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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NFI3
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 2004/07
		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	6526	14	1887	39	1597	30	3997	18	6447	15	2837	19	6653	13	4385	17	3776	15	1771	26	1590	22	6629	13	12173	10	3062	16	63328	4
	fir	5981	10	586	36	573	28	2107	19	1262	34	1173	28	3755	13	1333	26	582	29	823	33	271	38	573	31	353	32	499	34	19872	6
	pine	145	44	157	61	0	0	369	72	103	47	0	.	350	69	0	.	58	72	70	61	0	.	2559	24	2015	27	49	100	5875	15
	larch	0	0	17	100	100	100	51	74	84	100	0	.	69	100	99	100	271	48	0	.	53	74	2421	18	847	23	1922	23	5934	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	557	32	777	28	16	100	1350	21
	other conifers	332	37	35	70	17	100	87	52	443	42	0	.	17	100	75	50	0	.	69	100	49	73	95	55	483	44	0	.	1703	19
	beech	7289	10	2118	17	1412	21	3065	13	3713	14	958	35	3932	15	2612	16	1267	25	754	28	1427	28	1177	33	597	29	5920	15	36241	5
	maple	2042	14	397	27	401	36	556	30	2025	22	419	26	1021	38	853	25	1163	27	165	50	474	33	696	28	268	39	490	31	10970	8
	ash	884	25	760	35	593	25	369	24	1233	24	169	51	581	27	812	31	353	35	297	43	191	56	440	42	525	43	1205	29	8413	9
	oak	967	42	129	71	630	42	78	51	206	41	37	71	15	100	21	100	124	74	0	.	0	.	604	35	15	100	1304	36	4130	17
	sweet chestnut	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	6351	17	6418	17
	other broadleaves	2279	16	727	29	734	32	1119	28	1506	20	734	35	1099	20	1071	23	1290	25	601	38	1225	37	3572	19	3076	27	8742	14	27775	7
	indeterminable	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	26446	6	6811	15	6123	15	11797	10	17021	9	6328	14	17494	8	11263	12	8885	12	4549	18	5278	18	19322	8	21130	8	29560	7	192008	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4467	13	700	31	1163	27	2466	17	3558	17	2663	19	5150	13	3542	16	2345	15	1151	23	1838	30	4600	14	7447	10	2420	15	43511	4
	fir	2874	13	414	35	485	44	1395	20	733	21	666	30	1954	14	711	23	446	28	479	33	165	40	350	36	125	56	375	38	11172	6
	pine	458	35	210	62	172	63	51	74	153	37	0	.	191	100	0	.	53	100	208	52	0	.	1720	20	1132	26	102	62	4450	13
	larch	17	100	0	.	68	79	0	.	140	49	0	.	0	.	25	100	95	61	0	.	53	100	1677	18	710	24	1453	18	4237	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	507	32	847	24	16	100	1370	19
	other conifers	17	100	121	47	52	100	119	47	51	58	0	.	0	.	18	100	17	100	0	.	15	100	39	71	35	71	0	.	484	23
	beech	5771	10	1638	18	1071	19	2040	14	2665	13	577	30	2636	15	1954	19	790	28	651	32	747	27	724	32	544	32	3554	17	25362	5
	maple	1246	15	401	24	125	48	314	30	1070	19	562	28	719	25	821	22	465	29	170	41	554	32	155	40	158	40	193	37	6954	7
	ash	791	26	249	35	401	32	295	24	767	25	390	33	562	40	984	22	108	58	217	40	539	70	294	43	257	63	389	35	6242	10
	oak	699	33	296	48	610	37	174	43	279	31	60	74	48	71	36	100	35	71	18	100	0	.	164	59	15	100	671	28	3107	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	15	100	3747	15	3799	15
	other broadleaves	636	22	252	33	291	39	457	31	683	21	161	46	238	39	610	35	478	33	103	48	151	40	733	24	635	34	3497	12	8925	7
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	16976	6	4281	13	4438	14	7312	10	10100	8	5080	15	11497	8	8699	10	4832	12	2998	18	4063	20	11000	9	11920	8	16416	6	119613	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3080	10	285	26	760	22	1347	18	1812	14	1366	18	3115	12	2491	12	2025	13	1100	23	863	29	2674	12	4859	9	1944	16	27722	4
