Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
growing stock (stemwood)
development stage · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
development stage	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	-18	.	0	.	0	.	283	64	0	.	101	82	0	.	0	.	417	50	0	.	0	.	0	.	0	.	21	96	0	0	0	.	0	.	106	99	0	.	0	.	0	.	47	100	6	100	0	.	0	.	961	32
	fir	0	.	0	.	0	.	22	100	0	.	105	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	78
	pine	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	217	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	47	71	0	.	0	.	276	41
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	27	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	18	.	0	.	0	.	9	100	0	.	19	78	0	.	0	.	24	100	0	.	11	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	-2	1162	0	.	0	.	0	.	82	55
	maple	0	.	0	.	0	.	13	100	0	.	5	100	0	.	0	.	0	.	0	.	12	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	52
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100
	oak	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	5	77	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	53
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	0	.	0	.	0	.	333	55	0	.	236	70	9	.	0	.	658	46	0	.	24	.	0	.	0	.	21	96	0	0	0	.	0	.	139	86	0	.	0	.	0	.	57	120	80	70	0	.	0	.	1555	27
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	225	53	0	.	138	85	864	37	0	0	189	44	0	.	128	85	730	44	0	.	327	58	63	83	0	.	18	73	105	62	21	100	0	.	0	0	7	100	58	91	140	82	195	58	178	49	19	100	224	57	3630	16
	fir	33	88	0	.	8	100	249	54	0	.	106	93	0	.	42	100	9	100	20	79	68	57	106	100	0	.	0	0	76	74	0	.	4	100	43	79	0	.	1	100	0	.	126	56	15	100	3	100	146	65	1055	24
	pine	0	.	0	.	0	.	0	.	47	100	0	.	0	.	0	.	67	83	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	5	100	135	55
	larch	70	73	0	.	0	.	131	66	0	.	0	.	0	.	0	.	60	71	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	4	100	0	.	0	.	250	52	0	.	2	100	516	33
	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	.	1	100	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	9	68
	beech	398	42	0	.	25	71	318	33	0	.	68	52	0	.	0	.	0	.	19	92	217	64	37	100	0	.	0	.	114	66	0	.	1	100	0	.	75	73	13	100	22	100	33	73	2	100	39	100	182	43	1564	18
	maple	82	52	0	.	0	.	23	78	0	.	17	72	0	.	38	71	21	95	2	100	47	62	0	.	0	.	17	100	78	58	0	.	0	.	10	100	6	100	0	.	0	.	36	69	-6	240	0	.	-2	2404	368	28
	ash	1	659	0	.	0	.	15	61	2	100	51	67	0	.	8	100	0	.	0	.	5	84	2	100	0	.	0	.	17	100	39	100	0	.	0	.	1	100	0	.	0	.	0	.	14	100	0	.	30	87	184	35
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	12	100	0	0	0	.	20	100	32	71
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	16	100
	other broadleaves	36	48	0	.	0	.	39	43	5	100	17	78	0	.	6	62	29	60	0	.	5	78	0	.	0	.	1	100	39	55	0	.	0	.	0	.	0	.	1	70	0	.	128	76	24	77	0	.	4	71	335	32
	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	845	32	0	.	171	82	1638	25	53	97	449	37	0	.	222	71	917	38	41	80	686	41	209	79	0	.	36	81	432	44	60	100	6	75	52	67	88	64	93	69	162	72	530	38	482	39	61	76	610	37	7845	11
pole timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	752	37	0	.	178	78	1507	24	187	69	547	45	0	.	44	100	1023	25	88	66	787	30	123	61	208	100	48	.	244	47	329	59	60	65	196	61	533	50	252	40	333	58	1135	31	670	37	128	83	1273	31	10648	9
