Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-35calculated with twice the standard error, i.e. 29 ± 2 × 3

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6940	12	1445	28	2106	29	4677	17	5432	15	4739	17	8836	12	5194	15	5753	13	2845	19	2796	19	9046	12	16424	9	4570	14	80801	4
	fir	7017	9	536	35	739	27	2734	17	1601	27	1228	24	4719	13	1553	23	735	27	1186	27	303	35	627	28	367	39	502	32	23849	5
	pine	163	37	127	52	33	71	251	88	110	60	0	.	330	68	0	.	182	69	252	68	141	100	2977	25	2737	23	81	100	7383	14
	larch	17	100	0	.	369	90	33	100	89	64	88	100	104	100	102	100	305	54	0	.	195	49	3786	14	1707	19	3450	15	10245	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	629	31	1734	22	32	100	2396	17
	other conifers	421	42	103	62	37	71	126	44	430	45	0	.	33	71	113	41	173	82	87	100	49	73	156	53	871	38	0	.	2599	18
	beech	7280	9	2654	15	1442	20	2939	14	3705	11	845	34	3895	14	2855	17	1399	25	831	25	1538	29	1370	29	653	28	6544	14	37950	4
	maple	2606	17	670	23	452	38	588	34	1783	19	603	25	1175	30	1046	25	1531	23	280	42	506	36	1170	23	479	35	1133	29	14024	7
	ash	894	21	944	33	747	28	509	24	1420	21	216	49	727	26	801	30	526	36	526	35	330	44	738	27	642	38	1603	24	10622	8
	oak	1181	39	77	50	526	49	94	45	154	43	39	71	15	100	42	70	161	79	18	100	17	100	765	32	15	100	1396	35	4500	17
	sweet chestnut	0	.	0	.	36	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	247	86	0	.	8663	15	8947	15
	other broadleaves	2728	14	669	23	918	28	1172	22	2162	17	1333	27	1746	20	1536	24	2394	21	1512	40	1095	34	4338	15	4785	20	12298	11	38686	6
	indeterminable	106	53	17	100	17	100	17	100	34	71	81	54	66	63	68	50	127	43	18	100	0	.	108	41	161	40	151	48	971	16
	total	29352	5	7242	12	7423	14	13140	10	16921	8	9172	13	21645	8	13309	10	13286	10	7556	16	6971	15	25958	7	30575	7	40424	5	242973	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4184	12	907	32	1389	24	2094	19	3536	16	3354	17	5589	13	4096	15	3475	15	1661	19	1763	24	6009	12	9417	9	3187	13	50660	4
	fir	3425	12	345	35	374	42	1206	18	698	24	781	33	2079	13	875	24	530	25	531	27	132	39	416	30	210	38	427	33	12028	6
	pine	507	33	293	50	119	38	118	55	153	40	0	.	226	75	0	.	122	66	167	54	141	79	2092	20	1156	26	120	54	5214	12
	larch	17	100	17	100	118	87	17	100	88	59	0	.	0	.	51	100	108	58	0	.	88	66	2188	16	1417	19	2550	14	6659	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	651	26	1071	26	16	100	1738	19
	other conifers	34	71	86	53	34	100	119	47	103	58	0	.	17	100	18	100	87	82	0	.	18	100	35	71	53	58	0	.	604	22
	beech	5334	10	1510	18	1174	18	1669	14	2525	13	657	28	2911	14	1827	17	736	26	741	30	816	25	721	31	556	31	4010	16	25189	5
	maple	1243	14	467	26	103	53	391	28	1437	20	549	27	938	22	892	22	465	27	238	42	539	29	268	36	291	45	245	37	8067	7
	ash	568	24	315	32	441	26	260	27	721	22	359	34	484	34	1174	26	241	44	281	35	193	38	482	32	316	50	656	28	6489	9
	oak	733	34	243	44	584	30	175	40	140	35	40	71	92	61	36	100	54	58	18	100	0	.	291	36	15	100	726	23	3146	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	73	15	100	4656	14	4729	14
	other broadleaves	605	23	270	32	292	32	550	31	759	21	211	43	338	30	821	34	488	28	204	35	279	34	1189	20	1162	26	5023	12	12190	7
	indeterminable	19	100	0	.	0	.	0	.	0	.	22	100	67	51	18	100	15	100	0	.	15	100	35	71	36	71	20	100	248	27
	total	16669	6	4453	13	4628	13	6600	10	10160	8	5971	14	12740	8	9807	10	6322	11	3840	15	3985	16	14434	8	15716	7	21637	5	136962	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3105	11	278	28	745	25	1167	18	2222	14	1946	15	3639	10	3000	12	2933	13	1217	20	926	22	3425	11	6092	8	2214	15	32910	3