	fir	2220	13	132	37	201	39	899	19	420	22	461	24	1062	15	722	18	235	35	169	38	118	40	175	48	131	42	195	35	7139	6
	pine	478	25	275	42	115	45	132	46	336	25	0	.	135	54	13	71	25	77	80	67	0	.	703	21	806	25	68	60	3165	11
	larch	7	100	49	58	15	71	34	71	78	53	0	.	0	.	7	100	31	80	0	.	25	100	1140	19	837	21	1028	21	3250	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	241	32	386	26	0	.	637	20
	other conifers	0	.	66	78	0	.	31	64	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	50
	beech	3952	9	1380	17	768	22	1292	14	1304	13	567	52	1354	20	1442	16	438	26	162	34	418	28	136	51	300	28	1099	17	14612	5
	maple	610	18	138	31	7	100	107	43	451	21	87	52	264	29	416	25	271	30	76	44	191	33	78	56	58	49	59	56	2813	9
	ash	379	28	182	33	344	39	426	33	742	18	146	69	192	68	456	25	179	41	79	69	92	61	127	84	40	56	119	42	3501	10
	oak	331	26	155	51	218	36	135	36	170	30	21	100	28	81	16	71	8	100	26	76	0	.	26	78	0	.	199	35	1334	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	73	6	100	1052	18	1087	18
	other broadleaves	265	34	148	37	159	45	93	43	373	27	0	0	110	43	116	43	146	48	51	58	32	52	188	36	94	43	880	18	2657	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	0
	total	11323	5	2809	15	2587	13	4496	10	5709	8	2647	16	6260	9	5680	9	3368	11	1744	17	1738	18	5517	9	7517	7	6643	7	68037	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1985	10	315	24	711	22	1090	15	1326	12	1128	14	2198	11	1631	12	1349	15	605	22	581	21	1717	11	3366	10	1225	14	19227	3
	fir	1616	10	111	37	210	34	702	16	535	16	270	26	809	14	584	18	212	28	131	40	101	41	182	37	81	37	176	31	5720	5
	pine	293	26	145	35	153	43	117	29	365	24	0	.	124	70	25	58	38	84	22	74	0	.	178	37	375	24	56	66	1890	11
	larch	7	100	7	100	27	71	36	60	48	42	0	.	7	100	0	.	36	68	0	.	0	0	1048	16	820	15	567	16	2603	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	105	42	206	23	0	.	311	21
	other conifers	0	.	49	77	0	.	53	56	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	124	40
	beech	1971	9	787	15	487	21	860	15	1008	12	133	36	866	16	879	19	278	29	140	36	267	26	49	60	207	33	513	18	8445	4
	maple	301	19	84	33	69	82	42	59	129	26	55	39	93	35	192	26	82	33	32	52	65	34	14	71	30	61	8	100	1197	10
	ash	196	27	57	35	113	30	234	36	472	17	22	74	62	45	107	31	49	49	31	72	174	65	21	76	7	100	37	66	1583	12
	oak	118	30	92	46	142	41	130	36	166	24	0	.	34	73	0	.	0	.	7	100	0	.	27	77	33	71	120	51	869	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	436	22	451	21
	other broadleaves	59	48	67	36	75	43	71	65	162	31	0	.	19	58	41	67	25	61	6	100	18	74	31	61	35	53	246	22	856	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0
	total	6547	5	1714	12	1987	14	3336	9	4226	7	1607	13	4212	8	3459	9	2076	12	975	18	1207	18	3379	9	5159	7	3391	7	43276	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1273	10	203	24	399	26	731	16	845	15	689	17	1295	11	1243	13	882	15	409	21	343	23	855	14	1604	10	711	16	11481	4
	fir	736	13	49	43	163	34	493	17	407	18	153	29	618	14	374	17	228	27	58	47	69	46	72	45	116	34	104	33	3640	6
	pine	91	27	14	100	28	61	83	33	167	24	0	.	44	54	8	100	0	.	7	100	0	.	69	42	152	29	7	100	669	12
	larch	0	.	7	100	7	100	7	100	89	42	0	.	0	.	22	75	56	53	7	100	18	74	668	16	623	15	384	16	1889	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	100	30	0	.	106	29
	other conifers	0	.	0	.	8	100	47	47	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	83	39
