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
number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: economic region
unit: 1000 n
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 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	5873	13	1289	29	1794	33	4231	18	5016	15	2750	19	6787	14	3598	18	3963	15	1900	23	1926	22	6657	13	12148	10	3155	16	61087	4
	fir	6136	10	502	36	639	29	2598	17	1515	28	1138	25	4312	13	1352	24	564	27	846	30	236	40	536	28	334	43	465	33	21173	6
	pine	54	58	127	52	0	0	251	88	76	81	0	.	278	79	0	.	75	76	70	61	0	.	2424	28	1819	28	65	100	5237	17
	larch	0	0	0	.	349	95	33	100	89	64	88	100	87	100	102	100	251	59	0	.	35	71	2286	17	791	22	1493	25	5604	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	593	32	829	27	32	100	1455	20
	other conifers	367	41	103	62	20	100	126	44	430	45	0	.	33	71	113	41	0	.	87	100	49	73	139	56	532	41	0	.	1999	18
	beech	6921	10	2324	16	1392	19	2821	14	3371	11	809	35	3581	14	2542	18	1231	26	647	28	1387	30	1213	31	567	30	6054	15	34862	5
	maple	2501	17	592	24	435	37	554	35	1611	20	568	25	944	32	845	26	1430	24	201	48	338	39	730	28	405	36	591	33	11744	7
	ash	839	21	887	35	714	29	492	24	1225	23	216	49	617	27	599	27	359	42	415	42	138	50	466	34	492	41	1330	27	8788	9
	oak	989	43	42	72	456	51	77	51	154	43	21	100	15	100	42	70	123	87	18	100	0	.	680	34	15	100	1043	37	3676	18
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	5688	18	5705	18
	other broadleaves	2156	15	538	26	637	32	1121	22	1941	19	502	27	1019	21	978	23	1470	25	571	33	814	36	3298	17	3180	25	8862	14	27088	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	25836	5	6403	13	6452	15	12306	10	15428	9	6091	14	17673	8	10171	11	9466	11	4755	18	4923	17	19022	8	21112	8	28778	7	188417	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3783	13	784	35	1165	25	1914	21	3418	17	2298	20	4233	13	3231	16	2346	16	1133	23	1468	25	4718	14	7205	10	2480	14	40176	4
	fir	3110	13	345	35	374	42	1083	19	642	26	766	33	1846	14	770	25	515	26	457	30	98	46	380	32	194	40	356	38	10936	6
	pine	401	39	224	63	68	50	118	55	137	43	0	.	226	75	0	.	35	100	167	54	0	.	1769	21	998	27	84	65	4226	13
	larch	0	0	17	100	118	87	17	100	88	59	0	.	0	.	51	100	91	67	0	.	71	79	1420	18	726	25	1653	18	4251	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	563	28	759	27	16	100	1338	19
	other conifers	34	71	86	53	34	100	119	47	103	58	0	.	17	100	18	100	17	100	0	.	0	.	17	100	53	58	0	.	499	22
	beech	5082	10	1364	18	1174	18	1636	14	2354	14	657	28	2702	14	1743	17	632	29	662	32	658	27	685	32	469	34	3706	17	23526	5
	maple	1186	14	350	26	103	53	355	29	1387	21	549	27	790	22	857	23	405	30	156	49	484	30	232	35	179	37	229	38	7262	7
	ash	498	26	298	33	424	26	244	29	668	24	341	35	466	35	948	25	159	44	250	37	160	44	278	37	261	59	494	35	5489	9
	oak	733	34	208	42	496	33	141	44	140	35	40	71	48	71	0	0	54	58	18	100	0	.	234	42	15	100	600	24	2727	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	15	100	3902	15	3954	15
	other broadleaves	519	23	234	33	205	35	500	33	656	21	176	47	303	32	517	43	420	30	81	60	196	40	859	21	862	32	3424	12	8952	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
	total	15346	6	3910	13	4161	13	6127	10	9593	8	4826	16	10630	8	8135	10	4674	12	2924	18	3136	17	11193	9	11737	8	16945	6	113338	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2894	12	264	27	702	25	1075	19	2126	14	1525	17	2966	11	2441	13	2104	14	995	22	788	25	2719	12	5218	9	1989	16	27807	4
