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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NFI4
growing stock (stemwood)
development stage · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		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	-18	100	0	0	0	.	283	64	0	.	101	82	0	.	0	.	417	50	0	.	0	.	0	.	0	.	21	96	0	0	0	.	0	.	106	99	0	.	0	0	0	.	47	100	6	100	0	0	0	.	961	32
	fir	0	.	0	.	0	.	22	100	0	.	105	100	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	78
	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	.	0	.	0	0
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	13	100	56	62	0	.	0	.	285	39
	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	.	27	100	0	.	0	.	27	100
	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	18	100	0	.	0	.	9	100	0	.	19	78	0	.	0	.	24	100	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	-2	1162	0	.	0	.	0	.	82	55
	maple	0	.	0	.	0	.	13	100	0	.	5	100	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	52
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	oak	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100
	sweet chestnut	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 broadleaves	0	.	0	.	0	.	5	77	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	53
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	0	100	0	0	0	.	333	55	0	.	236	70	9	100	0	.	658	46	0	.	24	100	0	.	0	.	21	96	0	0	0	.	0	.	139	86	0	.	0	0	0	.	57	120	89	64	0	0	0	.	1564	27
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	254	48	0	.	138	85	875	36	0	0	237	40	0	.	128	85	966	36	0	.	332	57	63	83	0	.	18	73	106	61	21	100	0	.	0	0	7	100	63	84	143	80	210	54	203	44	19	100	224	57	4008	15
	fir	33	88	0	.	8	100	269	51	0	.	106	93	0	.	42	100	9	100	20	79	68	57	106	100	0	.	0	0	76	74	0	.	4	100	45	76	0	.	1	100	0	.	126	56	15	100	3	100	146	65	1077	23
	pine	0	.	0	.	0	.	0	.	47	100	0	.	0	.	0	.	70	79	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	5	100	138	54
	larch	70	73	0	.	0	.	131	66	0	.	0	.	0	.	0	.	64	68	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	86	75	0	.	0	.	254	51	0	.	2	100	606	30
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	76	0	.	1	100	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	12	54
	beech	411	41	0	.	25	71	318	33	0	.	68	52	0	.	0	.	14	100	19	92	217	64	37	100	0	.	0	.	124	61	0	.	1	100	0	0	75	73	18	73	22	100	33	73	2	100	39	100	182	43	1607	17
	maple	82	52	0	.	0	.	23	78	0	.	17	72	0	.	38	71	21	95	2	100	47	62	0	.	0	.	17	100	78	58	0	.	0	.	10	100	6	100	4	100	0	.	36	69	1	1336	0	.	-2	2404	380	27
	ash	1	659	0	.	0	.	15	61	2	100	51	67	0	.	8	100	0	.	0	.	5	84	2	100	0	.	0	.	17	100	39	100	0	.	0	.	1	100	13	100	0	.	0	.	14	100	0	.	30	87	197	33
	oak	107	100	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	6	100	0	.	12	100	0	0	0	.	20	100	164	68
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	16	100
	other broadleaves	36	48	0	.	0	.	59	43	5	100	17	78	0	.	6	62	58	56	0	.	5	78	0	.	6	100	1	100	39	55	0	.	0	.	0	0	0	.	38	62	6	100	128	76	26	71	0	.	4	71	435	26
	indeterminable	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	994	31	0	.	171	82	1709	24	53	97	497	35	0	.	222	71	1207	31	41	80	691	41	209	79	6	100	36	81	443	43	60	100	6	75	54	65	88	64	246	40	171	68	545	37	521	36	61	76	610	37	8642	10
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	752	37	0	.	178	78	1615	23	210	63	676	41	0	.	85	63	1412	21	88	66	924	29	123	61	237	88	116	70	306	43	329	59	60	65	246	51	533	50	426	33	333	58	1403	27	814	31	128	83	1273	31	12269	8
