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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NFI5
number of stems
development stage · main tree species
regional demarcation: canton
unit: n/ha
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.	36	45	0	.	0	.	0	.	0	.	66	30	0	.	23	75	0	.	0	.	0	.	60	.	0	.	0	.	10	72	93	73	89	39	0	.	13	57	0	.	0	.	0	.	41	23
	fir	0	.	0	.	0	.	18	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	82	0	.	0	.	10	90	5	85	0	.	0	.	4	64
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	66	20	.	0	.	0	.	0	.	0	.	0	.	0	.	51	70	0	.	7	82	0	.	0	.	4	90	5	87	0	.	0	.	4	48
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	87	0	.	0	.	6	70
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	70	.	3	94	0	.	0	.	0	.	105	.	0	.	50	.	0	.	0	.	0	.	0	.	0	.	0	.	187	62	0	.	0	.	0	.	0	.	16	90	0	.	0	.	50	.	14	50
	maple	0	.	0	.	50	.	6	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	90	0	.	0	.	0	.	2	58
	ash	0	.	0	.	0	.	3	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	82	0	.	0	.	5	87	0	.	0	.	0	.	1	70
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	79	0	.	0	.	0	.	0	99
	other broadleaves	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	120	.	65	29	0	.	20	.	0	.	105	.	87	30	70	.	23	75	0	.	0	.	0	.	60	.	0	.	238	64	10	72	113	67	89	39	0	.	58	50	15	28	0	.	50	.	74	18
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	8	96	0	.	24	45	67	23	0	.	62	34	0	.	7	81	116	17	4	90	134	39	78	49	0	.	83	66	18	77	20	71	0	.	111	36	22	72	12	87	37	85	37	39	27	56	24	85	26	62	54	12
	fir	11	51	0	.	0	.	19	33	27	71	5	96	0	.	0	.	0	.	46	73	49	40	273	82	0	.	0	.	2	97	0	.	5	85	24	39	0	.	0	.	0	.	8	52	0	.	37	67	24	44	18	33
	pine	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	8	87	0	.	14	51	0	.	0	.	0	.	0	.	0	.	9	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	63	2	46
	larch	3	97	0	.	0	.	3	84	0	.	0	.	0	.	0	.	9	43	0	.	0	.	0	.	0	.	0	.	4	96	0	.	0	.	0	.	0	.	11	89	0	.	21	87	10	62	0	.	0	.	4	35
	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	.	33	83	1	99	0	.	0	.	0	.	0	.	0	.	0	.	12	63	0	.	0	.	0	.	5	95	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	49
	beech	22	39	0	.	21	40	28	32	10	71	2	94	0	.	0	.	2	98	25	73	48	56	35	58	0	.	0	.	15	66	35	30	18	59	22	91	36	72	18	87	10	86	18	37	0	.	22	84	39	28	19	16
	maple	6	57	0	.	76	49	6	53	12	71	6	94	0	.	41	81	13	83	21	53	7	96	0	.	0	.	0	.	10	95	0	.	5	85	24	70	22	72	0	.	0	.	0	.	0	.	0	.	0	.	8	26
	ash	3	97	0	.	29	83	0	.	0	.	13	79	0	.	17	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	71	0	.	9	91	0	.	0	.	0	.	0	.	14	94	0	.	0	.	3	38
	oak	0	.	0	.	0	.	1	69	0	.	0	.	43	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	94	0	.	0	.	0	.	0	.	0	.	0	.	9	69	0	.	0	.	0	.	1	43
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	87	0	.	0	.	0	.	0	.	0	.	0	100
	other broadleaves	18	70	0	.	0	.	16	49	0	.	14	79	0	.	0	.	8	98	10	90	12	95	0	.	0	.	39	57	8	96	0	.	0	.	8	94	0	.	79	66	0	.	4	96	0	.	0	.	0	.	11	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	.
