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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NFI3
number of stems
development stage · main tree species
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		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 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	56	100	0	.	0	.	421	32	0	0	171	50	0	.	64	74	426	31	18	100	110	54	0	.	0	0	112	51	19	71	0	.	0	.	307	63	32	100	28	100	16	100	17	62	72	56	21	100	4	100	1895	16
	fir	0	.	0	.	0	.	98	73	0	.	70	76	0	.	0	.	8	71	24	100	46	58	0	.	0	.	0	0	0	.	0	.	0	.	47	63	11	100	0	.	0	0	7	100	40	80	0	.	8	71	359	30
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	10	100	0	.	0	.	14	76
	larch	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	39	44	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	8	71	0	.	0	.	51	36
	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	17	100	0	.	0	.	37	53	0	.	43	65	0	.	0	.	42	91	14	100	97	75	0	.	0	.	0	.	33	100	4	100	0	.	10	100	36	58	13	100	0	.	125	90	0	.	0	.	17	71	489	31
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	14	73	0	.	0	.	12	100	10	74	0	.	0	.	10	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	60	38
	ash	0	.	0	.	0	.	30	74	0	.	4	100	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	47	52
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	19	100	0	.	0	.	0	.	0	.	0	.	0	.	10	100	48	100	0	.	0	.	0	.	0	.	9	100	16	100	0	.	0	.	11	100	11	100	0	0	0	.	11	100	30	100	0	.	0	.	165	40
	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
	total	93	72	0	.	0	.	590	29	0	0	289	43	0	.	84	72	563	30	56	100	276	46	0	.	0	0	133	47	78	74	4	100	0	.	385	59	94	58	41	75	16	100	164	78	160	44	21	100	33	58	3081	14
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	206	52	0	.	58	84	585	27	14	100	119	50	0	.	91	66	823	22	199	90	40	59	0	.	21	100	44	92	89	55	39	80	32	59	85	73	0	.	78	59	189	61	346	47	199	32	10	100	226	57	3493	12
	fir	7	71	0	.	24	100	275	32	34	72	0	0	0	.	15	79	32	69	29	75	56	62	0	.	11	100	10	100	50	57	0	.	44	78	21	100	11	100	0	.	10	100	79	46	0	0	0	.	48	59	758	17
	pine	4	100	0	.	0	.	13	100	13	100	0	.	0	.	0	.	42	79	13	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	6	100	299	77	0	.	20	68	415	57
	larch	8	71	0	.	0	.	14	100	0	.	0	.	0	.	0	.	70	41	4	100	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	250	44	4	100	6	100	317	30	0	.	7	100	701	22
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	66	0	.	0	.	55	57
	other conifers	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	60	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	10	100	39	100	0	.	0	.	140	40
	beech	164	31	0	.	0	.	315	27	52	52	149	52	0	.	72	62	0	0	19	82	43	54	0	.	15	77	9	100	69	51	19	82	32	58	8	100	32	100	131	72	0	.	78	45	36	73	0	.	148	32	1391	13
	maple	37	51	0	.	10	100	79	37	13	100	50	54	0	.	40	61	10	100	20	100	37	59	0	.	0	.	0	.	9	100	0	.	11	100	0	.	11	100	19	71	0	.	51	66	45	91	4	100	18	71	464	18
	ash	4	100	0	.	10	100	52	57	0	.	37	73	0	.	20	71	0	.	0	.	17	72	25	100	0	.	0	.	0	.	22	100	5	100	0	.	0	.	131	58	0	.	11	100	10	100	0	.	40	71	385	26
