Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
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, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4705	13	1202	33	1303	34	4625	18	3886	16	3431	20	7278	14	3149	21	4915	14	2350	21	1957	23	7916	11	14644	9	5288	16	66649	4
	fir	6378	11	315	33	684	29	2883	17	1039	23	1097	23	4244	16	1248	24	574	26	948	25	180	43	557	27	139	35	643	29	20930	6
	pine	74	62	56	75	39	100	106	56	59	100	0	.	239	71	0	.	110	82	187	75	44	100	1772	32	2541	26	165	59	5392	17
	larch	0	.	74	80	450	84	33	100	288	52	131	70	50	76	49	100	235	52	0	.	128	61	3087	16	1723	20	2891	18	9139	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	706	31	1586	28	49	74	2340	21
	other conifers	350	47	103	62	0	.	124	38	431	50	0	.	50	58	134	47	270	63	144	72	67	60	156	60	644	33	20	100	2494	17
	beech	6651	10	2917	16	1741	30	2885	16	4042	14	804	32	3449	13	2039	19	1346	25	549	28	1262	28	1281	28	549	27	6155	14	35669	5
	maple	2748	16	960	26	738	43	824	25	1606	15	435	33	1219	28	998	25	1635	20	352	31	435	36	1399	27	373	35	1282	30	15003	7
	ash	871	26	572	29	379	29	396	34	896	24	351	41	418	34	323	30	547	33	488	44	251	60	866	28	410	35	1525	22	8293	8
	oak	918	50	138	55	378	46	59	74	184	43	21	100	0	0	0	.	106	85	18	100	35	100	718	38	15	100	870	41	3459	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	100	18	100	4138	20	4230	20
	other broadleaves	2293	15	698	24	969	26	1713	23	2361	17	1109	28	1567	21	964	23	2040	25	919	40	751	35	3821	15	3331	21	10498	11	33034	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
	total	24988	6	7035	13	6682	15	13647	10	14790	8	7378	13	18514	8	8905	12	11778	11	5956	16	5110	16	22355	7	25973	7	33522	6	206634	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2745	13	467	30	1042	29	2110	19	2711	16	2486	20	4602	12	2946	15	2657	15	1761	19	1253	24	5031	13	9338	9	3179	14	42328	4
	fir	3703	11	328	37	244	39	1526	20	745	24	753	27	2353	14	710	23	451	28	395	29	100	41	305	32	106	48	302	34	12020	6
	pine	234	42	139	49	17	100	151	69	108	48	0	.	278	79	0	.	17	100	146	56	61	77	1872	22	1021	23	16	100	4060	14
	larch	0	.	17	100	140	67	17	100	170	52	0	.	17	100	49	100	121	55	0	.	71	79	2042	16	1467	25	2569	15	6681	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	543	26	1266	22	16	100	1825	17
	other conifers	69	61	69	61	20	100	20	100	138	35	0	.	17	100	36	71	87	82	0	.	0	0	17	100	52	58	0	.	524	22
	beech	4316	11	1212	18	913	21	1213	14	2138	13	673	26	2484	15	1471	19	800	25	784	27	558	34	622	36	490	33	3930	16	21603	5
	maple	1504	13	505	24	282	32	242	33	1326	20	571	23	816	22	871	21	684	28	233	40	477	30	478	27	194	45	287	32	8470	7
	ash	458	27	297	34	255	34	208	33	351	26	251	37	373	40	567	28	174	37	137	43	136	43	424	38	238	74	649	34	4519	10
	oak	523	39	104	52	411	36	161	37	88	45	19	100	91	61	21	100	83	51	0	0	0	.	288	38	15	100	793	23	2598	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	71	15	100	3758	15	3811	15
	other broadleaves	687	25	243	31	296	34	399	31	1008	18	355	37	397	29	629	37	469	24	278	45	234	32	1281	19	1299	26	4362	12	11936	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
	total	14241	6	3381	12	3619	14	6047	11	8783	8	5108	14	11428	8	7299	10	5542	11	3733	15	2890	16	12941	8	15501	7	19862	5	120376	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2464	12	264	41	476	25	947	19	1665	15	1905	16	2905	12	2759	13	2213	14	1042	20	770	24	3033	11	5381	9	2015	15	27838	4
	fir	1770	12	183	41	219	38	860	19	395	26	568	29	1282	16	610	22	242	30	293	37	129	45	287	34	107	37	261	33	7208	6
	pine	500	31	178	32	89	57	84	51	109	32	0	.	189	57	6	100	60	89	145	53	53	100	678	22	578	26	94	71	2761	12
