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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NFI1
total number of stems NFI1
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 1983/85
		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	8403	15	1719	33	2157	24	4896	19	6884	16	6072	18	8884	13	5842	17	4165	15	2274	23	2178	26	8080	13	11627	10	2871	15	76051	4
	fir	7210	10	451	37	1070	33	3152	19	1337	19	1255	26	4069	12	1586	18	824	29	888	32	363	39	572	36	396	29	611	34	23786	6
	pine	334	35	399	63	241	93	168	82	209	39	0	.	464	66	0	.	141	87	199	57	0	.	4036	22	2503	24	101	69	8796	13
	larch	34	100	35	70	39	100	17	100	157	51	0	.	0	.	0	.	55	74	0	.	90	72	2578	18	935	23	1783	20	5723	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	389	34	844	24	0	.	1234	19
	other conifers	177	40	295	68	17	100	169	42	273	55	0	.	0	.	96	45	17	100	77	75	49	73	87	100	70	78	0	.	1327	23
	beech	10183	9	3611	16	2135	19	3675	13	5064	11	1123	31	4899	14	3214	18	1682	25	1047	32	1151	26	1200	34	1068	26	6572	15	46625	4
	maple	1999	15	615	26	210	50	354	32	1232	20	622	26	885	19	845	25	940	29	216	38	530	39	490	39	167	57	282	32	9387	7
	ash	1473	25	416	35	664	31	466	30	1516	19	701	36	1171	34	1287	28	433	38	188	40	407	57	342	37	420	67	914	27	10399	9
	oak	1245	37	104	41	1503	40	350	42	274	29	147	64	90	52	43	71	161	61	18	100	0	.	426	36	31	100	1291	38	5685	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	72	0	.	8737	15	8830	15
	other broadleaves	2539	18	696	29	992	38	1103	33	2539	17	900	27	1540	23	1885	28	1074	23	903	48	523	36	2339	19	2109	24	9074	11	28217	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	33597	6	8341	13	9028	14	14350	10	19486	8	10820	14	22002	8	14799	11	9494	12	5812	18	5291	19	20632	9	20171	8	32237	6	226060	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4479	11	973	29	2110	22	2749	15	3119	13	3265	17	5818	12	4841	15	3142	16	1661	21	1791	27	5213	12	8312	10	2592	17	50063	4
	fir	4189	12	280	34	479	41	2189	17	1113	20	844	28	2266	13	1227	19	364	30	519	38	233	36	310	42	158	40	166	48	14335	6
	pine	617	33	197	51	222	53	96	55	235	35	0	.	152	63	0	.	125	77	287	44	0	.	1884	20	1466	20	137	55	5417	11
	larch	17	100	35	70	54	73	103	85	102	53	0	.	0	.	0	.	54	58	0	.	71	79	1941	18	1159	18	1177	21	4714	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	378	29	684	29	0	.	1062	21
	other conifers	0	.	35	71	33	100	17	100	67	50	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	167	35
	beech	6777	9	2440	16	1378	20	2277	15	2856	12	605	28	2323	16	2546	17	1078	21	426	31	664	25	472	40	562	31	2648	16	27052	4
	maple	1244	15	344	29	56	76	281	30	1088	19	177	46	509	22	782	22	535	30	192	38	496	29	75	50	128	48	56	58	5965	7
	ash	896	21	383	35	576	33	431	24	887	20	132	55	392	31	876	24	236	43	154	72	179	59	140	49	184	64	78	50	5542	9
	oak	668	26	244	40	420	27	415	26	538	25	21	100	42	70	54	100	18	100	21	100	0	.	94	64	0	.	470	34	3004	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	15	100	2681	18	2715	18
	other broadleaves	792	22	275	31	470	46	373	30	960	20	83	54	214	39	459	32	231	37	103	41	180	42	314	32	351	52	2378	16	7184	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	19678	5	5205	13	5799	12	8931	9	10963	7	5128	13	11715	8	10784	10	5783	12	3363	16	3628	17	10840	9	13020	8	12383	7	127221	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3393	10	656	24	1773	20	2559	13	3270	11	2266	16	4150	11	3188	13	2674	15	1115	24	906	21	3006	12	5288	9	1687	16	35929	3
	fir	2593	11	328	34	309	40	1485	14	982	16	468	28	1434	14	1026	17	300	30	248	36	142	39	194	47	144	31	230	34	9884	5
	pine	574	25	396	29	305	40	204	39	721	24	0	.	73	49	14	72	46	72	77	60	0	.	503	21	868	21	111	67	3891	10
	larch	0	.	24	77	40	72	45	54	146	40	0	.	0	.	0	.	58	53	0	.	0	.	1269	15	1159	17	741	21	3482	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	212	38	472	25	0	.	693	20
	other conifers	0	.	0	.	7	100	63	69	30	81	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	117	45
	beech	3800	9	1087	16	861	21	1658	14	1304	14	312	34	1491	16	1344	19	357	27	146	40	423	28	96	56	250	35	753	18	13883	5
	maple	458	20	230	30	42	84	72	49	212	29	92	63	200	42	355	26	108	38	45	57	160	39	26	100	16	72	26	77	2041	10
	ash	285	30	139	34	175	35	230	30	726	17	26	76	149	34	176	34	125	47	31	72	62	60	33	100	15	100	41	84	2214	10
	oak	238	32	141	36	227	35	118	33	276	26	0	.	14	100	7	100	0	.	10	100	0	.	0	.	36	72	255	37	1321	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	70	6	100	573	21	621	20