	total	72	20	0	.	183	38	141	14	50	42	101	25	43	41	64	81	155	14	106	30	275	20	386	71	0	.	121	35	63	38	110	58	38	29	198	35	80	72	127	32	47	62	98	21	51	43	84	51	93	20	123	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	130	32	0	.	295	40	189	16	89	59	181	55	0	.	106	55	292	20	21	64	291	25	107	59	0	.	509	44	276	32	211	58	194	53	240	44	177	37	35	62	489	16	186	26	82	27	100	36	173	33	169	8
	fir	21	67	0	.	251	85	83	29	47	64	25	68	0	.	12	59	6	58	106	43	222	38	32	70	0	.	0	.	11	69	0	.	150	60	89	61	36	96	3	67	41	92	61	37	8	86	25	71	29	43	47	15
	pine	0	.	0	.	0	.	11	70	12	71	7	96	0	.	0	.	116	33	0	.	0	.	9	95	0	.	0	.	3	95	0	.	5	94	0	.	3	97	0	.	0	.	0	.	70	50	0	.	14	68	22	25
	larch	24	70	0	.	0	.	3	56	0	.	12	95	0	.	0	.	19	57	0	.	4	98	0	.	0	.	26	93	0	.	0	.	0	.	0	.	38	73	36	41	0	.	65	55	56	35	0	.	8	70	22	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	86	0	.	0	.	1	68
	other conifers	1	98	0	.	0	.	4	64	0	.	0	.	0	.	0	.	24	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	63	10	60	0	.	7	58	5	28
	beech	93	24	0	.	0	.	109	23	224	28	48	58	0	.	36	78	17	49	142	59	20	27	80	54	535	20	11	88	112	62	249	49	235	32	40	41	71	40	108	32	3	92	113	35	30	58	25	71	124	30	89	10
	maple	54	32	0	.	112	59	57	21	98	33	13	56	0	.	100	58	3	73	80	42	48	32	143	50	22	78	9	88	18	47	58	65	133	61	93	54	80	30	25	47	48	68	117	28	37	37	60	12	51	48	54	11
	ash	46	36	0	.	15	85	22	42	26	52	82	39	0	.	0	.	12	44	29	78	13	59	30	46	95	37	0	.	49	66	68	51	19	74	48	46	35	35	40	34	61	90	37	39	28	49	100	71	18	46	31	12
	oak	6	63	0	.	0	.	5	66	0	.	15	96	0	.	0	.	0	.	2	95	0	.	57	94	28	63	0	.	0	.	21	86	4	96	0	.	5	72	39	52	0	.	53	62	44	42	0	.	12	55	18	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	174	27	0	.	0	.	1	99	0	.	0	.	24	28
	other broadleaves	54	31	0	.	61	85	40	40	60	29	70	32	0	.	30	60	201	29	21	72	64	45	13	68	0	.	30	63	33	52	59	50	22	58	62	58	30	65	225	19	45	32	61	29	150	20	75	24	48	42	96	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	.
	total	430	11	0	.	735	31	522	8	556	12	453	21	0	.	284	17	698	8	400	18	663	13	471	13	680	21	585	38	503	20	666	18	763	13	572	18	475	16	684	8	688	14	700	8	520	8	385	12	484	11	577	3
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	148	30	350	.	179	22	249	19	100	67	236	31	0	.	152	34	251	19	52	73	247	25	198	59	775	52	459	46	310	27	120	68	18	44	382	4	243	31	39	47	74	43	178	23	263	28	330	.	184	29	196	8
	fir	32	68	160	.	32	65	72	30	60	87	74	63	0	.	18	85	9	61	138	40	82	61	6	83	47	42	102	57	31	63	3	93	26	64	0	.	119	48	15	69	206	41	47	33	0	99	270	.	19	46	42	14
	pine	5	69	0	.	0	.	12	71	25	87	4	72	0	.	0	.	52	48	24	65	0	.	0	.	58	75	21	94	0	.	48	58	24	88	0	.	2	94	9	86	0	.	1	99	142	34	0	.	6	60	29	23
	larch	7	59	0	.	0	.	0	.	5	87	9	97	0	.	0	.	36	35	0	.	5	72	0	.	0	.	0	.	20	97	10	67	0	.	0	.	9	94	43	47	0	.	6	98	96	22	0	.	0	.	24	19
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	99	0	.	0	.	22	63	0	.	0	.	6	39
	other conifers	25	56	0	.	0	.	5	52	0	.	0	.	0	.	13	85	1	99	10	95	5	72	0	.	0	.	0	.	8	66	9	93	24	96	0	.	6	94	0	.	0	.	0	.	1	99	0	.	36	85	6	36
	beech	153	34	70	.	41	76	127	27	290	48	113	47	0	.	26	85	35	52	155	28	43	61	0	.	0	.	133	58	236	36	181	37	191	26	0	.	28	73	220	23	245	34	53	30	21	68	20	.	82	28	112	11
	maple	15	42	70	.	207	76	29	31	38	55	39	46	0	.	174	42	20	53	19	75	19	60	112	45	56	85	118	95	58	38	27	63	58	18	0	.	44	80	10	66	65	42	82	29	0	.	0	.	88	27	37	13
