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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NFI5
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 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	5654	12	1375	31	1458	33	4896	17	4264	16	3361	18	8573	14	4321	17	5455	14	2079	23	2139	23	9069	11	14076	9	4154	17	70873	4
	fir	7037	10	366	32	735	28	2970	17	1176	21	1181	22	4545	15	1471	22	696	26	1016	27	263	35	609	28	191	37	663	29	22920	6
	pine	203	35	74	62	39	100	123	60	93	68	0	.	383	57	0	.	200	64	35	100	0	.	2635	26	2700	27	81	100	6564	16
	larch	0	.	74	80	469	85	33	100	288	52	175	73	67	81	98	100	235	50	0	.	90	72	2571	16	916	22	1554	23	6571	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	699	33	931	41	32	100	1662	27
	other conifers	387	45	103	62	0	.	141	39	431	50	0	.	33	71	134	47	0	.	121	100	67	60	173	56	766	43	20	100	2376	20
	beech	7347	10	3141	16	1980	28	3035	16	3997	14	887	32	3829	13	2269	19	1531	24	525	30	1576	28	1364	29	641	25	6150	14	38272	5
	maple	2953	17	1030	25	719	44	841	25	1625	15	453	32	1054	26	978	27	1640	21	290	36	347	46	915	27	333	38	792	31	13970	7
	ash	1144	23	821	29	533	35	499	30	1061	24	402	39	747	25	458	32	502	33	435	48	68	62	563	30	350	39	1517	24	9100	8
	oak	1128	43	193	45	516	42	129	43	201	42	39	71	15	100	0	.	123	87	18	100	0	.	788	36	15	100	1152	38	4318	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	18	100	7888	15	7942	15
	other broadleaves	2558	14	837	23	980	24	1622	24	2448	17	967	27	1387	21	1022	24	1624	23	656	34	827	35	4006	15	3172	23	10307	12	32413	6
	indeterminable	141	44	0	.	0	.	17	100	33	70	54	58	68	50	69	50	73	50	0	.	0	.	54	58	124	43	59	57	692	17
	total	28550	6	8015	13	7429	15	14306	10	15618	8	7519	13	20701	8	10819	11	12079	11	5175	18	5377	17	23483	8	24233	8	34370	6	217675	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3051	12	589	27	1125	28	2263	18	2914	15	2446	19	4926	12	3364	16	2896	15	1520	21	1444	23	5149	13	8467	9	2985	15	43140	4
	fir	3935	11	380	37	244	39	1595	19	781	23	768	27	2591	13	858	21	502	28	473	27	118	44	341	30	174	43	320	33	13080	6
	pine	408	36	190	41	52	74	151	69	144	44	0	.	278	79	0	.	18	100	164	51	0	.	2185	20	1229	23	52	58	4871	12
	larch	0	.	17	100	140	67	17	100	187	50	0	.	17	100	49	100	104	62	0	.	53	100	1727	17	815	23	1839	18	4965	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	561	28	811	27	16	100	1388	19
	other conifers	69	61	69	61	20	100	20	100	138	35	0	.	17	100	36	71	17	100	0	.	0	.	17	100	35	71	0	.	437	21
	beech	4725	10	1274	19	981	21	1196	15	2213	13	694	26	2588	15	1650	18	783	26	608	31	610	31	674	36	489	36	3751	17	22234	5
	maple	1534	14	472	24	282	32	260	33	1339	21	545	24	643	23	830	22	666	29	175	50	491	31	321	32	175	49	257	31	7990	7
	ash	514	25	349	31	289	33	208	33	425	24	305	34	496	34	627	26	220	43	156	40	185	46	232	40	226	78	650	34	4881	10
	oak	611	35	139	58	463	33	145	40	88	45	19	100	46	71	74	77	54	58	18	100	0	.	252	42	15	100	750	24	2674	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	15	100	4404	14	4438	14
	other broadleaves	683	23	289	30	330	32	433	31	1049	18	244	35	424	28	609	41	527	24	136	54	263	33	1180	19	1265	27	4311	13	11742	7
	indeterminable	54	58	0	.	0	.	0	.	17	100	22	100	67	51	0	.	15	100	0	.	0	.	36	71	58	75	18	100	287	27
	total	15584	6	3768	12	3925	13	6288	11	9296	8	5042	14	12094	8	8098	11	5802	11	3250	17	3163	17	12693	9	13773	8	19351	6	122129	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2735	12	285	40	546	25	1023	18	1708	15	1715	17	3339	12	2828	13	2389	14	925	21	800	23	3392	11	5433	9	2129	15	29246	4
	fir	1959	12	207	37	219	38	901	18	426	26	654	26	1315	15	616	22	282	29	306	37	109	39	312	35	144	31	269	33	7720	6