	oak	8	100	0	.	0	.	4	100	4	100	0	.	0	.	0	.	0	.	18	71	0	.	0	.	0	.	0	.	0	.	10	100	4	100	0	.	0	.	10	100	0	.	17	79	82	76	0	.	8	71	164	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	162	59	0	.	0	.	0	.	0	.	0	.	182	53
	other broadleaves	76	56	0	.	42	100	119	43	69	50	29	74	0	.	0	.	98	42	67	55	0	0	0	.	11	100	0	0	21	71	0	.	14	75	0	.	0	.	540	39	68	71	53	53	265	50	10	100	27	74	1509	19
	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
	total	525	29	0	.	145	71	1455	18	199	43	384	36	0	.	238	40	1156	19	379	59	194	39	25	100	57	56	63	82	259	36	90	58	146	44	114	60	53	100	1321	25	292	46	656	34	1337	26	25	100	542	29	9656	8
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	1599	28	66	71	510	62	3766	19	131	70	2617	26	0	.	273	49	6407	16	680	40	1703	25	777	56	420	67	692	54	1627	32	544	67	941	38	958	33	2268	31	809	34	314	45	2954	24	2659	25	333	55	3198	24	36246	6
	fir	233	48	0	.	148	92	431	32	0	.	335	51	0	.	0	.	111	48	201	43	487	41	143	77	0	.	0	.	61	49	56	100	541	42	251	55	543	48	274	56	62	71	407	31	126	47	51	75	56	47	4517	13
	pine	64	55	10	100	0	.	352	54	0	.	45	57	0	.	0	.	1603	26	165	74	117	100	0	.	0	.	0	.	0	.	138	69	71	100	20	100	122	63	0	.	4	100	92	90	1725	29	0	.	48	55	4579	16
	larch	24	61	11	100	0	.	113	54	0	.	29	100	0	.	0	.	1510	23	0	.	27	74	0	.	0	.	42	100	123	71	9	100	0	.	0	.	107	65	1328	26	0	.	275	40	1972	23	0	.	10	100	5581	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	184	49	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	65	0	.	0	.	345	37
	other conifers	94	47	30	100	0	.	179	61	0	.	0	.	0	.	0	.	243	44	20	100	44	100	0	.	0	.	0	.	10	100	26	100	0	.	40	100	193	91	9	100	0	.	166	63	31	75	0	.	47	73	1132	24
	beech	1009	37	0	.	12	100	1270	30	223	74	264	32	0	.	20	100	495	44	503	48	192	55	93	51	21	100	26	63	570	40	134	67	385	41	307	42	429	38	3448	21	28	63	554	33	633	41	6	100	234	39	10856	10
	maple	279	39	235	98	0	.	609	24	50	100	337	42	0	.	160	48	211	43	395	45	126	48	146	49	25	100	10	100	258	41	95	63	185	44	165	42	460	45	548	34	175	59	630	29	342	27	0	.	758	34	6199	10
	ash	398	34	0	.	0	.	246	41	7	100	350	47	0	.	67	77	464	46	183	65	310	68	67	65	11	100	68	86	451	41	106	92	111	44	490	50	360	35	996	24	119	71	384	29	367	36	51	73	714	35	6321	10
	oak	270	57	0	.	0	.	40	56	0	.	55	69	0	.	0	.	64	86	10	100	18	100	154	100	11	100	0	.	0	.	0	.	63	63	0	.	85	60	916	34	0	.	586	43	589	33	0	.	142	62	3002	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	335	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4018	20	0	.	0	.	52	76	0	.	0	.	4406	19
	other broadleaves	582	44	10	100	0	.	590	35	27	72	306	46	20	100	213	60	3456	21	225	50	161	44	50	66	0	.	64	85	367	49	57	61	208	63	357	49	394	38	7286	13	556	58	1062	29	3091	19	20	71	205	35	19307	8