	beech	732	14	374	19	204	24	457	17	643	14	49	55	327	22	306	21	151	28	47	49	150	35	50	61	80	36	243	20	3813	6
	maple	38	45	21	57	7	100	0	.	74	43	16	71	17	73	52	41	13	71	6	100	7	100	0	.	20	73	0	.	270	19
	ash	75	34	54	38	78	35	97	44	170	24	0	.	30	60	43	52	14	71	6	100	29	59	0	.	0	.	0	.	596	13
	oak	35	53	44	72	37	53	70	41	135	25	0	.	0	.	0	.	0	.	6	100	0	.	0	.	13	71	33	59	372	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	206	26	212	26
	other broadleaves	22	58	23	58	36	80	7	100	0	.	0	.	7	100	27	75	78	60	14	71	49	77	30	61	7	100	22	58	322	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0
	total	3001	6	788	14	965	15	1991	10	2552	8	907	15	2338	8	2075	10	1422	12	560	18	665	18	1751	10	2720	8	1718	8	23453	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	454	16	84	45	144	33	264	24	306	20	182	28	526	14	496	18	303	20	129	27	136	27	282	22	606	15	320	18	4231	5
	fir	297	19	28	50	55	43	241	21	128	30	97	45	254	22	173	24	93	36	79	52	0	.	29	61	14	71	29	50	1517	9
	pine	29	50	0	.	14	71	7	100	20	58	0	.	10	100	7	100	0	.	0	.	0	.	0	.	64	37	0	.	151	23
	larch	0	.	0	.	0	0	7	100	14	71	0	.	0	.	7	100	21	74	0	.	6	100	341	21	161	25	151	25	707	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	29	50	0	.	36	45
	other conifers	0	.	7	100	8	100	27	62	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	49
	beech	72	34	116	29	7	100	129	27	185	21	29	50	150	30	87	38	53	44	14	71	26	77	0	.	14	71	85	30	967	10
	maple	0	.	17	72	0	.	0	.	22	58	6	100	0	.	0	.	17	72	0	.	0	.	0	.	0	.	0	.	63	36
	ash	36	52	0	.	26	78	22	71	36	45	7	100	0	.	10	100	0	.	6	100	0	.	0	.	6	100	11	100	162	25
	oak	38	68	53	61	23	58	7	100	20	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	148	31
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	75	34	83	33
	other broadleaves	0	.	22	58	0	.	0	.	7	100	0	.	0	.	0	.	20	58	0	.	0	.	0	.	0	.	8	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	925	11	327	20	276	23	704	13	767	12	322	22	940	12	781	14	506	17	228	25	168	25	667	15	900	12	680	11	8190	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	146	25	8	100	30	62	62	40	124	27	91	34	193	21	216	25	103	27	113	30	117	37	117	28	216	20	161	27	1696	8
	fir	133	26	7	100	34	45	99	31	33	53	9	100	138	24	35	53	12	70	0	.	33	52	7	100	35	45	22	57	598	12
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	7	100	166	26	100	30	49	38	338	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	47	21	58	0	.	64	37
	other conifers	0	.	0	.	8	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71
	beech	22	58	14	70	22	75	39	63	86	38	0	.	30	60	14	100	14	71	7	100	9	100	6	100	18	74	44	41	324	18
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	10	100
	ash	0	.	14	100	8	100	0	.	23	58	0	.	0	.	14	71	7	100	0	.	0	.	0	.	0	.	0	.	67	36
	oak	0	.	9	100	8	100	0	.	35	45	0	.	0	.	8	100	0	.	8	100	0	.	0	.	0	.	0	.	67	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	46	46	46
	other broadleaves	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	21	58
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	307	17	51	46	125	31	200	23	308	18	100	34	367	16	297	20	136	23	128	28	175	29	338	18	391	15	330	17	3253	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	38	53	7	100	14	71	8	100	14	71	16	71	20	58	66	67	46	41	7	100	36	45	62	51	70	35	61	39	464	16
	fir	51	38	0	.	0	.	14	71	13	71	0	.	43	41	19	58	8	100	7	100	0	.	0	.	7	100	18	71	182	20
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	35	35	53	29	61	143	26
