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	23-35calculated 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. “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 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 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	5943	13	1289	29	1892	32	4231	18	5016	15	3704	19	7108	14	4111	16	4709	14	2466	20	2238	20	7244	12	14668	9	4174	14	68792	4
	fir	6231	9	502	36	639	29	2598	17	1515	28	1138	25	4312	13	1352	24	564	27	929	28	236	40	536	28	334	43	465	33	21351	6
	pine	54	58	127	52	0	0	251	88	76	81	0	.	278	79	0	.	75	76	216	63	106	100	2462	27	2245	25	65	100	5952	16
	larch	0	0	0	.	349	95	33	100	89	64	88	100	87	100	102	100	251	59	0	.	141	59	2887	15	1490	20	2978	17	8495	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	611	31	1526	23	32	100	2170	18
	other conifers	367	41	103	62	20	100	126	44	430	45	0	.	33	71	113	41	138	88	87	100	49	73	139	56	659	35	0	.	2264	17
	beech	6921	10	2447	16	1392	19	2821	14	3501	11	827	34	3581	14	2542	18	1266	25	736	26	1441	29	1284	29	567	30	6354	14	35681	5
	maple	2520	17	628	23	435	37	554	35	1666	19	585	25	1122	31	980	24	1467	23	264	44	459	39	1079	24	461	34	1064	30	13285	7
	ash	839	21	910	34	730	28	492	24	1334	22	216	49	617	27	744	29	489	37	510	36	330	44	666	28	547	37	1434	25	9860	8
	oak	989	43	42	72	456	51	77	51	154	43	21	100	15	100	42	70	123	87	18	100	17	100	715	33	15	100	1096	35	3782	18
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	100	0	.	5834	18	5998	17
	other broadleaves	2295	15	582	25	692	30	1155	22	1958	18	970	32	1249	20	1234	25	1854	23	1280	43	902	34	3684	16	4118	21	10580	12	32553	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	26159	5	6629	13	6621	15	12340	10	15739	8	7550	14	18402	8	11221	11	10938	11	6506	16	5919	16	21454	8	26630	7	34076	6	210183	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3855	12	784	35	1262	24	1914	21	3436	16	2738	19	4726	13	3617	15	2681	15	1428	20	1521	24	5179	13	8367	9	2936	13	44446	4
	fir	3130	13	345	35	374	42	1083	19	642	26	766	33	1879	13	770	25	515	26	494	28	98	46	380	32	194	40	374	36	11044	6
	pine	401	39	224	63	68	50	118	55	137	43	0	.	226	75	0	.	53	75	167	54	88	82	1793	21	998	27	84	65	4357	13
	larch	0	0	17	100	118	87	17	100	88	59	0	.	0	.	51	100	108	58	0	.	88	66	1821	17	1133	22	2412	15	5853	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	634	26	1034	25	16	100	1684	18
	other conifers	34	71	86	53	34	100	119	47	103	58	0	.	17	100	18	100	87	82	0	.	0	0	17	100	53	58	0	.	569	23
	beech	5099	10	1458	18	1174	18	1636	14	2373	14	657	28	2781	14	1743	17	650	28	741	30	731	26	685	32	522	32	3866	16	24116	5
	maple	1205	14	467	26	103	53	355	29	1387	21	549	27	938	22	874	23	450	28	238	42	521	29	249	33	291	45	245	37	7873	7
	ash	498	26	298	33	424	26	244	29	688	23	341	35	466	35	1135	27	222	42	281	35	178	41	427	34	316	50	601	30	6117	9
	oak	733	34	208	42	496	33	175	40	140	35	40	71	92	61	0	0	54	58	18	100	0	.	291	36	15	100	688	23	2949	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	73	15	100	4156	15	4229	15
	other broadleaves	536	22	270	32	223	33	550	31	674	21	193	44	303	32	729	35	451	29	153	42	214	38	1013	21	952	30	4274	13	10535	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	15491	6	4158	13	4277	13	6211	10	9667	8	5283	15	11428	8	8937	10	5270	11	3519	16	3439	16	12547	8	13893	7	19653	5	123772	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2970	11	264	27	702	25	1075	19	2126	14	1704	16	3254	11	2750	12	2463	14	1120	21	831	24	2922	11	5576	8	2128	15	29885	3
	fir	2094	12	172	46	220	36	794	19	491	22	419	26	1328	14	699	18	226	35	250	37	129	41	185	41	84	41	272	35	7364	6
	pine	503	27	179	31	130	56	108	48	255	27	0	.	156	62	13	71	31	64	94	60	70	100	748	20	753	26	43	71	3085	11
	larch	24	77	38	61	41	85	21	74	106	43	0	.	0	.	14	71	49	71	0	.	61	71	1467	16	1058	16	1374	16	4253	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	250	32	540	24	0	.	790	19
	other conifers	0	.	45	64	34	100	24	77	23	77	0	.	0	.	0	.	7	100	0	.	15	100	0	.	0	.	0	.	149	36
	beech	3818	9	1244	16	631	20	1112	18	1311	13	451	47	1442	17	1366	17	499	25	233	33	318	28	186	37	367	27	1174	17	14151	5
	maple	686	18	121	36	29	62	123	32	526	19	84	47	339	25	501	23	344	31	96	38	209	35	79	43	111	50	74	43	3324	8