	fir	87	54	0	.	43	85	489	37	20	100	55	78	0	.	71	100	120	64	299	71	537	35	25	75	4	100	0	.	82	75	0	.	109	60	131	79	289	55	9	92	9	100	356	34	87	80	44	95	47	71	2912	15
	pine	35	71	0	.	0	.	131	53	0	.	17	100	0	.	0	.	338	36	0	.	0	.	27	100	0	.	0	.	43	100	43	100	-6	100	0	.	9	100	15	100	0	.	10	100	439	50	0	.	111	59	1213	23
	larch	34	64	0	.	0	.	5	82	0	.	46	100	0	.	0	.	267	41	0	.	4	100	0	.	0	.	14	.	0	.	0	.	0	.	0	.	9	100	334	39	0	.	190	42	428	31	0	.	16	100	1346	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	98	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	98	0	.	0	.	138	70
	other conifers	72	55	0	.	0	.	56	56	0	.	0	.	0	.	0	.	28	48	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	50	100	0	.	14	66	8	85	0	.	10	100	235	32
	beech	462	60	0	.	15	100	446	32	295	54	141	45	0	.	97	83	112	59	111	74	120	81	48	49	69	100	0	.	105	72	76	56	55	77	93	64	43	51	644	34	7	100	176	39	179	68	0	100	220	48	3511	14
	maple	80	44	0	.	1	533	96	36	52	55	105	55	0	.	8	71	64	57	18	54	16	65	65	60	14	100	0	.	-1	1277	32	70	49	66	50	63	95	45	122	45	42	91	139	42	75	46	15	100	332	47	1471	15
	ash	127	50	0	.	3	100	38	52	5	74	81	54	0	.	17	72	32	49	66	89	24	61	74	63	19	100	0	.	43	71	30	94	93	86	123	53	106	51	177	37	28	100	86	39	86	48	13	87	60	49	1331	14
	oak	0	.	0	.	0	.	21	94	0	.	17	71	0	.	0	.	0	0	3	101	0	.	37	96	0	.	0	.	0	.	87	100	31	100	0	.	19	92	296	39	0	.	215	61	34	57	0	.	19	70	780	26
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1338	24	0	.	0	0	34	79	0	.	0	.	1537	23
	other broadleaves	91	45	0	.	12	100	147	40	54	63	46	67	0	.	32	91	774	30	26	71	51	54	26	56	0	.	2	.	33	78	17	59	77	79	111	58	64	59	1195	20	49	90	222	47	396	24	7	100	47	58	3478	11
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0
	total	1741	27	0	.	252	69	2937	18	613	41	1055	33	0	.	268	65	2985	17	605	52	1539	27	426	31	313	75	63	.	549	38	614	41	468	39	703	42	1170	31	4433	13	467	47	2542	21	2512	20	207	74	2136	24	28600	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	48	61	72	.	270	100	2338	22	66	112	1472	39	0	.	439	66	1877	26	219	98	916	39	420	68	391	68	152	71	1325	35	491	49	481	50	359	48	686	49	212	60	95	51	889	36	1034	41	101	.	850	32	15203	10
	fir	182	77	72	.	13	100	415	31	65	81	426	58	0	.	20	100	139	58	24	71	382	51	41	100	11	71	109	100	234	66	77	75	207	48	216	62	60	94	118	83	316	63	498	43	0	.	30	.	162	62	3815	15
	pine	9	100	0	.	0	.	258	83	47	62	14	100	100	86	0	.	562	47	56	100	0	.	24	100	0	.	16	100	0	.	108	59	59	59	37	100	69	75	0	.	0	.	107	90	608	36	0	.	66	60	2140	21
	larch	66	100	0	.	0	.	0	.	10	100	0	.	0	.	0	.	643	30	0	.	15	73	0	.	0	.	0	.	38	77	30	81	0	.	0	.	0	.	640	44	0	.	0	.	427	40	0	.	67	74	1934	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	212	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	77	0	.	0	.	228	42
	other conifers	6	100	0	.	0	.	111	98	0	.	0	.	0	.	2	100	1	100	0	.	22	100	0	.	0	.	0	.	7	81	57	100	15	100	0	.	0	.	0	.	0	.	103	100	1	100	0	.	12	100	337	48
	beech	193	55	7	.	44	91	1824	23	411	47	305	53	0	.	35	80	67	58	383	49	133	50	9	100	0	.	141	98	598	35	423	44	689	34	153	62	95	71	1581	27	356	48	758	37	102	58	0	.	527	34	8835	10
	maple	65	61	10	.	105	100	345	36	30	61	183	51	0	.	110	65	72	57	25	73	22	71	5	100	10	75	134	100	288	37	29	100	149	40	10	100	0	.	14	91	68	61	184	45	12	100	0	.	308	36	2175	14
	ash	164	62	0	.	43	100	181	51	25	73	389	52	0	.	42	100	93	69	194	58	56	70	0	.	189	100	13	100	464	37	60	87	63	62	0	.	150	79	197	66	109	78	193	41	10	85	0	.	337	38	2972	15
