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
growing stock (stemwood)
development stage · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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 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	0	.	0	.	395	45	0	0	221	67	0	.	126	96	276	59	34	100	183	72	0	.	0	.	83	100	0	0	0	.	0	.	145	76	0	.	0	.	130	100	28	100	-4	100	24	100	0	0	1640	24
	fir	0	.	0	.	0	.	15	83	0	.	111	100	0	.	0	.	0	0	85	100	30	66	0	.	0	.	0	0	0	.	0	.	0	.	46	91	0	.	0	.	0	.	30	100	0	.	0	.	0	.	317	47
	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	.
	larch	0	.	0	.	0	.	39	100	0	.	0	.	0	.	0	.	118	62	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	156	53
	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	44	100	0	.	0	.	15	87	0	.	28	59	0	.	0	.	39	71	14	100	52	71	0	.	0	.	0	.	6	100	11	100	0	.	0	.	30	100	0	.	0	.	99	96	0	.	0	.	36	100	374	34
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	26	77	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	77	48
	ash	0	.	0	.	0	.	6	77	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	37	81
	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	7	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	1	100	0	.	0	.	0	.	5	100	0	.	0	.	0	.	30	56
	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	51	100	0	.	0	.	470	43	0	0	359	56	0	.	150	95	433	52	132	100	293	54	0	.	0	.	83	100	38	100	11	100	0	.	192	71	30	100	0	.	130	100	174	95	-4	100	24	100	65	100	2631	20
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	143	72	0	.	2	100	586	53	26	100	77	70	0	.	90	100	503	43	0	.	77	100	0	.	0	.	0	.	65	92	23	84	57	84	7	100	0	.	48	100	147	81	122	62	109	71	1	100	167	66	2248	21
	fir	49	71	0	.	0	.	190	56	10	100	0	0	0	.	111	74	4	100	0	.	1	380	0	.	0	.	0	.	108	60	0	.	9	100	0	.	12	100	0	.	0	.	21	78	0	0	0	.	146	98	660	32
	pine	12	100	0	.	0	.	68	100	47	100	0	.	0	.	0	.	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	29	100	0	.	139	68	359	38
	larch	21	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	94	22	100	22	100	310	47	0	.	0	.	564	31
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	99	0	.	0	.	66	91
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	13	100
	beech	546	39	0	.	0	.	349	34	274	60	60	92	0	.	92	99	0	.	0	0	151	63	0	.	0	.	0	.	30	93	75	72	51	100	0	.	36	100	65	63	0	.	74	71	1	100	0	.	314	48	2119	18
	maple	78	58	0	.	2	100	26	68	14	100	12	76	0	.	2	100	0	.	0	.	9	77	0	.	0	.	0	.	4	100	0	.	9	100	0	.	14	100	2	100	0	.	7	74	8	100	10	100	0	.	197	29
	ash	20	100	0	.	1	100	10	71	0	.	47	71	0	.	3	100	0	.	0	.	64	96	43	100	0	.	0	.	0	.	45	100	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	90	278	36
	oak	18	100	0	.	0	.	17	100	32	100	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	64	100	0	.	0	.	0	.	2	100	0	.	20	100	18	100	0	.	50	71	233	38
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	63	0	.	0	.	0	.	0	.	0	.	90	63
	other broadleaves	25	63	0	.	9	100	29	52	18	72	16	84	0	.	0	.	21	84	5	100	0	.	0	.	0	.	0	.	2	100	0	.	22	100	0	.	0	.	13	80	0	.	63	90	5	77	5	100	3	100	235	30
	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
	total	911	33	0	.	13	100	1275	31	421	52	212	57	0	.	298	70	712	34	18	100	302	58	43	100	0	.	0	.	209	71	208	63	163	71	7	100	62	100	273	42	169	72	353	52	553	34	16	100	847	36	7064	12
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	506	39	0	.	135	87	1433	24	22	100	789	36	0	.	111	66	1336	26	86	69	1025	33	222	81	125	100	116	86	433	52	137	85	76	59	264	64	946	45	99	45	49	72	660	38	921	41	123	84	1298	31	10914	10
