Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
growing stock (stemwood)
site quality · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
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	0	0	352	71	0	.	4415	17	8	100	997	48	0	.	1276	41	5659	16	76	65	727	52	212	56	538	51	1899	29	1426	37	0	.	37	69	1461	31	0	.	606	44	1300	35	1450	24	1705	32	0	.	159	100	24303	8
	fir	14	100	0	.	0	.	573	37	59	95	119	96	0	.	117	85	0	.	578	41	24	79	454	45	-3	260	98	48	13	100	0	.	102	62	98	98	206	100	0	.	96	91	855	41	67	85	0	.	0	.	3469	17
	pine	74	100	0	.	0	.	136	54	89	63	0	.	0	.	0	.	404	31	151	52	71	66	44	100	4	100	217	83	36	74	0	.	119	56	69	83	0	.	22	100	4	100	0	.	28	74	0	.	0	.	1470	19
	larch	1	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	3534	16	0	.	0	.	0	.	0	.	0	.	99	100	0	.	0	.	0	.	0	.	480	33	0	.	0	0	1715	24	0	.	0	.	5828	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1104	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	804	27	0	.	0	.	1908	17
	other conifers	7	100	0	.	0	.	2	73	0	.	0	.	0	.	3	100	29	41	0	.	1	100	8	100	0	.	8	100	0	.	0	.	41	78	5	100	0	.	0	.	7	100	16	74	0	.	0	.	0	.	127	31
	beech	137	59	16	100	0	.	2288	23	464	48	552	52	0	.	185	62	0	.	1079	33	293	84	132	40	25	100	462	60	355	77	121	100	1272	31	174	64	0	.	0	.	188	95	491	40	203	73	0	.	137	84	8575	12
	maple	17	61	0	.	0	.	319	25	27	58	95	49	0	.	117	42	0	.	50	73	12	100	27	59	22	79	119	90	19	71	0	.	62	76	39	55	2	100	0	.	-7	112	166	33	119	47	0	.	17	100	1220	15
	ash	1	100	0	.	0	.	44	69	0	.	80	100	0	.	2	100	0	.	76	62	0	.	0	.	12	100	23	100	9	100	0	.	45	61	0	.	0	.	0	.	3	100	18	88	93	87	0	.	48	75	455	30
	oak	47	82	0	.	0	.	105	61	70	78	1	100	0	.	0	.	0	.	0	.	58	100	0	.	5	100	0	.	2	100	0	.	74	77	0	.	0	.	0	.	50	100	39	86	17	71	0	.	0	.	468	29
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	71	0	.	0	.	60	71
	other broadleaves	68	94	0	.	0	.	116	39	21	71	16	76	0	.	0	.	6	59	22	95	13	73	14	73	3	71	15	60	23	66	0	.	7	66	15	56	0	.	4	72	9	77	73	53	165	51	0	.	21	100	611	21
	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	367	56	368	71	0	.	8000	14	738	39	1860	34	0	.	1700	35	10736	11	2032	30	1199	45	891	38	606	47	2841	25	1982	32	121	100	1759	29	1862	29	208	100	1112	30	1649	35	3109	22	4975	17	0	.	382	68	48495	6
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	.	528	55	104	100	11451	12	0	.	2642	26	0	.	936	45	23748	8	336	63	834	45	1271	31	99	55	915	43	2880	27	0	.	156	69	1471	32	0	.	4889	17	1200	45	5288	17	12968	10	222	74	0	.	71938	4
	fir	0	.	157	66	0	.	2155	22	0	.	564	55	0	.	48	100	1134	34	443	57	128	63	1564	32	303	67	578	61	196	48	0	.	115	65	673	42	0	.	1129	29	217	59	2263	25	1255	30	90	88	0	.	13013	10
	pine	0	.	0	.	0	.	53	52	0	.	0	.	0	.	0	.	2326	19	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	0	8	100	0	.	128	59	116	100	0	0	1890	19	0	.	0	.	4544	13
	larch	0	.	0	.	0	.	40	70	0	.	6	100	0	.	0	.	5111	12	0	.	1	100	0	.	0	.	0	.	74	97	0	.	0	.	0	.	0	.	4014	15	0	.	197	51	7755	13	0	.	0	.	17197	8
	Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	665	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	243	36	0	.	0	.	929	23
	other conifers	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	13	64	0	.	0	.	0	.	0	.	1	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	13	58	0	.	0	.	74	50
	beech	0	.	158	78	6	100	1606	25	0	.	182	47	0	.	188	76	1050	24	99	76	6	72	402	32	163	70	109	59	972	32	0	.	154	64	214	59	0	.	2092	20	145	65	1007	25	348	43	0	.	7	100	8907	9
	maple	0	.	25	75	0	.	482	24	0	.	132	55	0	.	250	55	295	28	24	59	26	88	175	43	65	60	70	100	220	45	0	.	12	80	99	51	0	.	103	54	62	74	482	27	246	38	0	.	0	.	2770	11
	ash	0	.	0	.	0	.	64	59	0	.	118	49	0	.	165	61	131	46	10	100	0	.	37	71	84	61	4	100	62	60	0	.	12	100	46	79	0	.	83	59	127	90	181	55	315	37	0	.	0	.	1439	17
	oak	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	64	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	194	41	0	.	8	100	195	42	0	.	0	.	467	28
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	104	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	599	37	0	.	0	.	11	100	0	.	0	.	714	34
	other broadleaves	0	.	0	.	0	.	185	27	0	.	29	56	0	.	62	67	976	28	14	100	0	.	14	64	20	71	2	100	23	75	0	.	2	100	33	83	0	.	699	23	38	63	66	40	924	21	0	.	0	.	3088	12
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	0
	total	0	.	867	51	110	100	16088	11	0	.	3674	22	0	.	1649	36	35619	6	926	55	995	41	3465	24	733	56	1678	43	4457	22	0	.	450	50	2545	28	0	.	13938	9	1905	37	9499	15	26162	8	312	71	7	100	125080	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	782	32	1916	39	1933	32	7219	13	144	65	3677	22	0	.	966	39	6614	16	615	41	3464	19	2437	22	723	51	1149	39	4515	19	520	42	391	49	2469	25	411	43	1773	64	1792	32	4530	16	686	49	750	46	1859	28	51335	5
	fir	418	45	451	50	1119	39	8386	13	102	62	1228	32	0	.	215	83	971	29	1366	37	3375	20	2062	28	69	61	323	47	2061	23	33	96	305	37	1223	34	188	50	230	75	547	44	2676	19	320	62	244	58	1067	30	28978	6
	pine	26	100	0	.	0	.	756	44	176	47	159	100	0	.	0	.	0	.	136	65	176	71	79	73	0	.	0	.	53	100	193	53	76	62	0	.	181	72	0	.	104	65	32	58	0	.	0	.	267	38	2415	19
	larch	95	81	0	.	0	.	61	85	12	100	57	100	0	.	50	100	526	51	0	.	5	100	0	.	0	.	0	.	51	100	36	59	0	.	0	.	43	75	395	43	0	.	41	71	0	.	0	.	73	59	1446	24
	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	57	100	0	.	5	100	283	58	0	.	0	.	0	.	0	.	0	.	7	100	311	79	0	.	0	.	0	.	-7	310	66	100	10	72	0	.	0	.	82	98	0	.	26	63	0	.	0	.	12	63	853	37
	beech	1353	34	149	63	449	39	5412	15	860	36	1036	31	0	.	279	56	928	38	927	29	2073	23	453	32	189	71	567	46	2682	20	784	33	1153	27	834	28	546	37	3671	16	485	40	3071	18	316	56	175	58	1647	23	30039	6
	maple	158	55	0	.	259	43	917	23	156	39	252	37	0	.	29	75	227	49	105	38	373	27	237	54	59	81	72	49	1177	21	66	72	269	34	219	39	263	62	286	35	116	46	378	23	103	57	63	60	551	31	6334	8
