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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NFI2
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 1993/95
		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	1407	16	243	34	366	25	1069	20	1592	20	975	20	988	13	880	18	452	19	338	33	216	35	898	14	1381	13	289	17	11094	5
	fir	798	13	93	44	148	38	448	20	153	46	168	28	653	15	237	23	116	37	148	40	26	32	42	34	29	33	67	38	3128	7
	pine	28	105	66	58	50	97	18	74	58	57	0	.	33	80	0	.	2	100	16	77	0	.	459	29	266	27	9	100	1005	17
	larch	3	100	0	100	10	100	0	.	22	46	0	.	0	.	12	100	5	62	0	.	12	62	277	21	71	27	187	25	600	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	53	81	32	1	100	144	29
	other conifers	17	43	6	67	13	100	18	53	17	44	1	100	3	71	11	55	0	.	4	75	5	73	19	85	8	57	0	.	124	22
	beech	1325	12	582	17	357	23	497	15	683	15	222	43	552	18	367	18	232	42	113	39	123	29	117	36	140	43	933	19	6242	6
	maple	288	17	85	26	16	162	56	33	201	33	106	33	190	34	140	26	98	52	36	44	81	46	60	47	35	47	36	32	1427	10
	ash	209	28	82	40	114	42	35	30	228	35	88	38	121	30	220	28	32	42	42	34	54	64	74	33	61	74	141	27	1501	10
	oak	161	40	46	54	238	39	16	131	44	43	21	57	4	84	4	100	14	72	4	100	0	.	50	35	2	100	206	43	809	19
	sweet chestnut	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	1034	17	1038	17
	other broadleaves	279	18	90	32	125	37	148	40	247	31	127	25	163	28	187	30	150	24	68	54	98	36	287	23	289	24	993	12	3250	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4516	7	1293	13	1439	15	2306	12	3243	12	1707	15	2707	8	2059	11	1102	16	769	21	616	21	2346	10	2362	10	3896	7	30362	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2401	12	487	32	1006	24	1457	18	2009	15	1825	17	2746	13	2150	15	1367	15	745	29	826	36	2044	13	3566	11	1050	18	23679	4
	fir	1975	13	156	41	267	35	1271	20	547	23	517	29	1052	15	405	22	155	33	120	38	105	48	111	41	67	51	95	45	6844	7
	pine	243	39	63	52	55	60	46	67	74	37	0	.	46	100	0	.	29	71	115	52	0	.	459	24	499	23	41	69	1670	13
	larch	10	100	19	72	31	67	22	71	120	49	0	.	0	.	0	.	19	73	0	.	36	74	770	19	468	25	531	21	2028	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	111	37	276	30	0	.	387	24
	other conifers	5	100	50	59	0	.	62	68	25	61	0	.	0	.	0	.	4	100	0	.	-22	100	0	.	2	100	0	.	127	46
	beech	3402	10	1004	18	592	21	1256	17	1479	14	335	36	1375	20	1258	21	340	23	160	40	264	28	219	32	203	31	1010	19	12898	5
	maple	697	16	212	29	18	100	163	32	582	20	148	34	292	28	433	24	261	38	44	42	270	32	20	165	15	154	65	42	3221	8
	ash	470	27	230	36	255	29	245	23	482	19	216	51	220	43	448	22	93	52	77	76	167	52	85	47	61	59	100	37	3148	9
	oak	311	29	62	48	359	35	142	33	160	31	39	100	9	100	50	75	18	60	13	100	0	.	40	56	3	100	210	33	1416	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	72	4	100	1158	17	1198	17
	other broadleaves	298	22	94	40	183	46	212	44	362	25	30	52	124	37	219	28	114	32	31	62	71	41	149	29	132	39	1038	14	3056	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	9813	6	2378	13	2766	13	4875	10	5841	8	3110	14	5865	9	4964	10	2398	12	1305	21	1718	21	4045	9	5297	9	5298	7	59671	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3765	12	744	26	2260	23	2857	14	3739	11	2452	16	4557	11	3201	13	2533	17	1298	23	1055	30	2754	13	5638	10	1738	16	38591	4
	fir	3054	12	269	39	290	39	1946	16	1011	19	621	37	1854	14	1011	19	266	27	249	38	146	44	203	50	109	44	177	37	11207	6
	pine	553	25	332	34	240	52	215	46	679	22	0	.	100	57	34	58	12	72	86	54	0	.	538	21	654	24	81	67	3526	10
	larch	18	100	62	77	63	61	88	78	104	54	0	.	0	.	10	100	54	73	0	.	0	100	1077	18	792	17	724	22	2992	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	143	30	353	27	0	.	510	20
