Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
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 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	7615	9	1465	26	2164	19	3820	14	5482	11	4260	12	8582	9	5391	11	5806	10	2655	15	3016	13	8897	8	17316	7	5458	10	81927	3
	fir	6833	7	492	26	742	18	2651	15	1698	21	1331	18	4472	9	1840	15	590	21	947	23	338	32	1062	24	319	30	696	27	24011	4
	pine	299	39	91	46	29	58	133	88	129	36	0	.	383	43	30	76	423	52	206	50	108	77	2917	18	3546	17	106	55	8400	10
	larch	60	57	21	100	230	80	57	58	68	49	117	73	63	100	67	86	217	46	0	.	158	39	4332	11	2471	17	3426	12	11289	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	11	100	20	100	0	.	45	100	0	.	787	20	1479	17	38	61	2380	12
	other conifers	434	34	70	55	41	50	153	45	437	39	0	.	51	45	196	37	133	68	165	56	39	61	122	47	929	29	40	79	2810	14
	beech	7180	7	2464	12	1605	15	2814	12	4186	9	855	24	3604	10	2889	13	1626	21	686	20	1356	22	1275	23	892	20	7165	10	38597	3
	maple	2640	12	748	25	707	21	793	23	1852	13	667	18	1088	21	1170	21	1472	16	417	32	600	26	1020	18	453	27	1302	19	14930	5
	ash	968	17	720	27	893	20	695	20	1687	15	237	33	764	19	726	25	499	29	413	33	322	36	612	21	591	26	1837	15	10964	6
	oak	904	31	175	49	412	39	172	41	353	38	33	58	9	100	23	70	232	56	20	70	10	100	933	26	42	50	1213	25	4532	12
	sweet chestnut	0	.	0	.	40	61	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	172	71	0	.	7802	12	8024	11
	other broadleaves	2851	10	949	27	1327	20	1137	17	2238	12	1130	21	1808	16	1419	17	2388	16	1275	30	978	25	5186	11	4700	14	12797	7	40183	4
	indeterminable	151	32	40	50	9	100	20	71	19	71	58	48	70	43	40	50	154	29	20	71	0	.	89	33	122	34	164	31	955	12
	total	29934	4	7234	10	8200	10	12456	8	18148	6	8687	9	20904	6	13812	7	13542	8	6849	12	6925	11	27404	5	32860	5	42045	4	249002	1
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4297	9	683	26	1524	18	1828	15	3778	11	3229	12	5251	9	3763	11	3611	11	1628	15	1734	17	6301	8	10264	6	3254	10	51145	3
	fir	3363	9	272	28	381	27	1258	16	732	17	770	22	2105	10	989	16	431	20	387	23	240	35	615	21	249	25	432	24	12224	4
	pine	487	26	192	43	78	35	93	44	182	33	21	100	251	48	21	71	90	52	184	39	145	54	2038	15	1465	19	105	43	5351	9
	larch	40	79	28	58	90	63	19	71	118	37	0	.	0	.	39	78	61	57	0	.	87	51	2528	11	1643	13	2361	11	7013	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	32	100	20	100	0	.	0	.	0	.	871	17	1021	20	10	100	1954	13
	other conifers	99	63	69	43	29	74	87	40	178	33	0	.	30	58	21	71	64	69	0	.	10	100	21	71	122	39	0	.	730	17
	beech	5180	8	1351	13	1126	14	1538	12	2459	10	502	23	2697	11	1700	13	959	23	534	25	729	20	682	23	550	21	3851	11	23858	4
	maple	1303	11	493	19	272	31	455	20	1306	16	554	20	773	17	961	18	552	19	290	33	478	25	314	29	220	38	280	25	8252	5
	ash	673	19	348	24	428	19	299	24	854	15	271	29	389	27	848	22	251	30	206	31	141	33	399	25	241	39	1020	21	6366	7
	oak	726	26	299	33	413	26	126	34	282	25	44	60	50	61	21	100	108	54	10	100	0	.	262	26	39	62	661	19	3041	10
	sweet chestnut	0	.	0	.	29	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	53	9	100	4041	11	4130	10