	larch	0	.	14	71	38	84	0	.	45	60	9	100	0	.	0	.	82	61	0	.	74	73	1724	19	1127	15	1527	16	4639	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	310	31	649	24	0	.	968	19
	other conifers	0	.	31	81	7	100	59	51	41	85	0	.	0	.	0	.	7	100	0	.	15	100	0	0	0	.	0	.	160	35
	beech	3409	10	989	18	605	20	1073	16	1231	14	419	34	1287	14	1373	16	531	24	472	30	276	31	273	32	310	26	1712	15	13961	5
	maple	765	19	173	33	41	41	262	26	473	19	147	45	496	22	531	23	296	33	142	55	258	31	64	48	118	42	204	40	3970	8
	ash	236	26	142	39	390	35	215	25	491	22	182	51	226	67	261	29	184	39	110	66	103	44	190	38	124	46	151	34	3004	10
	oak	246	30	128	37	120	43	92	63	139	34	29	77	0	.	16	71	38	80	7	100	0	.	107	43	7	100	297	27	1226	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	62	0	.	1382	20	1469	19
	other broadleaves	218	31	80	46	122	48	208	30	316	24	20	71	69	60	162	34	79	43	51	48	40	57	128	32	135	36	1617	17	3245	10
	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
	total	9607	6	2183	13	2106	14	3799	10	4904	9	3279	13	6454	8	5718	9	3741	11	2263	15	1718	16	6882	8	8535	7	9262	7	70450	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1803	9	180	30	466	24	677	20	1096	12	1311	14	2064	11	1588	12	1660	13	706	24	665	22	1768	12	3623	8	1446	14	19054	3
	fir	1460	10	131	41	159	32	567	16	444	18	240	26	813	13	491	16	184	32	276	32	121	31	173	34	88	35	148	32	5295	5
	pine	225	25	94	35	54	47	61	37	211	24	0	.	117	59	7	100	21	58	37	54	14	100	273	25	379	23	41	52	1534	11
	larch	7	100	14	70	29	61	21	74	47	55	0	.	0	.	7	100	30	79	0	.	28	100	1192	16	925	14	835	14	3135	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	153	31	255	23	0	.	407	18
	other conifers	0	.	28	61	21	100	27	50	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	90	35
	beech	1955	9	762	16	471	21	789	14	738	13	136	36	867	18	1017	16	390	25	149	31	294	24	77	55	201	33	674	15	8520	4
	maple	347	16	83	37	23	75	48	43	138	26	63	41	112	29	168	24	106	31	48	42	124	29	56	60	41	55	44	47	1400	8
	ash	219	23	75	39	175	33	157	38	300	19	40	60	74	37	175	30	34	54	45	61	40	48	20	73	0	0	51	54	1404	10
	oak	179	32	119	40	131	32	95	33	155	29	19	100	34	61	7	100	8	100	25	72	0	.	21	75	12	100	155	31	960	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	6	100	600	20	620	20
	other broadleaves	199	39	49	42	42	47	30	60	215	25	6	100	45	41	0	.	63	55	6	100	25	61	52	43	42	72	484	18	1258	11
	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	6393	5	1535	12	1572	13	2472	10	3349	7	1817	12	4125	8	3461	9	2502	11	1293	17	1310	15	3792	9	5572	6	4484	6	43678	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1082	11	132	31	289	28	344	18	497	16	744	15	1162	11	1263	12	883	15	374	21	368	20	986	13	1906	9	898	18	10928	4
	fir	947	11	35	44	145	34	504	18	283	21	278	23	756	14	478	20	167	27	99	38	84	42	152	39	92	34	190	29	4210	6
	pine	105	29	31	50	47	62	54	39	128	30	0	.	43	54	17	100	7	100	22	74	7	100	68	41	198	28	14	100	742	13
	larch	7	100	7	100	8	100	14	71	63	36	0	.	0	.	14	100	35	53	0	.	12	100	814	14	621	16	538	15	2134	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	51	135	27	0	.	198	25
	other conifers	0	.	42	62	0	.	47	62	28	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	124	36
	beech	870	12	370	21	263	25	597	14	686	12	111	35	531	19	343	20	150	31	116	36	164	35	34	63	59	44	309	19	4604	5
	maple	116	26	35	44	18	72	28	50	70	35	45	41	40	45	105	34	31	60	12	70	27	50	0	.	52	41	0	.	578	12
	ash	28	50	45	49	88	36	124	38	224	20	0	.	31	75	52	42	28	50	18	74	38	46	7	100	0	.	40	63	724	12
