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
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
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 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	992	12	217	28	372	21	730	17	998	12	796	14	1092	10	779	13	524	12	307	20	353	20	932	10	1454	8	379	15	9925	3
	fir	895	9	60	27	178	23	471	18	273	15	222	22	576	11	277	15	72	23	87	29	82	46	132	27	59	28	51	31	3435	5
	pine	44	36	30	54	21	68	18	55	28	35	2	100	39	48	6	52	8	72	26	41	12	76	397	18	308	18	13	51	951	10
	larch	6	82	6	66	12	92	11	53	36	56	2	100	0	.	1	100	7	55	0	.	11	60	300	16	131	17	171	22	695	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	1	100	0	.	57	29	75	22	0	.	138	17
	other conifers	19	51	21	66	9	71	19	41	25	42	0	.	3	69	8	45	1	100	3	63	3	79	2	100	21	54	0	.	134	19
	beech	1458	8	631	13	421	15	595	12	854	10	126	28	659	12	525	14	237	24	147	31	161	27	150	29	110	22	774	13	6848	4
	maple	317	12	101	20	87	41	84	29	194	19	96	23	116	18	170	18	142	20	29	37	90	30	49	28	18	36	48	28	1539	6
	ash	244	18	97	24	179	25	85	28	313	17	102	30	158	29	227	20	54	32	38	32	68	45	46	30	42	51	134	21	1786	7
	oak	155	30	51	39	180	33	44	39	85	29	8	56	5	73	10	51	22	52	1	137	0	0	37	29	8	65	155	24	761	12
	sweet chestnut	0	.	0	.	9	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	8	73	0	.	953	11	972	11
	other broadleaves	288	13	105	22	150	28	107	24	361	13	81	20	202	22	231	25	162	19	76	31	74	33	334	15	163	19	1044	9	3378	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4419	5	1318	10	1619	10	2163	9	3166	6	1437	11	2854	6	2233	8	1230	10	714	14	854	15	2446	7	2388	6	3722	5	30562	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2401	9	497	26	1267	16	1414	13	2263	11	1733	12	2743	8	2547	12	1493	13	777	17	951	19	2396	10	3984	8	955	14	25421	3
	fir	2069	9	170	33	298	31	1245	14	786	14	468	23	1385	11	878	15	134	26	192	30	108	30	143	30	92	30	83	38	8051	5
	pine	272	23	92	41	89	48	53	48	170	26	3	100	76	37	22	100	21	66	125	33	37	60	619	17	592	17	100	46	2272	8
	larch	22	76	44	43	74	61	27	89	41	39	0	.	0	.	0	.	11	77	0	.	26	64	750	14	434	15	466	18	1896	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	11	100	4	100	0	.	4	100	0	.	190	28	201	23	1	100	411	17
	other conifers	7	100	7	71	9	100	10	100	45	44	0	.	0	.	-1	100	0	.	0	.	-2	100	0	.	5	73	0	.	80	33
	beech	3714	7	1243	12	874	15	1261	12	1669	10	405	21	1127	12	1283	14	381	20	233	36	410	22	140	31	188	32	801	13	13727	4
	maple	615	11	243	21	79	34	192	26	473	16	86	30	209	22	377	16	197	23	69	31	173	25	32	40	39	43	57	35	2841	6
	ash	504	16	216	26	279	24	178	24	508	15	126	40	166	24	424	20	59	35	59	57	43	71	65	43	45	55	72	40	2744	7
	oak	313	23	145	30	180	23	113	24	351	24	18	76	11	62	23	80	49	60	5	100	0	.	27	44	11	100	211	27	1457	10
	sweet chestnut	0	.	0	.	28	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	73	2	100	792	13	828	13
	other broadleaves	433	15	205	27	204	33	140	28	474	16	28	57	101	28	203	23	98	25	33	40	118	32	189	27	99	30	879	12	3205	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	10350	4	2863	9	3381	9	4634	8	6780	6	2868	10	5829	6	5760	8	2441	10	1497	14	1864	13	4558	7	5691	6	4418	5	62933	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4030	8	878	21	2553	15	3128	11	4927	9	2848	12	5494	8	3775	10	3082	11	1134	18	1422	16	2816	9	6092	7	1531	13	43708	3
	fir	3064	8	419	27	425	29	1722	12	1744	13	631	18	2152	12	1462	13	242	25	253	31	212	31	223	24	191	25	234	35	12974	4
	pine	607	18	353	23	314	32	202	34	979	15	0	.	94	39	40	42	18	51	136	43	27	64	466	18	799	16	161	53	4196	7
	larch	0	.	51	52	93	49	40	48	172	28	5	100	6	100	26	79	45	49	0	.	147	62	1271	11	1091	13	771	15	3719	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	4	100	4	100	0	.	158	23	323	21	4	100	498	15
