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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NFI2
total 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
grid: 1.4-km grid
state 1993/95
		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	7925	10	1427	22	2848	18	5583	16	8665	12	5663	12	8067	9	6315	12	4827	11	2430	18	2748	15	7891	9	12679	7	3760	12	80827	3
	fir	6450	7	587	27	912	25	2324	13	1498	16	1350	18	4174	9	1860	13	595	22	808	22	439	28	965	25	381	23	686	22	23029	4
	pine	323	31	275	52	147	57	82	70	387	37	10	100	379	43	50	61	164	63	133	48	114	64	3725	17	3102	18	115	55	9008	10
	larch	50	82	38	78	100	47	98	81	210	40	10	100	0	.	56	100	41	50	0	.	131	59	3353	12	1454	15	2170	14	7711	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	11	100	0	.	552	24	907	22	10	100	1511	16
	other conifers	362	41	105	66	136	63	155	39	336	35	9	100	62	47	74	42	0	.	92	58	29	74	165	82	581	35	0	.	2105	16
	beech	7576	7	2985	12	2302	13	3239	11	4201	8	990	23	4088	12	3115	12	1765	22	926	22	1321	21	1266	25	912	20	7088	11	41774	3
	maple	2043	10	595	19	606	33	397	23	1365	19	678	20	809	21	1078	20	1149	20	255	30	489	27	587	22	337	35	466	23	10854	6
	ash	1290	17	693	25	747	25	437	28	1172	17	351	31	708	22	1081	23	292	29	283	29	329	44	561	24	448	39	1519	18	9909	6
	oak	1053	27	341	42	1005	32	161	42	498	36	98	59	31	58	12	100	210	47	40	49	10	100	586	25	59	63	1423	23	5528	11
	sweet chestnut	0	.	0	.	49	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	67	0	.	8345	11	8490	11
	other broadleaves	2981	11	825	21	1233	23	923	26	2417	13	1097	18	1723	19	1660	19	1795	17	774	34	874	26	4102	13	3365	18	10835	8	34606	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	30052	4	7870	9	10084	10	13398	9	20749	6	10258	10	20073	6	15300	8	10837	9	5753	13	6485	12	23848	6	24227	6	36418	4	235353	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4491	9	700	24	1595	16	2219	13	3503	11	4076	12	5732	9	4188	10	3338	11	1479	17	2045	21	5271	9	8596	7	2677	12	49909	3
	fir	3237	8	254	26	454	25	1610	16	839	16	879	20	2038	11	1087	14	359	25	368	29	237	29	461	25	216	27	357	24	12395	4
	pine	499	25	202	34	128	40	91	49	229	25	10	100	249	46	32	100	68	73	332	37	129	54	1769	16	1499	17	154	49	5392	9
	larch	50	72	47	45	92	46	49	45	185	46	0	.	0	.	0	.	50	53	0	.	86	51	2455	12	1333	14	1592	14	5938	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	43	100	20	100	0	.	23	100	0	.	667	23	814	20	0	.	1565	14
	other conifers	39	61	107	53	80	100	60	58	82	46	0	.	10	100	10	100	10	100	11	100	9	100	0	.	60	52	0	.	479	25
	beech	6508	7	2096	12	1374	14	2123	13	2743	10	675	27	2625	13	2066	13	986	20	493	25	823	21	536	25	489	26	2719	12	26256	3
	maple	1295	11	441	20	269	31	404	22	989	15	312	24	526	17	870	17	535	22	148	34	426	27	151	32	108	35	287	28	6761	5
	ash	898	18	347	24	642	22	345	20	942	15	282	40	359	31	838	17	180	38	105	58	239	45	226	31	143	46	434	22	5982	7
	oak	817	24	291	34	581	27	219	28	374	23	32	74	49	60	68	56	175	49	10	100	0	.	117	37	29	75	625	20	3388	10
	sweet chestnut	0	.	0	.	20	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	53	9	100	3032	12	3144	12
	other broadleaves	814	14	367	24	352	28	359	23	942	16	115	35	528	24	614	22	466	22	204	27	270	28	705	18	560	24	3354	10	9650	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18648	4	4853	9	5587	9	7479	7	10828	6	6381	10	12159	6	9794	7	6167	9	3174	12	4265	13	12440	6	13858	6	15230	4	130861	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3352	8	506	21	1640	15	1705	11	2682	9	2308	11	4116	7	3165	10	2535	12	1080	16	1256	18	3370	9	5555	7	1933	12	35204	3