	pine	611	27	207	32	89	57	107	57	133	30	0	.	195	55	6	100	69	79	169	56	0	.	832	20	653	24	147	51	3218	11
	larch	0	.	14	71	52	64	0	.	45	60	9	100	0	.	0	.	119	52	0	.	28	100	1422	17	912	18	1062	18	3663	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	293	34	482	25	0	.	785	20
	other conifers	0	.	31	81	7	100	59	51	41	85	0	.	0	.	0	.	0	.	0	.	15	100	17	100	0	.	0	.	170	34
	beech	3563	10	1035	18	619	20	1094	16	1220	14	474	32	1314	14	1477	15	508	25	390	33	282	26	291	32	324	27	1698	15	14287	5
	maple	779	18	147	35	60	42	249	27	473	19	147	45	511	21	518	24	314	32	71	45	237	33	40	62	90	46	104	56	3741	8
	ash	383	32	150	38	390	35	233	25	503	22	182	51	287	54	319	27	148	50	110	66	122	44	160	43	103	51	144	35	3233	10
	oak	262	30	128	37	143	41	92	63	146	33	29	77	0	.	16	71	9	100	7	100	0	.	81	51	7	100	288	28	1208	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	79	0	.	1637	18	1686	18
	other broadleaves	253	29	87	44	139	53	191	31	308	25	9	100	69	60	180	35	113	38	51	48	52	46	86	38	153	34	1558	18	3249	10
	indeterminable	34	71	0	.	0	.	0	.	17	100	6	100	21	58	14	71	58	52	0	.	7	100	7	100	34	71	0	.	198	26
	total	10579	6	2291	13	2265	14	3949	10	5019	8	3223	13	7051	8	5975	9	4017	11	2029	17	1652	16	6983	8	8335	7	9035	7	72404	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1899	9	240	30	452	22	698	19	1145	12	1375	14	2056	12	1501	13	1707	13	684	24	561	21	1937	11	3654	8	1465	14	19373	3
	fir	1507	10	145	40	159	32	595	16	496	17	265	24	916	12	514	16	198	31	263	33	106	34	181	34	95	33	184	29	5624	5
	pine	251	24	115	33	61	43	68	34	237	23	0	.	127	61	7	100	28	79	22	58	0	.	300	27	378	23	34	60	1629	11
	larch	7	100	14	70	29	61	21	74	47	55	0	.	0	.	7	100	36	68	0	.	7	100	1113	16	775	16	739	16	2795	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	34	226	25	0	.	338	20
	other conifers	0	.	28	61	21	100	27	50	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	90	35
	beech	1999	9	785	16	492	20	771	13	773	13	129	37	847	16	1040	16	340	26	142	32	271	24	77	55	208	33	658	16	8532	4
	maple	333	16	90	35	23	75	48	43	138	26	63	41	112	29	162	25	106	31	48	42	124	30	56	60	23	57	44	47	1369	8
	ash	220	25	63	36	175	33	164	36	336	18	40	60	90	32	184	30	41	53	45	61	51	47	27	71	7	100	66	44	1509	10
	oak	186	31	119	40	131	32	89	34	144	30	19	100	34	61	0	.	8	100	25	72	0	.	21	75	12	100	151	32	938	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	6	100	721	18	742	18
	other broadleaves	176	42	56	43	49	43	30	60	217	25	6	100	45	41	0	.	54	62	6	100	37	52	59	39	27	50	456	20	1219	12
	indeterminable	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	29	62	14	100	7	100	7	100	14	71	0	.	86	33
	total	6585	5	1662	12	1592	13	2511	10	3541	7	1898	12	4227	8	3415	9	2552	11	1248	18	1164	15	3899	9	5424	7	4525	6	44244	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1124	11	146	30	283	29	367	18	544	15	735	16	1204	11	1208	12	940	14	419	21	384	20	1136	12	2024	9	997	21	11511	4
	fir	966	11	35	44	159	33	511	18	280	21	289	24	797	14	472	20	168	27	100	38	69	47	157	39	105	32	190	29	4299	6
	pine	105	29	31	50	47	62	67	37	135	29	0	.	49	57	17	100	0	.	15	100	0	.	78	46	206	29	14	100	765	13
	larch	7	100	7	100	8	100	14	71	63	36	0	.	0	.	14	100	29	61	0	.	12	100	780	15	595	17	470	16	1998	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	71	149	27	0	.	191	26
	other conifers	0	.	42	62	0	.	47	62	28	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	124	36
	beech	842	12	430	21	263	25	573	14	700	12	111	35	512	20	346	20	156	30	85	44	170	35	26	77	65	47	309	20	4588	5
	maple	116	26	35	44	18	72	28	50	70	35	38	45	40	45	92	36	31	60	12	70	27	50	0	.	42	45	0	.	549	13