	other broadleaves	744	17	423	34	399	26	424	25	940	13	169	33	410	21	625	26	528	26	292	27	265	27	1443	14	972	20	4755	9	12389	5
	indeterminable	40	50	0	.	9	100	10	100	0	.	11	100	49	45	21	71	9	100	21	71	35	60	82	35	71	38	20	71	377	17
	total	16952	4	4157	10	4777	9	6137	8	10829	6	5571	10	12037	6	9028	7	6664	8	3553	12	3864	12	15605	5	16866	5	20790	4	136830	1
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2977	8	301	21	747	18	1135	14	2258	10	2150	12	3434	8	2578	9	2729	10	1210	15	1174	16	3524	8	6260	6	2109	11	32586	2
	fir	2264	8	205	30	279	25	795	14	558	16	477	18	1301	10	785	14	248	24	199	29	113	32	206	26	145	31	261	29	7837	4
	pine	602	21	177	25	129	39	113	43	338	19	0	.	200	39	12	58	22	61	176	46	79	62	827	15	823	18	184	51	3683	8
	larch	18	64	33	44	45	55	55	44	117	39	0	.	0	.	16	50	61	49	0	.	92	40	1827	11	1131	12	1436	11	4831	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	40	100	4	100	6	100	22	100	0	.	409	20	517	19	10	100	1009	13
	other conifers	30	100	45	57	31	68	17	64	81	53	0	.	0	.	10	100	4	100	0	.	9	100	0	.	0	.	0	.	229	28
	beech	4005	7	1265	13	838	14	985	13	1349	12	486	29	1525	12	1296	12	433	21	244	26	480	19	198	25	310	24	1387	12	14801	3
	maple	715	13	187	23	153	38	148	26	465	15	112	33	283	20	391	19	313	22	113	37	160	29	70	35	95	40	163	32	3368	6
	ash	379	18	253	23	434	24	256	24	586	16	144	47	191	37	364	20	191	33	45	60	65	39	219	55	52	47	293	24	3474	8
	oak	337	26	166	30	165	26	123	32	194	24	25	57	23	73	22	60	36	75	13	73	0	.	40	46	22	84	367	19	1532	10
	sweet chestnut	0	.	0	.	21	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	38	66	0	.	1446	13	1509	12
	other broadleaves	350	18	190	31	140	36	134	25	368	19	23	62	87	40	137	28	175	28	73	33	44	46	284	26	222	28	1577	10	3803	6
	indeterminable	29	74	0	.	0	.	0	.	17	63	4	100	4	100	4	100	44	47	4	100	20	100	14	76	37	61	19	66	195	24
	total	11704	4	2822	9	2981	9	3761	8	6336	6	3421	10	7090	6	5621	7	4262	8	2099	12	2237	11	7658	6	9614	5	9251	4	78857	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2310	6	311	20	524	16	745	14	1245	9	1552	10	2487	7	1890	9	1836	10	605	16	780	14	1938	8	3858	6	1359	10	21440	2
	fir	1676	8	160	25	170	23	551	14	461	14	391	16	986	10	648	12	182	25	169	28	105	25	186	26	142	22	171	22	5998	4
	pine	347	19	123	26	78	36	59	27	263	17	0	.	124	45	18	60	37	46	67	38	35	53	262	20	411	15	73	42	1897	7
	larch	4	100	30	68	26	46	35	43	43	42	4	100	8	71	16	78	32	47	0	.	63	49	1304	11	921	12	813	12	3299	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	11	71	4	100	0	.	155	24	265	16	0	.	448	13
	other conifers	15	100	30	61	24	62	28	63	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	113	30
	beech	2292	6	882	11	594	14	814	10	914	10	282	21	762	13	927	12	262	21	170	25	285	19	75	32	156	28	711	12	9126	3
	maple	287	14	90	28	33	39	71	31	142	19	52	33	65	28	141	20	76	26	35	33	108	24	36	54	21	60	29	65	1186	7
	ash	223	20	112	23	175	20	151	30	449	13	33	50	93	26	201	22	36	42	22	71	95	31	31	46	9	72	94	27	1726	7
	oak	152	23	101	31	184	24	84	36	181	20	0	.	25	53	5	100	19	64	8	70	4	100	28	43	36	58	131	28	958	10
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	8	71	4	100	565	14	588	13
	other broadleaves	136	28	60	41	79	34	45	50	168	23	0	.	39	37	41	39	42	43	11	75	34	43	45	33	31	39	372	13	1103	8