	ash	157	67	0	.	254	55	529	36	95	63	266	42	0	.	79	70	225	49	262	61	392	70	19	62	395	75	0	0	663	33	123	68	184	50	475	45	150	52	717	36	88	49	611	37	45	71	26	100	334	33	6087	12
	oak	215	70	0	.	0	.	251	35	41	67	72	63	0	.	0	.	122	76	86	77	128	70	69	71	0	.	0	.	13	71	154	53	15	100	19	100	135	40	744	25	36	100	566	37	20	100	12	100	257	40	2955	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	411	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4321	15	0	.	6	100	26	100	0	.	0	.	4763	14
	other broadleaves	187	60	0	.	25	53	567	27	69	54	179	36	0	.	93	63	903	27	133	51	183	36	25	61	59	73	5	75	302	38	25	65	100	66	259	39	10	100	3478	14	158	49	361	36	22	53	5	78	159	51	7308	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	0	0	0	.	0	.	0	0
	total	3447	22	2516	37	4043	26	24382	9	1656	28	6927	17	0	.	1713	34	10925	12	3638	23	10480	14	5380	19	1493	40	2116	31	11510	14	1998	26	2502	23	5497	19	1928	30	15697	11	3327	24	12298	12	1537	35	1275	41	6227	17	142513	3
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	1836	27	0	.	58	72	5715	15	617	41	1146	37	4	75	175	73	0	.	1212	34	1997	24	563	47	362	100	211	71	1825	31	868	38	1172	35	120	68	2161	23	0	.	34	100	1213	31	0	.	67	100	3770	17	25124	7
	fir	966	35	0	.	433	80	5303	15	387	48	348	59	4	100	120	98	0	.	1502	33	2772	21	511	39	0	.	260	71	1656	29	151	69	793	31	155	57	984	39	0	.	28	94	1253	29	0	.	65	100	986	25	18679	8
	pine	42	61	0	.	0	.	405	36	32	80	172	52	67	100	0	.	0	.	95	85	0	.	0	.	0	.	0	.	41	100	137	56	125	49	0	.	224	50	0	.	40	100	148	54	0	.	0	.	573	27	2102	15
	larch	190	46	0	.	0	.	171	63	15	100	34	100	0	.	0	.	0	.	22	100	22	72	41	100	0	.	18	100	95	63	0	.	20	100	57	100	27	100	0	.	0	.	254	45	0	.	40	100	212	46	1217	19
	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	135	60	0	.	9	100	241	66	252	100	0	.	0	.	0	.	0	.	4	100	40	100	0	.	0	.	0	.	344	98	0	.	6	100	4	100	5	72	0	.	0	.	56	100	0	.	0	.	62	57	1157	40
	beech	3113	18	0	.	39	68	4707	15	1201	26	1595	30	0	.	282	56	0	.	1311	29	1268	28	317	40	108	100	228	66	1672	30	1254	29	1936	24	703	51	1188	24	208	78	216	86	2163	23	53	71	139	66	2518	19	26220	6
	maple	461	24	0	.	8	100	736	20	302	39	87	57	3	100	35	59	0	.	329	34	139	40	161	46	6	100	9	100	385	41	35	68	387	32	23	73	208	41	0	.	45	100	211	39	8	100	47	71	732	23	4355	9
	ash	585	32	0	.	0	.	725	33	248	53	879	39	9	100	15	100	0	.	422	35	183	37	43	82	21	100	89	96	727	36	108	61	221	37	101	77	628	31	30	74	0	.	559	31	11	89	89	72	897	29	6591	10
	oak	558	41	0	.	0	.	303	29	78	100	157	56	384	58	0	.	0	.	247	54	118	85	18	100	0	.	0	.	84	65	56	97	123	53	0	.	828	52	42	80	0	.	539	34	45	78	0	.	328	32	3909	16
	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	.	68	100	0	.	0	.	68	100
	other broadleaves	428	24	0	.	51	100	573	27	118	45	288	50	14	65	0	.	0	.	224	50	106	49	6	132	10	100	28	61	164	35	15	71	233	46	10	100	572	34	46	72	41	100	1013	39	65	76	2	100	534	30	4542	12
	indeterminable	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0