	fir	87	54	0	.	43	85	489	37	20	100	55	78	0	.	71	100	120	64	299	71	537	35	25	75	4	100	0	.	82	75	0	.	109	60	131	79	289	55	28	73	9	100	356	34	111	66	44	95	47	71	2954	15
	pine	35	71	0	.	0	.	131	53	0	.	17	100	0	.	0	.	403	32	0	.	0	.	27	100	0	.	0	.	43	100	43	100	-6	100	9	100	9	100	15	100	0	.	10	100	457	48	0	.	111	59	1304	22
	larch	34	64	0	.	0	.	5	82	0	.	46	100	0	.	0	.	483	32	0	.	4	100	0	.	0	.	14	100	0	.	0	.	0	.	0	.	9	100	501	31	0	.	190	42	580	27	0	.	16	100	1881	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	98	0	.	0	.	179	56
	other conifers	72	55	0	.	0	.	94	52	0	.	0	.	0	.	0	.	30	45	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	50	100	0	.	14	66	8	85	0	.	10	100	275	30
	beech	462	60	0	.	15	100	446	32	301	53	141	45	0	.	97	83	112	59	111	74	120	81	48	49	69	100	0	.	105	72	76	56	55	77	93	64	43	51	669	33	7	100	186	38	183	66	0	100	220	48	3557	14
	maple	80	44	0	.	1	533	96	36	96	54	105	55	0	.	14	59	64	57	18	54	16	65	65	60	30	71	35	100	10	142	32	70	49	66	81	49	95	45	177	36	42	91	142	41	101	37	15	100	332	47	1697	13
	ash	127	50	0	.	3	100	38	52	36	88	81	54	0	.	18	68	62	42	66	89	24	61	74	63	19	100	6	100	43	71	30	94	93	86	221	53	106	51	192	35	29	95	86	39	250	43	13	87	71	44	1689	14
	oak	0	.	0	.	0	.	21	94	0	.	17	71	0	.	0	.	0	0	3	101	0	.	37	96	0	.	0	.	0	.	87	100	31	100	0	.	19	92	299	39	0	.	215	61	36	53	0	.	19	70	785	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1406	23	0	.	0	0	34	79	0	.	0	.	1605	22
	other broadleaves	91	45	0	.	12	100	167	37	67	55	46	67	18	100	32	91	876	27	26	71	61	48	26	56	0	.	2	100	33	78	17	59	77	79	125	52	64	59	1628	20	133	61	313	37	445	22	7	100	47	58	4313	10
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0
	total	1741	27	0	.	252	69	3103	17	729	38	1184	31	18	100	316	56	3829	14	605	52	1685	26	426	31	358	67	172	59	622	35	614	41	468	39	906	37	1170	31	5392	12	553	42	2914	19	3097	18	207	74	2147	24	32509	5
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	48	61	72	100	270	100	2338	22	66	112	1619	36	0	.	439	66	2211	24	219	98	916	39	420	68	391	68	152	71	1473	33	491	49	481	50	359	48	686	49	203	63	95	51	889	36	1152	37	411	59	850	32	16251	9
	fir	182	77	72	100	13	100	415	31	65	81	426	58	0	.	20	100	139	58	24	71	382	51	41	100	11	71	109	100	439	58	77	75	207	48	216	62	60	94	118	83	316	63	498	43	0	.	197	86	162	62	4188	15
	pine	9	100	0	.	0	.	258	83	47	62	14	100	100	86	0	.	562	47	56	100	0	.	24	100	0	.	16	100	0	.	108	59	59	59	37	100	69	75	0	.	0	.	107	90	614	35	0	.	66	60	2146	21
	larch	66	100	0	.	0	.	0	.	10	100	0	.	0	.	0	.	728	28	0	.	15	73	0	.	0	.	0	.	38	77	30	81	0	.	0	.	0	.	655	43	0	.	0	.	454	38	0	.	67	74	2062	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	288	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	61	0	.	0	.	358	36
	other conifers	6	100	0	.	0	.	111	98	0	.	0	.	0	.	2	100	1	100	0	.	22	100	0	.	0	.	0	.	7	81	57	100	15	100	0	.	0	.	0	.	0	.	103	100	1	100	0	.	12	100	337	48
	beech	193	55	7	100	44	91	1824	23	411	47	305	53	0	.	35	80	67	58	383	49	133	50	9	100	0	.	141	98	656	33	423	44	689	34	153	62	95	71	1610	26	356	48	758	37	102	58	24	100	527	34	8946	10
	maple	65	61	10	100	105	100	345	36	30	61	183	51	0	.	110	65	72	57	25	73	22	71	5	100	10	75	134	100	334	35	29	100	149	40	10	100	0	.	15	82	68	61	184	45	12	100	17	100	308	36	2240	14