	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	4554	18	361	69	670	59	7597	14	438	53	4338	20	20	100	732	45	15083	10	2382	27	3186	21	1430	36	488	59	902	48	3491	23	1165	39	2505	23	2587	24	4963	20	19633	9	1259	37	7109	16	11725	12	461	52	5412	17	102491	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	885	34	152	77	259	80	3383	19	163	53	1492	31	4	100	961	50	4131	17	601	45	1385	33	372	48	594	63	428	71	1832	26	657	53	523	35	446	42	688	39	808	36	202	50	2118	21	3985	20	136	81	780	32	26982	7
	fir	327	60	36	100	9	100	1053	24	59	75	262	59	0	.	51	56	154	51	849	31	357	42	140	59	89	49	0	.	374	30	10	100	147	53	202	53	88	44	151	85	243	50	1013	25	507	44	214	76	47	56	6384	10
	pine	26	73	0	.	0	.	173	69	65	48	21	100	77	100	0	.	1134	33	82	59	20	72	75	71	0	.	22	100	44	91	24	58	114	86	76	88	23	62	106	100	0	.	54	79	1223	26	0	.	95	52	3453	16
	larch	136	71	0	.	0	.	58	100	0	.	0	.	0	.	0	.	670	30	0	.	10	100	0	.	0	.	0	.	158	65	8	100	18	81	0	.	33	100	566	31	0	.	0	.	1882	21	0	.	16	79	3555	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	232	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	270	46	0	.	0	.	502	39
	other conifers	26	73	0	.	0	.	244	55	56	100	0	.	0	.	10	100	31	100	0	.	4	100	0	.	0	.	0	.	12	100	0	.	20	100	10	100	0	.	10	100	0	.	40	100	10	100	0	.	10	100	484	33
	beech	397	32	26	100	32	85	3540	17	656	37	888	37	0	.	296	57	649	38	1170	25	330	45	181	72	344	60	252	96	1360	29	890	33	1242	24	309	44	298	43	2795	21	288	50	1164	25	615	41	27	100	555	31	18304	7
	maple	139	40	43	100	69	100	469	33	134	56	101	53	0	.	261	56	98	50	90	39	68	56	86	74	88	53	21	100	434	29	0	.	173	30	170	51	48	100	280	44	93	59	543	30	134	48	0	.	131	50	3673	11
	ash	169	50	18	100	17	100	227	38	266	59	509	36	0	.	371	81	479	32	264	45	108	53	52	100	261	89	31	100	633	30	37	79	194	44	29	72	83	58	588	37	105	76	326	38	182	42	38	100	149	31	5137	12
	oak	141	51	0	.	0	.	74	56	158	94	35	53	291	55	0	.	71	67	33	77	16	100	108	92	0	.	0	.	140	92	56	58	44	57	10	100	10	100	770	30	0	.	909	37	130	42	0	.	12	58	3011	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3009	21	0	.	0	.	30	100	0	.	0	.	3269	20
	other broadleaves	301	46	35	72	9	100	561	23	210	48	40	50	77	73	340	62	1420	24	269	50	41	63	61	63	47	83	21	100	555	38	19	71	308	39	101	48	19	71	2863	18	67	66	406	41	689	29	10	100	304	35	8776	9
	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	2545	22	311	71	394	73	9782	12	1767	28	3349	22	450	51	2291	38	9300	12	3358	20	2338	26	1075	35	1424	41	775	58	5543	18	1701	30	2784	20	1353	32	1290	30	11946	11	998	39	6573	15	9659	14	425	61	2099	21	83530	4
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	302	29	456	47	419	51	6889	13	337	35	2166	20	0	.	447	45	9411	10	1504	22	1025	26	850	27	237	55	799	42	1647	20	415	37	705	32	1530	25	519	34	719	30	1303	32	3622	13	4080	15	240	70	1309	19	40928	4
	fir	215	41	162	57	225	45	2931	15	397	30	575	35	0	.	118	79	274	34	1325	20	951	30	384	32	109	47	109	96	1017	24	45	53	582	23	216	38	155	73	230	34	249	43	1783	18	324	40	86	72	459	30	12921	6
	pine	111	48	8	100	0	.	197	35	177	42	50	75	4	100	0	.	1017	25	219	59	49	100	47	100	0	.	12	100	129	84	99	41	214	32	24	100	97	49	139	83	127	66	107	52	496	34	0	.	223	28	3546	12
