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)
development stage · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
development stage	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	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	12	100	96	85	0	.	0	.	0	.	1	100	455	44	0	.	37	100	0	.	6	.	113	75	54	100	0	.	0	.	30	72	0	.	0	.	1	100	23	100	160	77	0	.	0	.	986	28
	fir	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	0	.	0	.	0	100	11	100	0	0	0	.	0	.	40	76
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	1	100	0	.	0	.	4	76
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	0	13	72	0	.	0	.	68	50
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	20	100	19	100	0	.	0	.	0	.	8	100	4	100	0	.	32	100	0	.	8	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	103	44
	maple	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	71
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	14	100
	other broadleaves	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	2	100	0	.	0	.	9	82
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	32	100	123	69	0	.	0	.	0	.	8	100	490	42	0	.	69	71	0	.	14	.	113	75	54	100	0	.	0	.	66	73	0	.	51	60	11	74	34	100	176	71	0	.	0	.	1240	23
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	58	89	0	.	0	.	326	36	11	101	32	54	0	.	-9	121	833	22	94	76	170	44	34	80	0	.	252	54	183	52	0	.	17	71	87	55	39	67	92	55	69	55	119	47	66	49	4	100	82	49	2561	12
	fir	3	100	0	.	0	.	146	47	2	100	8	100	0	.	21	100	140	50	1	100	126	65	14	100	0	.	17	71	52	61	0	.	18	78	20	64	4	100	2	100	0	.	59	63	17	98	0	.	3	75	652	22
	pine	24	86	0	.	0	.	38	100	0	.	-1	100	0	.	0	.	74	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	90	0	.	0	.	49	85	1	100	0	.	25	77	221	35
	larch	7	100	0	.	0	.	0	.	0	.	19	100	0	.	0	.	214	38	14	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	5	71	57	51	11	100	13	88	186	36	0	.	2	100	535	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	85	0	.	0	.	16	85
	other conifers	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	3	71	0	.	0	.	0	.	1	100	10	46
	beech	91	46	0	.	34	100	87	46	59	100	32	89	0	.	91	54	72	50	0	.	0	.	-11	100	0	.	12	72	77	48	16	100	52	64	11	78	17	75	78	63	1	83	45	56	14	96	2	100	35	76	816	17
	maple	12	71	8	.	0	.	81	55	0	.	14	87	0	.	34	69	28	69	1	100	0	.	3	100	0	.	0	.	35	58	0	.	6	100	1	100	3	72	6	65	2	100	14	67	10	64	8	71	10	75	276	23
	ash	1	74	0	.	0	.	30	57	0	.	2	100	0	.	5	73	2	100	1	100	0	.	0	.	0	.	2	100	33	78	0	.	9	100	0	.	2	100	7	83	1	100	0	.	2	80	1	100	39	91	136	36
	oak	35	100	0	.	0	.	15	96	0	.	2	100	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	9	66	0	.	35	100	129	44
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	74	0	.	0	.	0	.	0	.	0	.	26	74
	other broadleaves	2	72	0	.	1	100	5	53	0	.	0	.	0	.	5	76	26	37	1	100	10	53	2	100	0	.	1	100	11	61	0	.	3	58	1	100	10	83	64	39	4	75	21	66	29	38	2	100	4	100	201	18