	fir	2558	9	272	25	216	27	1040	14	730	14	619	19	1555	11	947	13	204	25	253	28	106	28	270	28	110	28	173	37	9053	4
	pine	688	19	280	23	157	45	121	34	556	16	0	.	118	39	26	48	31	52	182	35	56	66	777	15	829	17	166	45	3988	7
	larch	19	71	63	47	47	59	49	77	58	42	4	100	8	71	21	53	28	74	0	.	78	50	1525	11	983	13	947	14	3831	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	8	71	0	.	0	.	346	21	475	20	0	.	853	14
	other conifers	0	.	34	100	20	71	26	62	42	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	126	37
	beech	4083	7	1519	11	1062	15	1415	11	1580	10	505	21	1305	12	1587	12	491	22	203	31	471	22	172	39	216	28	1040	13	15650	3
	maple	507	14	248	27	72	33	113	32	360	19	76	46	209	25	330	18	188	26	61	40	198	24	39	53	32	56	53	46	2487	7
	ash	481	20	184	24	224	26	340	43	680	14	91	38	138	30	350	20	85	36	30	88	74	35	37	54	34	48	104	36	2855	8
	oak	324	21	168	27	245	23	158	28	356	25	8	71	82	81	9	71	29	84	4	100	0	.	11	100	46	85	221	26	1662	10
	sweet chestnut	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	58	4	100	821	15	874	14
	other broadleaves	299	27	212	34	123	35	115	33	453	18	15	77	107	52	108	35	129	33	46	47	55	43	160	31	127	32	885	13	2834	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12312	4	3487	8	3824	9	5083	7	7497	5	3626	9	7662	6	6543	7	3729	9	1859	13	2295	12	6740	6	8412	5	6347	5	79416	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2405	6	403	18	1180	13	1443	10	2121	8	1525	11	2955	7	2086	9	1691	10	561	17	702	14	1627	8	3575	6	1086	11	23359	2
	fir	1524	7	145	26	255	21	812	11	707	12	358	17	1143	10	699	13	189	23	94	32	113	29	118	30	140	22	184	25	6481	4
	pine	321	19	142	25	127	35	95	28	412	15	0	.	126	45	40	41	41	53	30	55	17	60	235	23	422	16	50	48	2058	7
	larch	4	100	19	66	57	45	24	47	79	25	8	100	8	71	9	100	39	43	0	.	35	54	1164	10	855	10	592	12	2894	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	150	27	243	18	4	100	402	15
	other conifers	0	.	4	100	0	.	33	39	17	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	35
	beech	1824	7	846	13	497	16	827	11	1065	9	202	22	754	14	736	13	328	23	178	28	208	20	36	54	127	29	437	13	8066	4
	maple	141	19	74	27	5	100	20	53	110	25	36	33	62	37	135	24	57	32	34	39	41	35	4	100	8	71	13	75	739	9
	ash	109	22	99	28	107	28	135	35	357	15	31	52	53	34	94	24	19	45	19	60	34	40	12	58	14	59	27	41	1111	8
	oak	118	27	70	29	210	27	151	24	166	19	0	.	0	.	0	.	9	71	8	70	0	.	8	71	44	55	77	40	863	11
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	388	17	400	17
	other broadleaves	63	28	47	43	37	43	72	39	93	24	0	.	29	38	46	41	45	69	4	100	15	61	52	46	14	59	162	18	680	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6509	4	1850	9	2484	9	3612	7	5128	5	2162	9	5130	6	3845	7	2418	8	932	14	1164	11	3411	6	5444	5	3019	6	47105	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1264	7	189	22	414	16	892	11	953	10	801	14	1513	8	1211	10	812	12	331	17	403	17	833	12	1817	7	614	11	12047	3
	fir	744	11	66	29	137	24	411	15	431	15	226	19	694	11	318	15	117	23	66	37	49	35	80	37	73	27	85	27	3497	5
	pine	130	23	19	59	42	37	52	43	203	18	0	.	28	68	17	78	0	.	8	71	17	100	113	37	156	23	23	47	809	10
	larch	4	100	8	71	29	53	4	100	49	35	4	100	12	58	23	52	39	42	4	100	19	53	700	11	471	13	365	12	1732	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	35	36	107	24	0	.	146	20
	other conifers	0	.	0	.	9	100	16	61	21	83	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	48
	beech	508	12	433	13	190	21	344	14	516	12	97	29	365	18	314	15	179	31	75	47	99	32	12	58	54	36	174	20	3360	5
	maple	30	38	8	71	16	50	4	100	64	31	0	.	14	59	21	52	17	50	0	.	14	58	0	.	8	71	0	.	196	16
