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
total wood volume (stemwood)
ownership (2 categories) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/07
		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
ownership (2 categories)	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	.
public	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	3181	22	1250	38	1336	39	13711	11	972	47	5284	18	0	.	3737	28	26925	7	2410	25	3443	22	2066	22	586	51	2992	23	5781	18	1868	26	2069	25	4672	19	1126	27	5400	16	2979	26	9902	12	12465	10	495	46	3819	18	118469	3
	fir	1581	27	243	63	736	46	5956	13	427	47	1871	30	0	.	432	53	1790	23	2338	26	759	25	2577	21	149	71	714	28	1542	24	126	59	1207	27	1255	26	457	55	1138	31	613	40	4968	15	1018	32	121	54	486	38	32506	5
	pine	531	47	0	.	14	100	1193	32	175	49	253	70	91	100	0	.	2777	18	454	37	149	62	180	69	0	.	117	77	36	100	290	42	413	30	90	92	305	55	163	58	155	74	206	44	1538	20	0	.	747	27	9876	9
	larch	312	52	0	.	0	.	244	39	36	79	61	71	0	.	49	100	7152	11	20	100	21	89	42	100	0	.	8	100	212	48	41	71	14	100	52	100	11	100	3082	16	22	100	301	37	5166	11	55	100	197	43	17097	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1316	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	767	26	0	.	0	.	2087	16
	other conifers	95	57	0	.	0	.	32	56	153	100	0	.	0	.	0	.	30	43	3	100	229	77	7	100	0	.	3	100	6	100	0	.	29	55	3	100	1	100	42	100	5	100	196	74	10	77	0	.	7	100	850	33
	beech	3494	15	34	70	234	59	7059	12	2344	21	1384	25	0	.	846	33	1133	22	2587	19	936	31	657	23	129	84	1220	33	2259	24	1730	23	3505	16	1185	32	685	36	3659	16	443	47	4613	13	371	35	105	76	2050	18	42663	4
	maple	385	26	32	100	92	69	901	18	293	32	187	36	0	.	196	28	139	38	346	28	108	33	260	44	35	62	186	54	563	30	66	53	428	24	105	38	92	37	117	49	87	57	618	22	131	44	61	64	665	24	6093	7
	ash	770	31	0	.	82	75	510	27	307	53	499	42	0	.	168	57	91	42	405	33	113	59	81	64	243	89	30	85	554	29	172	44	243	35	192	52	259	44	223	53	41	63	761	31	115	48	50	79	1026	27	6936	9
	oak	275	51	0	.	0	.	291	33	245	48	84	52	285	64	0	.	51	98	195	43	67	75	82	71	0	.	0	.	94	60	140	54	173	54	0	.	261	43	232	40	8	100	698	28	52	42	0	.	552	28	3785	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	352	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1240	26	0	.	14	73	51	61	0	.	0	.	1656	22
	other broadleaves	404	32	4	100	13	100	590	30	280	37	332	51	5	100	130	52	1223	20	243	48	65	52	80	48	19	58	84	58	371	35	98	64	316	35	115	47	147	50	1629	16	98	57	909	33	596	24	10	71	407	40	8169	8
	indeterminable	0	.	0	.	2	74	30	59	0	.	5	60	0	.	5	100	77	38	0	.	10	100	0	.	0	.	1	100	18	83	0	.	0	.	0	.	1	100	14	72	0	.	20	65	82	51	0	.	0	.	265	22
	total	11028	12	1564	36	2509	35	30518	8	5232	18	9958	14	381	59	5564	23	43056	6	9001	14	5901	18	6033	16	1162	40	5355	18	11435	14	4531	20	8395	14	7670	16	3346	22	16941	7	4453	21	23205	9	22362	8	898	37	9956	13	250453	2
private	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	544	43	1174	44	1710	33	19923	9	115	73	3288	22	0	.	527	62	5970	19	508	34	7008	17	2686	23	631	48	854	61	4825	20	126	71	189	71	1682	36	2485	24	146	49	1079	45	3229	18	1852	24	144	96	3659	19	64354	5
	fir	363	40	245	51	745	48	9917	12	38	78	556	44	0	.	31	71	143	69	1323	30	5215	16	1163	31	184	84	349	82	2059	24	0	.	373	52	471	46	885	35	30	100	202	55	1165	31	179	52	97	59	1563	24	27297	6
	pine	286	77	0	.	0	.	457	34	127	51	357	62	50	83	0	.	288	42	9	100	64	100	0	.	0	.	0	.	40	100	65	88	51	100	0	.	204	42	0	.	42	100	23	72	617	35	0	.	581	32	3261	15
	larch	65	60	0	.	0	.	69	117	0	.	39	71	0	.	0	.	366	36	0	.	2	100	0	.	0	.	0	.	22	100	3	100	0	.	0	.	40	100	130	86	0	.	89	86	3134	26	0	.	130	85	4089	21
	Arolla pine	0	.	0	.	0	.	18	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	64	0	.	0	.	72	51
	other conifers	0	.	0	.	6	80	388	48	0	.	0	.	0	.	2	100	11	100	2	100	0	.	0	.	0	.	0	.	227	106	69	100	0	.	0	.	6	100	0	.	0	.	0	.	13	100	0	.	22	76	747	42
	beech	2218	30	201	56	349	38	7186	14	627	47	1716	29	0	.	405	65	564	49	910	36	2496	21	661	28	255	47	138	65	3087	24	344	62	1339	35	318	40	1005	27	965	30	216	54	1747	25	408	47	145	66	2340	20	29640	6