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	233	37	8	.	35	98	729	25	73	97	108	52	0	.	147	49	1391	18	111	66	306	46	55	80	0	.	284	49	398	34	16	100	106	48	120	46	115	47	333	31	91	46	319	36	351	26	17	59	234	37	5578	9
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	680	30	7	100	89	60	2281	17	88	60	938	26	14	88	186	61	1572	20	580	36	1144	23	239	45	204	43	518	40	858	24	252	47	269	37	415	36	678	35	303	29	404	35	811	20	665	25	264	45	966	24	14425	6
	fir	112	46	0	.	2	100	816	24	12	93	141	43	0	.	69	98	67	58	223	43	129	40	182	52	26	56	26	71	330	34	2	100	31	83	201	41	137	44	161	62	153	43	360	40	99	69	83	78	83	54	3446	11
	pine	151	46	0	.	0	.	116	40	83	47	40	52	44	100	0	.	359	26	73	68	25	104	0	.	0	.	66	96	104	67	63	56	6	73	5	100	39	60	1	100	37	100	18	52	855	19	0	.	43	63	2128	12
	larch	111	56	0	.	0	.	87	60	9	100	9	100	0	.	0	.	411	23	0	.	6	100	0	.	0	.	0	.	41	57	26	99	5	100	0	.	26	95	418	36	0	.	132	64	370	21	0	.	6	100	1656	14
	Arolla pine	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	69	50	0	.	0	.	0	.	0	.	33	100	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	41	0	.	0	.	242	30
	other conifers	58	52	0	.	0	.	61	65	13	100	0	100	0	.	0	.	24	51	0	.	3	82	0	.	0	.	0	.	-9	136	7	100	0	.	0	.	2	100	7	100	1	100	87	76	11	90	0	.	13	63	278	31
	beech	491	33	0	.	8	81	1104	20	220	46	178	38	0	.	172	64	214	36	234	36	149	53	32	67	85	64	210	49	275	36	157	41	257	36	115	57	58	45	1819	20	88	42	332	34	163	35	19	97	164	35	6543	8
	maple	132	37	15	100	1	100	205	27	35	57	42	50	0	.	46	79	56	45	32	60	55	46	34	95	30	48	29	66	218	35	14	91	38	50	21	53	66	88	117	34	94	49	195	36	42	46	10	100	90	41	1620	11
	ash	148	29	5	100	8	100	161	35	38	60	151	54	0	.	117	80	153	34	101	48	36	50	3	76	68	84	34	56	338	30	52	73	61	95	60	56	96	39	262	27	66	59	217	42	74	36	139	65	142	38	2531	11
	oak	69	43	0	.	0	.	72	54	92	50	30	84	210	55	0	.	39	64	13	78	0	.	65	72	1	100	9	100	2	100	6	72	7	89	0	.	39	47	451	25	2	100	322	32	66	35	0	.	18	61	1513	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	236	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1627	16	0	.	0	.	58	54	0	.	0	.	1921	14
	other broadleaves	86	38	12	100	8	100	291	30	91	59	52	43	2	71	52	65	798	17	54	44	30	43	15	91	1	71	57	40	158	42	21	58	65	46	82	56	135	49	1705	11	77	44	214	28	392	20	22	89	65	35	4484	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2038	17	39	72	117	64	5194	12	683	31	1580	21	269	51	643	44	3997	11	1309	24	1577	21	571	34	416	34	982	29	2331	17	599	34	738	25	899	27	1276	24	6871	8	923	25	2689	15	2918	11	538	38	1589	19	40786	4
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	661	32	473	44	641	44	6470	12	64	47	2687	21	0	.	938	46	5477	12	903	31	878	29	281	37	206	66	805	29	1274	28	292	47	737	27	948	31	433	41	247	51	879	39	2678	17	1883	19	104	75	1190	25	31151	5