	other conifers	0	.	46	100	0	.	60	63	28	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	147	45
	beech	4667	10	1688	15	1150	23	1764	14	1870	13	375	37	1585	16	1812	18	487	30	186	38	483	32	151	62	256	35	741	18	17217	5
	maple	597	18	253	32	36	51	80	43	276	24	57	71	299	33	457	19	241	31	68	43	176	37	50	72	47	58	17	100	2654	9
	ash	700	29	214	40	281	29	612	44	842	19	59	61	153	41	318	28	116	41	81	100	82	56	25	98	38	62	28	73	3549	12
	oak	283	27	219	36	291	36	101	43	421	33	0	.	101	83	6	100	0	.	0	.	0	.	13	100	12	100	162	43	1609	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	72	5	100	570	24	603	23
	other broadleaves	173	28	229	36	141	43	91	42	509	25	15	100	24	70	101	44	136	46	32	73	80	50	98	42	83	48	533	21	2245	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	13809	6	4057	12	4753	14	7815	9	9480	7	3578	14	8673	8	6950	9	3859	13	2000	19	2022	20	5080	10	7988	8	4784	8	84848	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4554	9	1064	25	2335	19	3755	13	5305	12	2951	16	5583	11	4292	12	3157	15	1157	26	1247	22	2489	12	6134	9	2046	16	46069	3
	fir	3053	10	185	34	574	32	2056	14	1595	17	698	26	2701	14	1715	21	457	33	248	39	242	41	234	40	235	34	457	32	14451	5
	pine	441	25	221	32	347	44	362	28	947	20	0	.	276	65	76	61	51	110	14	100	0	.	276	38	665	25	30	57	3706	11
	larch	19	100	13	100	48	58	38	76	235	34	0	.	16	100	0	.	118	51	0	.	38	73	1674	16	1377	15	752	19	4327	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	40	252	26	0	.	420	22
	other conifers	0	.	16	100	0	.	104	49	77	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	197	41
	beech	3529	11	1551	17	943	24	2252	15	2452	12	243	40	1889	19	1462	19	440	30	144	58	470	26	92	62	337	37	516	22	16319	5
	maple	301	24	190	34	0	.	21	71	175	33	80	39	98	41	246	36	97	38	56	52	64	41	10	100	-1	2190	16	100	1354	12
	ash	221	30	223	32	251	38	367	52	1001	18	14	100	100	43	191	35	44	50	70	71	89	42	12	100	9	100	-43	90	2547	12
	oak	182	35	100	56	221	47	376	33	416	23	0	.	0	.	0	.	0	.	9	100	0	.	0	.	87	71	136	65	1528	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-5	100	0	.	410	25	406	25
	other broadleaves	78	39	75	60	63	61	110	51	131	30	0	.	49	51	96	53	139	69	11	100	30	75	19	124	27	77	147	25	975	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12378	6	3637	13	4781	13	9442	9	12335	7	3986	14	10712	8	8077	10	4503	13	1709	20	2181	17	4969	10	9123	7	4466	9	92298	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4192	11	933	25	1337	21	3631	15	3402	14	1977	19	4718	11	3281	14	2273	16	969	23	1002	27	1767	16	4545	11	1765	15	35792	4
	fir	1794	15	196	49	307	43	1647	18	1287	20	655	33	2329	16	1072	19	553	27	316	47	169	48	182	52	272	37	130	47	10908	7
	pine	227	29	54	100	199	44	206	52	452	26	0	.	125	63	17	100	0	.	0	.	0	.	134	38	442	35	36	71	1891	14
	larch	0	.	3	100	24	100	19	100	191	45	0	.	21	100	70	74	188	57	18	100	95	71	1378	17	1335	18	735	20	4078	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	29	75	200	35	0	.	230	32
	other conifers	0	.	0	.	60	100	68	59	96	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	224	54
	beech	1558	16	1235	20	323	29	1422	19	1667	17	203	61	1228	25	785	28	379	32	167	48	232	51	16	100	196	45	325	36	9736	7
	maple	62	55	43	71	14	100	0	.	255	35	0	.	10	100	65	78	45	58	0	.	16	100	0	.	29	71	0	.	538	22
	ash	131	52	29	100	159	47	71	60	375	30	25	100	70	73	60	75	0	.	27	100	29	100	0	.	0	.	39	100	1014	18
	oak	141	50	205	44	165	65	123	50	438	29	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	17	100	1102	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	8	100	259	31	278	29