	total	8314	13	0	.	597	75	18879	10	3250	24	4707	22	485	54	627	48	0	.	5367	19	6646	16	1661	27	506	76	843	47	6993	19	2624	27	5016	18	1172	44	6825	15	327	62	403	71	7410	15	249	60	449	58	10611	11	93963	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	2617	21	2795	30	2096	30	28801	7	769	35	8462	14	4	75	3353	23	36021	6	2239	23	7023	14	4483	15	1721	34	4173	20	10647	13	1388	28	1755	26	5520	16	2572	21	7267	19	4327	21	12480	10	15359	9	1039	37	5788	14	172699	2
	fir	1398	28	609	41	1551	36	16417	9	549	37	2259	24	4	100	501	48	2105	23	3889	20	6298	14	4592	17	368	56	1259	34	3926	17	184	59	1315	22	2149	24	1377	32	1360	27	888	32	7047	13	1642	26	398	44	2054	20	64140	4
	pine	143	58	0	.	0	.	1350	28	297	35	331	55	67	100	0	.	2730	17	383	37	246	54	123	59	4	100	217	83	152	50	330	39	320	32	78	75	404	42	151	52	265	53	181	46	1918	18	0	.	840	22	10531	8
	larch	286	41	0	.	0	.	271	45	27	71	97	69	0	.	50	100	9171	9	22	100	28	59	41	100	0	.	18	100	319	45	36	59	20	100	57	100	70	60	4889	13	0	.	492	31	9470	12	40	100	285	37	25688	6
	Arolla pine	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	1769	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	1047	22	0	.	0	.	2836	13
	other conifers	198	50	0	.	14	74	561	41	252	100	0	.	0	.	3	100	42	35	11	74	352	71	8	100	0	.	9	91	344	98	66	100	57	58	9	72	5	72	82	98	7	100	105	57	13	58	0	.	75	49	2211	25
	beech	4603	16	323	48	494	36	14012	9	2525	19	3366	19	0	.	935	31	1978	22	3416	17	3640	17	1304	18	486	42	1366	30	5681	15	2159	21	4514	15	1924	24	1734	20	5971	12	1033	32	6732	12	920	30	314	43	4310	14	73741	3
	maple	636	22	25	75	266	42	2455	12	484	27	566	24	3	100	431	34	522	26	508	24	549	22	600	27	153	42	269	49	1802	17	101	53	730	22	380	27	473	39	389	29	216	39	1237	15	475	26	110	46	1299	18	14678	5
	ash	743	29	0	.	254	55	1362	23	343	42	1343	28	9	100	262	44	356	35	770	29	575	49	99	46	511	59	116	76	1461	23	231	46	462	27	622	37	779	27	830	32	218	56	1369	22	463	31	115	60	1279	22	14573	7
	oak	819	34	0	.	0	.	666	21	189	52	231	43	384	58	0	.	185	61	332	45	305	48	87	60	5	100	0	.	99	56	210	47	212	42	19	100	963	45	981	20	86	72	1153	24	276	33	12	100	585	25	7799	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	515	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4920	13	0	.	6	100	165	51	0	.	0	.	5605	12
	other broadleaves	684	24	0	.	75	69	1441	16	208	32	513	31	14	65	155	46	1885	20	393	34	302	28	59	37	91	51	50	39	513	25	39	48	342	37	317	33	583	33	4227	12	245	37	1514	28	1175	19	7	63	715	25	15548	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
	total	12128	11	3751	28	4750	24	67349	5	5643	16	17168	11	485	54	5689	18	57280	5	11963	12	19319	10	11396	11	3339	26	7478	17	24943	9	4744	18	9727	12	11076	13	8960	13	31074	6	7284	17	32316	7	32923	7	2036	30	17228	9	410050	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. 1195159. Birmensdorf, Swiss Federal Research Institute WSL

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