	maple	193	44	14	71	86	69	979	20	110	65	186	39	0	.	173	61	120	49	147	36	313	41	111	52	92	39	29	71	902	23	0	.	225	48	172	41	336	49	53	61	43	58	392	30	131	43	21	100	402	25	5229	9
	ash	209	62	0	.	255	61	938	28	111	65	647	41	43	100	543	80	232	51	571	47	827	61	17	87	215	71	5	100	1015	26	0	.	241	57	398	60	924	29	233	49	103	82	665	33	150	51	129	100	558	26	9028	11
	oak	525	64	0	.	0	.	346	29	3	100	119	66	380	74	0	.	56	100	181	79	334	59	16	100	4	100	0	.	29	73	86	93	23	71	15	100	607	47	626	27	70	71	536	37	132	56	0	.	76	47	4165	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3876	16	0	.	0	.	33	100	0	.	0	.	3998	16
	other broadleaves	208	46	1	100	53	85	900	23	13	73	69	47	1	100	221	95	440	32	125	51	179	47	22	63	30	100	0	.	282	38	3	100	78	87	157	53	258	52	1573	22	48	52	377	41	336	34	19	100	198	37	5593	10
	indeterminable	0	.	0	.	1	100	103	36	0	.	0	.	0	.	0	.	20	84	0	.	14	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	24	69	1	100	5	100	2	100	0	.	0	.	174	27
	total	4610	26	1636	42	3204	28	41223	7	1144	41	6977	17	474	73	1902	52	8297	15	3776	23	16452	13	4677	18	1412	36	1376	54	12493	15	697	48	2520	30	3213	27	6752	18	7656	12	1804	33	8227	15	7043	16	555	63	9529	14	157648	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	3724	20	2424	29	3047	25	33634	7	1086	43	8572	14	0	.	4264	26	32894	7	2919	21	10451	13	4753	15	1217	35	3846	22	10606	13	1995	25	2258	24	6353	17	3611	19	5546	16	4058	22	13131	10	14317	10	640	41	7478	13	182823	2
	fir	1944	23	489	40	1480	33	15873	9	465	43	2426	25	0	.	464	50	1933	22	3661	19	5974	14	3739	17	333	56	1064	33	3601	17	126	59	1580	24	1727	23	1342	30	1169	30	815	33	6133	14	1196	28	219	40	2049	20	59803	4
	pine	816	41	0	.	14	100	1650	25	302	36	610	47	141	71	0	.	3064	17	463	36	212	53	180	69	0	.	117	77	76	71	355	38	464	29	90	92	509	37	163	58	198	62	229	40	2155	17	0	.	1328	20	13137	8
	larch	377	44	0	.	0	.	314	40	36	79	100	52	0	.	49	100	7518	11	20	100	23	82	42	100	0	.	8	100	233	45	44	66	14	100	52	100	52	81	3212	16	22	100	390	35	8300	12	55	100	327	43	21186	6
	Arolla pine	0	.	0	.	0	.	18	75	0	.	0	.	0	.	0	.	1316	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	821	25	0	.	0	.	2159	15
	other conifers	95	57	0	.	6	80	419	45	153	100	0	.	0	.	2	100	41	41	5	74	229	77	7	100	0	.	3	100	233	103	69	100	29	55	3	100	8	86	42	100	5	100	196	74	23	67	0	.	29	63	1598	26
	beech	5713	15	235	49	582	32	14245	9	2972	19	3100	19	0	.	1251	30	1697	22	3497	16	3432	17	1318	18	384	42	1358	30	5345	17	2074	22	4845	15	1503	27	1690	21	4624	14	660	36	6360	12	779	29	250	50	4390	13	72303	3
	maple	578	23	46	72	178	48	1880	13	402	29	373	26	0	.	370	32	258	30	492	22	422	32	371	34	128	33	215	48	1465	18	66	53	652	23	277	29	429	39	170	39	130	43	1010	18	262	31	82	54	1066	18	11322	5
	ash	978	28	0	.	337	49	1447	20	418	42	1146	29	43	100	711	62	323	38	976	31	941	54	99	55	458	58	35	74	1569	20	172	44	484	33	590	44	1183	25	456	36	144	61	1426	23	266	35	179	75	1585	19	15964	7
	oak	801	46	0	.	0	.	637	22	248	47	203	44	665	50	0	.	107	70	376	44	400	50	98	61	4	100	0	.	123	49	226	48	196	49	15	100	869	35	858	22	78	64	1233	23	184	42	0	.	628	25	7950	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	441	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5115	13	0	.	14	73	84	54	0	.	0	.	5654	12
	other broadleaves	612	26	6	79	66	72	1490	18	293	36	400	43	6	80	351	63	1663	17	369	36	244	37	102	40	49	65	84	58	653	26	101	62	394	33	272	37	405	38	3202	13	147	42	1286	27	932	19	30	70	605	29	13762	6
	indeterminable	0	.	0	.	3	59	133	31	0	.	5	60	0	.	5	100	97	34	0	.	25	71	0	.	0	.	1	100	23	70	0	.	0	.	0	.	1	100	37	51	1	100	24	55	84	50	0	.	0	.	439	17
	total	15638	11	3200	27	5713	22	71741	5	6376	17	16935	11	854	48	7466	21	51354	5	12778	12	22352	10	10710	11	2574	27	6730	18	23927	10	5228	18	10916	13	10882	14	10097	14	24597	6	6257	18	31432	8	29405	7	1454	33	19484	9	408101	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. 1308482. Birmensdorf, Swiss Federal Research Institute WSL

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