	other broadleaves	297	27	218	35	212	37	85	39	252	27	15	100	38	63	63	44	199	38	15	100	82	44	102	45	118	55	537	25	2233	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11637	5	3219	12	3951	13	6518	8	7920	7	3179	14	7549	8	6173	9	3875	12	1687	18	1775	16	5501	9	8372	7	4953	8	76309	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2393	8	524	25	871	19	1705	13	2014	11	1271	15	2569	10	2029	11	1750	13	624	20	720	20	1594	12	3002	9	1126	14	22191	3
	fir	1146	11	91	33	175	43	762	15	682	17	235	24	1120	12	659	17	262	27	94	39	85	41	151	33	118	40	196	31	5774	5
	pine	253	22	131	36	149	42	218	28	556	22	0	.	131	63	63	64	65	64	7	100	0	.	201	35	360	24	7	100	2141	10
	larch	7	100	21	74	7	100	14	100	97	36	0	.	7	100	15	100	28	50	7	100	12	70	842	14	707	15	454	16	2218	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	47	176	29	0	.	240	24
	other conifers	0	.	0	.	55	100	40	62	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	54
	beech	1482	10	580	18	255	24	824	17	862	14	75	42	743	20	451	19	168	31	100	37	232	28	43	59	179	33	310	21	6304	5
	maple	96	29	49	37	14	71	15	71	107	34	29	51	24	59	66	45	61	40	12	70	42	41	0	.	21	58	0	.	537	13
	ash	103	33	50	38	66	39	50	51	235	23	0	.	54	50	77	37	7	100	12	100	35	51	0	.	0	.	18	72	708	12
	oak	79	32	53	46	102	51	93	41	179	26	0	.	0	.	0	.	0	.	6	100	0	.	0	.	20	76	38	51	571	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	6	100	317	22	331	21
	other broadleaves	29	60	42	71	30	71	36	45	63	42	0	.	20	58	15	71	46	53	20	58	12	70	37	53	7	100	66	44	424	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5588	5	1539	14	1724	13	3756	9	4830	7	1610	13	4668	8	3376	9	2388	11	883	17	1138	16	2938	9	4597	7	2533	8	41568	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1158	11	118	30	340	23	704	14	834	14	491	20	1096	12	1046	14	752	16	299	24	320	24	730	14	1407	11	537	16	9831	4
	fir	417	17	14	70	57	39	352	20	184	24	149	36	461	18	190	22	137	32	115	37	58	45	65	46	57	43	30	49	2286	7
	pine	77	35	14	100	48	51	63	50	113	28	0	.	35	76	0	.	0	.	0	.	0	.	35	45	158	33	14	71	558	15
	larch	7	100	7	100	7	100	0	.	42	41	0	.	16	71	7	100	63	43	21	100	25	70	438	17	410	18	265	21	1306	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	86	33	0	.	100	30
	other conifers	0	.	0	.	8	100	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	73
	beech	308	19	298	22	62	40	259	20	313	19	30	50	361	25	152	31	112	30	37	53	65	50	14	100	58	52	152	30	2223	8
	maple	35	66	7	100	0	.	7	100	44	41	6	100	0	.	18	74	14	71	0	.	0	.	0	.	6	100	0	.	137	26
	ash	22	58	0	.	33	51	20	71	79	32	8	100	7	100	7	100	7	100	0	.	0	.	0	.	12	100	0	.	196	21
	oak	34	60	44	47	30	61	20	74	107	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	242	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	116	28	123	27
	other broadleaves	7	100	22	58	15	71	15	100	31	61	0	.	7	100	0	.	31	60	0	.	15	100	23	74	0	.	23	57	188	23
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2065	8	523	16	599	16	1455	10	1746	9	684	17	1984	9	1420	12	1116	13	473	22	483	20	1326	11	2195	9	1144	10	17212	3
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	252	19	36	52	137	27	154	27	195	21	145	27	326	18	303	21	178	26	144	32	161	37	184	28	362	17	240	26	2817	6
	fir	137	23	7	100	15	71	64	33	48	38	15	100	230	23	41	41	19	58	0	.	0	.	13	71	12	100	24	58	624	12
	pine	7	100	14	100	0	.	14	100	27	50	0	.	11	100	7	100	0	.	0	.	0	.	0	.	28	50	0	.	108	29
	larch	0	.	0	.	0	.	7	100	7	100	0	.	0	.	17	72	7	100	0	.	7	100	208	25	162	25	92	33	507	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	29	62	0	.	36	53
	other conifers	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	beech	56	35	70	42	30	62	70	40	160	28	16	71	47	46	17	72	29	50	10	100	8	100	7	100	33	52	52	38	606	13
	maple	0	.	29	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	50
	ash	7	100	14	100	7	100	0	.	14	71	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	49	43
	oak	0	.	9	100	0	.	7	100	20	58	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	48	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	43	53	43
	other broadleaves	0	.	0	.	0	.	0	.	8	100	0	.	0	.	10	100	6	100	0	.	0	.	8	100	0	.	7	100	38	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	459	14	179	25	189	24	322	18	479	15	176	27	621	13	414	19	240	21	154	31	176	34	427	18	626	13	467	16	4929	5
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	98	31	7	100	33	53	43	41	47	43	30	50	56	35	189	31	75	35	14	71	58	47	71	34	167	21	91	38	978	10