	fir	2087	12	172	46	220	36	794	19	491	22	419	26	1328	14	686	19	226	35	216	42	129	41	185	41	84	41	254	37	7292	6
	pine	503	27	179	31	130	56	108	48	255	27	0	.	156	62	13	71	25	77	86	65	0	.	748	20	753	26	43	71	2999	11
	larch	24	77	38	61	41	85	21	74	106	43	0	.	0	.	14	71	49	71	0	.	20	76	1303	18	845	19	1068	19	3530	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	219	33	448	26	0	.	668	20
	other conifers	0	.	45	64	34	100	24	77	23	77	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	142	38
	beech	3818	9	1223	16	631	20	1112	18	1293	14	451	47	1326	17	1337	17	492	25	233	33	284	29	186	37	367	27	1174	17	13926	5
	maple	679	18	121	36	29	62	123	32	526	19	84	47	339	25	439	24	332	32	96	38	182	37	45	54	66	48	74	43	3134	8
	ash	365	23	175	31	417	34	307	30	636	19	200	55	223	49	401	26	199	42	76	59	99	43	241	88	41	68	90	38	3471	11
	oak	253	28	151	48	208	32	133	42	185	29	29	77	0	.	0	.	9	100	23	74	0	.	32	81	7	100	271	29	1301	13
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	29	73	0	.	1353	19	1388	18
	other broadleaves	275	28	97	46	152	54	188	29	360	27	6	100	42	44	184	33	120	45	62	48	43	50	92	46	92	40	1128	16	2840	9
	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	10897	5	2466	12	2566	13	3886	10	6008	8	2714	14	6381	8	5515	9	3556	11	1786	16	1561	17	5797	9	7921	7	7445	7	68500	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1951	9	277	26	528	21	865	18	1092	12	1229	15	2061	11	1556	13	1507	14	658	23	573	20	1589	11	3368	9	1236	14	18491	3
	fir	1702	10	139	36	174	31	601	18	455	18	269	25	778	14	564	17	195	29	210	38	93	35	199	36	90	32	132	31	5601	5
	pine	261	27	125	34	101	42	61	37	243	23	0	.	134	67	9	100	7	100	29	61	0	.	161	32	335	24	28	62	1493	12
	larch	0	.	7	100	20	73	34	60	47	55	0	.	7	100	0	.	36	68	0	.	0	0	1032	18	789	15	563	16	2535	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	105	44	205	23	0	.	320	21
	other conifers	0	.	28	71	21	100	47	62	13	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	117	36
	beech	2058	8	820	16	543	19	855	13	872	12	155	37	807	17	890	18	314	27	137	34	248	26	57	62	205	33	540	19	8502	4
	maple	271	18	91	36	19	72	42	47	118	29	51	50	74	37	186	24	75	35	32	44	89	33	28	81	15	100	7	100	1097	9
	ash	217	27	50	37	160	30	202	37	449	17	22	74	61	39	203	29	56	44	31	82	94	41	27	71	7	100	62	48	1642	9
	oak	154	26	96	46	152	37	83	33	168	26	0	.	36	62	9	100	8	100	7	100	0	.	21	75	26	77	138	39	897	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	6	100	517	19	538	19
	other broadleaves	100	55	42	47	68	42	65	59	186	26	0	.	38	46	37	64	25	61	6	100	31	66	53	43	29	49	281	21	962	12
	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	6716	5	1675	12	1785	12	2856	9	3642	7	1726	14	3994	8	3455	9	2239	12	1110	18	1127	15	3281	9	5074	7	3515	7	42194	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1176	10	195	29	267	31	590	16	797	14	685	17	1216	12	1206	13	849	15	376	21	369	21	955	13	1675	10	755	16	11112	4
	fir	893	12	42	41	202	29	517	16	301	21	193	31	616	14	435	19	194	25	47	46	62	52	78	44	93	34	163	30	3835	6
	pine	97	30	7	100	41	53	74	35	136	25	0	.	36	62	17	100	0	.	7	100	0	.	86	45	156	28	14	100	671	13
	larch	7	100	7	100	14	71	7	100	63	42	0	.	0	.	21	74	36	66	0	.	12	100	757	15	609	16	397	15	1930	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	93	32	0	.	114	28
	other conifers	0	.	21	100	0	.	20	74	27	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	76	45
	beech	833	12	378	22	212	27	448	17	711	13	78	37	396	21	306	22	149	29	70	41	150	33	33	71	79	36	245	21	4087	6
