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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NFI2
growing stock (stemwood)
site quality · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		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
site quality	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
low	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	287	58	166	63	0	.	4144	15	50	57	1404	29	0	.	1148	31	3866	13	112	38	646	38	320	40	268	41	1709	21	1256	26	0	.	83	38	940	27	0	.	327	38	758	33	1612	19	1375	24	0	.	165	72	20637	6
	fir	37	71	17	100	0	.	728	40	14	63	123	100	0	.	67	68	12	100	322	35	53	63	230	37	29	110	135	47	220	46	0	.	109	40	72	64	127	100	0	.	33	72	502	33	17	86	0	.	0	.	2846	15
	pine	77	58	0	.	0	.	226	46	155	52	0	.	0	.	0	.	359	27	157	49	23	87	7	100	0	.	101	69	23	100	26	100	205	38	72	78	0	.	14	100	27	71	0	.	106	40	0	.	19	100	1599	14
	larch	10	100	0	.	0	.	46	64	0	.	0	.	0	.	0	.	2275	13	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	124	35	0	.	0	100	1613	16	0	.	0	.	4081	10
	Arolla pine	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	1117	17	0	.	0	.	0	.	0	.	33	100	16	100	0	.	0	.	0	.	0	.	0	.	9	100	0	.	614	20	0	.	0	.	1791	13
	other conifers	4	88	2	100	0	.	28	83	0	.	0	.	0	.	1	100	20	63	0	.	0	.	3	100	6	100	1	100	0	.	0	.	13	59	0	.	0	.	0	.	4	72	3	82	0	100	0	.	0	.	85	33
	beech	422	37	48	74	0	.	1602	20	951	29	311	49	0	.	160	49	0	.	1043	26	152	60	292	35	142	62	221	63	315	51	38	100	1059	25	156	47	0	.	0	.	35	94	530	28	112	65	0	.	65	87	7655	9
	maple	31	46	0	.	0	.	151	24	32	53	48	49	0	.	31	41	0	.	75	51	0	.	18	61	68	84	25	74	24	58	0	.	47	48	25	57	0	.	0	.	0	.	121	30	27	72	0	.	10	100	733	14
	ash	32	44	0	.	0	.	51	46	34	63	22	100	0	.	21	92	0	.	81	60	1	100	6	100	13	82	1	100	25	56	0	.	39	40	0	0	0	.	0	.	0	.	77	62	47	67	0	.	48	71	496	19
	oak	58	48	0	.	0	.	40	49	35	78	2	100	0	.	0	.	0	.	0	.	19	100	0	.	1	100	0	.	0	0	0	.	75	58	1	100	0	.	0	.	15	100	34	61	16	47	0	.	0	.	297	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	66	0	.	0	.	45	66
	other broadleaves	55	77	3	100	0	.	109	47	45	45	9	57	0	.	5	79	1	100	48	50	4	71	8	63	22	100	6	52	24	46	0	.	61	48	6	67	0	.	0	.	9	76	63	48	81	51	0	.	9	100	566	17
	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	1012	30	237	56	0	.	7128	12	1316	27	1918	27	0	.	1433	27	7649	9	1838	23	898	35	885	29	549	41	2244	19	1902	23	64	100	1692	22	1273	25	127	100	466	30	890	31	2942	16	4053	13	0	.	317	60	40833	4
moderate	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	.	193	47	45	78	11944	10	0	.	1490	24	0	.	675	38	21303	6	316	52	1238	31	1285	26	135	53	871	31	1827	24	0	.	116	76	2096	24	0	.	3260	14	1034	32	5195	12	10749	8	217	61	0	.	63990	3
	fir	0	.	67	57	0	.	1896	19	0	.	468	37	0	.	242	54	683	26	260	42	53	58	894	24	206	57	253	56	349	60	0	.	67	64	387	33	0	.	837	27	210	56	1725	18	902	24	34	60	0	.	9531	8
	pine	0	.	0	.	0	.	76	54	0	.	0	.	0	.	0	.	2415	14	0	.	0	.	0	.	0	.	0	.	85	72	0	.	0	0	17	59	0	.	205	52	55	95	-3	100	1997	15	0	.	0	.	4848	10
	larch	0	.	0	.	0	.	48	95	0	.	0	.	0	.	0	.	3728	10	0	.	0	.	0	.	0	.	0	.	86	69	0	.	0	.	0	.	0	.	2378	14	0	.	65	52	5964	10	0	.	0	.	12270	6
	Arolla pine	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	273	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	198	33	0	.	0	.	475	23