	ash	164	62	0	.	43	100	181	51	25	73	389	52	0	.	42	100	93	69	194	58	56	70	0	.	189	100	13	100	464	37	60	87	63	62	0	.	150	79	197	66	109	78	193	41	10	85	0	.	337	38	2972	15
	oak	131	76	0	.	0	.	189	43	0	.	88	68	250	60	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	48	66	75	62	0	.	16	100	219	33	10	100	376	44	30	50	10	100	35	72	1491	19
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	697	34	0	.	0	.	26	100	0	.	0	.	829	30
	other broadleaves	143	59	0	0	4	100	251	43	28	75	14	52	3	100	0	.	354	37	110	91	35	100	4	100	4	100	0	0	61	87	19	71	63	64	6	62	119	100	892	31	45	66	99	63	220	52	0	.	165	44	2638	15
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	1012	32	161	100	479	72	5912	16	682	39	3038	26	353	58	647	58	4615	17	1011	39	1595	35	503	63	605	56	564	46	3474	25	1344	33	1800	28	782	41	1195	40	4605	16	999	43	3206	22	2690	22	659	61	2528	23	44459	6
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	333	39	978	44	563	53	5011	18	153	73	2241	29	0	.	980	49	8607	14	1169	36	760	49	603	37	106	79	75	100	1672	30	600	42	730	39	1296	33	955	34	1149	33	1926	37	2089	22	2432	24	190	78	2099	27	36717	6
	fir	216	50	358	50	458	56	1810	25	107	63	525	47	0	.	215	84	167	69	1796	30	870	37	408	45	44	100	188	68	661	36	1	100	125	50	557	43	494	50	287	51	229	63	1642	28	446	55	0	.	624	31	12229	9
	pine	0	.	0	.	0	.	266	43	23	142	122	92	0	.	0	.	543	38	144	63	129	66	107	100	0	.	0	.	31	100	209	56	70	54	0	.	246	53	17	100	197	61	69	60	239	46	0	.	516	31	2928	14
	larch	98	95	0	.	0	.	64	100	0	.	25	100	0	.	9	100	1824	22	0	.	0	.	0	.	0	.	0	.	115	82	9	100	17	100	0	.	46	100	527	37	0	.	14	100	930	37	55	100	141	60	3876	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	36
	other conifers	43	100	0	.	8	78	15	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	9	72	84	56
	beech	976	28	191	54	272	51	2678	22	1191	33	1411	32	0	.	400	45	423	50	1070	30	717	40	282	39	204	71	431	69	1156	35	815	35	1651	26	421	43	540	30	601	37	43	71	1997	22	128	83	147	67	1401	27	19146	7
	maple	255	41	8	100	57	76	262	30	121	49	7	139	0	.	60	46	16	72	198	33	35	88	28	75	14	94	27	100	370	33	0	.	146	39	87	62	62	46	2	100	33	100	234	29	13	73	68	71	268	31	2369	11
	ash	405	35	0	.	289	53	230	57	142	59	378	49	0	.	141	61	2	100	336	54	550	81	0	.	188	85	6	100	245	46	0	.	134	44	192	88	248	46	120	78	5	100	357	40	0	0	49	100	360	30	4379	15
	oak	37	100	0	.	0	.	185	38	69	80	10	100	0	.	0	.	59	100	50	80	150	71	16	91	0	.	0	.	0	0	103	81	38	59	0	.	218	48	92	75	19	100	228	44	5	100	0	.	306	39	1586	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	547	55	0	.	7	100	0	.	0	.	0	.	554	54
	other broadleaves	162	61	3	100	0	.	206	50	41	63	150	67	0	.	108	72	13	61	47	45	78	83	44	85	35	79	0	.	79	44	0	0	65	63	7	277	84	61	198	53	35	89	108	39	55	56	0	.	113	40	1630	16
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	2524	23	1539	40	1647	40	10727	14	1847	31	4871	20	0	.	1913	36	12062	12	4810	21	3290	28	1489	29	591	52	727	59	4330	21	1738	32	2976	22	2560	27	2892	26	3540	20	2487	32	6754	17	4249	21	508	59	5835	17	85908	4
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	1534	36	1477	41	1654	39	12162	12	313	72	2409	28	0	.	1583	40	11223	13	715	44	3946	26	1071	41	264	76	1519	33	5185	22	446	60	781	52	3135	24	815	35	1999	33	628	54	5096	18	4552	20	5	100	2119	28	64632	5
	fir	1227	34	127	72	1018	48	7593	14	259	62	829	44	0	.	162	100	701	39	792	47	3056	23	1690	34	239	71	538	56	1520	28	61	89	542	41	754	40	414	59	350	65	120	99	2678	21	115	77	44	100	843	35	25672	7
