662	31	465	36	407	33	437	31	244	37	748	25	16407	6
	oak	1187	23	707	33	1165	28	911	22	2003	22	43	63	86	55	60	55	51	57	111	48	1	100	205	37	130	71	957	20	7615	9
	sweet chestnut	0	.	0	.	2	100	0	.	6	100	0	.	0	.	0	.	7	100	0	.	0	.	194	59	18	100	5342	12	5569	12
	other broadleaves	1027	20	602	24	1108	33	925	26	1781	28	208	28	413	20	680	27	1173	32	333	31	345	30	987	18	986	21	4460	11	15029	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
	total	55484	3	15493	9	16449	10	29982	7	40712	5	17056	10	42099	6	35228	7	21093	9	11269	13	11345	13	30572	7	43639	5	37041	4	407462	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. 1218008. Birmensdorf, Swiss Federal Research Institute WSL

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