	ash	40	37	12	74	65	38	29	38	121	20	5	100	22	52	15	59	0	.	4	100	11	74	0	.	0	.	6	100	330	13
	oak	63	35	74	31	102	32	57	28	122	25	5	100	16	58	4	100	4	100	8	71	0	.	10	72	4	100	18	59	488	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	85	4	100	242	19	271	19
	other broadleaves	10	71	30	43	8	71	8	100	28	44	0	.	9	71	4	100	40	46	8	71	13	76	12	74	0	.	36	42	206	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2792	5	839	10	1013	10	1819	8	2508	6	1137	11	2673	6	1928	8	1212	10	505	16	625	14	1819	8	2695	6	1564	6	23127	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	378	12	52	37	144	21	290	15	302	15	223	17	571	11	454	14	271	16	145	27	143	25	282	18	646	11	255	18	4156	4
	fir	223	15	23	41	40	34	118	24	64	30	54	36	267	15	126	22	54	28	24	46	31	43	32	35	36	33	40	39	1130	7
	pine	18	51	16	61	4	100	8	71	43	33	0	.	11	72	4	100	4	100	0	.	0	.	4	100	34	45	4	100	149	19
	larch	0	.	0	.	4	100	4	100	11	58	0	.	13	74	14	58	8	71	0	.	8	71	286	16	119	21	114	20	581	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	25	41	43	35	0	.	72	26
	other conifers	0	.	4	100	4	100	16	60	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	42
	beech	110	22	98	28	39	40	113	28	141	19	27	42	46	38	62	36	46	51	8	71	12	74	0	.	11	74	66	26	780	9
	maple	0	.	14	58	0	.	4	100	0	.	4	100	0	.	4	100	4	100	0	.	0	.	0	.	0	.	0	.	29	38
	ash	13	58	8	100	15	58	16	74	25	41	0	.	0	.	8	71	4	100	0	.	0	.	0	.	7	100	0	.	97	24
	oak	17	63	16	60	21	67	4	100	39	34	7	100	0	.	12	73	0	.	0	.	0	.	0	.	4	100	9	71	129	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	82	26	87	25
	other broadleaves	0	.	17	58	8	71	4	100	12	75	0	.	0	.	0	.	8	71	0	.	0	.	0	.	0	.	9	72	59	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	759	9	248	17	278	16	578	11	642	10	314	15	907	9	688	11	399	14	177	23	193	21	635	11	900	9	578	10	7297	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	123	21	8	71	32	39	47	30	54	28	75	27	116	23	196	23	106	23	60	30	68	27	97	25	211	16	128	22	1322	7
	fir	88	24	4	100	20	45	50	33	28	38	16	50	99	23	20	45	4	100	0	.	11	58	4	100	20	45	16	50	381	11
	pine	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	12	58
	larch	4	100	0	.	4	100	4	100	8	100	0	.	0	.	0	.	4	100	0	.	0	.	131	21	68	27	51	30	274	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	45	27	38	0	.	47	29
	other conifers	0	.	0	.	4	100	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58
	beech	19	45	28	43	12	58	54	37	71	29	8	71	17	60	6	100	9	72	0	.	4	100	0	.	0	.	37	43	265	15
	maple	0	.	0	.	0	.	6	100	0	.	0	.	4	100	0	.	0	.	0	.	9	72	0	.	0	.	4	100	22	46
	ash	0	.	0	.	4	100	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	12	58
	oak	0	.	0	.	22	51	14	60	15	60	0	.	0	.	10	72	0	.	5	100	0	.	0	.	0	.	0	.	66	29
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	76	26	80	26
	other broadleaves	0	.	0	.	0	.	6	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	18	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	234	15	40	38	98	25	184	19	188	17	99	25	240	16	236	20	124	21	64	29	92	23	255	15	334	13	320	13	2510	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17	50	4	100	8	71	20	45	16	61	20	65	34	35	33	39	44	35	0	.	44	33	40	32	48	29	35	36	361	11
	fir	12	58	0	.	0	.	19	45	19	51	0	.	60	28	6	100	10	72	8	71	0	.	4	100	4	100	14	58	156	17
	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	.	33	35	26	49	18	50	77	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	beech	7	71	4	100	9	100	16	61	15	59	0	.	4	100	8	71	4	100	4	100	0	.	4	100	4	100	8	71	88	24
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	8	100	0	.	0	.	0	.	0	.	0	.	12	76