	oak	131	76	0	.	0	.	189	43	0	.	88	68	250	60	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	48	66	75	62	0	.	16	100	219	33	10	100	376	44	30	50	0	.	35	72	1480	19
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	697	34	0	.	0	.	26	100	0	.	0	.	829	30
	other broadleaves	143	59	0	.	4	100	251	43	28	75	9	60	3	100	0	.	354	37	110	91	35	100	4	100	4	100	0	0	61	87	19	71	63	64	6	62	119	100	849	32	45	66	99	63	136	58	0	.	165	44	2507	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
	total	1012	32	161	.	479	72	5912	16	682	39	2885	27	353	58	647	58	4119	18	1011	39	1595	35	503	63	605	56	564	46	3016	24	1344	33	1800	28	782	41	1195	40	4526	16	999	43	3206	22	2402	23	131	.	2528	23	42455	6
medium timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	333	39	978	44	563	53	4520	18	153	73	2241	29	0	.	932	51	8495	14	1169	36	760	49	603	37	24	71	75	100	1552	31	600	42	703	40	1243	35	955	34	1149	33	1900	38	2016	22	2432	24	190	78	2088	27	35674	6
	fir	216	50	358	50	458	56	1810	25	107	63	525	47	0	.	215	84	167	69	1796	30	870	37	408	45	44	100	188	68	638	37	1	100	125	50	557	43	494	50	287	51	204	70	1642	28	446	55	0	.	624	31	12180	9
	pine	0	.	0	.	0	.	266	43	23	142	122	92	0	.	0	.	543	38	144	63	129	66	107	100	0	.	0	.	31	100	209	56	70	54	0	.	246	53	17	100	143	75	69	60	239	46	0	.	516	31	2874	15
	larch	98	95	0	.	0	.	64	100	0	.	25	100	0	.	9	100	1614	24	0	.	0	.	0	.	0	.	0	.	22	71	9	100	17	100	0	.	46	100	527	37	0	.	14	100	661	33	55	100	141	60	3303	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	409	36
	other conifers	43	100	0	.	8	78	15	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	9	72	84	56
	beech	976	28	191	54	272	51	2358	21	1191	33	1411	32	0	.	400	45	423	50	1070	30	717	40	282	39	204	71	431	69	1156	35	815	35	1571	27	421	43	540	30	601	37	18	100	1997	22	128	83	147	67	1376	27	18696	7
	maple	255	41	8	100	57	76	262	30	121	49	7	139	0	.	60	46	16	72	198	33	35	88	28	75	14	94	27	100	367	33	0	.	114	42	87	62	62	46	2	100	0	.	234	29	13	73	68	71	262	32	2295	11
	ash	405	35	0	.	289	53	230	57	142	59	378	49	0	.	141	61	2	100	336	54	550	81	0	.	188	85	6	100	245	46	0	.	134	44	192	88	248	46	120	78	5	100	357	40	0	0	49	100	357	30	4376	15
	oak	37	100	0	.	0	.	185	38	69	80	10	100	0	.	0	.	59	100	50	80	150	71	16	91	0	.	0	.	0	0	103	81	38	59	0	.	218	48	92	75	0	.	228	44	5	100	0	.	286	41	1546	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	547	55	0	.	7	100	0	.	0	.	0	.	554	54
	other broadleaves	162	61	3	100	0	.	206	50	41	63	150	67	0	.	108	72	11	71	47	45	78	83	44	85	35	79	0	.	68	48	0	0	65	63	21	71	84	61	198	53	4	100	108	39	52	58	0	.	113	40	1597	16
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0
	total	2524	23	1539	40	1647	40	9916	13	1847	31	4871	20	0	.	1865	37	11737	12	4810	21	3290	28	1489	29	509	58	727	59	4080	22	1738	32	2836	23	2522	28	2892	26	3540	20	2273	34	6681	17	3978	21	508	59	5770	18	83589	4
old timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1534	36	1273	45	1654	39	11881	12	313	72	2409	28	0	.	1583	40	11012	13	715	44	3744	26	1071	41	264	76	1519	33	4866	23	446	60	781	52	3135	24	815	35	1999	33	628	54	4889	18	4430	20	5	.	2119	28	63086	5
	fir	1227	34	127	72	1018	48	7579	14	257	62	829	44	0	.	162	100	701	39	792	47	3056	23	1690	34	239	71	538	56	1339	30	61	89	542	41	754	40	414	59	350	65	120	99	2678	21	115	77	44	.	843	35	25475	7
