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
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 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	6870	12	1445	28	2008	30	4677	17	5432	15	3784	17	8359	13	4658	17	4928	14	2244	22	2346	21	8218	12	13724	9	3533	15	72225	4
	fir	6922	9	536	35	739	27	2734	17	1601	27	1228	24	4719	13	1553	23	735	27	1085	29	303	35	627	28	367	39	502	32	23653	5
	pine	163	37	127	52	33	71	251	88	110	60	0	.	330	68	0	.	182	69	70	61	0	.	2922	26	2241	26	81	100	6510	15
	larch	17	100	0	.	369	90	33	100	89	64	88	100	104	100	102	100	305	54	0	.	89	60	3027	16	936	20	1824	22	6984	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	611	31	949	25	32	100	1592	19
	other conifers	421	42	103	62	37	71	126	44	430	45	0	.	33	71	113	41	0	.	87	100	49	73	156	53	745	43	0	.	2300	19
	beech	7280	9	2531	15	1442	20	2939	14	3575	11	827	34	3895	14	2855	17	1363	25	724	27	1484	29	1300	30	634	28	6243	15	37093	4
	maple	2587	17	634	24	452	38	588	34	1728	19	585	26	997	31	911	27	1495	23	216	45	385	36	820	27	423	37	628	32	12451	7
	ash	894	21	921	34	730	28	509	24	1311	22	216	49	727	26	656	29	395	40	430	41	138	50	521	31	587	41	1498	26	9532	8
	oak	1181	39	77	50	526	49	94	45	154	43	39	71	15	100	42	70	161	79	18	100	0	.	730	33	15	100	1342	36	4394	17
	sweet chestnut	0	.	0	.	36	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	8405	15	8478	15
	other broadleaves	2590	14	625	23	809	29	1138	22	2144	17	847	24	1515	21	1262	23	1978	21	712	36	1007	36	3934	16	3829	22	10274	13	32665	6
	indeterminable	106	53	17	100	17	100	17	100	34	71	81	54	66	63	50	58	127	43	18	100	0	.	89	45	124	43	99	66	846	17
	total	29030	5	7016	13	7199	15	13106	10	16610	8	7696	13	20759	8	12202	11	11669	11	5605	17	5802	17	22992	8	24574	8	34463	6	218723	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4112	12	907	32	1291	24	2094	19	3518	16	2896	18	5095	13	3709	16	3091	15	1366	21	1710	24	5451	13	8133	10	2715	14	46089	4
	fir	3405	12	345	35	374	42	1206	18	698	24	781	33	2045	13	875	24	530	25	494	29	132	39	416	30	210	38	409	35	11920	6
	pine	507	33	293	50	119	38	118	55	153	40	0	.	226	75	0	.	71	100	167	54	0	.	2032	20	1156	26	120	54	4961	12
	larch	17	100	17	100	118	87	17	100	88	59	0	.	0	.	51	100	91	67	0	.	71	79	1769	17	907	22	1757	17	4902	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	581	28	777	27	16	100	1374	19
	other conifers	34	71	86	53	34	100	119	47	103	58	0	.	17	100	18	100	17	100	0	.	0	.	35	71	53	58	0	.	517	22
	beech	5317	10	1416	18	1174	18	1669	14	2507	13	657	28	2833	14	1827	17	719	26	662	32	743	26	721	31	484	33	3851	16	24580	5
	maple	1224	14	350	26	103	53	391	28	1437	20	549	27	790	22	875	23	420	29	156	49	503	30	251	38	179	37	229	38	7455	7
	ash	568	24	315	32	441	26	260	27	702	23	359	34	484	34	987	25	178	48	250	37	176	41	333	34	261	59	549	32	5862	9
	oak	733	34	243	44	584	30	141	44	140	35	40	71	48	71	36	100	54	58	18	100	0	.	234	42	15	100	637	25	2924	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	15	100	4402	14	4454	14
	other broadleaves	588	23	234	33	273	34	500	33	741	21	176	47	338	30	610	41	456	29	114	47	261	36	1018	20	1072	28	4154	12	10536	7
	indeterminable	19	100	0	.	0	.	0	.	0	.	22	100	67	51	18	100	15	100	0	.	15	100	35	71	36	71	20	100	248	27
	total	16525	6	4206	13	4512	13	6517	10	10087	8	5479	15	11943	8	9005	10	5642	11	3227	17	3612	17	12913	9	13298	8	18860	5	125822	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3029	11	278	28	745	25	1167	18	2222	14	1768	16	3326	11	2692	12	2565	13	1093	22	877	23	3211	11	5727	9	2075	16	30773	3