	larch	81	55	4	100	0	.	34	60	4	100	70	89	0	.	0	.	1393	17	0	.	0	.	0	.	0	.	0	.	19	100	34	77	8	71	0	.	0	.	668	36	0	.	83	56	1725	20	8	100	56	45	4187	12
	Arolla pine	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	269	38	0	.	0	.	0	.	0	.	102	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	162	51	0	.	0	.	551	30
	other conifers	0	.	0	.	10	100	40	50	0	.	0	.	0	.	0	.	145	86	0	.	0	.	0	.	0	.	0	.	0	.	4	100	89	90	0	.	0	.	0	.	0	.	90	64	10	100	0	.	40	61	428	38
	beech	1165	20	124	56	119	61	4565	16	1170	22	1036	25	0	.	739	37	401	29	1786	20	739	32	506	28	255	41	297	64	2089	21	880	27	2335	16	353	36	458	30	1173	30	133	93	3634	13	198	63	80	63	1081	22	25316	5
	maple	267	28	48	78	5	100	1221	21	235	29	147	32	29	74	66	39	29	58	383	24	18	74	133	55	115	35	30	100	633	28	69	75	594	24	114	45	82	54	37	76	44	75	967	25	167	55	132	54	357	32	5922	8
	ash	315	29	41	79	70	79	479	28	267	55	162	40	0	.	70	64	31	74	420	36	191	68	8	71	104	63	0	.	570	27	42	72	296	37	88	63	70	47	59	52	24	84	475	24	9	100	65	75	250	25	4106	10
	oak	77	41	0	.	0	.	200	30	59	46	40	64	156	64	0	.	63	100	41	53	39	61	4	100	0	.	5	100	40	68	101	45	298	48	0	.	69	48	125	46	13	100	478	35	8	71	0	.	33	57	1849	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	480	34	0	.	4	100	4	100	0	.	0	.	588	30
	other broadleaves	140	38	18	71	0	.	732	23	215	41	156	34	87	81	24	100	180	39	274	30	36	71	29	73	153	56	73	55	305	29	65	63	342	30	44	65	160	60	730	31	76	61	767	26	499	33	0	.	229	46	5336	9
	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	2674	16	862	38	848	38	17306	9	2861	19	4401	16	276	61	1464	30	13312	9	5952	13	3048	20	1960	21	972	33	1427	34	6448	15	1754	23	5463	14	2368	21	1610	23	4360	14	1971	26	12009	10	7682	12	612	40	4038	15	105678	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	652	23	1094	32	813	26	6094	12	45	54	984	21	0	.	560	38	8477	9	429	30	2056	18	914	22	155	56	1328	25	2377	16	87	44	328	28	1913	17	408	24	1195	21	663	39	2968	14	3154	15	363	36	1127	20	38188	4
	fir	686	24	162	43	350	35	4951	11	83	64	516	30	0	.	73	67	439	26	694	23	1901	17	942	21	4	100	423	35	871	20	20	100	544	43	572	26	237	36	284	46	186	76	1436	16	416	41	235	47	535	26	16559	5
	pine	92	33	0	.	5	100	174	45	30	88	44	75	10	100	0	.	324	37	19	53	39	82	0	.	0	.	0	.	41	65	7	100	41	55	74	95	47	41	24	100	0	.	21	83	219	47	0	.	296	27	1508	15
	larch	22	68	0	.	26	100	20	82	0	.	0	.	0	.	4	100	1344	15	4	100	21	100	4	100	0	.	7	100	101	55	10	100	0	.	15	72	4	100	493	22	0	.	129	39	1880	16	0	.	25	69	4108	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	413	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	194	35	0	.	0	.	607	24
	other conifers	10	100	0	.	0	.	66	58	0	.	8	100	0	.	0	.	0	.	10	100	8	100	0	.	21	100	0	.	65	63	0	.	10	100	10	100	10	100	0	.	0	.	13	100	0	.	0	.	34	89	265	27
	beech	2066	19	162	49	452	36	4079	12	549	31	967	23	0	.	124	47	556	29	602	21	1901	19	644	23	31	100	799	32	1764	19	292	37	571	23	755	23	532	25	788	33	217	55	1637	15	68	59	261	39	1588	15	21407	5
	maple	238	28	19	66	86	54	584	20	215	35	76	50	0	.	58	48	101	42	239	40	346	27	221	49	9	100	187	45	356	26	0	.	42	45	302	35	215	50	19	71	9	100	357	25	184	49	19	80	180	24	4065	8