	pine	90	72	0	.	17	100	662	49	0	.	172	78	10	.	0	.	360	40	74	88	41	100	0	.	0	.	0	.	0	.	0	.	89	63	8	100	71	54	0	.	0	.	10	100	179	86	0	.	230	53	2015	22
	larch	33	100	0	.	0	.	37	100	15	100	0	0	0	.	57	100	2994	22	20	100	0	.	43	100	0	.	0	.	120	73	0	.	0	.	52	100	0	.	807	40	0	.	117	49	3022	21	0	.	0	.	7315	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	53	0	.	0	.	349	41
	other conifers	0	.	0	.	0	.	72	98	0	.	0	.	0	.	0	.	0	.	0	.	236	79	0	.	0	.	0	.	277	105	0	.	4	100	3	100	1	100	0	.	0	.	189	98	0	.	0	.	0	.	782	51
	beech	2719	25	60	97	201	59	5530	16	633	38	873	38	0	.	333	60	734	43	1007	34	1433	29	475	43	54	71	598	50	2005	27	672	42	1605	28	871	43	725	36	580	48	259	84	2653	21	27	100	32	.	1569	25	25648	7
	maple	101	46	0	.	82	72	455	26	171	60	51	56	0	.	47	65	36	117	94	57	150	38	120	56	55	61	18	85	371	36	0	.	74	53	133	44	121	68	0	.	17	100	125	36	68	62	0	.	193	50	2480	12
	ash	236	61	0	.	0	0	738	34	52	100	195	61	28	.	1	1623	38	100	93	86	90	47	32	71	52	72	2	100	339	48	69	78	66	54	256	58	371	44	0	.	23	72	539	46	29	86	109	.	545	41	3903	14
	oak	243	54	0	.	0	.	189	42	79	100	0	.	214	.	0	.	0	.	237	58	7	100	0	.	0	.	0	.	73	60	2	100	46	100	16	100	543	64	14	100	0	.	243	54	0	0	0	.	161	54	2066	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	384	56	0	.	2	100	0	.	0	.	0	.	385	56
	other broadleaves	104	57	0	.	48	95	380	34	12	100	258	99	0	.	0	0	121	86	31	85	28	61	1	100	33	100	0	.	123	45	0	.	64	86	49	77	116	62	53	42	0	.	283	89	8	80	0	.	83	59	1795	23
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	6286	18	1460	46	3020	34	27523	9	1532	38	4787	22	252	.	2183	36	16115	11	3064	26	8785	18	3431	26	698	58	2675	32	9513	17	1250	43	3271	25	5275	21	3178	27	4187	21	1048	46	11726	14	8107	15	190	.	5744	19	135299	3
mixed	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	326	60	-1	.	72	.	9009	13	3	100	777	35	0	.	691	44	7980	13	581	34	1632	27	2342	20	455	56	1485	30	1594	29	0	.	41	74	420	48	177	51	2212	26	883	34	2778	19	4904	15	64	100	344	37	38768	6
	fir	142	71	0	.	0	.	5011	17	5	85	396	71	0	.	15	100	855	38	638	33	941	30	1854	23	33	88	233	46	828	34	0	.	329	54	131	71	42	68	323	48	303	47	1117	28	624	39	83	72	313	49	14218	9
	pine	78	79	0	.	0	.	80	46	100	63	169	86	0	.	0	.	684	40	94	58	0	.	0	.	0	.	147	100	0	.	0	.	117	59	0	.	46	100	105	71	41	92	13	100	529	28	0	.	69	90	2274	18
	larch	0	.	0	.	0	.	70	76	0	.	0	.	0	.	0	.	1488	18	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	0	.	0	.	1041	23	0	.	11	100	3327	21	0	.	61	71	6028	13
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	540	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	473	28	0	.	0	.	1011	21
	other conifers	0	.	0	.	0	.	111	91	177	100	0	.	0	.	0	.	10	73	2	100	0	.	7	100	0	.	4	100	-16	100	0	.	-7	291	0	.	0	.	0	.	6	100	5	100	1	100	0	.	21	100	322	64
	beech	249	42	0	.	0	.	2730	18	188	45	486	46	0	.	97	75	426	37	1003	32	810	37	686	25	109	72	368	44	984	33	122	.	934	36	92	91	245	46	1504	22	188	59	1020	26	484	45	31	100	300	41	13057	8
	maple	0	.	0	.	0	.	699	26	69	72	115	48	0	.	55	86	130	51	108	43	244	37	208	46	37	59	54	51	263	53	24	.	138	49	8	81	156	83	60	69	24	100	224	45	100	58	22	100	59	62	2798	12
	ash	84	98	0	.	0	.	243	37	228	64	193	50	0	.	73	100	120	59	232	43	203	51	19	82	-9	100	46	97	325	48	17	.	147	66	18	100	189	51	90	51	15	100	436	44	125	60	0	.	110	44	2905	14