	ash	28	50	45	49	95	36	124	38	225	21	0	.	31	75	52	42	28	50	18	74	44	42	7	100	0	.	33	72	733	13
	oak	63	37	30	60	56	56	99	30	152	25	0	.	0	.	0	.	0	.	6	100	0	.	8	100	27	81	57	41	499	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	337	24	343	24
	other broadleaves	7	100	28	61	69	53	45	54	45	41	0	.	6	100	39	52	44	73	6	100	18	74	23	57	14	100	38	52	383	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	6	100	13	71	0	.	0	.	0	.	0	.	14	71	0	.	0	.	34	45
	total	3258	6	829	14	997	14	1874	10	2241	8	1179	13	2654	8	2240	9	1396	12	661	17	726	17	2272	9	3234	7	2454	11	26016	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	559	15	97	28	106	36	213	24	269	19	353	19	516	17	617	17	430	17	247	25	192	28	414	18	725	13	402	17	5141	5
	fir	432	17	49	37	98	35	256	21	181	26	117	41	426	16	191	23	120	34	30	50	6	100	26	60	59	39	37	45	2027	7
	pine	15	71	0	.	14	71	27	50	20	75	0	.	17	72	7	100	0	.	0	.	0	.	7	100	65	37	0	.	171	22
	larch	0	.	0	.	7	100	7	100	20	58	0	.	0	.	0	.	7	100	0	.	6	100	423	19	357	19	193	23	1021	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	22	58	0	.	29	50
	other conifers	0	.	14	100	0	.	40	57	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	69	42
	beech	197	22	150	27	109	26	191	21	271	18	14	71	149	35	100	30	28	50	42	57	46	54	20	73	41	41	142	29	1500	8
	maple	7	100	0	.	7	100	0	.	41	47	0	.	6	100	27	76	23	59	0	.	0	.	0	.	0	.	0	.	112	30
	ash	39	53	14	70	14	71	36	45	70	34	0	.	7	100	28	61	10	100	0	.	12	100	0	.	0	.	10	100	240	19
	oak	19	73	7	100	30	50	32	51	46	60	0	.	7	100	0	.	0	.	0	.	0	.	0	.	6	100	15	71	163	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	98	27	105	26
	other broadleaves	0	.	14	100	21	57	7	100	9	100	0	.	7	100	8	100	31	82	0	.	6	100	7	100	7	100	15	71	132	29
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	total	1268	10	345	17	405	17	808	13	934	11	484	17	1136	11	979	14	648	15	318	23	269	23	919	13	1282	10	921	10	10717	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	182	22	49	47	37	46	86	32	113	30	132	28	221	18	224	24	213	21	109	33	140	33	185	26	302	18	210	26	2204	7
	fir	196	20	7	100	42	49	112	28	120	27	35	52	172	23	120	27	87	35	56	62	13	71	37	60	34	45	30	49	1061	9
	pine	7	100	0	.	0	.	14	71	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	48	38
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	15	71	14	71	0	.	7	100	163	28	106	27	86	28	398	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	21	58	0	.	50	38
	other conifers	0	.	0	.	0	.	20	58	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	58
	beech	28	61	35	45	20	58	94	34	119	28	22	58	78	35	29	79	18	57	0	.	9	100	6	100	25	79	44	41	527	13
	maple	0	.	16	71	0	.	0	.	7	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	50
	ash	15	71	0	.	0	.	10	100	7	100	8	100	0	.	7	100	0	.	6	100	0	.	0	.	6	100	0	.	59	36
	oak	0	.	18	70	14	71	0	.	27	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	66	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	33	104	33
	other broadleaves	0	.	16	71	21	58	0	.	7	100	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	435	14	140	26	135	25	336	17	433	15	202	23	472	13	394	18	338	17	171	29	169	31	419	18	515	13	482	15	4641	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	0	.	17	72	33	44	53	39	32	52	46	42	66	36	36	45	23	58	29	50	43	41	113	30	55	43	594	12
	fir	51	42	0	.	33	53	49	42	50	42	9	100	72	37	28	59	15	71	0	.	12	70	0	.	15	71	7	100	342	16
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	40	63	40	65	45	199	24
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	15	71	0	.	29	61
	other conifers	0	.	7	100	0	.	13	71	14	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	47