	fir	302	46	206	46	181	56	2110	17	85	63	756	29	0	.	44	63	179	41	628	31	742	33	345	33	32	69	325	38	322	34	9	100	336	30	342	40	24	64	270	52	141	57	958	21	106	40	10	100	253	44	8705	8
	pine	186	38	0	.	0	.	143	56	99	47	41	64	18	100	0	.	755	24	154	56	62	69	41	82	0	.	9	100	2	100	130	52	170	37	157	53	98	52	104	77	48	76	106	48	495	37	0	.	206	40	3024	12
	larch	63	75	0	.	0	.	84	61	0	.	108	90	0	.	25	100	1353	17	0	.	12	100	0	.	0	.	13	100	11	87	41	100	27	74	0	.	0	.	392	39	0	.	19	100	1056	20	34	100	50	52	3289	11
	Arolla pine	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	338	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	115	55	0	.	0	.	456	27
	other conifers	1	100	0	.	2	100	31	96	50	100	0	.	0	.	1	100	1	100	0	.	0	.	0	.	6	100	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	56	95	0	.	0	.	4	85	156	51
	beech	1106	19	75	64	40	62	2543	15	877	29	922	26	0	.	130	82	315	41	1494	21	468	35	288	38	274	45	454	41	990	29	478	30	1981	17	356	36	275	37	694	28	58	63	1748	17	235	42	44	60	617	28	16461	6
	maple	252	24	15	59	34	100	454	22	135	40	46	48	1	100	79	55	7	71	210	30	41	57	77	49	18	110	34	45	209	27	0	.	280	27	24	56	85	71	0	.	0	.	230	22	52	48	45	72	187	37	2513	9
	ash	418	26	0	.	57	65	248	28	69	86	240	37	0	.	51	100	-4	100	312	56	25	54	0	.	92	81	18	82	263	32	23	66	191	40	82	73	255	48	8	81	0	.	278	29	35	63	54	100	304	30	3021	11
	oak	244	34	0	.	0	.	186	37	31	84	38	72	120	72	0	.	21	100	0	.	81	65	3	100	0	.	0	.	168	83	42	54	57	55	1	100	15	100	89	62	0	.	351	31	23	51	0	.	42	46	1511	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	570	31	0	.	31	100	11	100	0	.	0	.	653	28
	other broadleaves	467	41	9	73	5	100	265	29	50	40	61	49	11	100	44	65	90	49	74	51	51	64	13	49	44	64	16	77	101	42	1	100	47	44	38	55	73	63	174	52	3	100	218	36	185	39	11	100	101	55	2154	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3700	15	779	37	960	39	12537	9	1460	25	4899	16	150	72	1312	36	8575	10	3775	17	2360	21	1047	26	671	39	1674	24	3341	19	1016	29	3831	15	1948	24	1257	29	2547	17	1129	33	6674	11	4196	13	303	52	2953	18	73095	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1752	23	404	50	862	34	11220	10	492	42	3506	18	0	.	721	37	9616	10	1241	24	3088	20	1914	21	267	57	1938	26	3462	18	625	35	1076	31	2627	21	1390	28	987	28	483	41	6468	13	3919	14	466	43	3421	16	61945	4
	fir	1005	28	92	71	562	39	5285	13	317	42	633	38	0	.	192	65	307	37	1521	25	1694	20	1251	23	205	58	197	70	1460	26	89	64	865	23	399	39	231	58	222	42	186	66	1636	18	217	47	149	49	1462	19	20178	6
	pine	283	42	21	100	0	.	421	43	403	39	305	57	0	.	0	.	793	24	193	59	64	70	56	74	0	.	0	.	141	49	110	52	223	36	0	.	245	40	129	77	76	75	113	82	381	36	0	.	631	23	4590	11
	larch	138	64	6	100	0	.	168	48	8	100	35	100	0	.	0	.	1860	15	11	100	0	.	0	.	0	.	0	.	198	58	10	100	0	.	27	100	0	.	300	31	0	.	68	66	2117	17	0	.	109	65	5054	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	484	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	36	0	.	0	.	683	23
