Result query system

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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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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. “protective forest [2022]”) – is assigned to all measurement cycles.

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
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
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	774	17	210	30	294	48	841	21	727	18	549	22	1113	17	421	21	645	15	303	22	296	26	1052	14	2081	11	616	16	9920	5
	fir	880	12	39	32	134	30	489	20	188	32	72	94	660	17	222	25	88	36	130	42	23	44	93	36	18	39	67	36	3103	7
	pine	12	68	-21	130	2	100	14	61	12	100	0	.	28	75	0	.	8	78	16	71	-18	100	224	33	249	26	14	65	540	20
	larch	0	.	7	88	63	89	5	100	54	54	15	73	10	89	4	100	25	50	0	.	12	68	481	22	182	20	283	23	1139	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	56	145	29	3	85	269	30
	other conifers	46	42	11	63	0	.	21	58	55	46	0	.	3	61	20	47	19	66	12	71	8	69	9	56	36	33	1	100	244	17
	beech	1038	11	409	18	250	29	438	17	539	15	114	39	508	19	317	24	208	39	81	33	200	39	115	32	58	31	957	14	5231	6
	maple	363	24	140	30	135	41	164	26	216	26	70	30	184	34	136	28	194	23	34	50	67	38	175	25	53	34	177	31	2108	8
	ash	126	24	113	32	89	43	84	52	201	33	54	41	55	46	72	39	67	53	108	55	42	67	120	30	64	34	216	24	1412	10
	oak	100	46	35	79	70	50	6	72	19	42	1	100	0	0	0	.	14	84	2	100	2	100	61	35	4	100	95	42	410	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	5	100	668	20	685	20
	other broadleaves	259	18	83	27	102	26	291	24	322	19	131	29	202	23	139	24	220	24	117	37	102	39	477	16	452	22	1202	12	4100	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
	total	3598	7	1027	14	1139	18	2353	12	2334	10	1006	17	2764	10	1331	12	1488	12	802	19	734	19	2940	9	3346	8	4300	6	29162	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1500	13	259	31	609	29	1218	20	1671	17	1207	20	2073	12	1622	15	1228	16	763	20	599	31	2387	15	4313	10	1347	15	20796	4
	fir	1913	13	163	37	156	44	945	22	422	27	330	44	1253	15	546	30	237	31	223	33	35	43	167	36	33	49	134	35	6556	7
	pine	198	47	107	60	14	100	48	57	54	46	0	.	101	73	0	.	4	100	68	63	16	72	651	24	462	25	5	100	1727	14
	larch	0	.	14	100	83	72	9	100	73	48	0	.	9	100	34	100	68	64	0	.	31	87	866	17	545	26	975	18	2706	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	189	29	390	23	5	100	585	18
	other conifers	24	60	35	61	6	100	5	100	66	39	0	.	8	100	16	71	30	74	0	.	0	0	4	100	11	59	0	.	205	22
	beech	2277	11	653	20	500	26	698	18	1054	16	373	28	1334	16	777	19	372	31	367	28	235	38	279	38	200	34	1777	17	10896	5
	maple	643	14	274	25	103	37	131	34	784	21	271	25	360	25	511	21	204	36	139	38	218	42	181	29	89	41	91	39	4000	7
	ash	265	33	141	34	167	38	115	37	204	29	223	44	195	48	326	32	74	50	71	42	69	44	158	36	114	74	317	33	2440	11
	oak	287	39	53	50	247	39	93	43	50	48	12	100	33	62	7	100	37	60	0	0	0	.	101	39	6	100	306	24	1233	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	71	5	100	1417	16	1437	15
	other broadleaves	283	25	107	31	145	37	238	34	409	17	111	35	145	28	284	36	174	26	102	44	75	32	445	23	393	27	1722	15	4635	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
	total	7391	6	1806	12	2031	15	3499	12	4786	9	2528	15	5510	8	4123	11	2429	11	1733	16	1279	21	5444	9	6561	8	8097	6	57217	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2840	12	367	38	624	27	1254	20	2112	15	2232	16	3152	12	3254	13	2332	14	1080	22	856	27	3098	12	5657	9	2286	19	31144	4
	fir	2327	13	222	41	322	37	1094	20	418	35	606	28	1631	17	865	22	253	32	317	37	183	47	315	35	133	36	380	34	9068	7
	pine	452	32	209	31	109	52	82	68	170	31	0	.	152	53	8	100	36	83	135	50	31	100	556	22	550	27	44	101	2535	11
	larch	0	.	5	162	55	84	0	.	61	57	10	100	0	.	0	.	72	57	0	.	83	77	1533	20	1028	15	1333	17	4180	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	207	31	450	23	0	.	670	18