	maple	77	30	28	49	14	71	0	.	76	38	29	50	18	71	47	41	60	78	12	70	17	71	0	.	35	51	8	100	421	17
	ash	60	35	43	41	99	31	146	46	223	21	0	.	36	66	37	45	14	71	12	100	29	59	7	100	0	.	0	.	708	14
	oak	35	53	23	72	28	61	109	39	118	28	0	.	0	.	0	.	0	.	6	100	0	.	8	100	20	76	18	72	364	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	243	26	249	25
	other broadleaves	7	100	29	61	54	58	14	71	8	100	0	.	7	100	18	71	70	66	6	100	12	70	23	57	14	71	39	45	301	22
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	3186	6	772	15	932	14	1924	10	2460	8	985	14	2324	9	2086	9	1372	13	538	18	651	17	1969	10	2781	8	1889	8	23869	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	466	15	84	37	89	33	248	23	210	22	300	24	493	14	548	17	300	20	195	24	179	27	317	21	599	13	358	18	4386	5
	fir	343	19	56	35	70	48	277	20	241	22	137	44	324	19	100	26	129	31	65	43	6	100	15	100	40	41	22	58	1825	8
	pine	15	71	0	.	14	71	7	100	20	58	0	.	18	72	7	100	0	.	0	.	0	.	0	.	57	39	0	.	138	24
	larch	0	.	0	.	0	0	7	100	20	58	0	.	0	.	0	.	14	71	0	.	6	100	318	23	227	22	187	23	779	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	35	45	0	.	43	41
	other conifers	0	.	0	.	8	100	40	52	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	43
	beech	128	28	156	27	66	34	178	25	188	20	29	50	132	31	85	33	48	44	14	71	39	56	6	100	21	58	121	31	1212	9
	maple	0	.	7	100	0	.	0	.	28	50	0	.	6	100	18	100	17	72	0	.	7	100	0	.	0	.	0	.	83	34
	ash	30	50	23	58	35	83	7	100	40	47	7	100	15	71	14	71	0	.	6	100	0	.	0	.	0	.	10	100	187	24
	oak	12	100	16	71	29	50	14	71	45	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	131	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	68	33	76	31
	other broadleaves	0	.	23	58	14	71	0	.	7	100	0	.	0	.	18	100	12	70	0	0	6	100	0	.	0	.	8	100	88	33
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	994	11	363	18	325	20	776	12	806	12	474	20	988	11	791	14	521	16	280	21	244	23	670	16	980	11	790	11	9002	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	146	25	14	70	43	59	90	29	129	28	70	36	240	19	204	26	140	28	127	29	124	34	101	33	222	22	182	24	1830	8
	fir	131	26	7	100	33	60	84	33	20	58	21	59	148	23	107	31	26	50	33	51	19	73	15	71	35	45	30	49	709	11
	pine	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	28	50
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	16	71	0	.	0	.	7	100	195	24	100	28	71	34	396	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	44	14	71	0	.	50	37
	other conifers	0	.	7	100	8	100	13	71	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	51
	beech	36	52	28	50	15	71	45	56	86	31	7	100	34	52	32	59	12	70	7	100	9	100	6	100	18	74	44	41	380	15
	maple	0	.	9	100	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	23	58
	ash	7	100	7	100	9	100	10	100	15	71	0	.	0	.	18	72	7	100	0	.	0	.	0	.	6	100	0	.	79	32
	oak	0	.	18	70	0	.	0	.	28	50	0	.	0	.	8	100	0	.	8	100	0	.	0	.	7	100	0	.	69	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	37	76	37
	other broadleaves	0	.	0	.	21	58	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	334	17	90	29	128	29	248	20	299	18	104	31	423	15	384	18	191	23	176	25	167	29	352	17	417	15	410	15	3722	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	29	63	7	100	7	100	20	58	53	39	35	45	39	41	66	67	77	40	7	100	22	58	61	53	111	26	63	35	596	14
	fir	51	38	0	.	7	100	20	58	27	61	0	.	36	53	14	71	8	100	0	.	13	71	7	100	7	100	8	100	197	20
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	42	42	47	29	61	122	28
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	15	71	0	.	22	58