	other broadleaves	46	72	75	75	21	128	0	.	29	87	0	.	0	0	10	100	187	48	22	100	56	75	34	100	0	.	13	100	494	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8151	8	2772	15	2610	15	7186	10	8192	9	2859	17	8500	9	5361	11	3625	13	1532	21	1599	22	3552	12	7026	9	3318	10	66283	3
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1402	18	367	44	574	30	1417	21	1664	18	852	26	2317	15	1924	19	989	22	667	34	627	37	837	27	2019	16	1063	24	16721	6
	fir	892	22	68	71	119	51	725	29	255	48	206	54	1305	21	364	34	186	47	153	51	174	52	60	71	147	50	151	51	4806	10
	pine	57	71	46	100	0	.	35	100	123	50	0	.	43	100	32	100	0	.	0	.	0	.	0	.	97	50	0	.	433	27
	larch	0	.	0	.	0	.	31	100	34	100	0	.	39	100	91	73	32	100	0	.	69	72	758	22	490	27	409	26	1954	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	71	49	58	0	.	88	45
	other conifers	0	.	0	.	36	100	100	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	63
	beech	480	28	478	39	167	66	451	41	734	28	140	52	276	40	129	73	148	50	28	100	79	74	0	.	81	74	224	37	3414	13
	maple	0	.	37	100	0	.	22	100	0	.	21	100	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	95	53
	ash	51	71	75	100	58	72	47	100	141	50	0	.	0	.	42	100	31	100	0	.	0	.	0	.	43	100	0	.	489	29
	oak	26	100	50	100	0	.	0	.	162	46	0	.	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	286	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	36	121	36
	other broadleaves	0	.	27	100	38	71	15	100	62	100	0	.	0	.	0	.	49	71	0	.	0	.	0	.	0	.	26	100	217	40
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2909	13	1148	23	991	24	2843	15	3175	13	1218	24	3980	12	2647	17	1436	18	848	30	949	28	1695	17	2925	12	1995	15	28760	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	599	29	45	100	196	45	169	50	436	34	224	42	249	44	992	27	413	31	287	36	340	35	382	36	958	22	736	30	6027	9
	fir	528	34	40	100	27	100	300	43	230	41	0	.	795	30	122	58	0	.	0	.	46	100	34	100	164	59	52	100	2338	16
	pine	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	72	0	.	106	59
	larch	38	100	0	.	44	100	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	562	30	361	32	182	51	1221	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	50	72	59	0	.	141	39
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	59	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	100
	beech	71	73	261	56	46	100	281	54	522	34	0	.	156	60	76	100	58	100	0	.	0	.	0	.	0	.	66	58	1537	21
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	73	0	.	0	.	0	.	52	73
	ash	0	.	0	.	0	.	0	.	39	100	0	.	0	.	51	100	0	.	0	.	0	.	0	.	0	.	0	.	90	71
	oak	0	.	0	.	41	100	132	59	80	71	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	0	.	279	39
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	197	36	224	34
	other broadleaves	0	.	0	.	0	.	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	55	73
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1236	21	346	48	355	38	882	26	1383	20	224	42	1258	22	1241	24	505	28	312	34	438	30	1075	21	1620	17	1253	21	12128	7
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	102	71	62	100	107	71	226	50	48	100	68	71	147	58	103	71	129	60	0	.	147	51	140	51	219	42	27	100	1527	17
	fir	109	71	0	.	0	.	113	71	109	71	0	.	364	41	0	.	150	73	53	100	0	.	0	.	0	.	124	72	1021	25
	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	.	148	50	73	71	37	100	258	38
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	24	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	123	100	98	100	33	100	0	.	0	.	37	100	34	100	45	100	0	.	39	100	20	100	0	.	428	42