	fir	40	63	0	.	50	90	196	55	0	.	203	55	0	.	0	.	142	51	227	86	213	46	54	57	0	.	0	.	143	61	36	100	178	57	89	86	196	53	208	77	5	100	147	41	20	64	20	100	19	59	2187	17
	pine	27	74	0	.	0	.	56	62	0	.	12	100	0	.	0	.	340	38	33	100	0	.	0	.	0	.	0	.	0	.	88	69	10	100	0	.	51	80	0	.	0	.	2	100	386	45	0	.	126	59	1131	22
	larch	43	72	0	.	0	.	35	135	0	.	34	100	0	.	0	.	279	42	0	.	5	71	0	.	0	.	8	100	22	100	3	100	0	.	0	.	11	100	264	34	0	.	97	48	285	30	0	.	10	100	1095	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	84	100	0	.	0	.	131	73
	other conifers	44	58	0	.	0	.	23	61	0	.	0	.	0	.	0	.	16	56	3	100	0	.	0	.	0	.	0	.	0	.	69	100	0	.	0	.	6	100	42	100	0	.	39	79	7	100	0	.	7	100	257	36
	beech	368	51	0	.	8	100	480	51	109	95	55	66	0	.	0	.	92	59	182	62	93	66	7	170	5	100	10	100	102	60	5	77	85	52	79	61	10	174	889	34	6	100	78	60	154	61	0	.	99	71	2915	17
	maple	12	200	0	.	0	.	90	51	0	.	77	58	0	.	37	61	50	50	19	76	28	80	24	86	0	.	0	.	68	75	21	70	38	48	31	68	103	41	79	54	55	83	95	42	42	47	0	.	237	42	1107	15
	ash	51	47	0	.	0	.	34	84	0	.	42	65	0	.	16	71	33	54	47	100	24	58	13	172	9	100	0	.	171	54	21	100	50	76	91	61	239	64	89	52	23	100	98	52	64	54	12	100	156	68	1283	19
	oak	84	100	0	.	0	.	20	83	0	.	23	83	0	.	0	.	1	100	2	100	0	.	48	100	4	100	0	.	0	.	0	.	10	77	0	.	44	73	207	44	0	.	123	59	88	71	0	.	8	73	662	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	162	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	874	31	0	.	0	.	30	76	0	.	0	.	1066	29
	other broadleaves	36	74	0	.	0	.	108	66	0	.	67	54	0	.	44	75	593	32	24	54	66	56	11	62	0	.	15	89	10	66	7	72	71	76	126	59	21	54	928	22	44	90	182	60	354	25	2	100	20	82	2728	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	0
	total	1211	28	0	.	193	75	2474	21	131	82	1304	29	0	.	208	58	3091	17	623	51	1454	29	378	51	143	88	148	77	949	36	386	49	518	38	681	41	1628	33	3677	15	182	61	1522	26	2435	21	157	80	1980	26	25476	6
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	20	102	0	.	458	75	2718	22	221	57	746	51	0	.	1097	68	1547	28	460	54	1012	38	189	70	271	67	258	100	1132	33	803	45	403	46	85	75	522	49	309	56	39	69	1273	26	1409	26	129	100	644	36	15747	10
	fir	188	77	0	.	10	100	825	30	10	74	154	67	0	.	14	100	67	71	442	62	375	47	100	67	12	58	0	.	375	43	0	.	33	81	155	76	99	53	2	100	171	58	653	41	181	68	18	100	106	54	3990	15
	pine	19	100	0	.	0	.	271	64	53	56	0	.	91	100	0	.	352	50	171	60	17	100	114	100	0	.	19	100	0	.	60	74	23	73	0	.	27	93	0	.	0	.	39	80	556	36	0	.	197	58	2009	19
	larch	65	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	399	43	0	.	19	100	0	.	0	.	0	.	34	84	21	100	14	100	0	.	40	100	253	53	0	.	0	.	901	27	0	.	61	72	1806	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	116	69	0	.	0	.	135	61
	other conifers	3	100	0	.	0	.	240	65	0	.	0	.	0	.	2	100	0	.	0	.	16	100	0	.	0	.	0	.	6	100	0	.	13	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	282	56
	beech	97	65	0	.	23	90	2314	21	607	43	525	45	0	.	291	74	194	58	380	40	332	61	40	88	78	100	176	100	1195	31	759	40	711	33	82	81	134	47	925	33	216	55	626	38	219	65	0	.	322	37	10244	10
	maple	50	53	0	.	41	100	248	36	38	71	66	75	0	.	133	69	9	62	59	48	36	66	0	.	25	72	0	.	416	31	0	.	173	39	9	100	0	.	19	69	44	54	140	54	14	73	0	.	111	48	1630	14
	ash	144	84	0	.	34	100	228	45	140	98	682	44	0	.	467	89	104	59	271	44	45	59	0	.	234	93	0	.	558	33	37	86	62	58	0	.	60	52	142	78	93	90	163	57	55	72	0	.	172	42	3690	18
