Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3
growing stock (stemwood)
site quality · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/06
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
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	21	100	217	61	0	.	3662	15	52	58	852	34	0	.	1151	32	4563	13	131	35	758	38	341	44	263	47	1745	21	1025	29	0	.	73	43	1131	25	0	.	353	38	837	32	1742	20	1548	25	0	.	195	71	20661	6
	fir	28	87	0	.	0	.	662	43	21	59	74	97	0	.	104	67	21	81	343	36	55	62	271	42	7	67	123	38	161	51	0	.	116	42	73	66	124	100	0	.	24	95	543	32	38	66	0	.	0	.	2788	15
	pine	63	63	2	100	0	.	234	40	161	53	0	.	0	.	0	.	327	27	147	42	40	63	15	100	0	.	96	64	5	100	0	.	193	39	103	72	0	.	15	100	33	68	0	.	114	40	0	.	28	100	1575	14
	larch	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	2812	13	0	.	0	.	0	.	0	.	15	100	46	100	0	.	0	.	0	.	0	.	244	27	0	.	4	100	2028	15	0	.	0	.	5149	9
	Arolla pine	0	.	0	.	0	.	20	73	0	.	0	.	0	.	0	.	1200	17	0	.	0	.	0	.	0	.	47	100	16	100	0	.	0	.	0	.	0	.	4	100	13	100	0	.	727	18	0	.	0	.	2028	12
	other conifers	8	73	3	100	0	.	39	78	0	.	0	.	0	.	1	100	30	42	0	.	0	.	4	100	4	100	2	100	0	.	0	.	17	59	3	83	0	.	0	.	3	100	5	75	0	.	0	.	0	.	119	30
	beech	362	42	44	98	0	.	1558	19	862	31	337	52	0	.	224	50	0	.	1113	25	197	63	295	35	81	68	247	57	349	51	83	100	1127	25	116	54	0	.	0	.	27	85	696	26	118	65	0	.	102	77	7939	9
	maple	27	47	0	.	0	.	217	27	37	52	39	57	0	.	30	52	2	100	88	53	2	100	17	54	14	70	77	66	15	62	0	.	46	51	48	44	0	.	0	.	2	100	151	25	31	53	0	.	14	73	857	13
	ash	30	44	0	.	0	.	89	44	70	60	28	100	0	.	21	91	0	.	120	48	2	100	0	.	4	100	4	79	39	54	0	.	40	47	1	100	0	.	0	.	1	100	75	65	45	66	0	.	62	72	630	19
	oak	63	49	0	.	0	.	38	55	36	71	2	100	0	.	0	.	0	.	0	.	29	100	0	.	2	100	0	.	0	.	0	.	57	64	1	100	0	.	0	.	22	100	52	53	45	50	0	.	0	.	347	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	60	0	.	0	.	34	60
	other broadleaves	84	59	4	100	0	.	137	43	42	46	8	61	0	.	4	100	2	73	31	51	5	71	10	61	8	100	10	47	6	56	0	.	51	48	14	52	0	.	0	.	10	69	72	48	106	56	0	.	11	100	614	18
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	685	33	270	54	0	.	6658	12	1281	29	1340	28	0	.	1537	28	8957	9	1972	22	1087	35	953	30	384	38	2365	19	1663	24	83	100	1720	23	1490	23	124	100	616	25	972	30	3341	16	4834	12	0	.	412	61	42741	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	.	368	47	56	75	11332	11	0	.	1574	22	0	.	939	33	22510	6	396	48	921	36	1340	23	155	49	792	34	1733	24	0	.	106	75	1681	25	0	.	3664	13	1114	32	6065	12	12086	8	279	59	0	.	67110	3
	fir	0	.	66	59	0	.	2387	19	0	.	410	40	0	.	235	56	828	24	282	45	107	59	1073	25	141	65	312	57	379	62	0	.	116	63	454	33	0	.	1017	24	211	46	2067	18	1100	23	57	62	0	.	11241	8
	pine	0	.	0	.	0	.	74	58	0	.	0	.	0	.	0	.	2531	14	0	.	0	.	0	.	0	.	0	.	95	86	0	.	0	0	10	71	0	.	222	58	61	93	0	0	1906	14	0	.	0	.	4899	10
	larch	0	.	0	.	0	.	44	60	0	.	0	.	0	.	0	.	4349	10	0	.	0	.	0	.	0	.	0	.	85	64	0	.	0	.	0	.	0	.	3028	12	0	.	113	49	6727	9	0	.	0	.	14346	6