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	48	100	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	109	71
	oak	0	.	0	.	0	.	0	.	185	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	185	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	46	226	46
	other broadleaves	0	.	0	.	0	.	90	100	58	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	148	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	211	50	62	100	277	55	590	34	433	37	68	71	511	34	140	58	313	44	97	71	147	51	351	34	312	34	414	35	3926	12
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	57	100	0	.	72	100	0	.	81	100	0	.	0	.	200	58	0	.	37	100	202	51	48	100	61	100	85	100	844	27
	fir	57	100	0	.	0	.	0	.	0	.	0	.	75	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	198	58
	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	.	87	71	0	.	60	100	147	59
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	35	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	87	100	0	.	73	100	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	228	58
	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	.	60	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	53	121	53
	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	114	71	87	100	72	100	73	100	209	58	0	.	75	100	267	50	0	.	37	100	202	51	171	50	61	100	266	46	1633	19
>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	.	0	.	55	100	0	.	44	100	0	.	0	.	0	.	134	72	71	100	304	46
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	100	96	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	69	100	120	71
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	33	100	0	.	70	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	.	485	49	485	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	55	100	0	.	44	100	0	.	0	.	37	100	218	52	721	38	1075	29
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18479	7	3944	19	8254	16	14581	10	18277	9	11323	13	21359	8	17023	10	11358	12	5498	20	5662	18	11361	10	24656	7	8870	12	180646	2
	fir	12259	8	1007	28	1730	25	8507	12	5187	13	2865	22	11128	10	4995	14	1884	23	1287	30	908	33	867	30	1023	27	1348	28	54997	4
	pine	1550	19	782	24	890	38	882	27	2374	16	0	.	624	50	160	48	94	76	231	54	0	.	1866	19	2688	17	196	47	12336	8
	larch	88	54	97	53	221	47	198	53	706	32	0	.	77	71	183	52	450	36	18	100	250	50	6731	12	5018	12	3687	14	17724	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	718	23	1316	20	1	100	2050	15
	other conifers	23	40	118	75	110	89	413	44	244	67	1	100	61	96	11	55	4	100	4	75	-17	112	19	85	10	58	12	100	1013	28
	beech	15033	7	6885	12	3702	18	8094	11	9508	9	1518	24	7061	14	5926	15	2117	19	843	27	1651	22	633	31	1233	27	3816	13	68018	3
	maple	1945	11	821	22	85	47	341	24	1489	15	411	30	890	20	1356	17	742	23	204	29	660	27	139	37	124	43	133	34	9341	6
	ash	1781	22	852	25	1165	24	1439	34	3109	13	401	38	663	29	1330	19	315	37	297	56	421	44	195	34	212	52	265	29	12447	7
	oak	1105	23	682	30	1316	26	890	24	1966	17	60	68	114	76	108	60	32	62	64	56	0	.	103	37	104	72	731	25	7274	9
	sweet chestnut	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	57	17	100	4580	13	4698	13
	other broadleaves	873	16	590	29	570	31	668	31	1432	19	172	25	359	22	612	23	775	30	163	38	336	30	587	21	531	23	2770	11	10440	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	53137	4	15779	10	18044	10	36013	7	44291	5	16751	11	42335	6	31705	7	17785	10	8609	16	9871	14	23321	7	36933	6	26410	5	380985	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. 1217899. Birmensdorf, Swiss Federal Research Institute WSL

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