	other conifers	0	.	18	71	8	100	63	49	50	84	0	.	0	.	0	.	5	100	0	.	-24	100	0	0	0	.	0	.	120	50
	beech	3922	10	1171	18	841	22	1237	17	1600	15	419	30	1480	15	1597	16	568	24	455	30	287	33	250	34	236	29	1491	15	15553	5
	maple	853	17	190	31	52	43	293	27	512	21	104	42	491	24	577	23	313	33	136	53	266	30	49	47	108	43	170	43	4115	8
	ash	275	24	184	35	485	32	278	24	643	21	242	54	288	61	326	28	215	39	119	69	86	45	146	43	65	46	106	38	3459	10
	oak	235	29	181	42	149	51	78	54	139	34	26	72	0	.	16	73	25	75	9	100	0	.	74	41	6	100	270	26	1209	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	60	0	.	906	19	958	19
	other broadleaves	184	34	78	44	124	48	189	30	333	25	19	71	64	57	130	32	44	89	48	49	35	52	150	42	94	34	1271	17	2764	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
	total	11089	6	2626	13	2768	14	4568	10	6039	8	3657	13	7258	8	6774	9	3876	11	2300	16	1803	18	6431	9	8326	7	8258	8	75773	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3551	10	438	30	964	27	1598	20	2471	13	2714	14	4106	11	3313	13	3386	14	1418	25	1267	22	3323	12	7104	9	2774	16	38429	3
	fir	3276	11	292	36	358	34	1299	17	1086	20	503	27	1811	14	1239	16	424	33	565	34	278	32	344	34	186	35	306	31	11970	5
	pine	364	26	175	38	112	47	166	37	482	26	0	.	243	62	8	100	24	59	64	54	17	100	351	26	663	25	64	58	2733	11
	larch	12	100	30	71	73	61	39	74	107	55	0	.	0	.	13	100	70	84	0	.	29	100	1972	16	1666	15	1253	14	5266	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	172	31	323	24	0	.	495	19
	other conifers	0	.	57	58	50	100	49	51	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	192	36
	beech	4026	9	1685	17	1103	21	1624	14	1715	14	377	35	1849	18	2149	16	798	27	273	31	662	26	92	68	362	36	1064	16	17777	5
	maple	674	16	157	39	30	75	79	43	273	27	100	38	208	31	301	26	102	51	90	44	182	32	84	73	65	54	55	48	2401	9
	ash	480	25	156	38	402	34	352	36	635	19	87	59	191	37	390	34	71	55	113	61	67	45	54	85	0	0	109	61	3106	10
	oak	332	32	230	42	243	36	178	34	340	32	24	100	93	66	12	100	8	100	37	74	0	.	20	76	22	100	222	32	1762	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	10	100	5	100	850	23	871	22
	other broadleaves	276	33	98	43	82	47	69	61	386	26	11	100	70	43	0	.	100	56	15	100	47	60	66	46	57	73	732	20	2009	11
	indeterminable	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	12991	5	3318	13	3417	14	5454	10	7515	8	3816	12	8571	8	7426	9	4989	12	2577	18	2549	15	6488	9	10454	7	7445	7	87010	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3392	11	479	32	914	29	1218	19	1753	17	2357	16	3803	12	4177	12	2747	15	1141	21	1253	21	2792	13	5771	10	2530	18	34328	4
	fir	3239	12	141	45	491	34	1866	18	1066	21	958	23	2674	14	1817	21	560	27	350	38	310	43	457	39	323	33	619	28	14870	6
	pine	292	29	88	50	163	63	165	40	421	30	0	.	121	55	54	100	9	100	55	77	12	100	147	42	529	27	27	100	2082	13
	larch	22	100	28	100	22	100	44	71	224	37	0	.	0	.	58	100	105	53	0	.	45	100	2124	15	1715	16	1246	15	5630	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	110	48	247	27	0	.	357	24
	other conifers	0	.	144	68	0	.	171	66	101	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	445	39
	beech	2745	12	1281	23	901	25	1993	15	2493	12	373	36	1711	19	1046	22	497	34	271	37	501	33	101	65	176	47	715	21	14803	5
	maple	322	27	103	45	25	71	65	51	216	36	115	43	125	48	271	34	75	58	43	73	54	50	0	.	118	42	0	.	1530	13
	ash	85	51	165	47	350	38	434	40	832	22	0	.	89	76	194	44	61	52	57	75	113	47	15	100	0	.	88	64	2483	13
	oak	188	39	96	60	146	59	373	32	521	24	0	.	0	.	0	.	0	.	20	100	0	.	23	100	70	76	100	46	1538	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	8	100	542	27	563	26