	oak	8	100	0	.	0	.	120	59	0	.	79	74	259	63	0	.	0	.	52	89	48	100	1	100	0	.	0	.	5	100	41	68	36	59	0	.	17	100	253	37	0	.	351	48	44	78	0	.	49	61	1364	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1274	29	0	.	0	.	28	100	0	.	0	.	1383	27
	other broadleaves	103	77	0	.	4	100	250	37	49	60	6	76	5	100	234	90	475	32	198	62	10	84	2	100	5	100	0	0	219	53	67	90	85	52	3	70	0	0	1056	28	22	71	93	74	173	59	0	.	212	49	3271	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
	total	698	38	0	.	570	71	7214	15	1118	38	2259	29	355	59	2238	51	3249	19	2033	30	1910	32	446	53	625	51	453	72	3939	23	1788	33	1552	28	334	63	899	38	4233	16	584	49	3338	21	3696	18	147	100	1873	26	45551	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	438	33	472	51	493	57	7678	14	580	70	2381	28	0	.	434	54	7707	14	1303	35	1498	34	883	35	401	57	259	86	1730	31	531	40	644	42	1324	35	703	38	1229	35	1541	42	4036	19	3889	20	25	100	2339	24	42519	6
	fir	540	47	130	67	312	54	3124	20	292	59	401	49	0	.	17	100	305	51	1281	32	1204	33	342	38	271	59	81	100	896	35	66	94	510	34	237	42	258	82	335	45	177	73	1725	23	416	51	22	100	661	30	13602	9
	pine	305	76	0	.	0	.	462	36	75	61	175	83	14	100	0	.	1180	33	108	82	80	100	27	100	0	.	0	.	40	100	186	60	183	38	0	.	246	65	61	100	153	75	129	62	224	51	0	.	298	35	3944	15
	larch	187	76	0	.	0	.	68	81	27	100	39	71	0	.	0	.	1808	24	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	711	39	0	.	33	60	977	27	55	100	115	63	4039	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	245	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	71	0	.	0	.	285	43
	other conifers	0	.	0	.	4	100	75	98	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	12	65	1	100	0	.	7	83	101	73
	beech	1309	29	37	71	89	79	3315	17	1108	34	1078	33	0	.	613	44	309	43	1259	29	673	38	391	38	294	48	2	100	1762	35	744	35	2321	23	343	46	463	38	540	34	98	85	2901	18	125	83	55	62	1280	25	21112	7
	maple	240	36	7	100	0	.	777	23	197	41	69	58	0	.	33	66	6	100	255	31	29	87	78	55	95	40	0	.	486	38	21	100	346	36	37	84	94	71	40	100	17	100	267	28	101	55	72	60	324	34	3592	10
	ash	387	36	0	.	67	89	382	35	147	51	122	51	0	.	111	81	3	100	420	59	525	92	9	100	200	69	0	.	466	37	7	100	300	47	152	100	88	53	131	78	9	100	526	37	12	100	38	100	504	31	4606	16
	oak	45	78	0	.	0	.	152	41	56	77	54	100	134	100	0	.	0	.	45	79	120	81	16	100	0	.	0	.	32	100	79	100	59	48	0	.	224	48	192	57	0	.	182	45	17	100	0	.	88	52	1495	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	384	42	0	.	5	100	0	.	0	.	0	.	512	35
	other broadleaves	107	54	3	71	0	.	240	41	112	46	111	53	0	.	52	100	148	54	59	41	69	100	10	74	44	71	1	100	194	49	16	82	101	56	3	100	101	60	230	46	12	78	258	43	169	48	0	.	209	64	2250	14
	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	3558	22	649	45	966	47	16273	11	2597	30	4431	21	148	100	1261	40	11834	12	4730	20	4199	25	1755	26	1305	38	343	73	5604	21	1669	32	4466	19	2097	30	2178	30	3852	18	2005	35	10073	13	5970	17	267	60	5825	18	98057	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	1837	34	1876	34	1608	34	11167	13	187	100	2402	27	0	.	1630	40	14053	12	955	34	4929	23	1841	28	326	72	1634	36	5479	21	384	59	882	45	3752	22	1201	29	1440	34	1167	36	4734	19	3691	21	155	88	2433	25	69760	5
	fir	887	33	359	49	982	44	7928	14	7	100	1052	41	0	.	161	91	950	35	1223	36	3209	21	1354	31	0	.	792	40	1643	28	3	100	556	47	1058	33	637	47	287	64	192	75	2372	22	251	73	125	58	813	36	26841	7