	fir	2225	12	190	42	220	36	845	19	525	22	460	26	1382	13	744	18	297	31	242	39	147	38	185	41	102	36	254	37	7815	6
	pine	568	26	212	31	177	48	141	54	276	25	0	.	156	62	13	71	31	71	110	71	0	.	895	20	777	25	79	54	3438	10
	larch	24	77	38	61	48	74	21	74	106	43	0	.	0	.	14	71	86	59	0	.	20	76	1421	17	895	18	1092	19	3765	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	244	36	455	26	0	.	699	21
	other conifers	0	.	45	64	34	100	24	77	23	77	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	142	38
	beech	3930	9	1258	16	647	20	1136	17	1308	14	463	46	1410	17	1396	16	499	25	233	33	293	29	186	37	373	28	1226	17	14358	5
	maple	679	18	121	36	37	53	123	32	532	19	99	42	339	25	458	24	332	32	96	38	190	39	45	54	66	48	74	43	3191	8
	ash	365	23	175	31	417	34	314	30	676	19	200	55	241	48	419	25	217	42	76	59	105	41	241	88	41	68	98	36	3586	11
	oak	278	28	151	48	215	31	133	42	192	28	29	77	0	.	0	.	9	100	23	74	0	.	32	81	7	100	271	29	1339	13
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	29	73	0	.	1518	18	1553	17
	other broadleaves	288	27	115	42	152	54	188	29	373	26	21	77	42	44	191	33	120	45	68	44	59	51	126	39	145	45	1259	15	3146	9
	indeterminable	35	100	0	.	0	.	0	.	17	100	6	100	0	.	7	100	40	61	0	.	0	.	0	.	7	100	7	100	119	40
	total	11421	5	2582	12	2693	13	4091	10	6257	8	3045	14	6896	8	5933	9	4198	11	1941	16	1706	16	6613	9	8596	7	7952	6	73924	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2048	9	298	26	613	20	894	17	1167	11	1406	14	2322	10	1644	12	1794	13	672	23	614	19	1797	11	3568	8	1363	14	20201	3
	fir	1765	10	153	35	174	31	624	18	496	17	295	24	891	13	587	17	202	29	224	35	93	35	199	36	111	32	154	30	5968	5
	pine	296	24	139	34	108	41	68	34	250	22	0	.	134	67	9	100	36	65	29	61	0	.	188	33	349	23	28	62	1633	11
	larch	0	.	7	100	28	59	34	60	47	55	0	.	7	100	0	.	36	68	0	.	7	100	1090	17	851	14	600	17	2708	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	112	41	212	23	0	.	334	20
	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	845	16	564	19	910	13	879	12	155	37	873	17	905	18	328	27	137	34	256	25	57	62	211	33	562	18	8754	4
	maple	271	18	97	35	19	72	42	47	118	29	51	50	74	37	192	23	75	35	32	44	96	31	36	85	15	100	7	100	1125	9
	ash	217	27	50	37	160	30	209	35	462	16	22	74	78	34	203	29	56	44	31	82	94	41	27	71	7	100	62	48	1678	9
	oak	154	26	96	46	152	37	83	33	168	26	0	.	36	62	9	100	8	100	7	100	0	.	21	75	26	77	138	39	897	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	6	100	601	19	622	18
	other broadleaves	107	51	49	47	75	42	65	59	199	27	0	.	38	46	37	64	32	52	6	100	37	57	61	40	35	44	306	21	1047	12
	indeterminable	8	100	7	100	0	.	0	.	0	.	0	.	30	51	0	.	29	61	20	76	0	.	7	100	14	71	0	.	114	28
	total	6942	5	1769	12	1912	12	2976	9	3799	7	1928	13	4482	8	3586	9	2613	11	1159	18	1196	15	3603	9	5404	7	3830	7	45198	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1222	10	202	28	302	28	605	16	819	14	828	16	1275	12	1271	13	997	14	422	21	419	20	1047	13	1877	10	790	16	12076	4
	fir	907	12	42	41	208	29	530	16	322	20	205	32	674	13	455	18	194	25	55	42	68	50	101	39	93	34	163	30	4016	6
	pine	97	30	7	100	41	53	81	37	136	25	0	.	42	65	17	100	0	.	7	100	0	.	93	45	163	29	14	100	698	13
	larch	7	100	7	100	14	71	7	100	63	42	0	.	0	.	21	74	36	66	0	.	12	100	811	14	630	16	397	15	2005	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	100	30	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	840	12	406	23	226	26	448	17	731	13	78	37	410	21	320	22	149	29	70	41	150	33	33	71	79	36	275	19	4215	5