	other broadleaves	37	73	97	68	233	55	117	54	122	44	0	.	26	100	92	51	161	65	22	100	31	71	54	59	43	71	106	42	1141	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	10321	6	2621	15	3245	15	6445	10	7748	8	3803	13	8549	8	7709	9	4214	12	1960	17	2317	16	5835	10	8999	8	6003	10	79770	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2500	15	449	30	448	34	1071	24	1244	19	1650	21	2287	17	3079	16	1814	17	897	24	779	28	1522	19	3337	13	1473	17	22552	5
	fir	1882	19	102	58	342	35	1296	22	965	25	556	44	1849	18	997	23	611	33	140	52	75	71	101	75	294	40	140	50	9348	8
	pine	47	71	0	.	63	71	115	50	103	75	0	.	82	71	41	100	0	.	0	.	0	.	17	100	221	35	14	100	703	22
	larch	0	.	0	.	35	100	40	100	94	58	0	.	0	.	0	.	23	100	0	.	27	100	1556	19	1356	20	747	21	3879	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	71	68	59	0	.	107	46
	other conifers	0	.	75	100	0	.	241	57	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	382	43
	beech	951	22	704	27	452	28	986	21	1581	19	79	71	628	33	420	32	146	51	192	54	244	61	67	74	169	42	505	29	7124	8
	maple	59	71	0	.	30	100	0	.	205	47	0	.	34	100	101	86	95	52	30	100	0	.	0	.	0	.	0	.	555	28
	ash	158	52	81	70	60	71	196	45	313	33	0	.	32	100	154	59	73	71	0	.	28	100	0	.	0	.	55	100	1149	19
	oak	87	74	34	100	167	50	132	50	269	54	0	.	0	0	0	.	0	.	0	.	0	.	0	.	33	100	35	100	757	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	179	33	195	31
	other broadleaves	0	.	73	100	139	51	22	100	32	100	0	.	0	0	25	100	175	81	32	100	35	100	26	100	74	78	47	73	680	29
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	5685	10	1517	18	1735	17	4097	13	4836	11	2285	19	4912	12	4818	13	2939	15	1290	22	1189	23	3345	13	5553	10	3230	10	47432	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1041	23	329	47	232	47	566	31	721	31	519	32	1330	19	1367	25	1193	21	764	32	863	30	1023	28	1641	18	1101	28	12690	7
	fir	1157	20	53	100	250	48	572	30	766	27	211	51	1055	24	687	27	550	35	371	61	96	71	185	61	271	46	197	50	6419	10
	pine	41	100	0	.	0	.	90	71	72	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	71	0	.	266	38
	larch	41	100	0	.	0	.	0	.	0	.	0	.	0	.	84	71	62	71	0	.	51	100	796	26	569	26	438	28	2042	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	88	50	65	58	0	.	152	38
	other conifers	0	.	0	.	0	.	131	58	150	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	281	60
	beech	202	56	233	45	129	59	573	36	714	28	107	59	406	39	228	72	137	58	41	100	41	100	30	100	119	70	171	41	3131	14
	maple	0	.	72	71	0	.	0	.	35	100	19	100	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	153	46
	ash	79	71	0	.	0	.	58	100	37	100	43	100	0	.	54	100	0	.	38	100	0	.	0	.	33	100	30	100	372	34
	oak	0	.	115	71	67	75	0	.	271	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	463	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	168	38	186	36
	other broadleaves	0	.	75	71	101	59	0	.	42	100	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	28	100	283	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2561	14	877	27	780	26	1989	18	2806	15	899	26	2792	14	2419	18	2007	17	1214	28	1051	28	2139	18	2771	14	2133	16	26439	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	351	48	0	.	158	71	152	58	458	39	259	52	391	39	539	33	278	42	135	62	218	41	282	43	921	27	446	39	4589	12
	fir	460	39	0	.	253	53	378	43	426	43	63	100	611	37	329	51	64	100	0	.	134	70	0	.	154	72	50	100	2922	16
	pine	0	.	0	.	0	.	0	.	49	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	453	39	306	44	478	37	1237	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	75	70	71	0	.	139	51
	other conifers	0	.	61	100	0	.	123	71	114	100	0	.	81	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	380	46
	beech	48	100	0	.	0	.	0	.	171	58	0	.	44	100	67	100	0	.	96	71	0	.	0	0	0	.	171	45	597	29
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	27	100
	ash	0	.	67	100	0	.	0	.	76	100	0	.	0	.	141	71	0	.	0	.	0	.	0	.	0	.	0	.	284	50
	oak	0	.	0	.	0	.	0	.	218	51	0	.	0	.	45	100	0	.	38	100	0	.	0	.	0	.	0	.	301	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	153	46	153	46
