Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
total wood volume (stemwood)
ownership (2 categories) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
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	3285	15	788	37	813	35	11108	9	582	29	5695	13	19	100	3650	18	32891	5	2241	19	2243	19	2006	16	692	35	4342	14	6571	13	1439	23	2177	18	4985	14	860	26	6147	11	2994	19	11248	9	14283	8	703	31	3238	15	125000	2
	fir	1823	20	158	58	605	43	6570	11	598	36	1947	20	0	.	635	37	2227	17	2533	17	1075	26	2507	17	146	59	599	24	1721	18	114	47	1223	20	1411	19	330	51	1495	21	534	33	4978	11	1553	20	332	36	751	29	35865	4
	pine	509	27	2	100	10	100	825	30	357	31	187	57	48	100	0	.	3014	13	542	30	124	46	181	48	0	.	143	68	220	49	210	36	380	25	187	52	188	43	286	52	149	51	193	36	2032	15	0	.	539	24	10326	7
	larch	233	39	3	100	0	.	227	33	14	73	52	60	0	.	40	87	8534	8	27	69	8	73	24	100	0	.	27	74	403	40	76	78	98	53	56	71	20	78	4153	10	0	.	470	30	7338	8	32	100	127	36	21961	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1696	14	0	.	0	.	0	.	0	.	48	100	22	100	0	.	0	.	0	.	0	.	8	70	32	100	0	.	1033	17	0	.	0	.	2840	10
	other conifers	152	33	5	100	0	.	124	61	166	72	56	100	0	.	0	.	71	33	-2	233	160	71	4	100	0	.	2	100	8	57	34	100	13	126	3	73	45	98	28	97	11	57	346	45	17	56	0	.	34	70	1277	20
	beech	3561	12	70	61	145	49	6782	9	2430	17	1871	19	0	.	757	25	1144	19	3207	14	765	27	1089	18	239	42	1074	28	2218	16	1784	17	4067	11	1430	21	808	25	4782	11	189	44	4588	11	668	25	114	48	2350	16	46133	3
	maple	479	17	53	98	56	65	962	14	367	25	268	26	0	.	287	28	188	27	486	21	139	26	295	29	53	40	246	45	439	25	90	45	393	23	219	27	211	44	213	26	82	41	854	15	182	28	72	45	612	22	7246	5
	ash	880	19	2	100	51	77	621	24	352	35	639	26	0	.	106	44	203	24	553	24	72	48	65	45	154	76	99	45	519	24	153	35	291	31	176	43	331	30	459	26	47	47	920	22	132	37	105	49	944	20	7871	6
	oak	588	25	0	.	0	.	455	27	247	41	187	37	145	58	0	.	56	62	177	33	52	78	103	61	0	.	19	100	80	46	255	41	212	30	25	97	155	42	351	28	5	100	1022	24	153	26	28	81	514	23	4830	8
	sweet chestnut	3	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	292	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2052	15	0	.	6	71	42	55	0	.	0	.	2398	14
	other broadleaves	566	23	10	83	4	100	626	20	235	32	370	46	25	71	83	34	1293	14	346	24	107	47	82	33	63	56	122	36	313	30	45	41	270	27	156	29	527	53	2386	10	118	44	956	24	936	16	19	68	422	25	10079	6
	indeterminable	3	100	0	.	1	71	41	46	0	.	2	77	0	.	9	74	94	30	12	88	5	72	5	100	0	.	14	68	23	66	0	.	23	76	14	72	1	100	23	43	22	66	44	39	80	31	0	.	18	69	432	14
	total	12081	8	1092	33	1684	32	28341	6	5347	14	11275	11	237	53	5566	16	51704	4	10122	10	4751	16	6361	12	1348	28	6734	13	12538	10	4200	15	9147	10	8661	12	3474	17	22384	5	4184	16	25624	6	28448	5	1405	25	9550	10	276259	1
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	495	43	1119	33	2455	24	23573	7	203	43	3702	17	0	.	407	42	6785	12	1134	23	5740	14	2936	18	446	41	581	43	4986	14	261	45	184	45	1704	24	2179	23	236	37	1045	31	5182	13	2591	17	386	46	3620	15	71950	3
	fir	458	43	369	36	898	34	11225	9	127	42	903	33	0	.	41	59	115	60	1179	24	4833	13	1776	22	110	75	268	69	2724	18	0	.	537	31	634	35	760	29	14	100	208	44	1831	21	142	49	267	45	1725	19	31144	5
	pine	56	64	20	100	0	.	447	26	152	78	190	64	24	81	0	.	168	40	10	74	58	73	17	100	0	.	0	.	145	54	43	75	156	51	0	.	208	37	120	75	50	72	46	55	492	30	0	.	563	25	2964	12
	larch	93	67	6	100	67	100	109	49	0	.	22	77	0	.	0	.	716	27	0	.	131	99	0	.	0	.	0	.	73	63	10	73	0	.	1	100	51	63	170	52	0	.	103	57	3205	18	0	.	160	43	4915	13
	Arolla pine	0	.	0	.	0	.	50	76	0	.	0	.	0	.	0	.	10	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	161	44	0	.	0	.	220	36