	other conifers	24	87	0	.	0	.	69	61	0	.	0	.	0	.	0	.	0	.	0	.	76	100	0	.	0	.	0	.	2	71	23	100	1	71	1	100	0	.	0	.	0	.	22	88	0	.	0	.	12	100	231	41
	beech	1830	19	51	65	166	43	4703	14	1488	22	1518	24	0	.	192	53	101	30	1390	20	718	27	636	23	91	60	176	76	2239	21	642	27	1833	19	456	41	593	26	393	28	118	86	2762	15	17	66	199	52	1939	18	24250	5
	maple	111	62	0	.	52	57	378	21	145	38	69	45	6	.	62	68	22	58	165	29	113	40	48	44	102	61	80	62	439	26	46	49	117	40	58	50	129	53	0	.	18	74	159	33	10	61	27	70	283	31	2639	9
	ash	264	32	4	100	16	73	293	29	238	41	133	52	2	.	20	98	42	76	261	37	385	64	69	57	42	65	22	100	300	31	21	108	67	41	150	66	283	39	22	79	0	.	515	27	38	72	62	62	366	27	3614	11
	oak	203	34	0	.	0	.	160	28	93	70	91	42	34	.	0	.	102	73	196	48	57	84	27	64	0	.	0	.	62	54	106	57	185	39	9	88	273	34	49	91	30	100	780	30	2	100	17	100	178	39	2653	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	366	48	0	.	8	90	0	.	0	.	0	.	374	47
	other broadleaves	167	42	0	.	18	100	307	32	77	34	40	90	10	.	22	70	43	36	93	33	86	51	62	47	43	100	13	64	153	34	6	69	170	45	29	53	147	53	93	47	15	103	247	37	45	41	2	100	194	30	2082	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5777	14	577	46	1676	29	23005	8	3262	18	6329	15	51	.	1208	33	13371	8	5071	15	6279	15	4063	16	749	43	2426	25	8457	13	1677	25	4537	15	3755	19	3291	19	2562	17	926	37	12779	10	6945	11	921	36	8596	12	128293	3
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2087	25	349	59	840	36	11597	10	278	68	1781	25	0	.	1082	33	8639	11	454	39	3589	19	1406	24	-5	.	451	42	4324	18	279	54	876	36	1493	26	1360	28	1390	24	1147	32	3306	16	2532	18	288	87	2176	20	51720	4
	fir	664	34	17	100	254	57	5171	14	51	49	441	36	0	.	113	94	474	33	284	40	1660	22	1158	27	19	.	63	60	1107	27	0	.	369	44	444	37	490	43	255	55	47	77	1890	20	377	45	56	72	654	36	16058	7
	pine	182	42	0	.	8	100	416	35	0	.	74	65	4	.	0	.	507	40	99	62	0	.	16	100	0	.	0	.	19	100	0	.	55	69	7	100	80	47	31	75	0	.	8	100	143	70	0	.	500	35	2149	16
	larch	70	55	0	.	0	.	67	51	0	.	36	100	0	.	0	.	1739	18	0	.	105	100	22	100	0	.	0	.	136	57	10	100	0	.	24	100	16	100	553	33	0	.	74	61	1842	18	0	.	129	65	4821	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	200	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	71	0	.	0	.	216	34
	other conifers	0	.	0	.	0	.	92	71	0	.	48	100	0	.	0	.	0	.	0	.	36	97	0	.	0	.	0	.	96	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	290	45
	beech	1915	20	0	.	334	60	3293	16	955	33	652	37	0	.	126	53	426	41	661	34	1238	26	407	38	56	.	327	78	703	30	241	41	1054	26	568	38	458	33	256	44	64	64	1185	20	45	79	44	100	1635	20	16644	7
	maple	87	40	5	100	11	100	134	39	101	45	66	57	0	.	39	64	26	89	17	57	140	37	60	81	0	.	0	.	173	37	1	100	42	52	24	51	54	65	0	.	7	100	176	35	4	100	0	.	117	43	1285	13