	other conifers	0	.	0	.	8	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71
	beech	0	.	0	0	0	.	0	.	49	42	0	.	7	100	9	100	7	100	7	100	0	.	7	100	0	.	22	58	108	27
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	10	100	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	24	58
	oak	0	.	0	.	9	100	7	100	27	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	45	46	53	42
	other broadleaves	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	80	33	14	70	30	50	57	35	170	23	35	45	89	30	95	49	91	36	14	71	41	41	141	29	183	21	167	23	1207	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	0	.	7	100	0	.	0	.	7	100	14	71	7	100	0	.	9	100	22	58	7	100	15	72	7	100	103	27
	fir	22	58	0	.	0	.	7	100	7	100	0	.	8	100	0	.	0	.	7	100	0	.	0	.	0	.	18	72	68	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	.	22	58	8	100	0	.	30	50
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	oak	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	61	30	61
	other broadleaves	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	50	0	.	14	71	30	81	30	64	7	100	23	58	14	71	0	.	16	71	22	58	36	45	23	58	54	42	298	18
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	0	.	7	100	7	100	0	.	0	.	7	100	7	100	7	100	0	.	14	71	7	100	23	58	19	72	105	27
	fir	0	.	0	.	0	.	0	.	14	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58
	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	.	7	100	14	100	22	74
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	14	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	.	51	49	51	49
	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	7	100	0	.	7	100	7	100	14	71	0	.	14	71	7	100	7	100	0	.	14	71	14	71	37	45	83	38	213	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16333	8	2913	21	4608	20	9040	13	12840	10	8900	14	18055	10	12864	11	11293	12	5401	18	5485	18	17131	10	30584	7	10246	11	165693	3
	fir	14475	8	1262	29	1718	23	5981	12	3713	17	2944	23	9403	10	4027	15	1856	21	1881	25	656	27	1416	25	878	27	1447	26	51657	4
	pine	1337	24	662	30	353	36	625	47	875	18	0	.	846	60	46	48	142	58	359	51	0	.	5188	20	4139	20	234	66	14807	10
	larch	38	55	70	44	543	66	119	44	413	29	88	100	94	93	204	76	477	41	0	.	151	62	7382	11	4146	13	5475	14	19199	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1566	24	2406	20	49	100	4030	15
	other conifers	401	38	290	44	133	71	390	29	624	37	0	.	57	52	131	37	17	100	87	100	65	77	156	53	585	39	16	100	2952	15
	beech	18876	7	6294	12	4033	15	7095	10	8930	8	2186	27	8987	11	6950	13	2885	21	1778	26	2776	22	2194	29	1726	24	11906	13	86616	3
	maple	4715	11	1197	18	600	29	1074	23	3745	15	1287	22	2171	20	2392	18	2320	21	497	35	1130	25	1035	27	699	29	909	28	23771	5
	ash	2016	17	1482	25	1865	21	1418	19	3264	14	786	32	1418	25	2229	19	794	31	790	32	519	34	1019	38	807	45	1987	26	20396	6
	oak	2176	29	554	31	1378	31	564	24	882	17	89	57	99	52	58	54	194	68	81	59	0	.	975	34	91	72	2085	25	9224	11
	sweet chestnut	0	.	0	.	17	100	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	89	66	28	100	11972	13	12120	13
	other broadleaves	3057	14	969	20	1151	25	1911	21	3158	16	684	26	1410	19	1751	25	2123	22	727	31	1103	32	4325	16	4177	25	13749	11	40295	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	63424	4	15692	10	16399	11	28217	7	38450	6	16963	12	42540	6	30653	8	22118	9	11599	14	11887	14	42475	7	50265	6	60076	4	450759	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. 1327002. Birmensdorf, Swiss Federal Research Institute WSL

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