	fir	2232	12	190	42	220	36	845	19	525	22	460	26	1382	13	756	18	315	30	282	35	147	38	185	41	119	34	272	35	7929	6
	pine	568	26	212	31	177	48	141	54	276	25	0	.	156	62	13	71	38	61	125	63	70	100	895	20	785	25	79	54	3537	10
	larch	24	77	38	61	48	74	21	74	106	43	0	.	0	.	14	71	86	59	0	.	61	71	1646	15	1125	16	1422	15	4591	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	275	33	547	24	0	.	821	19
	other conifers	0	.	45	64	34	100	24	77	23	77	0	.	0	.	0	.	7	100	0	.	15	100	0	.	0	.	0	.	149	36
	beech	3930	9	1279	16	647	20	1136	17	1326	13	463	46	1525	17	1426	16	506	25	233	33	326	28	186	37	373	28	1226	17	14583	5
	maple	686	18	121	36	37	53	123	32	532	19	99	42	339	25	521	23	344	31	96	38	217	36	79	43	111	50	74	43	3381	8
	ash	365	23	226	29	417	34	314	30	684	19	200	55	241	48	419	25	256	37	76	59	105	41	278	77	41	68	98	36	3721	10
	oak	278	28	151	48	215	31	133	42	192	28	29	77	0	.	9	100	9	100	23	74	0	.	39	69	7	100	304	27	1388	13
	sweet chestnut	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	71	66	0	.	1518	18	1595	17
	other broadleaves	305	26	125	39	158	51	188	29	373	26	21	77	42	44	191	33	160	42	68	44	59	51	185	32	174	39	1486	15	3536	9
	indeterminable	35	100	0	.	0	.	0	.	17	100	6	100	0	.	7	100	40	61	0	.	0	.	0	.	14	71	26	77	145	36
	total	11529	5	2665	12	2700	13	4091	10	6283	8	3224	13	7325	8	6357	8	4695	10	2121	15	1927	15	7264	8	9388	7	8718	6	78286	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2099	9	312	25	613	20	894	17	1177	11	1432	14	2518	10	1865	12	2000	13	701	22	728	18	1851	11	3748	8	1413	14	21351	3
	fir	1773	10	153	35	174	31	624	18	505	17	301	24	891	13	633	17	202	29	265	32	100	33	199	36	111	32	161	29	6092	5
	pine	296	24	139	34	108	41	68	34	250	22	0	.	134	67	9	100	43	57	53	56	28	79	199	32	362	23	35	53	1724	11
	larch	0	.	7	100	28	59	34	60	47	55	0	.	7	100	0	.	36	68	0	.	23	75	1237	16	942	13	709	15	3069	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	133	37	231	22	0	.	373	19
	other conifers	0	.	28	71	21	100	47	62	13	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	117	36
	beech	2090	8	866	16	564	19	910	13	879	12	162	36	911	16	940	17	328	27	160	31	293	25	66	55	211	33	584	18	8962	4
	maple	285	18	97	35	19	72	42	47	118	29	51	50	74	37	192	23	75	35	40	41	96	31	36	85	30	71	7	100	1162	9
	ash	217	27	50	37	160	30	209	35	485	16	22	74	78	34	203	29	56	44	31	82	94	41	27	71	7	100	62	48	1702	9
	oak	154	26	96	46	152	37	83	33	178	25	0	.	36	62	9	100	8	100	15	70	0	.	21	75	26	77	153	37	931	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	6	100	601	19	622	18
	other broadleaves	107	51	49	47	82	39	65	59	218	26	0	.	38	46	37	64	56	52	6	100	37	57	61	40	35	44	335	20	1125	11
	indeterminable	8	100	7	100	0	.	0	.	0	.	0	.	30	51	0	.	29	61	20	76	0	.	7	100	20	58	0	.	121	27
	total	7031	5	1803	12	1919	12	2976	9	3870	7	1967	13	4715	7	3887	9	2850	11	1292	17	1398	14	3844	8	5730	6	4068	7	47350	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1252	10	202	28	302	28	605	16	826	14	874	16	1360	12	1434	12	1045	13	422	21	432	19	1068	13	1994	9	826	15	12642	3
	fir	922	12	42	41	208	29	530	16	338	20	205	32	674	13	455	18	201	24	61	39	68	50	111	37	93	34	163	30	4071	6
	pine	97	30	7	100	41	53	81	37	136	25	0	.	42	65	17	100	7	100	7	100	0	.	93	45	170	28	21	74	719	13
	larch	7	100	7	100	14	71	7	100	63	42	0	.	0	.	21	74	43	58	0	.	20	73	882	14	686	15	502	14	2251	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	45	100	30	0	.	135	25
	other conifers	0	.	21	100	0	.	20	74	27	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	76	45
	beech	890	12	448	22	226	26	448	17	745	13	78	37	429	21	339	21	149	29	76	39	150	33	33	71	79	36	284	19	4372	5
	maple	85	29	28	49	14	71	0	.	76	38	29	50	18	71	60	39	60	78	12	70	17	71	0	.	35	51	8	100	441	17