	Arolla pine	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	262	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	223	34	0	.	0	.	494	23
	other conifers	0	.	0	.	0	.	24	77	0	.	0	.	0	.	0	.	26	59	0	.	0	.	0	.	0	.	0	.	4	71	0	.	0	.	0	.	0	.	0	100	2	100	2	100	14	67	0	.	0	.	73	36
	beech	0	.	101	66	18	100	1301	20	0	.	196	44	0	.	183	45	877	19	200	51	59	59	435	27	87	74	142	51	687	28	0	.	112	54	75	54	0	.	1876	16	79	65	1337	17	261	31	1	100	47	80	8077	7
	maple	0	.	23	69	0	.	463	21	0	.	60	40	0	.	243	40	162	25	11	72	18	77	100	44	39	52	13	64	140	40	0	.	7	63	95	51	0	.	28	159	24	58	457	21	128	26	0	.	0	.	2010	10
	ash	0	.	12	100	0	.	56	36	0	.	44	59	0	.	82	59	224	33	6	78	0	.	42	73	30	64	1	100	51	75	0	.	4	100	73	70	0	.	94	42	50	100	102	45	192	32	0	.	18	100	1081	14
	oak	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	129	65	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	124	40	0	.	0	0	139	33	0	.	0	.	403	27
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	92	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	770	31	0	.	0	.	5	100	0	.	0	.	810	29
	other broadleaves	0	.	1	100	0	.	342	42	0	.	16	53	0	.	51	55	699	19	11	72	1	100	6	71	12	54	6	56	32	47	0	.	1	100	20	52	0	.	981	18	30	52	79	31	821	15	0	.	6	100	3114	9
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	0	.	571	42	74	71	16033	9	0	.	2299	20	0	.	1733	26	32633	5	906	36	1105	33	2995	18	463	45	1265	34	3213	20	0	.	346	45	2408	22	0	.	11806	7	1571	28	10223	10	23603	6	337	59	71	86	113657	2
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	607	32	1246	34	2544	21	9900	10	140	38	3872	15	0	.	988	47	6319	13	869	27	4666	15	2433	17	418	47	1409	29	5420	14	627	37	753	35	3047	18	666	34	1044	27	1277	29	5283	13	364	46	832	35	1794	19	56517	4
	fir	506	37	377	41	945	28	7346	11	192	47	1726	23	0	.	98	91	955	23	1124	24	3053	16	1907	19	43	52	176	41	2369	18	12	96	488	31	834	26	257	47	284	44	287	41	2560	17	169	48	427	36	1061	23	27195	5
	pine	193	46	24	100	8	100	462	42	330	44	101	81	0	.	0	.	55	100	281	47	130	50	183	48	0	.	3	100	119	64	123	47	204	36	0	.	176	52	122	74	45	72	41	49	0	.	0	.	233	33	2833	13
	larch	184	56	7	100	74	100	57	61	5	100	107	84	0	.	29	100	505	35	0	.	108	99	0	.	0	.	0	.	194	64	68	68	0	.	22	100	43	64	254	31	13	100	116	52	174	100	0	.	50	51	2009	18
	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	21	74	0	.	1	100	116	54	0	.	0	.	0	.	0	.	0	.	2	100	133	80	0	.	0	.	0	.	12	178	37	100	3	73	0	.	0	.	23	100	0	.	127	70	0	100	0	.	15	56	490	33
	beech	2059	19	91	49	723	33	4998	14	1292	25	1256	23	0	.	412	44	615	31	1295	21	1744	19	646	26	150	44	549	42	2581	16	750	26	1582	19	855	22	676	31	3328	17	194	41	3022	14	253	38	318	38	1755	17	31144	4
	maple	165	33	62	64	120	45	687	17	158	30	224	29	0	.	73	37	96	40	206	28	328	27	195	40	50	36	87	40	810	19	54	53	219	27	98	34	222	47	220	30	95	43	404	22	93	39	68	50	376	25	5109	6