	ash	41	54	0	.	31	71	242	44	24	76	53	62	4	.	11	100	6	100	46	79	96	43	0	.	0	.	0	.	18	75	8	100	25	77	11	100	85	48	4	100	3	100	162	50	11	77	0	.	348	35	1230	16
	oak	233	36	0	.	12	100	273	30	60	58	64	74	81	.	0	.	0	.	130	72	33	65	0	.	0	.	0	.	9	100	208	50	51	64	35	100	241	52	10	61	15	100	296	47	1	100	0	.	244	35	1996	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	891	30	0	.	0	.	0	.	0	.	0	.	906	30
	other broadleaves	56	45	0	.	0	.	177	57	92	62	10	84	1	.	0	.	43	43	32	70	47	86	23	76	6	.	0	.	44	54	0	.	41	59	20	72	88	71	66	36	21	69	98	71	34	61	0	.	131	39	1029	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5335	15	371	59	1490	34	21463	8	1561	28	3225	21	89	.	1372	30	12074	10	1723	25	6944	15	3092	19	76	.	841	41	6629	16	747	36	2514	24	2626	23	2872	22	3456	15	1305	30	7194	13	5006	14	388	77	5950	14	98345	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	16	72	206	63	311	48	5524	12	29	100	1493	24	0	.	210	41	4378	12	300	34	919	30	816	26	340	41	405	36	1499	22	0	.	38	52	687	28	55	59	1607	18	294	44	1791	17	3213	13	211	59	111	45	24451	5
	fir	0	.	114	61	109	80	3214	15	50	97	459	43	0	.	59	74	364	43	619	29	784	31	566	25	6	272	43	60	522	31	0	.	86	63	171	45	81	100	144	53	21	100	635	27	228	37	48	54	94	52	8418	8
	pine	0	.	0	.	0	.	107	41	18	100	15	100	0	.	0	.	333	31	73	64	0	.	7	100	0	.	28	100	79	72	0	.	81	55	5	100	26	100	65	61	36	74	0	.	227	34	0	.	49	59	1149	15
	larch	0	.	0	.	65	100	4	100	0	.	0	.	0	.	0	.	867	18	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	1039	17	0	.	39	100	2196	17	0	.	37	83	4267	10
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	298	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	9	100	0	.	341	26	0	.	0	.	653	19
	other conifers	0	.	2	100	0	.	5	100	0	.	0	.	0	.	0	.	17	74	1	100	0	.	3	100	0	.	1	100	6	100	0	.	9	67	0	.	3	100	0	.	4	72	0	.	1	100	0	.	17	63	68	29
	beech	299	41	76	66	213	43	1182	19	63	64	322	37	0	.	215	51	104	35	315	32	253	46	227	32	30	100	53	85	482	25	0	.	142	46	177	39	55	63	751	23	15	77	287	28	36	100	18	96	196	43	5510	8
	maple	6	100	28	81	74	61	220	28	25	100	67	43	0	.	32	92	29	50	99	49	8	51	35	37	19	62	7	71	115	40	0	.	24	59	118	43	14	71	17	59	0	.	121	38	18	49	17	100	108	51	1198	12
	ash	150	69	32	79	108	63	153	37	7	100	110	44	0	.	15	219	76	73	167	39	60	77	10	71	0	.	0	.	108	46	0	.	29	86	17	88	39	55	92	42	0	.	121	61	73	49	0	.	78	42	1446	15
	oak	74	66	0	.	0	.	5	100	0	.	13	89	0	.	0	.	84	71	4	73	116	72	0	.	0	.	0	.	0	0	0	.	6	90	0	.	17	100	105	47	12	100	0	.	30	66	0	.	45	60	513	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1131	22	0	.	0	.	62	100	0	.	0	.	1239	21
	other broadleaves	70	61	3	100	8	100	127	27	25	84	49	47	0	.	15	81	76	42	90	38	30	59	3	60	1	100	2	74	159	36	0	.	22	70	16	71	43	84	519	23	16	71	88	46	135	58	0	.	39	70	1535	12