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	54	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	1	100	2	100	11	89	0	.	0	.	39	43
	beech	0	.	77	76	20	100	1217	21	0	.	203	45	0	.	167	53	773	21	179	51	59	74	377	30	136	57	71	55	635	34	0	.	76	64	78	58	0	.	1330	17	50	58	1066	17	211	32	1	100	33	72	6758	8
	maple	0	.	15	59	0	.	393	24	0	.	27	50	0	.	169	44	115	26	15	72	35	73	95	42	49	55	9	63	117	46	0	.	10	71	76	55	0	.	26	72	17	72	313	22	52	38	0	.	0	.	1536	11
	ash	0	.	8	100	0	.	44	40	0	.	37	62	0	.	27	137	137	35	1	100	0	.	10	71	33	76	1	100	54	59	0	.	2	100	17	140	0	.	32	45	35	100	47	56	116	31	0	.	6	100	607	17
	oak	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	109	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	132	45	0	.	1	100	87	30	0	.	0	.	332	30
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	786	30	0	.	0	.	10	100	0	.	0	.	823	29
	other broadleaves	0	.	1	100	0	.	193	23	0	.	24	45	0	.	58	42	458	19	7	64	0	.	5	83	10	63	6	52	21	54	0	.	1	100	19	51	0	.	554	20	23	54	71	32	526	17	0	.	9	100	1986	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	362	45	65	71	15820	9	0	.	2248	21	0	.	1339	29	30043	5	778	36	1384	30	2665	19	569	44	1212	31	3175	20	0	.	271	47	2691	22	0	.	9541	8	1425	29	8480	11	20824	6	252	61	49	77	103194	3
good	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	651	30	1050	33	2370	21	10969	9	177	42	5162	14	0	.	952	43	5678	12	1075	23	5046	15	2417	17	567	39	1704	28	5714	14	617	36	599	31	2833	18	509	40	1040	25	1399	28	4981	13	213	52	997	34	1832	18	58550	4
	fir	476	42	284	40	771	30	7599	10	166	48	1499	23	0	.	90	99	836	25	1172	23	2815	16	1810	20	53	50	128	45	2296	19	9	100	671	30	945	25	278	51	216	45	242	39	2363	16	118	52	269	38	1196	24	26302	5
	pine	187	38	21	100	8	100	467	41	392	37	153	74	0	.	0	.	46	100	296	43	128	50	114	49	0	.	3	100	88	63	134	48	204	35	0	.	185	49	111	75	94	59	31	46	0	.	0	.	253	31	2913	13
	larch	197	52	6	100	65	100	128	53	9	100	106	92	0	.	25	100	473	35	0	.	117	90	0	.	0	.	0	.	207	56	77	64	0	.	24	100	42	69	282	30	11	100	107	53	195	83	0	.	54	51	2124	16
	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	7	100	0	.	0	.	96	54	0	.	0	100	0	.	0	.	1	100	1	100	114	74	0	.	0	.	0	.	-5	278	7	100	1	71	0	.	0	.	7	100	2	73	122	69	0	.	0	.	11	63	364	36
	beech	2107	19	72	48	678	33	4887	13	1052	26	1293	23	0	.	329	45	464	38	1339	20	1668	20	586	29	243	43	499	41	2432	17	620	26	1738	19	813	23	545	31	2606	15	152	34	2780	14	165	43	289	40	1912	17	29270	4
	maple	169	46	42	64	161	40	461	18	128	32	168	29	0	.	46	59	53	60	207	30	273	25	130	47	44	53	104	50	811	18	31	51	189	25	120	35	185	49	108	34	92	49	253	25	49	40	69	52	343	26	4234	7
	ash	218	29	28	87	206	39	385	27	78	44	335	32	0	.	154	68	138	49	422	43	413	60	21	90	148	64	16	72	476	23	67	61	122	45	240	48	155	42	320	23	23	59	408	29	14	58	51	72	376	28	4814	10
	oak	306	30	0	.	12	100	168	33	94	45	59	47	0	.	0	.	137	50	67	51	126	55	99	51	0	.	9	100	32	77	208	44	83	56	42	84	117	38	545	23	14	86	610	33	3	71	17	100	310	31	3059	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	309	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3830	11	0	.	0	.	3	100	0	.	0	.	4142	11
	other broadleaves	156	44	2	100	22	57	445	25	67	36	111	37	0	.	66	63	613	20	136	29	160	34	44	47	62	74	45	42	336	25	18	55	38	44	135	39	11	86	2032	11	87	42	317	24	69	38	0	910	191	35	5162	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	.