	pine	90	72	0	.	17	100	662	49	0	.	172	78	10	100	0	.	360	40	74	88	41	100	0	.	0	.	0	.	0	.	0	.	89	63	8	100	71	54	0	.	0	.	10	100	179	86	0	.	230	53	2015	22
	larch	33	100	0	.	0	.	37	100	15	100	0	0	0	.	57	100	3161	21	20	100	0	.	43	100	0	.	0	.	120	73	0	.	0	.	52	100	0	.	919	37	0	.	117	49	3079	21	0	.	0	.	7652	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	53	0	.	0	.	349	41
	other conifers	0	.	0	.	0	.	72	98	0	.	0	.	0	.	0	.	0	.	0	.	236	79	0	.	0	.	0	.	277	105	0	.	4	100	3	100	1	100	0	.	0	.	189	98	0	.	0	.	0	.	782	51
	beech	2719	25	60	97	201	59	5673	15	1203	38	873	38	0	.	333	60	734	43	1007	34	1433	29	475	43	54	71	598	50	2144	26	672	42	1605	28	871	43	725	36	580	48	259	84	2653	21	27	100	32	100	1569	25	26499	7
	maple	101	46	0	.	82	72	480	25	173	60	51	56	0	.	47	65	36	117	94	57	150	38	124	54	55	61	18	85	371	36	0	.	74	53	133	44	121	68	0	.	17	100	162	36	68	62	0	.	193	50	2550	12
	ash	236	61	0	.	0	0	738	34	80	74	195	61	28	100	1	1623	38	100	93	86	90	47	32	71	52	72	2	100	407	42	69	78	66	54	256	58	371	44	0	.	23	72	600	43	29	86	109	100	545	41	4060	14
	oak	243	54	0	.	0	.	189	42	79	100	0	.	214	100	0	.	0	.	237	58	7	100	0	.	0	.	0	.	73	60	2	100	46	100	16	100	543	64	14	100	0	.	243	54	0	0	0	.	161	54	2066	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	384	56	0	.	2	100	0	.	0	.	0	.	385	56
	other broadleaves	104	57	0	.	48	95	581	41	33	70	258	99	0	0	0	0	141	75	31	85	28	61	1	100	33	100	0	.	131	42	0	.	64	86	49	77	554	80	53	42	0	.	457	67	12	63	0	.	83	59	2661	25
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	6286	18	1664	42	3020	34	28187	9	2155	33	4787	22	252	100	2183	36	16513	11	3064	26	8988	18	3436	26	698	58	2675	32	10228	16	1250	43	3271	25	5275	21	3616	27	4299	20	1048	46	12206	13	8291	15	190	100	5744	19	139323	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	326	60	-1	100	72	100	10100	13	3	100	905	33	0	.	833	39	9071	12	581	34	1750	26	2342	20	455	56	1637	28	1846	27	0	.	41	74	622	37	177	51	2345	24	973	32	3212	17	5088	15	64	100	344	37	42787	5
	fir	142	71	0	.	0	.	5162	17	5	85	396	71	0	.	15	100	855	38	638	33	941	30	1854	23	33	88	233	46	840	34	0	.	329	54	241	56	42	68	338	46	303	47	1117	28	624	39	83	72	313	49	14505	9
	pine	78	79	0	.	0	.	105	40	100	63	169	86	0	.	0	.	694	39	94	58	0	.	0	.	0	.	147	100	105	72	0	.	117	59	1	100	46	100	133	60	41	92	13	100	531	28	0	.	69	90	2443	17
	larch	0	.	0	.	0	.	75	71	0	.	0	.	0	.	0	.	1918	15	0	.	0	.	0	.	0	.	0	.	33	92	0	.	0	.	0	.	0	.	1451	20	0	.	11	100	3564	20	0	.	61	71	7112	11
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	612	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	554	26	0	.	0	.	1164	19
	other conifers	0	.	0	.	0	.	111	91	177	100	0	.	0	.	0	.	11	70	2	100	0	.	7	100	0	.	4	100	-16	100	0	.	-7	291	0	.	0	.	0	.	6	100	5	100	1	100	0	.	21	100	323	64
	beech	249	42	0	.	0	.	2794	18	188	45	486	46	0	.	97	75	495	35	1003	32	810	37	686	25	109	72	368	44	1079	32	122	100	934	36	204	58	245	46	1526	21	188	59	1020	26	515	43	31	100	300	41	13449	8
	maple	0	.	0	.	0	.	804	24	69	72	115	48	0	.	96	59	281	59	108	43	244	37	208	46	37	59	54	51	263	53	24	100	138	49	15	64	156	83	65	65	24	100	228	44	126	49	22	100	59	62	3137	12
	ash	84	98	0	.	0	.	318	37	228	64	193	50	0	.	87	85	120	59	232	43	203	51	19	82	-9	100	46	97	339	46	17	100	147	66	39	71	189	51	155	42	15	100	436	44	135	56	0	.	110	44	3105	13