	ash	7	97	0	.	26	76	12	48	13	87	26	44	0	.	74	54	10	70	14	81	15	46	16	83	299	85	40	68	38	48	0	.	48	31	269	65	26	84	36	56	33	50	29	43	9	48	0	.	22	58	26	19
	oak	24	96	0	.	0	.	5	59	0	.	15	51	0	.	0	.	0	.	2	95	2	97	0	.	0	.	0	.	0	.	14	60	11	53	0	.	5	61	32	33	0	.	67	53	10	54	0	.	1	98	15	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	27	0	.	0	.	5	82	0	.	0	.	21	27
	other broadleaves	50	39	0	.	0	.	29	30	0	.	30	52	0	.	40	87	68	48	51	50	8	97	0	.	99	68	6	94	66	64	17	48	71	59	118	65	31	75	149	20	30	59	39	43	51	31	0	.	40	40	59	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	466	11	650	.	485	31	540	9	531	13	547	14	0	.	498	11	535	10	464	13	425	16	333	19	1334	22	881	18	766	14	429	15	472	11	769	35	514	15	667	6	653	19	501	10	620	12	620	.	477	13	571	3
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	39	43	343	41	528	23	177	15	23	46	178	42	25	71	106	33	281	10	36	50	56	30	147	34	354	46	100	88	142	24	95	30	51	36	196	24	62	58	54	46	452	31	124	22	246	20	81	87	76	25	152	6
	fir	36	89	21	72	60	41	57	24	45	50	96	46	0	.	20	76	6	60	145	29	91	28	89	61	25	33	75	67	27	33	25	79	50	37	56	36	89	59	13	79	108	45	81	21	38	48	43	53	16	36	47	10
	pine	0	.	0	.	0	.	15	64	17	35	1	98	85	71	0	.	28	38	0	.	20	61	0	.	0	.	0	.	0	.	26	49	3	77	0	.	15	78	1	99	36	80	6	59	40	42	0	.	14	35	13	19
	larch	1	97	0	.	0	.	0	.	0	.	1	98	0	.	0	.	32	30	0	.	0	.	0	.	0	.	0	.	0	.	2	95	1	98	0	.	0	.	76	34	0	.	0	.	66	34	0	.	2	55	14	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	49
	other conifers	3	98	0	.	0	.	3	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	73	0	.	0	.	0	.	0	.	0	.	0	.	10	84	0	.	0	.	7	60	2	43
	beech	112	20	19	71	147	44	132	14	218	23	131	23	0	.	106	22	13	49	128	34	127	30	98	37	176	48	264	41	131	17	187	24	216	16	117	29	107	41	137	30	48	85	104	19	50	64	74	42	92	19	109	6
	maple	27	34	0	.	0	.	28	31	38	41	21	72	0	.	35	30	3	61	24	36	7	50	2	95	36	45	12	88	51	27	0	.	41	31	21	57	17	47	5	56	39	94	30	24	2	98	67	74	22	26	21	11
	ash	17	58	0	.	31	84	14	38	31	44	57	51	0	.	21	93	0	.	28	54	4	97	0	.	25	89	0	.	31	33	12	95	24	50	11	84	11	73	5	61	64	69	29	36	9	72	30	50	14	31	17	14
	oak	8	85	0	.	0	.	3	62	23	56	11	53	275	30	0	.	0	.	0	.	5	97	2	95	0	.	0	.	0	.	0	.	3	74	0	.	23	67	11	47	0	.	19	45	7	59	0	.	7	40	7	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	46	0	.	0	.	0	.	0	.	0	.	4	45
	other broadleaves	30	36	0	.	16	84	23	25	27	45	5	58	100	35	6	93	20	41	42	58	13	50	6	94	50	72	12	88	10	41	0	.	12	66	13	52	74	60	72	35	7	94	20	31	17	42	0	.	26	42	23	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	274	11	383	36	782	20	451	8	421	16	501	17	485	7	294	11	395	6	403	12	323	13	344	14	665	10	462	24	399	10	347	15	402	12	414	13	398	18	426	8	755	15	423	8	475	9	295	15	275	9	410	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	26	32	303	17	110	24	98	10	20	57	150	16	100	.	199	41	215	9	40	27	106	25	73	34	131	56	257	19	126	17	46	47	25	39	141	17	61	24	99	21	251	34	95	20	214	12	125	26	56	23	124	4
	fir	26	32	59	39	122	31	100	10	53	41	52	35	100	.	28	80	10	39	116	18	131	18	117	21	49	62	78	37	49	18	14	62	42	46	50	37	46	41	28	50	60	53	84	21	23	39	0	.	25	32	59	6
	pine	2	77	0	.	0	.	4	52	0	.	4	69	0	.	0	.	7	36	6	75	0	.	5	98	0	.	0	.	0	99	5	93	7	51	1	99	5	72	0	.	38	94	0	99	1	76	0	.	6	44	4	21