	pine	168	56	0	.	14	100	568	52	0	.	187	73	10	100	0	.	537	33	121	58	99	74	0	.	0	.	0	.	36	100	17	100	143	54	74	90	130	48	0	.	0	.	0	.	163	85	0	.	496	36	2761	17
	larch	61	71	0	.	0	.	117	75	0	.	0	.	0	.	49	100	3102	19	20	100	0	.	42	100	0	.	0	.	158	61	0	.	0	.	52	100	0	.	812	37	0	.	227	54	3115	20	0	.	128	86	7881	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	200	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	48	0	.	0	.	430	33
	other conifers	0	.	0	.	0	.	70	98	0	.	0	.	0	.	0	.	0	.	2	100	44	100	0	.	0	.	0	.	243	99	0	.	3	100	3	100	1	100	0	.	0	.	141	100	0	.	0	.	0	.	507	57
	beech	2621	23	190	57	319	47	5362	17	562	40	884	39	0	.	195	64	700	40	1361	28	1758	25	498	29	0	.	1086	34	2011	27	455	47	1368	31	918	39	903	34	379	48	274	68	1888	22	25	100	195	61	2033	22	25985	6
	maple	176	43	40	83	94	69	301	33	131	61	15	55	0	.	57	47	45	88	144	47	252	50	184	63	8	100	69	45	322	33	0	.	61	63	158	37	205	71	1	100	15	100	224	44	40	62	0	.	251	36	2793	12
	ash	203	52	0	.	0	0	581	38	51	102	162	65	43	100	21	74	36	85	216	43	250	49	0	.	0	.	30	84	252	57	62	78	56	100	317	62	689	34	0	.	10	100	521	42	11	123	129	100	442	44	4085	14
	oak	299	44	0	.	0	.	210	39	91	100	36	100	205	100	0	.	50	100	215	55	169	96	33	100	0	.	0	.	75	57	35	74	88	100	15	100	553	51	17	80	37	100	336	44	3	100	0	.	353	39	2819	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	873	41	0	.	8	100	0	.	0	.	0	.	881	41
	other broadleaves	194	47	0	.	45	100	401	35	2	100	154	100	0	0	0	.	69	55	62	54	7	61	27	58	0	.	45	91	117	43	0	.	77	100	83	46	157	50	55	46	3	100	432	60	32	54	19	100	113	46	2096	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	0	0	.	0	.	0	.	0	.	0	0
	total	6445	18	2465	33	3061	30	26705	9	1032	39	4893	22	258	100	2113	37	19743	10	4318	22	10716	16	3978	22	334	73	3657	27	10337	16	955	48	3235	28	6428	19	4477	22	3864	20	1697	33	10885	14	7560	15	623	59	7062	16	146841	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	394	48	0	.	157	72	6618	14	0	0	758	36	0	.	409	49	5125	14	4	370	1060	30	1581	28	0	.	1115	33	827	35	25	100	32	95	276	54	181	72	2085	26	609	45	1560	23	2886	18	145	100	272	48	26119	6
	fir	68	100	0	.	23	100	2983	20	56	100	361	73	0	.	152	100	245	47	201	62	543	38	1884	26	0	.	93	66	294	51	21	100	65	71	42	83	136	75	229	66	192	71	827	42	211	49	31	100	240	54	8899	11
	pine	161	73	0	.	0	.	71	47	109	66	171	85	0	.	0	.	450	36	0	.	15	100	26	100	0	.	84	100	0	.	0	.	56	100	0	.	16	100	94	68	35	94	13	100	553	26	0	.	15	100	1869	17
	larch	0	.	0	.	0	.	18	100	9	100	0	0	0	.	0	.	1350	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1014	26	0	.	0	.	2124	29	0	.	14	100	4529	16
	Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	784	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	276	36	0	.	0	.	1076	21
	other conifers	48	100	0	.	1	100	12	100	153	100	0	.	0	.	0	.	16	62	0	.	170	100	7	100	0	.	3	100	-16	100	0	.	9	84	0	.	0	.	0	.	5	100	0	.	0	.	0	.	15	103	423	56
	beech	548	55	0	.	128	71	2099	24	165	47	396	59	0	.	28	76	327	47	241	61	159	52	379	31	0	.	58	60	116	46	15	100	271	51	56	85	106	62	1698	24	66	64	625	36	245	46	0	.	236	43	7962	11
	maple	-2	100	0	.	41	95	421	25	23	54	132	45	0	.	70	79	144	43	0	.	38	45	85	33	0	.	146	67	88	62	24	100	14	100	35	100	11	100	27	100	0	.	240	38	31	68	0	.	97	48	1666	13
	ash	112	100	0	.	200	72	179	48	79	84	70	69	0	.	60	100	129	73	0	.	13	76	32	65	0	.	5	100	107	62	0	.	0	.	25	90	96	83	60	55	10	77	87	66	99	73	0	.	176	61	1539	20