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	614	40	461	48	888	41	10544	10	215	62	2527	22	0	.	546	35	6673	10	1667	22	2170	24	1667	21	396	42	539	32	2991	18	0	.	436	39	1191	25	334	48	5469	10	407	40	3082	15	6559	10	293	53	775	32	50446	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5253	14	1439	26	2755	19	37514	5	961	30	10437	10	14	88	3129	20	30970	5	3571	14	9825	10	4691	12	1017	26	4483	15	11654	10	1449	22	3013	17	6288	12	3955	16	4627	11	3277	17	15196	7	12438	7	1337	28	7947	10	187239	2
	fir	2087	19	429	31	1109	26	16742	7	518	29	2437	17	0	.	499	37	1530	18	3276	15	5134	12	3516	13	288	43	671	29	3794	14	99	58	1706	16	1605	18	967	28	1054	23	549	30	5549	10	1044	22	346	31	2548	15	57496	3
	pine	826	21	21	100	8	100	1243	21	603	28	473	39	66	72	0	.	2820	13	592	28	151	44	121	47	0	.	103	68	344	33	303	31	535	22	174	48	500	24	330	41	201	41	294	39	2103	15	0	.	1454	17	13264	6
	larch	390	32	6	100	65	100	409	28	17	71	207	54	0	.	25	100	6476	8	25	71	123	86	22	100	0	.	13	100	414	34	87	58	32	65	51	71	46	63	2784	12	11	100	345	33	7779	8	34	100	333	35	19692	5
	Arolla pine	0	.	0	.	0	.	7	71	0	.	0	.	0	.	0	.	1390	15	0	.	0	.	0	.	0	.	33	100	16	100	0	.	0	.	0	.	0	.	1	100	9	100	0	.	812	17	0	.	0	.	2267	11
	other conifers	83	45	2	100	2	100	259	36	63	82	49	99	0	.	1	100	45	40	1	100	116	73	3	100	6	100	1	100	95	102	29	81	15	51	1	100	8	59	7	100	7	48	166	53	12	85	0	.	64	39	1033	19
	beech	5731	10	202	38	815	29	12931	7	3662	14	3623	14	0	.	934	24	1236	19	4094	12	2857	15	1578	16	543	29	1231	29	4767	12	1534	16	5319	10	1683	19	1456	16	4004	11	344	35	6359	9	511	24	326	36	4585	11	70327	2
	maple	600	18	71	42	171	38	1479	11	441	21	304	21	7	91	291	29	168	26	524	18	357	21	258	28	168	40	150	37	1190	14	61	42	507	18	252	25	351	32	140	29	121	40	894	14	137	25	107	40	796	17	9546	4
	ash	1024	17	41	64	220	37	1127	15	377	31	688	22	6	79	218	52	274	30	888	24	603	42	82	49	203	48	76	43	1060	16	104	46	383	27	320	38	760	23	395	20	70	56	1294	17	233	25	256	44	1277	15	11979	5
	oak	858	18	0	.	12	100	711	17	276	33	238	30	444	38	0	.	246	41	344	39	287	39	107	48	1	100	9	100	242	59	361	34	306	28	44	81	607	26	704	20	59	60	1749	18	131	25	17	100	562	21	8315	7
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	340	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4624	10	0	.	39	82	131	54	0	.	0	.	5133	10
	other broadleaves	847	25	24	59	40	55	1171	16	335	26	211	27	23	64	139	35	1075	14	344	20	253	28	118	31	95	54	90	30	625	18	28	46	348	25	186	30	496	27	2621	9	143	29	887	18	822	16	38	61	533	19	11494	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17698	7	2235	22	5197	17	73595	4	7254	11	18668	8	560	36	5236	15	46571	4	13658	8	19706	8	10495	9	2321	20	6860	13	24201	7	4055	15	12163	9	10605	10	9146	11	21290	4	4792	14	32771	5	26153	5	2459	22	20097	7	397785	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. 1230959. Birmensdorf, Swiss Federal Research Institute WSL

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