	oak	179	80	0	.	0	.	80	59	50	71	45	100	0	.	0	.	72	100	10	100	137	78	33	100	5	100	0	.	8	78	0	.	10	100	0	.	99	49	273	43	65	71	123	74	106	66	0	.	126	57	1422	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	184	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1857	24	0	.	0	.	30	100	0	.	0	.	2071	22
	other broadleaves	71	65	0	.	0	.	406	45	36	88	43	56	0	.	41	76	353	32	85	45	56	53	30	48	0	.	32	53	247	57	0	.	23	67	-13	108	44	100	718	24	35	66	353	42	213	44	0	.	176	69	2948	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	.	0	0	0	0	0	.	0	.	0	0
	total	1128	35	-1	.	72	.	18432	10	855	39	2224	29	0	.	973	40	12842	11	2753	24	4023	21	5179	16	631	57	2369	25	4264	22	164	.	1733	30	656	40	998	39	8188	12	1561	31	6079	15	10917	12	200	71	1581	30	87821	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3200	21	2322	31	2875	27	30403	7	722	40	7736	15	0	.	3817	24	31534	7	2773	22	8167	15	4622	15	1342	35	3317	20	9686	14	1888	25	2066	27	5459	17	3174	19	5883	16	3979	22	11947	10	13654	10	506	43	6900	14	167969	2
	fir	1887	25	557	38	1541	36	15576	9	455	40	2442	24	0	.	525	49	1991	23	3568	20	5854	15	4123	17	331	54	1068	34	3196	18	139	57	1316	24	1841	23	1299	30	1088	30	952	32	6417	13	1287	28	204	44	2135	19	59792	4
	pine	212	44	0	.	17	100	1397	30	217	41	495	46	110	79	0	.	2555	18	369	37	186	52	159	71	0	.	163	91	74	72	360	39	330	31	45	85	441	35	137	57	184	62	207	51	1995	19	0	.	997	22	10650	8
	larch	300	43	0	.	0	.	306	41	25	72	71	74	0	.	67	87	7281	11	20	100	18	62	43	100	0	.	14	100	210	47	39	66	17	100	52	100	55	85	3352	16	0	.	345	29	8161	12	55	100	287	38	20718	7
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	1342	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	823	24	0	.	0	.	2164	15
	other conifers	121	49	0	.	8	78	365	46	177	100	0	.	0	.	2	100	39	39	-5	145	259	72	7	100	0	.	4	100	273	107	57	100	12	226	3	100	4	74	50	100	6	100	320	66	15	58	0	.	52	52	1770	27
	beech	5014	16	258	46	557	33	13216	9	2719	19	3303	19	0	.	961	30	1786	23	3594	16	3441	18	1538	19	436	42	1539	30	4962	16	2108	21	4855	15	1633	27	1723	19	4923	13	850	35	6635	12	922	30	249	46	4174	14	71394	3
	maple	583	23	18	71	245	52	1893	14	442	30	484	26	0	.	317	31	339	29	445	22	526	22	426	29	129	34	249	57	1367	19	86	52	523	21	311	28	439	37	197	35	152	42	941	18	262	31	105	53	1151	20	11631	5
	ash	1017	24	0	.	335	48	1445	22	452	39	1288	25	28	100	282	44	285	36	922	27	929	50	128	42	439	57	67	70	1434	21	215	42	503	29	592	40	1065	24	585	30	180	51	1611	22	263	34	171	70	1438	20	15676	7
	oak	589	37	0	.	0	.	663	21	197	52	160	48	473	55	0	.	130	71	299	48	308	49	86	59	5	100	0	.	82	55	241	52	201	38	16	100	895	41	894	21	75	63	1197	24	176	42	0	.	648	25	7336	10
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4838	13	0	.	9	84	90	53	0	.	0	.	5391	12
	other broadleaves	606	26	3	100	63	75	1433	19	175	33	529	52	3	100	187	47	1641	19	299	38	253	34	105	40	72	59	35	49	570	29	37	47	292	35	176	44	427	37	3014	13	133	44	1193	28	829	19	7	100	589	27	12674	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	0	0	0	.	0	0	0	0
	total	13536	11	3158	29	5641	22	66691	5	5581	16	16508	11	614	53	6159	19	49373	5	12284	12	19942	11	11237	11	2756	27	6455	18	21853	10	5169	18	10114	12	10129	14	9523	14	24967	5	6511	17	30820	7	28478	7	1297	33	18369	9	387165	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. 1231014. Birmensdorf, Swiss Federal Research Institute WSL

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