	larch	2	99	0	.	0	.	0	100	2	95	0	.	0	.	2	94	37	17	1	98	0	.	1	98	0	.	0	.	2	62	0	.	0	.	1	98	0	.	29	32	0	.	2	71	74	20	10	71	4	53	13	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	47	0	.	0	.	2	30
	other conifers	0	.	0	.	5	95	1	74	31	95	0	.	0	.	0	.	0	.	0	.	5	77	0	.	0	.	0	.	5	78	0	.	3	98	2	98	2	98	0	.	0	.	2	99	0	.	0	.	0	.	1	36
	beech	121	14	60	65	43	44	94	11	95	23	90	26	0	.	47	47	21	32	120	23	132	21	82	19	68	66	94	32	86	21	122	22	99	18	84	21	60	17	56	37	53	77	87	16	1	99	115	9	94	17	74	5
	maple	10	34	3	93	37	45	17	19	32	63	11	44	0	.	17	56	2	60	16	47	12	48	21	41	72	27	9	65	42	26	0	.	23	53	13	45	31	45	0	.	11	63	15	28	13	51	10	71	29	36	16	10
	ash	8	59	0	.	36	65	7	27	0	.	11	56	100	.	8	71	1	71	10	48	4	49	9	74	38	53	6	96	12	50	8	66	5	48	11	59	29	32	0	.	14	80	10	48	4	61	0	.	15	29	8	12
	oak	4	44	0	.	0	.	2	30	7	95	0	.	130	.	0	.	1	100	12	53	2	99	0	.	0	.	0	.	1	99	27	56	3	69	1	99	18	42	3	59	0	.	2	53	1	99	0	.	8	45	3	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	30	0	.	0	.	0	.	0	.	0	.	4	33
	other broadleaves	11	45	0	.	20	65	16	22	4	95	3	69	0	.	0	.	11	45	29	62	6	62	3	98	45	58	6	95	4	56	0	.	28	43	12	37	49	67	29	38	12	94	12	41	7	50	25	71	18	36	14	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	.
	total	212	9	425	11	373	13	338	4	245	16	320	9	430	.	301	29	314	5	351	11	397	9	310	9	404	13	450	14	327	8	222	18	236	11	316	9	301	11	314	9	437	19	310	11	347	8	285	4	255	8	321	2
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	28	60	120	.	98	61	176	10	23	93	134	45	0	.	291	35	263	10	45	30	144	20	129	15	126	51	292	24	164	25	7	82	79	80	165	26	66	56	133	33	321	28	135	13	190	11	20	71	40	36	170	5
	fir	23	96	160	.	146	65	92	14	21	65	79	40	0	.	4	97	8	41	174	26	153	18	139	18	80	19	75	45	45	37	0	.	55	42	49	59	21	82	8	61	110	38	77	22	17	42	350	0	28	36	59	7
	pine	15	96	0	.	0	.	6	48	41	46	10	97	0	.	0	.	15	38	6	58	0	.	3	99	0	.	75	98	0	.	20	82	26	61	31	96	10	61	1	99	13	67	1	99	36	30	0	.	6	55	13	21
	larch	9	96	0	.	0	.	1	73	0	.	0	.	0	.	0	.	50	19	0	.	1	99	0	.	0	.	0	.	10	98	0	.	0	.	0	.	0	.	44	28	0	.	0	99	81	18	0	.	1	97	24	12
	Arolla pine	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	41	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	29	0	.	0	.	11	24
	other conifers	8	96	0	.	0	.	1	73	0	.	0	.	0	.	0	.	3	76	2	98	0	.	1	99	0	.	3	98	5	99	0	.	25	51	0	.	0	.	2	69	22	96	2	99	0	.	0	.	3	97	2	28
	beech	41	17	100	.	20	0	64	16	105	38	125	41	0	.	88	43	16	47	93	32	87	25	51	19	97	65	64	45	165	25	410	21	114	25	35	76	104	34	83	22	67	63	94	18	30	41	20	71	56	22	66	7
	maple	40	34	0	.	0	.	24	20	30	75	80	34	0	.	14	60	7	39	30	30	25	26	31	37	65	18	6	49	47	43	83	82	43	32	18	66	14	92	5	53	4	96	24	32	11	42	20	71	38	39	20	10
	ash	28	52	0	.	0	.	6	36	47	72	31	41	0	.	25	59	13	60	34	44	21	46	3	59	0	.	5	97	22	39	17	82	4	95	0	.	50	32	13	32	0	.	12	46	5	61	0	.	14	35	12	15
	oak	20	44	0	.	0	.	3	69	52	65	4	95	0	.	0	.	1	100	5	98	6	59	1	99	0	.	0	.	4	62	50	82	68	86	0	.	23	58	13	41	16	68	10	67	6	100	0	.	7	53	7	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	26	0	.	0	.	0	99	0	.	0	.	9	25
	other broadleaves	102	30	0	.	0	.	28	26	23	65	71	42	0	.	63	60	16	27	52	41	17	31	21	42	27	65	10	72	48	45	0	.	15	68	27	32	47	64	106	24	51	57	22	34	31	22	0	.	53	38	38	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	.