	beech	8	100	0	.	0	.	0	.	20	58	0	.	7	100	8	100	0	.	15	71	0	.	7	100	0	.	37	45	102	27
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	8	100
	ash	0	.	7	100	0	.	0	.	9	100	0	.	0	.	17	72	0	.	0	.	0	.	0	.	0	.	0	.	33	51
	oak	0	.	0	.	0	.	0	.	29	50	0	.	0	.	8	100	0	.	10	100	0	.	0	.	0	.	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	47	53	47
	other broadleaves	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	105	30	14	70	58	39	95	28	182	22	41	46	133	26	128	26	51	38	48	41	49	38	136	27	206	21	217	23	1462	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	0	.	0	.	7	100	13	71	9	100	6	100	0	.	31	50	0	.	36	45	22	58	49	43	18	72	206	20
	fir	29	50	0	.	0	.	7	100	7	100	0	.	6	100	6	100	0	.	7	100	7	100	0	.	0	.	25	58	95	28
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45	7	100	0	.	43	41
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	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	.	32	52	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	39	46
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	10	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	71
	oak	0	.	0	.	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	45	53	53	48
	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	44	41	0	.	0	.	30	50	66	34	9	100	12	71	14	71	31	50	7	100	49	38	80	30	63	37	89	35	494	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	71	0	.	7	100	7	100	0	.	7	100	14	71	14	71	7	100	0	.	14	71	7	100	23	58	18	72	133	24
	fir	15	71	0	.	0	.	0	.	0	.	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	.	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	.	44	57	44	57
	other broadleaves	0	.	0	.	0	.	7	100	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	28	50	0	.	7	100	14	71	16	100	7	100	21	57	14	71	7	100	0	.	14	71	14	71	37	45	76	41	258	19
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15280	8	2781	22	4030	20	9593	13	11025	11	10165	14	20902	10	14143	11	14102	11	6007	18	5739	18	21354	10	34866	7	12434	13	182423	3
	fir	16126	8	1190	28	1690	22	6995	12	3517	14	3318	21	10848	11	4276	15	2068	21	2251	24	704	27	1663	24	818	26	1726	24	57188	4
	pine	1601	23	618	26	302	40	557	33	783	21	0	.	1049	52	37	55	314	57	404	50	0	.	6037	19	5251	20	328	58	17280	10
	larch	21	58	127	51	705	64	92	48	650	36	184	73	85	84	184	81	543	37	0	.	204	65	8307	12	4553	12	6022	14	21675	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1778	24	2663	24	49	100	4500	17
	other conifers	455	39	293	49	47	72	367	28	686	37	0	.	57	52	170	40	17	100	121	100	82	64	208	56	801	42	44	70	3349	16
	beech	18708	7	6848	12	4464	17	6954	10	9345	9	2332	24	9326	11	6926	13	3364	20	1807	26	2963	23	2464	27	1792	24	12790	13	90082	3
	maple	5722	12	1790	19	1108	34	1426	19	3694	13	1252	23	2366	18	2608	18	2780	18	595	30	1240	26	1332	26	663	31	1197	27	27773	5
	ash	2344	18	1449	22	1496	24	1283	19	2644	15	936	30	1658	26	1693	20	949	29	769	35	482	34	989	27	693	49	2420	23	19805	6
	oak	2268	30	634	29	1354	30	593	23	855	18	106	50	103	49	98	66	194	68	85	57	0	.	1150	33	91	72	2412	26	9943	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	127	74	45	72	15330	12	15510	12
	other broadleaves	3677	13	1327	18	1616	22	2335	20	4082	14	1226	24	1938	18	1858	25	2399	20	855	30	1203	31	5363	15	4637	23	16692	11	49210	5
	indeterminable	237	33	7	100	0	.	17	100	67	50	88	44	170	30	83	43	175	34	14	100	14	100	126	37	230	32	77	50	1305	12
	total	66437	4	17064	10	16813	11	30212	7	37348	6	19606	11	48502	6	32075	8	26922	9	12909	14	12631	14	50898	7	57103	6	71520	5	500040	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. 1358378. Birmensdorf, Swiss Federal Research Institute WSL

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