	other conifers	0	.	0	.	15	73	52	72	25	78	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	98	45
	beech	4369	7	1497	12	1154	17	1929	12	1907	10	514	23	1538	14	1669	13	432	22	199	27	534	22	39	56	183	35	647	13	16610	4
	maple	439	16	216	25	53	41	100	40	249	22	87	40	158	33	256	22	99	32	40	49	113	28	20	73	13	58	16	66	1859	8
	ash	325	22	210	22	243	27	284	24	834	14	75	48	160	27	288	29	73	41	53	51	51	42	23	71	10	100	33	64	2662	8
	oak	284	25	151	29	307	22	214	26	320	22	4	100	15	72	7	71	22	83	7	100	0	.	0	.	49	66	172	33	1552	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	59	2	100	385	17	405	16
	other broadleaves	202	24	185	31	152	32	66	37	308	20	14	71	78	61	77	35	120	34	33	68	54	46	72	34	61	55	383	19	1805	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	13321	4	3959	9	5308	9	7736	7	11464	5	4178	10	9699	6	7600	7	4137	9	1858	14	2560	12	5114	7	8814	6	4336	6	90085	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4811	7	834	24	1981	13	3776	10	4863	9	2812	12	5710	8	4379	9	3034	11	1084	17	1734	15	2539	10	6118	7	1680	11	45353	2
	fir	2816	8	310	22	397	26	1690	13	1766	13	725	17	2579	10	1551	14	368	24	150	34	183	35	231	29	316	30	306	25	13388	4
	pine	556	18	271	30	243	35	329	24	1056	15	0	.	164	57	108	47	69	56	27	53	22	62	306	26	598	17	42	68	3790	8
	larch	9	100	35	70	53	52	22	81	216	29	26	100	30	61	33	76	39	46	8	100	87	52	1530	11	1190	13	776	12	4057	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	110	28	275	25	-1	100	384	20
	other conifers	0	.	0	.	67	100	43	58	49	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	159	55
	beech	2741	9	1410	12	715	16	1877	13	1941	10	400	25	1351	15	1091	14	363	26	197	33	389	25	38	59	224	34	371	16	13108	4
	maple	171	22	54	36	24	58	44	52	229	25	46	37	54	44	82	37	65	36	11	71	56	35	0	.	16	59	5	100	858	11
	ash	157	25	76	36	176	31	125	37	440	18	19	75	68	41	156	29	5	100	13	100	42	49	0	.	6	100	22	75	1305	10
	oak	194	31	156	27	257	31	240	24	344	21	6	100	15	71	0	.	6	100	6	100	0	.	10	115	39	51	50	39	1323	11
	sweet chestnut	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	415	18	427	18
	other broadleaves	74	34	94	44	53	52	66	36	174	31	0	.	35	44	35	55	70	50	41	55	13	73	52	51	5	100	93	37	803	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11529	4	3242	10	3971	9	8212	7	11076	6	4035	10	10006	6	7434	7	4018	9	1536	14	2526	13	4821	7	8790	6	3758	7	84955	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3285	9	354	26	1117	16	2628	12	3035	11	1698	15	4048	9	3283	11	2193	12	728	20	968	21	2040	12	4612	8	1243	14	31232	3
	fir	1623	12	105	41	245	30	1032	16	764	18	511	25	1874	13	792	18	333	26	229	41	171	38	186	36	253	33	187	33	8304	6
	pine	285	26	87	48	147	41	164	39	554	21	0	.	55	74	25	100	0	.	0	.	14	100	181	56	298	27	38	50	1847	12
	larch	26	71	25	71	32	59	9	100	150	30	0	.	80	71	43	58	108	47	29	100	73	71	1355	12	942	15	641	16	3514	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	30	54	203	27	0	.	240	24
	other conifers	0	.	0	.	29	71	33	103	25	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	50
	beech	956	16	866	18	259	32	861	18	1189	14	170	33	873	21	529	20	335	25	77	43	177	44	20	111	119	51	288	28	6719	6
	maple	27	123	22	71	25	61	21	71	111	44	7	100	10	100	60	47	24	59	0	.	0	.	0	.	8	100	0	.	316	23
	ash	81	38	47	62	90	47	106	44	221	31	13	100	12	100	45	58	12	100	0	.	7	100	0	.	15	100	0	.	649	17
	oak	163	49	183	37	125	39	100	40	320	21	9	100	12	100	33	73	0	.	0	.	0	.	0	.	11	100	22	101	979	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	196	22	202	22