	oak	63	37	30	60	56	56	108	29	163	24	0	.	0	.	0	.	0	.	6	100	0	.	8	100	27	81	57	41	518	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	6	100	297	26	312	25
	other broadleaves	15	71	28	61	62	51	45	54	45	41	0	.	6	100	39	52	47	59	6	100	12	70	23	57	25	71	57	42	409	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	3233	6	756	14	975	14	1864	9	2185	8	1179	12	2570	8	2311	9	1347	12	655	17	713	16	2166	9	3121	7	2407	10	25483	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	558	15	90	29	106	36	222	23	250	19	370	20	509	16	673	16	424	17	217	24	173	29	397	18	764	13	371	16	5123	5
	fir	395	18	21	58	77	35	249	22	189	25	108	42	359	18	197	23	120	34	30	50	14	71	20	73	53	39	30	50	1860	8
	pine	15	71	0	.	14	71	27	50	20	75	0	.	17	72	7	100	0	.	0	.	0	.	7	100	58	35	7	100	172	21
	larch	0	.	0	.	7	100	7	100	20	58	0	.	0	.	0	.	7	100	0	.	6	100	429	19	336	19	209	21	1021	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	22	58	0	.	36	45
	other conifers	0	.	14	100	0	.	40	57	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	69	42
	beech	206	22	138	27	102	27	203	21	306	20	14	71	135	32	92	31	28	50	42	57	46	54	20	73	41	41	151	28	1523	8
	maple	15	71	0	.	7	100	0	.	41	47	0	.	6	100	27	76	35	52	8	100	0	.	0	.	0	.	0	.	140	27
	ash	39	53	14	70	14	71	36	45	63	33	0	.	7	100	28	61	17	71	0	.	12	100	0	.	0	.	10	100	241	19
	oak	19	73	7	100	30	50	32	51	53	53	0	.	0	0	0	.	0	.	0	.	0	.	0	.	6	100	8	100	156	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	75	31	82	30
	other broadleaves	0	.	14	100	27	50	7	100	9	100	0	.	0	0	8	100	31	82	8	100	6	100	7	100	18	72	15	71	151	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	1248	10	298	18	383	17	822	12	959	11	492	17	1033	12	1033	13	662	14	305	21	257	23	901	13	1297	10	884	10	10572	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	183	22	49	47	37	46	89	32	107	31	99	32	228	18	217	24	238	20	127	32	153	31	178	27	309	17	203	26	2216	7
	fir	190	20	7	100	42	49	90	29	125	27	35	52	155	24	98	27	87	35	56	62	13	71	37	60	34	45	30	49	999	10
	pine	7	100	0	.	0	.	14	71	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	48	38
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	15	71	14	71	0	.	7	100	180	26	117	26	93	27	433	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	21	58	0	.	50	38
	other conifers	0	.	0	.	0	.	20	58	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	58
	beech	35	53	38	45	20	58	88	36	113	28	22	58	70	37	38	65	18	57	6	100	9	100	6	100	18	74	44	41	525	13
	maple	0	.	16	71	0	.	0	.	7	100	6	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	36	45
	ash	15	71	0	.	0	.	10	100	7	100	8	100	0	.	7	100	0	.	6	100	0	.	0	.	6	100	6	100	65	34
	oak	0	.	18	70	14	71	0	.	37	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	76	32
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	67	40	75	37
	other broadleaves	0	.	16	71	21	58	0	.	7	100	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	437	14	143	25	135	25	309	18	435	15	169	25	453	14	375	18	370	16	195	27	182	29	438	17	527	13	450	15	4620	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	0	.	17	72	20	58	53	39	32	52	55	39	82	32	43	41	23	58	43	41	43	41	141	26	75	37	675	11
	fir	58	39	0	.	33	53	49	42	50	42	9	100	72	37	39	51	8	100	0	.	12	70	0	.	15	71	7	100	353	16
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	37	56	43	87	37	224	22
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	75	15	71	0	.	36	53
	other conifers	0	.	7	100	0	.	13	71	14	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	47
	beech	8	100	0	.	0	.	0	.	20	58	0	.	7	100	8	100	0	.	15	71	0	.	0	0	0	.	37	45	95	28
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	8	100