	total	314	18	380	.	264	13	401	5	342	15	535	11	0	.	484	20	440	6	440	13	454	9	380	8	395	38	530	16	509	13	586	18	428	18	324	12	334	11	484	12	605	14	378	7	439	7	410	3	246	12	430	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	64	20	288	19	170	22	150	6	46	37	160	15	29	61	175	22	239	5	37	19	158	12	116	13	299	39	302	17	161	12	87	28	60	31	170	14	107	19	75	19	283	15	129	9	186	8	89	34	99	16	149	3
	fir	25	29	56	30	105	34	79	8	44	28	58	22	19	90	14	44	7	22	130	13	135	14	117	16	39	29	65	27	34	16	11	58	56	27	52	24	51	28	12	30	95	24	68	11	16	24	99	37	23	17	50	4
	pine	3	59	0	.	0	.	7	28	16	30	4	45	33	90	0	.	29	20	6	41	3	51	4	58	11	96	29	89	0	76	20	37	11	35	5	94	6	37	2	74	16	56	2	46	48	21	0	.	9	27	13	11
	larch	8	49	0	.	0	.	1	40	1	69	3	67	0	.	0	99	35	11	0	100	2	56	0	99	0	.	3	99	5	57	3	62	0	99	1	99	9	69	42	17	0	.	12	46	73	11	2	96	3	42	18	7
	Arolla pine	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	18	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	17	24	0	.	0	.	4	17
	other conifers	5	45	0	.	6	72	2	28	6	99	0	.	0	.	1	99	4	40	2	74	3	46	1	99	0	.	1	99	5	39	2	99	7	54	1	99	1	70	0	70	6	99	4	49	2	56	0	.	9	56	3	15
	beech	97	11	46	44	45	31	90	7	176	16	90	16	0	.	63	23	17	20	115	15	83	14	64	14	151	34	84	25	122	14	183	16	165	11	66	17	66	16	116	13	76	31	86	10	23	27	47	29	89	12	80	3
	maple	26	19	6	75	60	36	25	11	51	23	26	24	0	.	47	30	6	26	29	21	21	18	41	28	51	25	24	74	41	16	19	48	49	26	28	34	37	24	9	32	26	35	43	16	13	25	28	51	39	18	25	5
	ash	20	25	0	.	27	42	9	18	23	33	34	23	19	90	21	36	6	38	20	27	11	29	8	34	87	62	9	54	23	22	17	39	18	27	22	39	28	20	20	26	26	43	20	19	10	28	21	68	15	20	16	7
	oak	9	38	0	.	0	.	3	26	15	45	7	39	150	38	0	.	1	72	5	44	3	43	8	88	4	96	0	.	1	49	15	39	12	65	0	99	14	28	21	26	4	73	23	31	11	33	0	.	7	27	8	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	14	0	.	0	100	1	65	0	.	0	.	10	13
	other broadleaves	38	17	0	.	18	52	24	14	28	25	27	23	39	66	27	46	40	20	36	25	20	28	13	35	49	39	13	33	24	29	10	48	25	30	24	29	42	35	123	11	28	33	26	17	45	14	15	55	32	19	38	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	294	7	396	15	432	13	390	3	407	8	409	7	291	33	349	12	408	4	381	6	438	6	371	6	691	16	530	10	417	7	367	10	402	8	369	8	362	8	521	5	561	9	412	5	444	4	301	16	326	6	412	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. 1283400. Birmensdorf, Swiss Federal Research Institute WSL

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