	ash	262	36	40	87	246	41	646	25	91	41	443	35	0	.	310	82	230	40	416	37	478	59	21	75	229	67	16	82	760	21	71	53	127	40	359	42	217	37	550	21	58	51	413	28	19	58	59	69	325	28	6385	9
	oak	402	47	0	.	13	100	173	29	85	66	78	48	0	.	0	.	180	59	84	47	124	53	96	59	0	.	11	100	28	71	197	46	73	53	46	82	90	40	692	20	23	82	797	31	3	71	20	100	297	31	3512	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	277	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3918	11	0	.	3	100	12	100	0	.	0	.	4210	10
	other broadleaves	213	35	1	100	11	61	423	22	74	36	65	37	0	.	168	78	753	20	170	31	154	37	45	47	93	78	30	48	344	27	51	66	61	52	146	34	12	61	2450	10	98	47	479	24	91	39	4	76	180	46	6115	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
	total	4612	16	1847	29	4686	18	24807	7	2365	20	7871	13	0	.	2078	36	9985	10	4447	15	10919	12	5525	13	982	33	2280	24	12636	10	1990	21	3510	17	5406	15	2360	23	12885	6	2089	21	13247	9	1179	28	1728	27	6087	13	145519	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	2932	17	64	70	509	52	8630	10	646	40	1571	25	15	100	397	54	97	100	2297	22	1952	20	629	34	70	100	126	49	2438	20	958	27	1585	21	250	46	2573	18	0	.	85	62	2149	19	86	57	33	57	5024	13	35118	5
	fir	1637	21	40	100	433	52	6184	12	392	44	319	40	0	.	109	86	0	.	1670	23	1937	20	579	35	0	.	83	72	1078	21	96	51	918	24	246	47	792	33	0	.	28	100	1169	24	31	100	55	61	1277	23	19072	6
	pine	536	32	0	.	0	.	416	25	46	56	243	48	64	81	0	.	0	.	121	57	0	.	0	.	0	.	0	.	142	57	112	44	108	43	50	81	260	32	0	.	18	100	236	42	0	.	0	.	1080	20	3431	11
	larch	195	42	2	100	0	.	181	40	15	100	23	72	0	.	0	.	0	.	25	71	12	89	24	100	0	.	5	100	113	52	12	100	48	60	30	100	18	87	0	.	0	.	300	39	8	100	32	100	237	39	1282	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	75	50	0	.	3	100	172	54	122	77	37	100	0	.	0	.	0	.	2	72	13	75	0	.	0	.	0	.	148	98	28	100	2	100	2	100	37	76	0	.	0	.	68	72	0	.	0	.	47	55	756	28
	beech	3207	14	9	100	141	66	5297	12	1255	25	1941	22	0	.	336	48	0	.	1367	20	1114	28	270	33	45	100	224	51	1518	24	1025	22	2575	16	558	40	952	20	79	73	119	90	1962	18	89	51	35	65	2470	15	26588	5
	maple	404	19	16	71	10	100	560	21	240	34	73	44	7	81	55	45	0	.	306	24	85	40	40	50	9	100	15	79	230	28	12	71	343	30	40	38	217	29	10	100	17	100	246	31	16	60	42	60	429	18	3422	7
	ash	673	21	-1	100	14	100	734	22	360	43	526	30	24	100	33	58	0	.	344	29	275	34	39	72	10	100	32	61	652	25	60	53	270	36	77	55	789	24	18	78	10	100	784	24	53	76	172	56	1000	19	6948	7
	oak	572	26	0	.	0	.	481	22	115	62	181	39	423	41	0	.	0	.	156	45	133	75	9	100	0	.	0	.	236	70	125	61	172	41	0	.	483	37	24	84	42	100	1114	23	37	64	0	.	285	26	4587	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	56	82	61	81	0	.	0	.	130	52
	other broadleaves	609	42	11	77	26	100	480	26	181	43	184	55	32	58	6	100	3	100	171	39	65	48	41	65	0	.	45	62	191	31	15	50	301	31	27	51	406	27	72	53	25	97	573	34	195	50	18	70	331	24	4008	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