	oak	347	95	0	.	0	.	106	43	40	71	0	.	0	.	0	.	56	100	0	.	52	100	0	.	0	.	0	.	0	.	0	0	0	.	0	.	19	100	181	50	42	82	178	65	10	72	0	.	67	58	1097	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	930	28	0	.	0	.	21	100	0	.	0	.	993	26
	other broadleaves	107	66	0	.	0	.	395	41	94	87	0	0	0	.	9	71	128	34	3	100	56	69	43	80	0	.	8	67	35	54	10	100	19	81	6	78	110	100	536	27	45	72	59	41	111	60	0	.	17	88	1791	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
	total	1783	46	0	.	551	64	12914	12	728	41	1889	31	0	.	728	45	8798	11	449	58	2106	28	4036	20	0	.	1511	29	1451	31	94	100	465	51	440	49	674	52	6858	13	1004	37	3591	21	6568	15	175	100	1150	36	57964	5
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	3338	21	2349	29	2853	25	30595	7	1036	45	7376	15	0	.	3897	27	30548	7	2841	21	9785	14	4715	16	1123	35	3464	22	9667	14	1902	25	2093	25	5852	17	3553	19	5211	16	3683	22	12414	10	12900	10	602	43	7152	13	168948	2
	fir	1772	24	489	40	1378	34	15260	9	375	48	2282	25	0	.	456	50	1714	23	3458	20	5576	15	3733	17	283	56	966	35	3457	17	126	59	1351	25	1626	24	1339	30	1060	30	736	35	5775	14	1080	30	215	40	1986	20	56495	4
	pine	692	40	0	.	14	100	1496	26	284	35	545	45	115	81	0	.	2900	17	432	36	211	52	166	72	0	.	103	83	76	71	350	38	416	29	74	90	471	38	155	57	188	63	207	44	1911	18	0	.	1271	21	12074	8
	larch	377	44	0	.	0	.	276	44	36	79	74	60	0	.	49	100	7194	11	20	100	23	82	42	100	0	.	8	100	213	48	44	66	14	100	52	100	52	81	3107	16	22	100	378	35	7710	12	55	100	327	43	20071	7
	Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	1302	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	807	24	0	.	0	.	2124	15
	other conifers	95	57	0	.	6	80	419	45	153	100	0	.	0	.	2	100	32	42	5	74	229	77	7	100	0	.	3	100	233	103	69	100	29	55	3	100	8	86	42	100	5	100	192	75	23	67	0	.	29	63	1584	27
	beech	5533	15	227	49	566	33	13934	9	2826	19	3026	20	0	.	1220	31	1661	22	3437	16	3218	17	1315	18	377	42	1332	31	5222	17	2063	22	4806	15	1479	27	1682	22	4496	14	659	36	6293	12	769	30	250	50	4320	14	70711	3
	maple	555	23	46	72	178	48	1863	13	402	29	373	26	0	.	354	33	255	30	477	22	419	32	371	34	128	33	215	48	1401	18	66	53	640	23	271	29	426	39	168	39	130	43	984	18	237	29	82	54	1021	18	11061	5
	ash	918	26	0	.	303	53	1421	20	418	42	1126	29	43	100	678	63	306	38	954	30	923	55	97	55	443	58	35	74	1554	20	172	44	484	33	585	44	1172	25	421	38	144	61	1395	23	240	38	179	75	1507	20	15518	7
	oak	801	46	0	.	0	.	625	21	219	50	192	46	598	49	0	.	107	70	326	40	389	52	98	61	4	100	0	.	112	47	219	49	193	49	15	100	857	36	851	22	78	64	1191	23	180	42	0	.	615	26	7670	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	407	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4426	14	0	.	14	73	78	52	0	.	0	.	4925	13
	other broadleaves	578	27	3	71	57	80	1423	18	276	37	354	48	5	100	342	64	1434	18	351	37	209	42	94	42	49	65	69	63	591	27	99	62	375	32	222	38	389	38	2818	13	125	43	1093	29	844	20	26	77	575	31	12401	6
	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
	total	14658	11	3114	28	5355	22	67325	5	6026	17	15347	11	760	48	6997	22	47860	5	12303	12	20980	11	10637	11	2407	27	6196	18	22527	10	5111	18	10400	13	10179	14	9948	14	22758	6	5770	18	29935	8	26778	7	1409	34	18803	9	383583	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. 1230976. Birmensdorf, Swiss Federal Research Institute WSL

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