	other broadleaves	0	.	0	.	71	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	859	29	129	71	482	39	654	31	1511	22	323	46	1126	25	1121	24	342	39	270	43	379	35	804	27	1451	21	1299	23	10749	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	128	71	0	.	0	.	55	100	117	71	181	58	48	100	0	.	299	45	0	.	214	50	125	61	320	41	217	58	1702	19
	fir	216	58	0	.	0	.	68	100	58	100	0	.	62	100	60	100	0	.	68	100	154	71	0	.	0	.	204	58	890	28
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	100	0	.	58	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	229	45	122	72	38	100	389	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	71	0	.	0	.	57	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	113	100	284	53	0	.	0	.	56	100	0	.	0	.	0	.	0	.	0	.	0	.	453	43
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	25	100
	ash	0	.	0	.	0	.	67	100	52	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	71
	oak	0	.	0	.	0	.	44	100	45	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	145	59	174	52
	other broadleaves	0	.	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	344	45	0	.	55	100	347	47	556	35	181	58	110	71	115	71	299	45	68	100	392	40	440	31	500	34	603	32	4010	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	81	100	0	.	71	100	84	100	0	.	36	100	131	71	180	58	49	100	0	.	90	71	66	100	310	50	184	72	1283	24
	fir	286	59	0	.	0	.	0	.	0	.	0	.	133	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	419	51
	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	.	108	71	129	100	237	63
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	36	100	0	.	75	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	.	196	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	196	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	414	55	414	55
	other broadleaves	0	.	0	.	63	100	66	100	94	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	223	59
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	367	51	0	.	134	71	150	71	290	75	36	100	264	61	180	58	49	100	0	.	90	71	106	73	454	39	727	40	2847	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16160	7	2531	19	4314	19	8056	12	11274	10	11704	12	18435	8	17952	10	13972	11	6502	17	6434	17	15670	9	31455	7	12974	12	177433	2
	fir	15635	8	1012	29	2305	22	8006	12	5395	14	3299	21	11738	10	6763	13	2787	21	2164	28	1289	30	1662	26	1410	24	2097	24	65564	4
	pine	1405	21	559	27	463	43	680	27	1362	19	0	.	727	45	112	62	81	49	338	45	58	60	1947	19	2794	17	168	48	10693	8
	larch	75	64	83	46	331	39	137	51	614	26	25	81	19	94	192	48	424	36	0	.	278	52	10010	11	7597	10	6921	11	26705	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	1091	21	1794	18	9	94	2906	13
	other conifers	71	34	403	66	63	91	805	41	585	61	0	.	92	88	36	41	54	68	12	71	-16	122	13	58	47	32	83	98	2249	25
	beech	15210	6	6135	12	4175	17	7661	11	10151	9	1843	23	7960	13	6656	12	2726	20	1777	23	2170	21	932	30	1320	27	6850	11	75566	3
	maple	2914	10	936	19	375	25	732	18	2240	14	679	22	1402	17	1898	15	1010	17	472	27	839	24	490	26	432	30	494	25	14914	5
	ash	1468	18	907	24	1552	25	1583	25	2993	15	649	38	851	38	1657	21	562	30	507	38	405	32	493	30	277	41	921	30	14824	7
	oak	1229	23	745	32	1090	28	904	21	2070	24	62	67	126	58	81	61	85	53	106	61	2	100	279	33	151	72	1028	20	7958	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	165	51	23	82	5442	13	5635	12
	other broadleaves	1040	19	611	26	1115	28	992	19	1739	16	271	25	507	18	669	23	914	36	336	32	325	29	1219	18	1113	21	5109	11	15961	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
	total	55207	4	13921	10	15785	11	29555	7	38424	5	18533	10	41857	6	36016	7	22632	9	12214	13	11784	13	33972	6	48414	5	42096	4	420409	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. 2308771. Birmensdorf, Swiss Federal Research Institute WSL
https://doi.org/10.21258/1942261