	ash	0	.	7	100	0	.	0	.	9	100	0	.	0	.	17	72	0	.	0	.	0	.	0	.	0	.	0	.	33	51
	oak	0	.	0	.	0	.	0	.	29	50	0	.	0	.	8	100	0	.	10	100	0	.	0	.	0	.	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	46	45	46
	other broadleaves	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	113	29	14	70	58	39	82	31	182	22	41	46	142	25	154	24	51	38	48	41	64	33	145	26	226	21	251	21	1571	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	0	.	0	.	7	100	13	71	27	58	6	100	0	.	38	45	0	.	29	50	22	58	49	43	25	59	231	19
	fir	22	58	0	.	0	.	7	100	7	100	0	.	6	100	6	100	0	.	7	100	14	71	0	.	0	.	25	58	95	28
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45	18	72	7	100	61	36
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	12	100	32	52	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	51	42
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	10	100	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	71
	oak	0	.	0	.	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	38	60	45	53
	other broadleaves	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	37	45	0	.	7	100	42	46	66	34	27	58	12	71	14	71	38	45	7	100	50	38	80	30	73	35	95	33	549	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	0	.	7	100	7	100	0	.	7	100	14	71	21	58	7	100	0	.	14	71	7	100	30	50	18	72	140	23
	fir	26	59	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	51
	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	.	15	71	14	100	29	61
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	7	100	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	51	50	51
	other broadleaves	0	.	0	.	7	100	7	100	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	33	51	0	.	14	71	14	71	30	71	7	100	21	57	21	58	7	100	0	.	14	71	14	71	51	38	83	39	311	17
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13608	8	2384	23	3744	20	9047	13	10277	11	10413	14	18823	10	12700	10	13078	11	6600	16	5426	18	19382	9	36184	7	13518	12	175182	3
	fir	14949	8	1020	30	1605	22	6734	13	3277	14	3088	21	10047	11	3876	15	1833	21	2106	22	667	25	1532	24	634	26	1637	24	53003	4
	pine	1160	24	499	28	260	38	497	33	655	22	0	.	883	57	37	55	215	53	538	43	179	80	4670	21	4795	19	338	53	14724	10
	larch	21	58	127	51	671	64	92	48	632	36	140	71	67	81	135	74	524	38	0	.	326	59	9587	12	6404	12	8770	11	27496	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1860	22	3954	20	65	79	5889	15
	other conifers	419	40	293	49	47	72	349	28	686	37	0	.	75	46	170	40	364	64	144	72	82	64	173	56	696	32	44	70	3543	14
	beech	17452	7	6427	12	4115	18	6860	10	9306	9	2180	24	8829	11	6388	13	3263	19	2133	23	2608	23	2313	27	1669	23	13012	12	86553	3
	maple	5494	12	1772	19	1108	34	1404	19	3661	13	1268	23	2689	20	2700	17	2794	18	794	26	1336	23	1997	24	778	29	1817	26	29611	5
	ash	1867	17	1153	22	1300	23	1154	20	2348	14	832	32	1129	34	1430	20	985	28	804	33	581	33	1508	27	778	43	2433	22	18301	6
	oak	1948	32	544	30	1141	31	555	23	870	17	88	56	125	51	52	50	234	55	67	68	35	100	1142	32	91	72	2180	24	9071	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	238	67	45	72	10448	13	10739	13
	other broadleaves	3412	15	1128	19	1560	22	2408	20	3972	14	1490	26	2085	18	1803	23	2734	22	1269	37	1068	31	5314	14	4849	21	17040	10	50132	5
	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	0
	total	60329	4	15346	10	15551	11	29098	7	35684	6	19498	11	44753	6	29290	8	26039	8	14455	13	12307	13	49715	6	60878	5	71300	4	484244	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. 1327037. Birmensdorf, Swiss Federal Research Institute WSL

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