	ash	163	25	22	100	84	70	373	26	81	53	122	42	39	100	7	71	103	65	198	31	247	39	0	.	0	.	24	72	260	38	12	74	12	74	183	45	250	34	0	.	20	100	525	28	87	46	81	66	182	26	3075	10
	oak	153	26	0	.	4	100	112	33	28	71	54	55	47	100	0	.	201	91	63	61	82	66	11	100	0	.	0	.	39	51	52	53	13	100	23	85	150	42	38	54	9	100	315	32	9	100	5	100	156	28	1564	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	648	27	0	.	135	70	0	.	0	.	0	.	799	25
	other broadleaves	347	46	0	.	38	100	677	24	63	62	92	89	0	0	4	100	218	39	238	41	52	48	133	55	15	100	71	48	303	44	0	.	51	62	159	28	268	48	474	26	50	72	447	34	227	41	4	100	263	35	4193	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
	total	4429	15	1460	29	1859	23	17132	8	1095	27	2863	17	95	100	830	33	12191	8	2496	18	6652	13	2869	17	235	49	2841	20	6178	13	480	36	1612	23	4005	14	2121	19	3964	14	1155	32	7983	10	6439	11	967	30	4386	13	96339	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	293	45	0	.	194	54	8222	10	19	71	918	26	0	.	730	35	10348	11	74	42	1425	24	1569	25	0	.	1958	25	1832	23	4	100	116	55	306	38	178	56	2647	18	918	32	2813	18	4880	13	17	100	247	35	39709	5
	fir	179	72	0	.	8	100	2939	13	67	95	476	44	0	.	139	77	282	32	282	38	803	29	1818	21	0	.	190	45	247	42	4	100	40	81	133	63	76	61	393	38	228	54	1124	26	370	35	73	100	68	63	9938	8
	pine	210	48	0	.	0	.	378	37	52	54	43	74	0	.	0	.	1200	24	27	59	19	84	75	62	0	.	291	91	184	65	0	.	51	66	21	100	4	100	129	64	147	58	77	89	1118	25	0	.	29	74	4056	13
	larch	4	100	0	.	0	.	37	58	4	100	0	0	0	.	0	.	2638	13	0	.	0	.	0	.	0	.	0	.	28	63	0	.	0	.	0	.	0	.	1824	17	0	.	14	77	2180	17	0	.	10	100	6738	9
	Arolla pine	0	.	0	.	0	.	9	72	0	.	0	.	0	.	0	.	1612	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	74	49	100	0	.	1083	23	0	.	0	.	2782	15
	other conifers	18	100	0	.	10	100	49	60	61	100	0	.	0	.	0	.	320	55	0	.	74	71	9	100	0	.	10	100	150	83	0	.	30	75	0	.	20	100	0	.	41	100	10	100	0	.	0	.	127	80	931	28
	beech	767	31	0	.	128	76	2617	17	211	47	657	49	0	.	282	53	647	33	221	46	603	41	888	21	0	.	230	39	562	37	13	100	299	53	79	78	145	51	3114	18	101	72	1009	24	539	41	0	.	246	36	13360	8
	maple	125	48	0	.	35	76	849	21	77	43	249	33	0	.	139	55	444	33	35	69	217	39	156	29	0	.	351	78	194	39	22	100	20	100	74	68	85	59	246	35	29	100	583	29	277	32	0	.	178	43	4387	11
	ash	161	50	0	.	185	57	514	33	69	63	46	51	0	.	43	92	315	62	68	64	188	50	61	54	0	.	39	59	204	39	0	.	10	100	28	73	249	58	449	32	20	71	159	35	346	56	0	.	123	50	3276	13
	oak	227	45	0	.	0	.	106	46	84	74	4	100	0	.	0	.	84	52	0	0	25	100	30	100	0	.	0	.	4	100	0	0	0	.	0	.	21	100	278	39	24	71	209	55	162	46	0	.	40	53	1299	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2103	19	0	.	0	.	100	96	0	.	0	.	2242	18
	other broadleaves	418	32	0	.	57	100	1011	22	114	57	224	82	0	.	58	59	1443	28	89	76	485	36	140	42	0	.	78	49	259	37	4	100	69	77	103	75	119	74	2519	15	126	51	485	27	818	25	0	.	143	45	8764	8