	oak	179	80	0	.	0	.	91	53	50	71	45	100	0	.	0	.	92	81	10	100	137	78	33	100	5	100	0	.	9	73	0	0	10	100	0	.	99	49	307	39	65	71	123	74	133	54	0	.	126	57	1516	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	184	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1862	24	0	.	0	.	135	81	0	.	0	.	2180	22
	other broadleaves	71	65	0	.	0	.	438	42	36	88	43	56	0	.	41	76	381	30	85	45	58	52	30	48	0	.	36	49	262	54	0	.	23	67	53	118	44	100	815	22	35	66	481	41	230	41	0	.	176	69	3337	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	.	0	0	0	0	0	.	0	.	0	0
	total	1128	35	-1	100	72	100	19992	10	855	39	2352	28	0	.	1170	36	14713	10	2753	24	4143	21	5179	16	631	57	2525	24	4761	20	164	100	1733	30	1176	34	998	39	9000	11	1651	30	6645	14	11636	11	200	71	1581	30	95058	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	3229	21	2526	29	2875	27	32385	7	744	39	8189	14	0	.	4047	22	33907	6	2773	22	8628	14	4622	15	1454	33	3537	20	10588	13	1888	25	2092	26	5764	16	3174	19	6186	15	4098	21	12945	10	14247	9	816	38	6910	14	177626	2
	fir	1887	25	557	38	1541	36	15761	9	457	40	2442	24	0	.	525	49	1991	23	3568	20	5854	15	4123	17	331	54	1068	34	3619	17	139	57	1316	24	1953	23	1299	30	1121	29	977	31	6417	13	1311	28	371	51	2135	19	60763	4
	pine	212	44	0	.	17	100	1422	29	217	41	495	46	110	79	0	.	2632	18	369	37	186	52	159	71	0	.	163	91	179	52	360	39	330	31	55	71	441	35	165	50	238	53	207	51	2020	19	0	.	997	22	10974	8
	larch	300	43	0	.	0	.	311	40	25	72	71	74	0	.	67	87	8395	10	20	100	18	62	43	100	0	.	14	100	306	44	39	66	17	100	52	100	55	85	4139	13	0	.	345	29	8917	11	55	100	287	38	23475	6
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	1532	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	958	22	0	.	0	.	2489	13
	other conifers	121	49	0	.	8	78	402	43	177	100	0	.	0	.	2	100	45	35	-5	145	259	72	7	100	0	.	4	100	273	107	57	100	12	226	3	100	4	74	50	100	6	100	320	66	15	58	0	.	52	52	1813	27
	beech	5027	16	258	46	557	33	13742	9	3295	20	3303	19	0	.	961	30	1869	22	3594	16	3441	18	1538	19	436	42	1539	30	5263	15	2108	21	4936	15	1745	25	1723	19	5004	13	875	34	6645	12	958	29	273	43	4199	14	73286	3
	maple	583	23	18	71	245	52	2024	13	487	28	484	26	0	.	365	28	490	37	445	22	526	22	430	29	145	33	284	51	1426	18	86	52	556	21	349	26	439	37	263	29	185	39	986	17	322	27	122	47	1157	20	12416	5
	ash	1017	24	0	.	335	48	1520	21	512	35	1288	25	28	100	297	42	314	34	922	27	929	50	128	42	439	57	73	65	1516	20	215	42	503	29	712	36	1065	24	678	27	182	51	1672	21	437	31	171	70	1453	19	16407	6
	oak	696	35	0	.	0	.	694	21	197	52	160	48	473	55	0	.	151	63	299	48	308	49	86	59	5	100	0	.	82	54	241	52	201	38	16	100	895	41	936	20	95	54	1197	24	205	37	10	100	668	25	7615	9
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4911	13	0	.	9	84	194	59	0	.	0	.	5569	12
	other broadleaves	606	26	3	100	63	75	1708	20	209	30	534	52	21	87	187	47	1822	17	299	38	265	32	105	40	79	55	39	45	605	28	37	47	292	35	244	41	864	54	3624	12	253	37	1586	25	987	18	7	100	589	27	15029	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	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	13685	11	3362	28	5641	22	69962	5	6320	16	16966	11	632	51	6452	18	53598	5	12284	12	20415	10	11241	11	2890	26	6720	17	23858	9	5169	18	10254	12	10892	13	9960	14	27082	5	6908	17	32328	7	30572	7	1826	31	18446	9	407462	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. 1231037. Birmensdorf, Swiss Federal Research Institute WSL

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