	indeterminable	4	100	4	100	4	100	4	100	0	.	0	.	17	50	0	.	20	53	16	61	0	.	8	71	26	41	4	100	106	21
	total	7445	3	1903	9	1898	9	2585	7	3879	5	2315	9	4618	5	3887	7	2558	8	1109	13	1509	10	4076	6	5878	5	4326	5	47987	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1330	8	183	21	308	20	504	14	799	11	928	12	1474	9	1341	10	1031	10	429	18	434	15	995	10	2156	7	791	11	12703	3
	fir	984	9	70	38	183	25	478	13	332	16	259	20	753	10	463	13	150	21	53	35	80	33	116	27	120	23	136	26	4178	4
	pine	125	27	26	65	30	46	51	35	179	19	0	.	25	65	22	71	8	71	12	58	21	83	134	29	191	19	23	52	847	10
	larch	8	71	11	58	39	45	12	58	60	31	12	100	11	73	16	61	27	51	0	.	19	52	937	10	710	14	547	11	2410	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	68	29	111	21	4	100	191	17
	other conifers	0	.	12	100	16	81	12	75	16	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	60	40
	beech	894	9	495	15	283	17	425	13	727	10	140	24	383	16	391	14	203	30	70	33	144	24	28	58	58	31	333	14	4574	4
	maple	95	23	15	50	16	50	12	74	67	31	24	41	18	51	61	30	37	70	7	71	20	51	0	.	20	52	9	71	399	13
	ash	84	23	46	37	76	27	100	38	182	18	17	61	21	66	47	32	16	50	11	74	21	52	8	71	4	100	16	60	650	10
	oak	70	45	50	35	106	32	99	28	111	22	5	100	5	100	4	100	8	100	4	100	0	.	8	71	24	53	31	42	523	12
	sweet chestnut	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	306	17	325	17
	other broadleaves	37	80	33	50	49	44	16	50	33	40	0	.	8	71	18	50	79	58	8	71	11	57	37	45	18	51	58	30	407	17
	indeterminable	0	.	0	.	0	.	5	100	0	.	4	100	0	.	0	.	4	100	0	.	4	100	12	58	8	71	0	.	37	33
	total	3628	5	941	11	1121	10	1714	8	2507	6	1389	10	2698	6	2362	7	1571	9	594	15	754	12	2343	7	3423	6	2258	6	27305	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	549	11	55	35	124	23	211	19	231	18	312	17	622	10	610	12	354	14	163	19	212	18	370	14	834	10	342	14	4991	4
	fir	427	14	67	28	78	30	255	16	227	17	162	26	395	14	169	20	92	27	54	39	11	58	27	55	50	29	36	33	2050	6
	pine	38	40	4	100	12	58	8	71	47	35	0	.	15	59	8	71	4	100	0	.	0	.	12	58	62	31	4	100	214	16
	larch	0	.	4	100	21	69	8	71	20	45	0	.	8	100	6	100	8	71	0	.	4	100	418	14	233	15	201	16	931	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	10	72	4	100	0	.	19	45	59	30	0	.	97	23
	other conifers	0	.	0	.	4	100	24	53	12	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	37
	beech	145	20	179	20	95	26	206	18	236	14	50	31	133	24	148	20	48	40	27	42	38	46	12	58	20	45	117	22	1455	6
	maple	0	.	4	100	0	.	0	.	24	41	0	.	4	100	10	100	17	60	0	.	4	100	0	.	0	.	0	.	63	30
	ash	26	41	17	50	37	58	12	58	54	30	4	100	13	58	13	58	4	100	4	100	4	100	0	.	0	.	10	71	197	17
	oak	14	71	40	47	29	38	24	41	65	28	0	.	5	100	5	100	0	.	0	.	0	.	5	100	0	.	13	58	198	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	82	0	.	123	22	142	22
	other broadleaves	4	100	21	45	18	64	0	.	12	58	0	.	0	.	10	100	7	71	4	100	4	100	0	.	6	100	29	48	114	23
	indeterminable	0	.	0	.	0	.	4	100	0	.	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	12	58
	total	1201	8	390	14	417	14	753	10	929	9	532	13	1194	8	983	10	544	12	257	17	278	16	887	10	1265	8	874	8	10504	2