	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	2404	24	0	.	617	53	16732	8	759	36	2617	22	0	.	1392	30	19372	8	796	34	3840	20	4745	15	0	.	3149	22	3664	22	47	100	635	42	745	35	898	36	13731	9	1683	27	6484	14	11872	10	90	100	1211	32	97483	3
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	3994	15	1768	24	2254	22	29359	6	710	25	8468	11	4	100	3126	20	40022	5	3505	16	7744	11	4481	15	1428	34	5361	15	9422	10	1746	30	2645	18	5544	12	4093	19	6284	11	3605	17	14837	8	19028	7	1120	27	6892	13	187441	2
	fir	1648	19	361	33	764	27	12678	7	640	24	2234	18	0	.	396	38	1301	15	3403	12	4600	12	3425	13	213	32	733	27	2620	13	135	48	1898	19	1440	18	1121	27	1331	21	979	26	5851	10	1784	19	660	34	1221	17	51437	3
	pine	509	24	18	71	5	100	1288	22	337	27	202	33	91	86	0	.	5320	13	525	35	244	54	197	43	0	.	325	82	397	42	269	39	496	29	215	48	297	31	398	45	278	43	358	36	5090	14	0	.	711	16	17570	7
	larch	275	39	15	77	26	100	281	33	8	71	99	69	0	.	4	100	7663	8	8	71	58	53	4	100	0	.	49	87	450	33	61	50	26	60	15	72	144	54	5129	11	4	100	506	26	9966	8	8	100	123	29	24922	5
	Arolla pine	0	.	0	.	0	.	27	71	0	.	0	.	0	.	0	.	2720	15	0	.	0	.	0	.	0	.	102	100	24	100	0	.	0	.	0	.	0	.	29	74	49	100	0	.	1891	16	0	.	0	.	4843	11
	other conifers	159	34	30	100	21	71	578	31	118	71	8	100	0	.	10	100	790	31	40	61	131	53	9	100	21	100	10	100	238	56	30	88	149	58	61	70	224	79	18	71	62	74	328	38	90	53	0	.	258	44	3381	13
	beech	5585	11	313	35	742	27	16423	7	2860	15	4005	14	0	.	1533	23	2789	16	4315	12	3905	14	2312	13	667	35	1614	25	6448	11	2232	18	4864	11	1822	16	1931	15	11464	10	767	31	8201	8	2089	21	373	31	3870	10	91123	3
	maple	1085	16	345	69	206	43	3812	10	724	19	960	19	29	74	733	26	893	21	1162	19	825	18	742	22	237	28	613	47	1894	14	186	44	1025	17	836	20	902	27	1149	21	351	36	3135	12	1149	17	156	47	1622	19	24770	4
	ash	1210	17	81	53	366	36	1921	14	690	32	1230	21	39	100	578	54	1393	24	1133	21	1071	27	212	37	376	64	162	45	2118	16	220	49	627	23	818	33	1012	21	2223	16	288	42	1879	14	1002	25	235	38	1462	19	22346	5
	oak	876	23	0	.	4	100	537	18	334	49	188	31	494	39	0	.	483	44	165	32	180	38	307	61	11	100	5	100	224	60	219	29	423	36	33	66	335	27	2137	18	47	50	2513	19	981	23	5	100	390	26	10889	8
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	741	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10421	10	0	.	139	68	186	58	0	.	0	.	11487	9
	other broadleaves	1883	19	63	48	146	55	3690	11	698	23	847	30	185	50	651	38	6862	13	1163	20	775	25	414	26	225	42	316	28	1826	18	145	38	992	22	774	27	971	24	14412	8	944	36	3231	14	5620	12	45	46	1171	17	48050	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	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	17224	8	2994	21	4533	17	70595	4	7118	12	18241	8	841	36	7032	16	70978	4	15419	8	19534	8	12104	9	3176	23	9289	13	25661	7	5242	15	13145	9	11558	10	11030	11	54997	4	7375	13	40980	5	48875	5	2600	20	17721	7	498258	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. 1356646. Birmensdorf, Swiss Federal Research Institute WSL

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