	ash	60	35	43	41	99	31	146	46	223	21	0	.	36	66	37	45	14	71	18	74	29	59	7	100	0	.	13	100	727	14
	oak	35	53	23	72	35	66	109	39	136	26	0	.	0	.	0	.	0	.	6	100	0	.	8	100	20	76	18	72	389	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	259	25	265	24
	other broadleaves	57	88	29	61	61	53	14	71	8	100	0	.	7	100	18	71	137	58	14	71	12	70	23	57	26	59	55	43	461	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	14	71	0	.	0	.	29	50
	total	3405	6	848	15	1000	14	1960	10	2579	8	1185	14	2566	8	2380	9	1663	12	617	17	735	17	2275	9	3210	7	2156	8	26579	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	480	15	84	37	89	33	264	22	225	21	368	22	607	14	640	16	336	19	202	23	207	25	340	20	711	13	380	17	4934	5
	fir	371	18	56	35	70	48	277	20	248	22	159	39	356	19	146	31	129	31	65	43	6	100	15	100	47	38	29	50	1974	8
	pine	15	71	0	.	14	71	7	100	20	58	0	.	18	72	7	100	0	.	0	.	0	.	0	.	65	40	7	100	152	24
	larch	0	.	0	.	7	100	7	100	20	58	0	.	0	.	0	.	14	71	0	.	6	100	346	22	241	22	215	22	856	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	35	45	0	.	49	38
	other conifers	0	.	0	.	8	100	40	52	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	43
	beech	148	25	187	25	66	34	193	24	195	20	29	50	132	31	105	30	48	44	20	58	39	56	6	100	21	58	121	31	1311	9
	maple	0	.	7	100	0	.	0	.	28	50	0	.	6	100	18	100	31	60	0	.	7	100	0	.	0	.	0	.	96	32
	ash	30	50	23	58	35	83	7	100	47	43	7	100	15	71	14	71	7	100	6	100	0	.	0	.	0	.	10	100	201	23
	oak	12	100	16	71	29	50	21	58	55	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71	148	23
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	82	0	.	83	30	119	32
	other broadleaves	0	.	23	58	14	71	0	.	7	100	0	.	0	.	18	100	12	70	7	100	6	100	0	.	12	100	8	100	107	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	13	71
	total	1057	10	395	18	332	19	814	12	853	11	570	18	1134	11	948	14	578	15	301	21	271	22	764	15	1132	10	868	10	10016	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	153	24	14	70	43	59	106	27	129	28	120	39	275	19	204	26	181	24	134	30	124	34	117	30	257	21	218	22	2075	7
	fir	131	26	7	100	33	60	84	33	27	50	21	59	148	23	126	28	26	50	33	51	19	73	21	58	35	45	30	49	741	11
	pine	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	28	50
	larch	7	100	0	.	0	.	0	.	0	.	0	.	0	.	16	71	0	.	0	.	7	100	209	23	112	27	94	32	444	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	44	14	71	0	.	50	37
	other conifers	0	.	7	100	8	100	13	71	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	51
	beech	45	46	60	46	21	58	45	56	93	29	7	100	43	46	41	51	12	70	7	100	9	100	6	100	18	74	44	41	452	15
	maple	0	.	9	100	0	.	0	.	0	.	6	100	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	30	50
	ash	7	100	7	100	9	100	10	100	15	71	0	.	0	.	18	72	7	100	0	.	0	.	0	.	6	100	0	.	79	32
	oak	0	.	18	70	0	.	0	.	28	50	0	.	0	.	8	100	0	.	8	100	0	.	0	.	7	100	0	.	69	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	92	37	101	34
	other broadleaves	0	.	0	.	21	58	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	350	16	121	29	135	28	264	19	313	17	155	32	466	15	413	17	240	20	183	26	167	29	398	16	464	14	484	14	4153	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	29	63	7	100	7	100	20	58	53	39	35	45	48	38	79	59	106	35	7	100	29	50	68	49	122	26	110	27	719	13
	fir	51	38	0	.	7	100	28	50	27	61	0	.	36	53	14	71	15	71	0	.	13	71	7	100	7	100	8	100	212	19
	pine	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	39	61	39	67	40	189	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	15	71	0	.	36	45
	other conifers	0	.	0	.	8	100	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71