	ash	365	23	226	29	417	34	307	30	644	19	200	55	223	49	401	26	238	38	76	59	99	43	278	77	41	68	90	38	3606	11
	oak	253	28	151	48	208	32	133	42	185	29	29	77	0	.	9	100	9	100	23	74	0	.	39	69	7	100	304	27	1350	13
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	71	66	0	.	1353	19	1430	18
	other broadleaves	292	27	108	42	158	51	188	29	360	27	6	100	42	44	184	33	132	42	62	48	43	50	151	36	121	35	1356	15	3202	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	11005	5	2548	12	2572	13	3886	10	6035	8	2893	14	6785	8	5938	9	4000	11	1953	16	1775	16	6376	9	8657	7	8168	6	72590	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2002	9	291	25	528	21	865	18	1102	12	1255	14	2247	10	1777	12	1698	14	687	22	681	19	1642	11	3548	8	1277	14	19601	3
	fir	1710	10	139	36	174	31	601	18	463	18	276	25	778	14	610	17	195	29	243	34	100	33	199	36	90	32	140	30	5716	5
	pine	261	27	125	34	101	42	61	37	243	23	0	.	134	67	9	100	14	71	53	56	21	75	171	31	348	23	35	53	1576	11
	larch	0	.	7	100	20	73	34	60	47	55	0	.	7	100	0	.	36	68	0	.	15	100	1149	16	879	14	672	14	2868	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	126	39	224	22	0	.	359	19
	other conifers	0	.	28	71	21	100	47	62	13	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	117	36
	beech	2074	8	841	16	543	19	855	13	872	12	162	36	845	16	925	17	314	27	160	31	278	24	66	55	205	33	562	18	8702	4
	maple	285	18	91	36	19	72	42	47	118	29	51	50	74	37	186	24	75	35	40	41	89	33	28	81	30	71	7	100	1135	9
	ash	217	27	50	37	160	30	202	37	472	16	22	74	61	39	203	29	56	44	31	82	94	41	27	71	7	100	62	48	1665	9
	oak	154	26	96	46	152	37	83	33	178	25	0	.	36	62	9	100	8	100	15	70	0	.	21	75	26	77	153	37	931	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	75	40	65	59	204	25	0	.	38	46	37	64	49	58	6	100	31	66	53	43	29	49	304	20	1033	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	6804	5	1709	12	1792	12	2856	9	3713	7	1765	13	4218	8	3756	9	2461	11	1235	17	1308	15	3492	9	5393	7	3738	7	44241	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1206	10	195	29	267	31	590	16	797	14	725	16	1300	12	1369	12	897	14	376	21	375	20	969	13	1778	10	790	15	11636	4
	fir	909	12	42	41	202	29	517	16	317	21	193	31	616	14	435	19	201	24	53	42	62	52	89	40	93	34	163	30	3890	6
	pine	97	30	7	100	41	53	74	35	136	25	0	.	36	62	17	100	7	100	7	100	0	.	86	45	163	28	14	100	685	13
	larch	7	100	7	100	14	71	7	100	63	42	0	.	0	.	21	74	43	58	0	.	20	73	827	14	664	15	495	14	2168	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	93	32	0	.	121	27
	other conifers	0	.	21	100	0	.	20	74	27	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	76	45
	beech	882	12	420	21	212	27	448	17	724	13	78	37	414	21	324	22	149	29	76	39	150	33	33	71	79	36	253	20	4244	5
	maple	85	29	28	49	14	71	0	.	76	38	29	50	18	71	60	39	60	78	12	70	17	71	0	.	35	51	8	100	441	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	.	13	100	721	14
	oak	35	53	23	72	28	61	109	39	136	26	0	.	0	.	0	.	0	.	6	100	0	.	8	100	20	76	18	72	383	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	243	26	249	25
	other broadleaves	57	88	29	61	61	53	14	71	8	100	0	.	7	100	18	71	131	58	14	71	12	70	23	57	26	59	55	43	454	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	3338	6	814	15	938	14	1924	10	2508	8	1025	14	2427	8	2281	9	1502	12	558	18	665	17	2070	9	2958	8	2060	8	25069	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	480	15	84	37	89	33	248	23	210	22	333	23	552	14	603	17	315	20	195	24	185	26	333	20	653	13	366	18	4645	5
	fir	351	18	56	35	70	48	277	20	248	22	137	44	324	19	146	31	129	31	65	43	6	100	15	100	40	41	22	58	1886	8
	pine	15	71	0	.	14	71	7	100	20	58	0	.	18	72	7	100	0	.	0	.	0	.	0	.	57	39	7	100	145	23
	larch	0	.	0	.	0	0	7	100	20	58	0	.	0	.	0	.	14	71	0	.	6	100	325	23	227	22	201	22	800	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	35	45	0	.	49	38
	other conifers	0	.	0	.	8	100	40	52	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	43
	beech	142	26	187	25	66	34	178	25	195	20	29	50	132	31	96	31	48	44	20	58	39	56	6	100	21	58	121	31	1281	9
	maple	0	.	7	100	0	.	0	.	28	50	0	.	6	100	18	100	31	60	0	.	7	100	0	.	0	.	0	.	96	32
	ash	30	50	23	58	35	83	7	100	40	47	7	100	15	71	14	71	7	100	6	100	0	.	0	.	0	.	10	100	195	23
	oak	12	100	16	71	29	50	14	71	55	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	142	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	77	0	.	68	33	95	32