	ash	0	.	0	.	4	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	72
	oak	4	100	4	100	0	.	4	100	25	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	36	65	36
	other broadleaves	0	.	0	.	0	.	8	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	18	60
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	40	32	12	58	24	48	74	27	79	26	20	65	98	24	51	32	67	29	12	58	44	33	84	23	86	23	144	21	834	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	0	.	5	100	0	.	4	100	4	100	4	100	17	50	4	100	8	71	20	45	8	71	8	71	19	50	103	20
	fir	4	100	0	.	0	.	4	100	0	.	4	100	8	71	4	100	0	.	0	.	4	100	0	.	4	100	0	.	32	35
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	16	50	0	.	9	72	29	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	4	100	0	.	10	71	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	50
	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	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	39	49	43	45
	other broadleaves	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8	71	4	100	5	100	13	58	20	45	8	71	12	58	21	45	4	100	8	71	24	41	32	35	20	45	67	33	246	14
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	8	71	4	100	9	71	0	.	4	100	0	.	22	45	12	74	58	29
	fir	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	72	18	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	.	4	100	12	58	11	58	28	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	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	.	4	100	0	.	0	.	0	.	0	.	0	.	19	100	23	84
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	35	79	35
	other broadleaves	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	5	100	0	.	4	100	4	100	4	100	8	71	8	71	9	71	0	.	8	71	12	58	38	34	130	27	229	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19958	6	3287	15	7867	11	12201	10	18299	7	14694	10	23116	7	17668	8	13636	9	6093	14	7431	14	19418	7	33159	5	10518	9	207347	2
	fir	14840	6	1352	19	2033	18	6392	10	4316	10	3510	14	10037	7	5067	10	1531	18	1621	20	990	22	1932	20	983	18	1566	19	56170	3
	pine	1979	16	934	22	604	29	449	26	1834	13	21	100	911	35	169	39	308	43	685	33	334	54	6623	14	6051	13	512	41	21415	6
	larch	130	65	175	35	332	37	232	45	603	28	27	100	41	59	124	52	210	27	4	100	358	37	9667	8	5322	9	5869	9	23094	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	98	100	24	100	12	58	38	100	0	.	1813	18	2629	16	13	77	4628	12
	other conifers	401	41	253	48	257	62	310	29	501	30	9	100	77	49	84	39	10	100	104	53	38	78	165	82	642	35	4	100	2854	15
	beech	20636	5	8014	9	5484	11	8142	8	10337	7	2505	19	9205	9	7899	10	3808	18	1888	20	2937	18	2027	22	1813	19	11587	10	96281	2
	maple	4014	8	1381	14	968	27	947	18	2888	13	1106	18	1624	13	2441	13	1958	16	499	24	1180	21	781	21	493	27	822	21	21103	4
	ash	2832	14	1343	19	1807	17	1309	19	3303	10	760	25	1281	19	2392	16	580	25	441	29	688	38	836	21	646	37	2090	16	20306	5
	oak	2396	20	965	25	2187	22	768	19	1603	17	150	45	179	43	115	40	428	42	76	43	10	100	733	23	187	61	2373	18	12168	8
	sweet chestnut	0	.	0	.	94	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	249	50	21	83	13169	9	13533	9
	other broadleaves	4167	10	1504	18	1763	20	1496	19	3959	11	1227	17	2395	17	2432	18	2483	15	1037	28	1227	23	5031	13	4067	17	15293	7	48081	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	71354	3	19209	7	23396	7	32245	6	47642	4	24008	8	48963	5	38414	6	24965	7	12484	11	15193	10	49276	5	56013	4	63817	3	526978	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. 1297045. Birmensdorf, Swiss Federal Research Institute WSL

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