	total	4474	15	1505	29	4293	19	25604	7	2164	20	8886	12	0	.	1662	31	8749	10	4716	14	10860	11	5220	13	1117	29	2508	24	12386	11	1787	23	3645	16	5153	15	2027	23	11095	6	2116	21	11973	9	828	32	1691	27	6478	13	140935	2
very good	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	4315	16	30	81	340	51	10458	10	734	38	2381	21	14	88	353	55	122	100	2067	20	2896	19	669	34	46	100	200	45	2857	19	832	28	2216	21	418	44	3446	17	0	.	86	67	3408	18	102	66	123	60	5950	12	44063	4
	fir	1574	21	60	100	338	53	6520	11	338	39	348	31	0	.	99	87	0	.	1522	25	2213	19	582	31	0	.	155	65	929	21	90	63	859	22	201	50	562	34	0	.	63	100	960	24	7	94	43	70	1352	20	18817	6
	pine	562	27	0	.	0	.	473	28	56	65	319	45	66	72	0	.	0	.	138	58	0	.	0	.	0	.	0	.	148	51	143	46	126	42	85	72	315	26	0	.	24	100	266	43	0	.	0	.	1181	20	3903	10
	larch	183	39	0	.	0	.	187	39	8	100	102	54	0	.	0	.	0	.	25	71	6	100	22	100	0	.	0	.	121	48	10	100	32	65	27	100	4	74	0	.	0	.	172	55	6	100	34	100	279	41	1217	17
	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	72	50	0	.	2	100	136	54	63	82	48	100	0	.	0	.	0	.	0	.	2	73	0	.	0	.	0	.	98	97	23	100	1	100	1	100	8	59	0	.	0	.	39	60	0	.	0	.	52	46	544	27
	beech	3202	13	4	100	117	66	5224	12	1659	22	1816	21	0	.	278	47	0	.	1534	20	978	26	323	32	22	100	440	60	1385	25	876	21	2446	16	636	40	911	19	68	77	108	93	1983	17	24	144	35	71	2575	15	26644	5
	maple	400	19	14	71	11	100	474	22	280	30	62	54	7	91	46	48	0	.	226	27	50	43	14	66	7	100	11	77	238	25	30	68	261	30	30	43	167	42	6	100	12	100	208	39	9	62	38	61	443	24	3043	8
	ash	774	22	5	100	14	100	647	21	265	42	295	34	6	79	16	60	0	.	384	29	189	35	45	75	9	100	58	52	506	25	37	66	220	40	63	49	605	27	42	75	13	100	762	22	57	56	205	52	846	19	6061	7
	oak	494	24	0	.	0	.	500	21	147	52	177	38	444	38	0	.	0	.	277	46	141	61	8	100	0	.	0	.	210	67	153	53	147	39	1	100	490	32	28	88	30	100	1104	22	25	74	0	.	252	29	4628	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	89	0	.	39	82	72	86	0	.	0	.	123	57
	other broadleaves	636	31	19	74	18	100	423	31	223	37	68	58	23	64	9	71	3	100	153	33	90	51	62	49	1	100	33	58	244	30	11	82	248	33	26	63	485	27	36	55	24	68	436	31	147	58	38	59	324	23	3780	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	12212	9	132	78	840	44	25043	7	3773	17	5616	15	560	36	802	40	129	100	6326	13	6564	14	1725	25	86	71	896	39	6738	14	2204	20	6555	13	1488	31	6992	13	188	47	360	57	9376	11	447	36	517	43	13254	9	112823	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	5253	14	1439	26	2755	19	37514	5	961	30	10437	10	14	88	3129	20	30970	5	3571	14	9825	10	4691	12	1017	26	4483	15	11654	10	1449	22	3013	17	6288	12	3955	16	4627	11	3277	17	15196	7	12438	7	1337	28	7947	10	187239	2