	other broadleaves	0	.	23	58	14	71	0	.	7	100	0	.	0	.	18	100	12	70	0	0	6	100	0	.	12	100	8	100	99	31
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	1030	10	395	18	325	20	776	12	831	12	507	19	1047	11	902	15	556	15	286	21	250	23	719	15	1046	10	819	11	9488	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	153	24	14	70	43	59	90	29	129	28	70	36	266	19	204	26	167	26	127	29	124	34	117	30	257	21	196	23	1956	7
	fir	131	26	7	100	33	60	84	33	20	58	21	59	148	23	118	30	26	50	33	51	19	73	15	71	35	45	30	49	719	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	209	23	112	27	94	32	444	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	45	46	60	46	15	71	45	56	86	31	7	100	34	52	41	51	12	70	7	100	9	100	6	100	18	74	44	41	430	15
	maple	0	.	9	100	0	.	0	.	0	.	6	100	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	30	50
	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	.	10	100	0	.	76	37	85	34
	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	350	16	121	29	128	29	248	20	299	18	104	31	449	15	404	18	226	21	176	25	167	29	392	16	464	14	447	14	3974	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	48	38	79	59	106	35	7	100	29	50	61	53	122	26	90	31	692	13
	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	.	61	39	54	43	53	43	167	24
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	15	71	0	.	36	45
	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	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	80	33	14	70	36	45	57	35	170	23	35	45	98	29	108	45	120	32	14	71	48	38	166	26	205	20	217	20	1369	8
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	.	22	59	14	71	15	71	0	.	9	100	22	58	7	100	15	72	14	71	132	24
	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	.	7	100	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	81
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	29	50	0	.	20	58	30	81	30	64	22	59	23	58	22	58	0	.	16	71	22	58	36	45	23	58	61	39	334	17
>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	30	50	19	72	112	26
	fir	0	.	0	.	0	.	0	.	21	58	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50
	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	.	15	71	14	100	29	61
	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	.	57	45	57	45
	other broadleaves	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	0	.	7	100	7	100	58	67	0	.	14	71	7	100	7	100	0	.	14	71	14	71	52	38	90	36	278	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16654	8	2927	21	4803	19	9040	13	12869	10	10586	14	19522	9	14531	10	13044	11	6415	16	6020	17	18481	10	35015	7	11990	10	181897	3
	fir	14628	8	1262	29	1718	23	5981	12	3751	17	2950	22	9437	10	4141	14	1863	21	2074	23	664	27	1426	25	878	27	1491	25	52265	4
	pine	1337	24	662	30	353	36	625	47	875	18	0	.	846	60	46	48	180	49	536	42	285	87	5261	20	4586	19	249	63	15841	10
	larch	38	55	70	44	543	66	119	44	413	29	88	100	94	93	204	76	501	39	0	.	338	54	8767	11	5641	12	8293	11	25108	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1734	22	3488	18	49	100	5280	14
	other conifers	401	38	290	44	133	71	390	29	624	37	0	.	57	52	131	37	232	86	87	100	65	77	156	53	712	34	16	100	3293	15
	beech	18982	7	6657	12	4033	15	7095	10	9117	8	2211	26	9237	11	7054	13	2945	21	1981	24	2966	22	2274	28	1779	24	12396	13	88727	3
	maple	4782	11	1351	18	600	29	1074	23	3800	15	1305	22	2497	21	2620	17	2434	20	651	30	1315	24	1437	22	929	33	1398	26	26191	5
	ash	2016	17	1556	24	1881	20	1418	19	3424	13	786	32	1418	25	2561	21	1035	30	916	29	729	31	1405	31	918	41	2211	24	22274	6
	oak	2176	29	554	31	1378	31	597	23	921	17	89	57	143	48	67	49	194	68	89	54	17	100	1073	31	91	72	2274	23	9664	11
	sweet chestnut	0	.	0	.	17	100	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	328	75	28	100	12379	13	12765	13
	other broadleaves	3279	14	1059	19	1258	24	1995	20	3248	15	1169	29	1640	18	2219	25	2636	21	1515	39	1209	30	4924	15	5258	21	16583	10	47992	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	64293	4	16387	10	16718	11	28334	7	39049	6	19184	11	44891	6	33575	7	25080	9	14263	13	13608	13	47265	6	59321	5	69328	4	491296	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. 1327010. Birmensdorf, Swiss Federal Research Institute WSL

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