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	194	17	12	58	49	37	81	23	102	23	171	21	240	14	236	18	198	19	132	27	110	26	218	23	382	14	202	17	2329	5
	fir	164	18	8	71	36	40	61	30	79	28	32	48	143	18	124	22	27	38	28	42	19	53	24	41	29	38	41	37	812	8
	pine	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	58	0	.	21	45
	larch	4	100	0	.	0	.	0	.	0	.	0	.	4	100	13	58	4	100	0	.	4	100	204	18	90	22	89	23	413	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	43	19	45	0	.	46	31
	other conifers	0	.	4	100	4	100	8	71	16	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	47
	beech	58	29	57	31	51	31	53	36	88	23	16	50	44	37	46	34	7	71	4	100	9	71	4	100	11	74	49	37	499	10
	maple	0	.	5	100	0	.	0	.	0	.	4	100	0	.	0	.	8	71	0	.	5	100	0	.	0	.	0	.	21	45
	ash	16	61	4	100	14	58	16	75	21	45	0	.	0	.	14	59	4	100	0	.	0	.	0	.	4	100	0	.	93	24
	oak	5	100	17	50	16	79	0	.	33	47	0	.	0	.	5	100	0	.	5	100	0	.	0	.	8	71	4	100	93	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	110	23	115	22
	other broadleaves	0	.	6	100	13	58	4	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	10	73	41	34
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	445	11	114	21	182	19	228	16	340	13	223	19	431	11	438	13	252	16	169	23	147	23	486	14	556	11	506	10	4515	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	37	40	8	71	8	71	20	45	53	32	33	35	69	26	58	48	87	26	12	74	24	41	66	32	122	19	103	21	700	9
	fir	43	32	0	.	12	58	20	45	33	39	12	58	47	33	33	39	13	58	0	.	11	58	8	71	8	71	13	57	253	13
	pine	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	8	71
	larch	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	26	38	37	59	32	166	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	50	20	45	0	.	36	33
	other conifers	0	.	4	100	4	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58
	beech	13	58	11	58	0	.	16	50	51	34	0	.	4	100	17	60	8	71	4	100	0	.	4	100	0	.	16	50	144	18
	maple	0	.	0	.	0	.	6	100	0	.	0	.	4	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	13	59
	ash	0	.	0	.	4	100	5	100	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	18	50
	oak	4	100	10	73	10	71	4	100	16	50	4	100	0	.	10	72	0	.	0	.	0	.	0	.	0	.	0	.	58	28
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	69	31	73	30
	other broadleaves	0	.	10	73	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	19	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	96	23	43	32	43	32	75	25	160	18	49	29	128	19	123	27	107	23	16	61	39	32	163	18	192	15	265	14	1500	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	0	.	4	100	0	.	4	100	13	58	13	58	18	51	19	60	16	77	27	38	12	58	12	58	23	40	164	17
	fir	28	43	0	.	0	.	4	100	4	100	8	71	9	71	0	.	0	.	8	71	0	.	0	.	8	71	14	58	83	23
	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	.	39	34	17	50	10	100	66	28
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	58	4	100	0	.	16	50
	other conifers	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	beech	4	100	0	.	0	.	0	.	9	71	4	100	4	100	4	100	0	.	0	.	0	.	0	.	0	.	8	71	33	36
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100
	ash	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	oak	0	.	10	73	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	61