	other broadleaves	9	100	78	47	37	58	42	59	103	41	0	.	21	100	0	.	51	53	0	.	14	100	27	84	0	.	42	42	424	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	6454	6	1768	12	2106	12	4996	9	6472	7	2408	13	6985	7	4817	9	3055	11	1064	19	1423	17	3845	9	6462	7	2657	8	54512	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1356	14	159	40	565	23	776	21	799	17	807	19	1640	13	1269	15	857	20	548	29	621	27	634	22	2121	12	709	22	12862	5
	fir	741	17	34	71	83	45	378	24	203	32	242	35	991	18	314	28	120	39	17	100	66	78	47	58	75	67	142	39	3453	9
	pine	15	100	40	73	0	.	32	100	154	37	0	.	34	76	18	100	14	100	0	.	0	.	7	100	104	50	0	.	417	23
	larch	0	.	0	.	19	100	19	100	67	62	0	.	0	.	42	71	21	100	0	.	22	100	620	18	475	21	276	27	1562	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	58	76	42	0	.	99	35
	other conifers	0	.	16	100	0	.	42	71	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	51
	beech	312	25	319	31	267	36	406	26	626	22	73	51	154	44	87	51	156	45	17	100	57	72	14	73	93	51	119	29	2699	10
	maple	0	.	108	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	114	43
	ash	23	71	55	73	14	100	0	.	38	72	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	155	38
	oak	0	.	37	71	117	58	66	59	131	40	15	100	0	.	62	61	0	.	0	.	0	.	0	.	0	.	12	100	439	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	110	32	110	32
	other broadleaves	16	100	0	.	0	.	21	100	15	100	0	.	0	.	19	100	19	100	0	.	0	.	16	100	0	.	13	73	118	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2463	10	768	19	1065	19	1740	14	2033	11	1137	18	2843	10	1833	14	1187	16	583	27	772	23	1362	14	2943	10	1381	14	22110	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	573	25	27	100	126	53	290	31	377	31	131	41	555	23	806	26	505	25	185	46	203	37	398	28	1028	17	468	26	5671	8
	fir	412	27	20	100	69	75	159	41	171	42	0	.	673	23	69	58	29	100	50	100	51	71	12	100	98	58	105	60	1919	13
	pine	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	40	71
	larch	19	100	0	.	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	405	24	213	31	146	37	804	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	46	66	44	0	.	118	32
	other conifers	0	.	0	.	30	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100
	beech	51	71	106	51	52	71	122	45	175	43	22	100	55	75	74	58	15	100	0	.	0	.	19	100	0	.	110	46	802	18
	maple	0	.	0	.	0	.	34	100	0	.	8	100	0	.	12	100	0	.	0	.	19	71	0	.	0	.	9	100	82	49
	ash	0	.	0	.	21	100	73	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	53
	oak	21	100	0	.	43	71	74	58	122	45	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	271	29
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	211	35	222	34
	other broadleaves	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1076	19	153	41	362	29	769	20	865	20	161	42	1283	17	961	22	548	24	247	40	273	31	897	17	1426	14	1051	17	10071	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	105	51	33	100	60	71	83	59	29	100	16	100	70	59	137	50	73	59	84	60	200	38	129	41	220	34	134	40	1372	14
	fir	58	71	0	.	0	.	68	71	0	.	0	.	386	31	28	100	21	100	0	.	0	.	30	100	30	100	67	73	688	22
	pine	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100
	larch	0	.	0	.	0	.	0	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	143	39	139	44	29	75	339	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	16	100	0	.	28	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	46	100	35	100	224	38	94	58	0	.	14	100	0	.	0	.	19	100	0	.	0	.	28	78	0	0	459	26
	maple	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	11	100	0	.	0	.	0	.	0	.	0	.	22	71
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	62	72	29	100	0	.	112	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	203	39