	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	.	41	45	0	.	21	74	17	71	7	100	7	100	0	.	7	100	0	.	7	100	108	28
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100
	oak	0	.	0	.	0	.	18	73	20	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	46
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	45	46	53	42
	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	89	31	7	100	14	71	50	42	96	28	16	71	85	31	102	46	61	36	22	58	42	41	164	27	127	26	160	24	1034	10
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	7	100	14	71	20	74	0	.	7	100	15	71	0	0	21	58	11	100	95	30
	fir	7	100	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	72	39	45
	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	.	14	71	7	100	0	.	21	58
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	8	100	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58
	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	59	37	59
	other broadleaves	0	.	7	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	79
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	15	71	7	100	29	78	37	54	7	100	14	71	20	74	0	.	7	100	15	71	21	58	27	50	66	42	272	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	0	.	8	100	7	100	0	.	0	.	0	.	8	100	7	100	0	.	7	100	7	100	15	71	8	100	74	32
	fir	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	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	.	53	50	53	50
	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	.	8	100	7	100	0	.	0	.	7	100	8	100	7	100	0	.	7	100	14	71	30	50	74	42	170	24
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17974	8	3487	27	4825	17	9971	12	14434	11	8978	14	19164	9	14098	11	10837	11	5293	19	5526	20	16943	11	30377	7	9924	11	171831	3
	fir	13915	8	1326	29	1720	26	5951	13	3546	16	2830	23	8640	10	3952	14	1817	21	1746	27	756	28	1389	27	862	25	1436	26	49886	4
	pine	1493	22	801	30	481	37	758	40	1157	16	0	.	854	56	53	48	174	50	387	51	0	.	5229	19	4551	19	282	55	16220	9
	larch	38	55	80	43	217	60	134	42	452	31	0	.	76	91	170	74	509	35	7	100	162	64	7555	12	4146	13	5597	15	19143	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1488	25	2380	20	32	100	3910	16
	other conifers	349	35	277	47	92	79	364	30	580	37	0	.	24	77	93	44	17	100	69	100	65	77	134	58	518	43	16	100	2600	15
	beech	19809	7	6434	13	3971	15	7890	10	9652	9	2314	29	9315	12	7311	13	2997	20	1784	26	3043	22	2149	30	1760	24	11465	13	89894	3
	maple	4236	10	1058	19	608	29	1019	23	3772	15	1145	22	2114	25	2335	17	2012	22	449	33	1307	26	942	27	535	29	750	27	22282	5
	ash	2361	18	1316	26	1571	21	1454	21	3444	14	734	34	1427	32	2427	20	711	30	636	35	1024	50	882	43	834	46	1761	27	20580	7
	oak	2189	28	777	36	1669	30	612	25	1046	18	118	57	125	52	80	61	167	68	66	54	0	.	821	34	84	74	2326	26	10082	11
	sweet chestnut	0	.	0	.	66	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	89	66	28	100	12047	13	12238	13
	other broadleaves	3262	14	1246	22	1308	27	1768	25	2731	16	896	32	1473	19	1865	24	2037	22	775	35	1476	34	4554	18	3848	25	13402	11	40639	6
	indeterminable	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	65627	4	16803	11	16529	11	29922	7	40814	6	17015	12	43213	6	32384	8	21294	9	11212	14	13358	15	42174	7	49921	6	59038	4	459304	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. 1326981. Birmensdorf, Swiss Federal Research Institute WSL

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