	maple	77	30	28	49	14	71	0	.	76	38	29	50	18	71	47	41	60	78	12	70	17	71	0	.	35	51	8	100	421	17
	ash	60	35	43	41	99	31	146	46	223	21	0	.	36	66	37	45	14	71	18	74	29	59	7	100	0	.	0	.	714	14
	oak	35	53	23	72	35	66	109	39	118	28	0	.	0	.	0	.	0	.	6	100	0	.	8	100	20	76	18	72	371	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	259	25	265	24
	other broadleaves	7	100	29	61	54	58	14	71	8	100	0	.	7	100	18	71	76	68	6	100	12	70	23	57	14	71	39	45	308	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	14	71	0	.	0	.	29	50
	total	3253	6	807	15	993	14	1960	10	2523	8	1139	14	2463	8	2185	9	1533	12	597	18	714	17	2159	10	3019	8	1971	8	25316	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	466	15	84	37	89	33	264	22	218	21	336	23	539	14	579	17	322	19	202	23	200	26	324	21	656	13	373	18	4652	5
	fir	363	18	56	35	70	48	277	20	241	22	159	39	356	19	100	26	129	31	65	43	6	100	15	100	47	38	29	50	1914	8
	pine	15	71	0	.	14	71	7	100	20	58	0	.	18	72	7	100	0	.	0	.	0	.	0	.	65	40	0	.	145	25
	larch	0	.	0	.	7	100	7	100	20	58	0	.	0	.	0	.	14	71	0	.	6	100	339	22	241	22	194	23	828	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	35	45	0	.	43	41
	other conifers	0	.	0	.	8	100	40	52	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	43
	beech	135	27	156	27	66	34	193	24	188	20	29	50	132	31	94	31	48	44	14	71	39	56	6	100	21	58	121	31	1242	9
	maple	0	.	7	100	0	.	0	.	28	50	0	.	6	100	18	100	17	72	0	.	7	100	0	.	0	.	0	.	83	34
	ash	30	50	23	58	35	83	7	100	47	43	7	100	15	71	14	71	0	.	6	100	0	.	0	.	0	.	10	100	194	23
	oak	12	100	16	71	29	50	21	58	45	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	138	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	83	30	90	28
	other broadleaves	0	.	23	58	14	71	0	.	7	100	0	.	0	.	18	100	12	70	7	100	6	100	0	.	0	.	8	100	95	31
	indeterminable	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	13	71
	total	1021	11	363	18	332	19	814	12	821	12	538	19	1066	11	830	13	542	16	295	21	265	23	705	15	1066	10	833	11	9491	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	146	25	14	70	43	59	106	27	129	28	120	39	249	19	204	26	154	27	134	30	124	34	101	33	222	22	203	23	1949	8
	fir	131	26	7	100	33	60	84	33	27	50	21	59	148	23	116	30	26	50	33	51	19	73	21	58	35	45	30	49	730	11
	pine	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	28	50
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	16	71	0	.	0	.	7	100	195	24	100	28	71	34	396	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	44	14	71	0	.	50	37
	other conifers	0	.	7	100	8	100	13	71	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	51
	beech	36	52	28	50	21	58	45	56	86	31	7	100	43	46	32	59	12	70	7	100	9	100	6	100	18	74	44	41	395	15
	maple	0	.	9	100	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	23	58
	ash	7	100	7	100	9	100	10	100	15	71	0	.	0	.	18	72	7	100	0	.	0	.	0	.	6	100	0	.	79	32
	oak	0	.	18	70	0	.	0	.	28	50	0	.	0	.	8	100	0	.	8	100	0	.	0	.	7	100	0	.	69	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	37	92	37
	other broadleaves	0	.	0	.	21	58	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	334	17	90	29	135	28	264	19	305	17	155	32	440	15	392	18	206	22	183	26	167	29	358	17	417	15	447	14	3893	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	29	63	7	100	7	100	20	58	53	39	35	45	39	41	66	67	77	40	7	100	22	58	68	49	111	26	77	32	617	14