	fir	2087	19	429	31	1109	26	16742	7	518	29	2437	17	0	.	499	37	1530	18	3276	15	5134	12	3516	13	288	43	671	29	3794	14	99	58	1706	16	1605	18	967	28	1054	23	549	30	5549	10	1044	22	346	31	2548	15	57496	3
	pine	826	21	21	100	8	100	1243	21	603	28	473	39	66	72	0	.	2820	13	592	28	151	44	121	47	0	.	103	68	344	33	303	31	535	22	174	48	500	24	330	41	201	41	294	39	2103	15	0	.	1454	17	13264	6
	larch	390	32	6	100	65	100	409	28	17	71	207	54	0	.	25	100	6476	8	25	71	123	86	22	100	0	.	13	100	414	34	87	58	32	65	51	71	46	63	2784	12	11	100	345	33	7779	8	34	100	333	35	19692	5
	Arolla pine	0	.	0	.	0	.	7	71	0	.	0	.	0	.	0	.	1390	15	0	.	0	.	0	.	0	.	33	100	16	100	0	.	0	.	0	.	0	.	1	100	9	100	0	.	812	17	0	.	0	.	2267	11
	other conifers	83	45	2	100	2	100	259	36	63	82	49	99	0	.	1	100	45	40	1	100	116	73	3	100	6	100	1	100	95	102	29	81	15	51	1	100	8	59	7	100	7	48	166	53	12	85	0	.	64	39	1033	19
	beech	5731	10	202	38	815	29	12931	7	3662	14	3623	14	0	.	934	24	1236	19	4094	12	2857	15	1578	16	543	29	1231	29	4767	12	1534	16	5319	10	1683	19	1456	16	4004	11	344	35	6359	9	511	24	326	36	4585	11	70327	2
	maple	600	18	71	42	171	38	1479	11	441	21	304	21	7	91	291	29	168	26	524	18	357	21	258	28	168	40	150	37	1190	14	61	42	507	18	252	25	351	32	140	29	121	40	894	14	137	25	107	40	796	17	9546	4
	ash	1024	17	41	64	220	37	1127	15	377	31	688	22	6	79	218	52	274	30	888	24	603	42	82	49	203	48	76	43	1060	16	104	46	383	27	320	38	760	23	395	20	70	56	1294	17	233	25	256	44	1277	15	11979	5
	oak	858	18	0	.	12	100	711	17	276	33	238	30	444	38	0	.	246	41	344	39	287	39	107	48	1	100	9	100	242	59	361	34	306	28	44	81	607	26	704	20	59	60	1749	18	131	25	17	100	562	21	8315	7
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	340	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4624	10	0	.	39	82	131	54	0	.	0	.	5133	10
	other broadleaves	847	25	24	59	40	55	1171	16	335	26	211	27	23	64	139	35	1075	14	344	20	253	28	118	31	95	54	90	30	625	18	28	46	348	25	186	30	496	27	2621	9	143	29	887	18	822	16	38	61	533	19	11494	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	17698	7	2235	22	5197	17	73595	4	7254	11	18668	8	560	36	5236	15	46571	4	13658	8	19706	8	10495	9	2321	20	6860	13	24201	7	4055	15	12163	9	10605	10	9146	11	21290	4	4792	14	32771	5	26153	5	2459	22	20097	7	397785	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. 1195094. Birmensdorf, Swiss Federal Research Institute WSL

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