	total	10841	9	142	73	1135	44	23134	7	3373	19	5097	15	565	39	937	39	105	100	6458	14	5586	15	1629	26	135	71	529	38	6747	14	2444	20	6321	13	1280	30	6526	13	211	48	343	57	8657	11	577	38	387	46	12182	9	105341	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	3560	15	1894	25	3108	19	33524	5	837	31	7869	10	15	100	3476	20	33490	5	3692	16	8297	11	4742	12	905	28	4072	15	10617	10	1585	22	2517	17	6110	12	3240	16	5061	11	3312	17	15240	7	14085	7	1144	29	7014	11	179406	2
	fir	2170	18	484	34	1378	25	16579	7	605	32	2528	18	0	.	546	36	1804	16	3419	15	5152	12	3829	13	191	49	694	30	3987	13	108	47	1638	17	1607	18	1172	27	1301	21	550	28	6339	10	1338	20	539	30	2338	16	60295	3
	pine	792	25	26	93	8	100	1186	21	537	31	344	41	64	81	0	.	2913	13	549	29	170	41	198	45	0	.	99	63	361	38	234	32	504	23	163	52	436	28	359	44	157	46	277	37	2021	14	0	.	1342	17	12739	6
	larch	379	34	9	79	74	100	281	30	20	80	130	70	0	.	29	100	7666	8	25	71	120	90	24	100	0	.	20	80	438	35	80	59	48	60	52	72	61	52	3526	10	13	100	533	27	8937	8	32	100	287	34	22785	4
	Arolla pine	0	.	0	.	0	.	27	60	0	.	0	.	0	.	0	.	1462	15	0	.	0	.	0	.	0	.	47	100	16	100	0	.	0	.	0	.	0	.	6	74	13	100	0	.	950	16	0	.	0	.	2522	11
	other conifers	103	40	3	100	3	80	351	34	122	77	37	100	0	.	1	100	56	36	4	63	146	73	4	100	4	100	2	100	164	89	65	71	22	49	5	64	37	76	23	98	5	72	203	50	14	64	0	.	62	44	1437	19
	beech	5628	11	245	37	882	29	13154	8	3409	15	3731	15	0	.	1156	24	1492	17	3975	12	3114	15	1646	15	364	32	1162	26	5136	12	1858	16	5396	10	1604	19	1628	17	5284	12	419	34	7018	9	722	21	354	34	4374	11	73749	2
	maple	597	16	101	44	131	42	1927	10	435	22	395	20	7	81	401	26	259	22	611	17	432	22	353	26	113	27	191	33	1195	15	66	45	615	19	280	23	439	28	257	31	138	34	1259	12	268	20	110	38	819	15	11398	4
	ash	964	18	51	73	261	39	1524	15	521	32	1040	22	24	100	447	58	455	26	886	22	756	39	102	44	273	57	53	45	1502	15	131	37	441	25	509	32	1006	20	662	19	118	50	1374	16	310	26	230	45	1405	15	15045	5
	oak	1037	24	0	.	13	100	696	17	235	40	261	30	423	41	0	.	309	44	240	33	286	43	105	54	2	100	11	100	271	61	322	37	303	29	46	81	573	31	840	18	87	59	1963	18	224	25	20	100	583	20	8849	7
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	317	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4696	10	0	.	59	78	112	49	0	.	0	.	5184	9
	other broadleaves	906	30	17	57	37	72	1383	16	297	28	273	39	32	58	229	59	1458	13	382	23	225	29	101	34	112	65	90	35	573	19	66	52	413	25	207	25	418	26	3503	8	163	33	1203	19	1213	14	22	58	528	22	13850	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	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	16137	8	2830	22	5895	17	70632	4	7019	12	16608	8	565	39	6284	16	51680	4	13783	8	18697	8	11103	9	1964	21	6439	13	24258	7	4516	14	11896	9	10584	10	9010	11	25519	4	4974	14	35467	5	30193	5	2452	22	18752	7	407258	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. 1195114. Birmensdorf, Swiss Federal Research Institute WSL

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