	fir	81	33	7	100	16	100	21	58	29	50	0	.	113	28	13	71	6	100	15	100	6	100	0	.	15	70	30	60	352	16
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	14	71
	larch	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	31	48	38	36	45	219	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	53	15	71	0	.	50	43
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	8	100	8	100	13	71	31	51	0	.	17	72	7	100	8	100	0	.	0	.	7	100	0	.	7	100	107	27
	maple	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	17	73	0	.	0	.	0	.	23	60
	ash	0	.	0	.	7	100	17	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	59
	oak	0	.	0	.	8	100	17	72	28	50	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	60	36
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	93	50	100	47
	other broadleaves	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	185	23	21	58	79	35	120	25	141	23	36	45	187	21	210	28	89	34	35	54	80	38	242	21	252	17	257	24	1934	7
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	71	7	100	15	71	23	58	0	.	9	100	7	100	22	58	14	71	8	100	43	47	15	71	35	45	14	71	225	19
	fir	7	100	0	.	0	.	0	.	0	.	0	.	45	41	9	100	7	100	0	.	0	.	7	100	0	.	18	72	93	29
	pine	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	21	58	0	.	36	45
	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	8	100	21	58	7	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	12	70	0	.	63	33
	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	.	7	100	8	100	0	.	22	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	50	33	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	21	58	21	58	30	50	51	48	28	50	9	100	52	38	30	50	21	58	15	71	43	47	43	41	68	32	65	36	500	13
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	0	.	7	100	0	.	7	100	0	.	6	100	7	100	0	.	7	100	22	58	0	.	23	58	0	.	93	28
	fir	14	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	70
	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	0	.	7	100	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	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	51	35	51
	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	28	50	0	.	13	71	0	.	22	58	0	.	6	100	7	100	0	.	7	100	22	58	21	58	23	58	42	46	191	20
>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	.	9	100	0	.	0	.	7	100	0	.	0	.	14	71	0	.	7	100	38	45
	fir	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	18	73
	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	7	100	14	71
	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	.	52	49	52	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	.	0	.	0	.	0	.	9	100	0	.	0	.	7	100	0	.	0	.	21	58	14	71	78	38	137	26
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20204	8	4039	21	7442	16	12833	11	16370	9	13556	13	22911	9	17465	11	12757	12	6147	19	6197	19	18907	10	30223	8	9165	12	198215	3
	fir	15800	8	1178	27	2121	26	8026	13	4376	13	2965	22	9738	10	4751	13	1920	22	1879	29	888	30	1311	29	900	25	1314	27	57166	4
	pine	1862	20	1151	28	965	37	771	30	1868	17	0	.	866	53	84	57	376	58	571	45	0	.	6659	19	5390	17	369	52	20932	9
	larch	72	55	122	42	153	63	185	60	551	29	0	.	23	72	39	58	266	37	28	100	205	57	7427	12	4609	12	4561	14	18241	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	1121	23	2313	20	0	.	3442	15
	other conifers	177	40	330	66	119	71	309	36	405	46	0	.	7	100	96	45	17	100	77	75	65	77	104	85	70	78	0	.	1777	20
	beech	22606	7	8100	13	4743	16	8798	10	10604	9	2163	23	9883	12	7732	14	3435	19	1774	28	2543	21	1840	31	2163	23	10494	14	96878	3
	maple	3832	11	1274	19	323	41	729	25	2682	15	932	26	1619	15	2066	17	1659	24	466	32	1244	27	591	37	338	36	365	32	18119	6
	ash	2786	19	1002	27	1528	24	1213	20	3457	14	867	34	1772	28	2430	21	809	33	385	41	683	49	515	32	632	62	1052	25	19132	7
	oak	2264	27	601	25	2299	30	1020	23	1451	16	168	57	145	48	116	59	179	64	61	59	0	.	520	39	87	75	2063	27	10974	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	175	61	28	100	12690	13	12893	13
	other broadleaves	3665	15	1252	21	1719	30	1619	25	3852	15	998	26	1819	21	2432	26	1588	22	1041	44	812	30	2823	19	2585	23	12085	10	38291	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	73266	4	19049	10	21412	10	35504	7	45616	6	21649	11	48782	6	37212	8	23014	9	12429	15	12636	14	41993	7	49338	6	54159	5	496061	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. 1344338. Birmensdorf, Swiss Federal Research Institute WSL

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