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	49	85	49
	other broadleaves	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	162	41	140	51	124	50	414	33	290	32	16	100	481	26	165	45	105	47	102	53	200	38	314	26	432	24	315	27	3261	10
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	56	71	0	.	37	100	0	.	40	100	22	100	94	71	21	100	18	100	19	100	73	63	0	.	173	49	43	75	596	24
	fir	69	72	0	.	0	.	39	100	0	.	84	71	0	.	0	.	0	.	31	100	48	100	0	.	49	100	0	.	320	36
	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	.	75	59	0	.	31	100	106	51
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	18	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	20	100	0	.	37	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	74
	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	.	52	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	72
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	129	41	145	38
	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	125	51	0	.	58	74	39	100	130	51	105	60	94	71	21	100	18	100	50	72	121	55	94	51	238	42	203	34	1296	16
>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	.	32	100	0	.	20	100	90	81	0	.	52	100	0	0	28	100	105	72	326	39
	fir	37	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	118	61	155	52
	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	.	31	100	45	72	64	71	140	46
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	17	100	0	.	34	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	.	48	100	48	100
	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	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	487	40	487	40
	other broadleaves	0	.	54	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	67	83
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	37	100	54	100	0	.	0	.	36	100	32	100	0	0	20	100	90	81	0	.	52	100	47	74	90	51	835	28	1292	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17607	5	2999	15	8079	11	12825	8	17331	7	10895	9	21446	6	17013	7	11867	9	4867	14	6575	12	11884	7	25830	5	7248	9	176466	2
	fir	11784	6	1117	21	1696	19	6803	10	5707	10	2884	15	10616	8	5371	10	1318	18	1009	24	921	26	1005	21	1164	19	1293	20	52687	3
	pine	1778	14	873	20	814	27	836	22	2961	12	5	100	462	39	218	37	131	44	313	32	112	63	1977	15	2718	13	352	37	13553	6
	larch	82	43	161	40	304	41	129	42	710	20	34	100	117	68	145	45	232	33	37	100	366	42	6482	8	4660	10	3373	10	16833	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	21	100	11	100	4	100	8	100	0	.	670	17	1251	16	4	135	1969	12
	other conifers	26	62	44	54	159	59	198	47	170	52	0	.	28	88	7	54	1	100	3	63	0	1233	9	77	26	47	0	.	670	24
	beech	13602	5	6116	9	3797	12	7274	9	8492	7	1711	18	5770	10	5258	10	1917	16	889	21	1727	18	420	24	946	27	3158	9	61077	3
	maple	1570	9	745	15	267	25	475	22	1256	12	331	22	557	17	958	13	538	16	148	26	457	21	101	27	93	31	136	28	7630	4
	ash	1335	14	701	17	1002	18	851	20	2354	10	335	28	563	18	1164	16	202	29	163	37	210	36	134	29	118	43	261	20	9394	5
	oak	1130	18	785	19	1237	18	851	16	1872	13	60	44	57	46	135	38	98	49	31	58	0	0	74	31	118	56	623	22	7073	6
	sweet chestnut	0	.	0	.	41	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	52	46	24	74	3763	10	3883	10
	other broadleaves	1021	11	721	25	596	23	460	20	1460	13	123	21	437	20	564	18	519	21	183	29	274	25	691	19	327	21	2466	9	9843	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	49936	3	14262	7	17994	7	30702	5	42312	4	16378	8	40075	5	30845	6	16828	7	7652	11	10644	10	23499	5	37275	4	22677	4	361078	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. 1217878. Birmensdorf, Swiss Federal Research Institute WSL

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