	fir	51	38	0	.	7	100	28	50	27	61	0	.	36	53	14	71	15	71	0	.	13	71	7	100	7	100	8	100	212	19
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	42	49	43	43	52	144	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	15	71	0	.	22	58
	other conifers	0	.	0	.	8	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71
	beech	0	.	7	100	0	.	0	.	49	42	0	.	7	100	9	100	7	100	7	100	0	.	7	100	0	.	22	58	115	26
	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	.	57	42	65	39
	other broadleaves	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	80	33	21	58	30	50	65	33	170	23	35	45	89	30	95	49	98	34	14	71	41	41	148	28	190	20	207	21	1284	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	0	.	7	100	0	.	0	.	7	100	14	71	13	71	0	.	9	100	29	50	15	71	15	72	7	100	123	24
	fir	29	50	0	.	0	.	7	100	7	100	0	.	8	100	0	.	0	.	7	100	0	.	0	.	0	.	18	72	75	32
	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	.	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	.	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	.	37	52	37	52
	other broadleaves	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	36	45	0	.	14	71	30	81	30	64	7	100	23	58	20	58	0	.	16	71	29	50	51	38	23	58	62	39	340	16
>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	.	21	58	7	100	23	58	19	72	112	26
	fir	0	.	0	.	0	.	0	.	14	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	14	100	22	74
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	49	51	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	0	.	7	100	7	100	14	71	0	.	14	71	7	100	7	100	0	.	21	58	14	71	37	45	83	38	220	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17936	8	3233	21	5110	19	9834	12	13557	10	11180	13	21226	9	14842	11	13935	12	6150	18	6362	18	20240	10	34056	7	11156	11	188816	3
	fir	15797	8	1328	29	1825	23	6334	12	3958	16	3148	22	10267	10	4443	14	2128	21	2205	25	782	27	1570	25	973	26	1567	25	56325	4
	pine	1654	23	777	27	491	36	672	44	954	17	0	.	905	57	46	48	321	57	383	51	0	.	6130	19	4773	19	321	57	17427	10
	larch	72	73	70	44	584	61	119	44	413	29	88	100	111	94	204	76	567	37	0	.	213	56	8725	11	4625	12	5992	14	21782	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1639	24	2565	20	49	100	4263	15
	other conifers	455	39	290	44	150	74	390	29	624	37	0	.	57	52	131	37	17	100	87	100	65	77	191	54	797	41	16	100	3270	15
	beech	19612	7	6647	12	4141	15	7339	10	9329	8	2216	27	9602	11	7444	13	3125	21	1855	26	2974	22	2316	28	1821	24	12345	13	90766	3
	maple	4838	11	1246	18	625	30	1145	23	3919	15	1320	22	2224	20	2501	18	2399	20	513	34	1212	24	1151	27	717	30	946	27	24756	5
	ash	2140	17	1534	24	1898	20	1465	19	3443	13	804	31	1580	24	2342	19	868	31	811	31	541	33	1129	35	902	46	2218	25	21677	6
	oak	2393	28	624	31	1550	30	587	23	888	17	107	50	99	52	94	51	231	65	81	59	0	.	1025	33	91	72	2422	26	10191	11
	sweet chestnut	0	.	0	.	36	71	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	126	75	28	100	15504	12	15707	12
	other broadleaves	3581	14	1080	19	1398	24	1928	21	3473	15	1044	23	1941	19	2135	25	2681	20	914	32	1382	31	5162	16	5096	22	16046	10	47861	5
	indeterminable	168	41	24	77	17	100	17	100	51	58	115	43	163	41	75	46	218	32	39	62	22	76	154	32	181	35	126	59	1370	13
	total	68648	4	16854	10	17826	11	29830	7	40615	6	20022	11	48176	6	34257	8	26508	9	13036	14	13552	14	49558	7	56624	6	68707	4	504211	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. 1296673. Birmensdorf, Swiss Federal Research Institute WSL

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