	beech	0	.	7	100	0	.	0	.	49	42	0	.	7	100	9	100	7	100	7	100	0	.	7	100	0	.	22	58	115	26
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	10	100	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	24	58
	oak	0	.	0	.	9	100	7	100	27	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	57	42	65	39
	other broadleaves	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	80	33	21	58	36	45	65	33	170	23	35	45	98	29	108	45	127	31	14	71	48	38	173	26	212	20	264	18	1451	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	0	.	7	100	0	.	0	.	22	59	14	71	21	58	0	.	9	100	29	50	15	71	15	72	14	71	153	22
	fir	29	50	0	.	0	.	7	100	7	100	0	.	8	100	0	.	0	.	7	100	0	.	0	.	0	.	18	72	75	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58	8	100	0	.	30	50
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	0	.	0	.	21	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	14	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100
	oak	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	52	37	52
	other broadleaves	0	.	0	.	7	100	23	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	81
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	36	45	0	.	20	58	30	81	30	64	22	59	23	58	28	50	0	.	16	71	29	50	58	35	23	58	68	37	383	15
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	0	.	7	100	7	100	0	.	0	.	7	100	7	100	7	100	0	.	21	58	7	100	30	50	19	72	119	25
	fir	0	.	0	.	0	.	0	.	21	58	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50
	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	.	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	.	57	45	57	45
	other broadleaves	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	0	.	7	100	7	100	58	67	0	.	14	71	7	100	7	100	0	.	21	58	14	71	52	38	90	36	285	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18257	8	3247	21	5306	18	9834	12	13600	10	12890	13	22893	9	16538	10	15837	11	7199	16	7055	16	21946	10	38810	7	12950	10	206363	3
	fir	15950	7	1328	29	1825	23	6334	12	3996	16	3154	22	10301	9	4558	14	2153	21	2430	23	789	27	1580	25	990	25	1610	25	56999	4
	pine	1654	23	777	27	491	36	672	44	954	17	0	.	905	57	46	48	393	48	604	43	380	85	6256	19	5296	18	343	54	18771	9
	larch	72	73	70	44	584	61	119	44	413	29	88	100	111	94	204	76	592	36	0	.	399	49	10376	11	6314	11	9023	11	28363	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1821	23	3754	18	49	100	5633	14
	other conifers	455	39	290	44	150	74	390	29	624	37	0	.	57	52	131	37	267	82	87	100	83	64	191	54	924	36	16	100	3664	15
	beech	19718	7	7010	12	4141	15	7339	10	9524	8	2241	26	9853	11	7547	13	3185	21	2076	24	3171	21	2396	27	1912	23	12835	13	92948	3
	maple	4905	11	1399	18	625	30	1145	23	3974	15	1338	22	2550	20	2729	17	2514	19	666	30	1396	23	1553	23	947	33	1468	26	27208	5
	ash	2140	17	1608	23	1915	20	1465	19	3603	13	804	31	1580	24	2674	21	1108	30	937	29	751	30	1533	29	1012	41	2442	23	23572	6
	oak	2393	28	624	31	1550	30	620	23	928	17	107	50	143	48	103	47	231	65	89	54	17	100	1124	31	91	72	2611	25	10631	11
	sweet chestnut	0	.	0	.	36	71	0	.	7	100	0	.	0	.	0	.	7	100	0	.	0	.	437	74	28	100	16023	12	16538	12
	other broadleaves	3803	13	1170	18	1560	23	2011	20	3563	15	1565	25	2172	18	2620	24	3253	19	1812	36	1488	29	5796	14	6194	19	19212	10	56220	5
	indeterminable	168	41	24	77	17	100	17	100	51	58	115	43	163	41	93	42	218	32	39	62	22	76	172	31	231	32	197	44	1528	12
	total	69517	4	17549	10	18199	11	29947	7	41236	6	22301	11	50727	6	37243	7	29768	9	15939	13	15553	13	55180	6	66502	5	78778	4	548438	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. 1296717. Birmensdorf, Swiss Federal Research Institute WSL

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