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	47	25	47
	other broadleaves	0	.	0	.	4	100	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	21	67
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	36	37	10	73	16	50	17	80	26	48	25	41	26	41	22	45	19	60	24	56	27	38	67	25	41	32	84	25	439	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	0	.	4	100	4	100	0	.	0	.	9	71	8	71	8	71	4	100	16	50	8	71	60	45	32	42	155	22
	fir	0	.	0	.	0	.	0	.	12	58	4	100	4	100	0	.	0	.	0	.	4	100	0	.	4	100	8	71	36	33
	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	15	61	32	39
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	8	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	.	11	100	11	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	4	100	0	.	0	.	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	0	.	0	.	0	.	7	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	58
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	38	77	38
	other broadleaves	10	100	5	100	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	41	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17	64	5	100	4	100	4	100	42	57	4	100	14	58	8	71	16	62	4	100	23	41	16	50	80	35	147	24	383	15
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19317	6	3017	18	5456	13	8347	10	13951	8	12648	9	22182	7	15893	8	15679	8	6855	13	7527	12	22330	7	41265	5	13673	8	208140	2
	fir	15781	5	1275	21	1880	15	6074	10	4137	12	3448	15	10215	7	5052	10	1732	17	1844	19	922	24	2244	19	1073	18	1808	19	57483	3
	pine	1901	17	612	22	356	30	462	36	1141	13	21	100	998	37	110	39	584	42	645	32	387	56	6190	14	6515	13	496	41	20420	7
	larch	133	54	127	40	450	45	190	31	427	23	134	65	95	70	174	52	410	31	0	.	428	33	11658	7	7266	10	8958	8	30450	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	96	100	48	100	34	63	76	100	0	.	2369	16	3500	14	62	56	6184	10
	other conifers	578	35	235	36	157	45	330	28	753	30	0	.	85	38	228	34	202	66	165	56	58	57	143	48	1051	28	49	68	4031	12
	beech	19770	5	6704	9	4592	11	6851	8	10020	7	2335	17	9157	8	7418	9	3548	19	1741	19	3041	16	2277	21	1996	17	13648	9	93098	3
	maple	5039	8	1542	16	1181	19	1485	17	3856	11	1413	17	2234	15	2734	15	2483	14	862	24	1382	19	1440	17	808	27	1787	17	28248	4
	ash	2369	13	1499	17	2065	15	1535	15	3838	10	706	23	1471	17	2217	16	1000	24	701	27	648	25	1270	22	901	28	3271	14	23492	4
	oak	2214	21	868	25	1333	21	632	20	1252	17	111	43	116	37	95	35	403	49	60	47	15	76	1276	24	171	51	2420	17	10965	8
	sweet chestnut	0	.	0	.	114	83	10	100	4	100	0	.	0	.	0	.	4	100	0	.	0	.	298	61	17	100	14562	9	15008	9
	other broadleaves	4132	10	1696	26	2033	18	1773	16	3782	10	1322	21	2353	14	2251	17	3224	15	1664	26	1336	22	7002	11	5949	14	19606	7	58122	4
	indeterminable	224	25	44	46	22	60	42	44	35	47	80	38	139	31	65	39	232	23	61	41	59	49	208	21	263	25	207	29	1681	9
	total	71459	3	17619	8	19639	7	27730	6	43196	4	22217	8	49140	4	36284	6	29535	7	14674	10	15803	9	58705	4	70774	4	80545	3	557321	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. 1296754. Birmensdorf, Swiss Federal Research Institute WSL

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