	other conifers	6	100	0	.	5	79	169	47	0	.	0	.	0	.	1	100	6	100	2	74	9	91	0	.	6	100	0	.	165	108	31	100	0	.	0	.	9	85	0	.	0	.	13	100	1	100	3	100	34	48	458	43
	beech	1995	24	209	40	827	33	6764	11	1135	31	2245	21	0	.	355	45	558	37	1176	23	2485	17	762	25	213	41	305	64	2952	15	292	42	1519	24	451	35	827	23	1191	24	375	44	2580	17	281	40	251	49	2586	16	32332	5
	maple	200	33	20	89	103	62	1175	14	80	56	297	30	9	78	175	41	187	51	182	31	360	22	103	39	73	36	73	44	731	18	2	100	202	32	193	34	314	37	157	34	65	50	501	22	178	27	27	62	349	22	5757	6
	ash	300	31	86	65	238	42	974	21	154	41	557	29	16	100	149	48	243	47	468	30	717	38	32	70	135	79	10	51	872	20	1	100	176	40	380	38	675	26	569	21	72	73	803	25	296	28	103	70	613	20	8638	7
	oak	385	34	0	.	13	100	294	24	157	72	86	52	284	59	0	.	180	50	104	81	196	44	8	100	3	100	0	.	148	73	140	69	113	53	10	100	422	48	605	20	48	60	656	28	95	47	0	.	146	42	4093	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	106	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3794	13	0	.	80	100	94	78	0	.	0	.	4073	13
	other broadleaves	496	51	4	61	57	57	857	21	59	39	93	32	19	70	90	56	607	23	118	31	163	31	157	81	33	66	15	65	255	22	2	100	65	66	146	42	245	38	2199	14	84	43	599	29	636	21	5	100	231	25	7236	8
	indeterminable	6	60	0	.	6	100	141	27	10	80	5	100	0	.	0	.	63	63	0	.	19	68	5	100	0	.	0	.	21	64	0	.	6	83	5	100	0	.	9	60	0	.	7	77	13	55	4	100	0	.	319	19
	total	4492	17	1833	28	4668	20	45777	5	2076	25	8099	14	352	56	1218	30	9743	10	4372	16	14709	10	5796	15	1019	32	1252	37	13072	10	781	38	2959	20	3524	19	5689	15	9064	9	1947	24	12401	10	8183	11	1047	34	10026	11	174098	2
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	3780	14	1907	24	3268	20	34681	5	785	24	9397	10	19	100	4057	17	39677	5	3375	14	7983	11	4942	12	1138	27	4923	13	11557	10	1700	21	2361	17	6689	12	3039	18	6383	10	4040	16	16430	7	16873	7	1089	26	6857	11	196949	2
	fir	2281	18	527	31	1503	27	17795	7	725	30	2851	17	0	.	675	34	2342	16	3712	13	5908	12	4283	13	256	47	866	27	4445	13	114	47	1760	17	2045	17	1090	25	1510	21	742	26	6809	10	1695	19	600	28	2476	15	67010	3
	pine	565	25	22	90	10	100	1272	22	509	32	377	43	72	72	0	.	3183	13	551	30	181	39	198	45	0	.	143	68	365	36	252	32	535	23	187	52	396	28	406	43	199	42	239	31	2524	14	0	.	1102	17	13289	6
	larch	326	34	9	75	67	100	336	27	14	73	74	48	0	.	40	87	9250	8	27	69	139	93	24	100	0	.	27	74	476	35	86	69	98	53	57	70	70	50	4323	9	0	.	574	27	10543	7	32	100	287	28	26876	4
	Arolla pine	0	.	0	.	0	.	50	76	0	.	0	.	0	.	0	.	1706	14	0	.	0	.	0	.	0	.	48	100	22	100	0	.	0	.	0	.	0	.	8	70	32	100	0	.	1194	16	0	.	0	.	3060	10
	other conifers	158	32	5	100	5	79	293	37	166	72	56	100	0	.	1	100	77	31	0	2109	169	67	4	100	6	100	2	100	173	102	65	71	13	126	3	73	54	83	28	97	11	57	358	43	18	55	3	100	68	42	1735	19
	beech	5556	11	279	34	972	29	13546	7	3565	15	4116	14	0	.	1112	22	1702	18	4383	12	3250	14	1852	14	452	29	1379	26	5171	11	2076	16	5586	10	1881	18	1635	17	5972	9	563	33	7168	9	948	21	365	37	4936	11	78465	2
	maple	679	15	73	75	159	46	2137	10	446	22	566	20	9	78	462	23	375	29	667	17	498	17	398	24	126	27	319	36	1170	14	92	44	596	18	412	21	525	28	371	21	147	32	1355	13	360	19	99	37	961	16	13003	4
	ash	1180	16	88	64	289	37	1595	16	505	27	1196	19	16	100	255	33	446	28	1021	19	789	34	97	38	289	55	109	41	1391	15	154	35	467	24	556	29	1006	20	1027	16	119	48	1723	16	428	22	208	43	1556	14	16509	5
	oak	973	20	0	.	13	100	749	19	405	37	272	30	429	43	0	.	236	41	281	36	248	38	110	58	3	100	19	100	228	50	395	36	325	27	34	75	577	37	956	16	54	54	1679	18	248	24	28	81	660	20	8923	7
	sweet chestnut	3	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	398	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5846	10	0	.	86	93	136	56	0	.	0	.	6471	9
	other broadleaves	1062	27	14	61	62	53	1482	15	293	26	463	37	44	51	173	33	1900	12	465	19	270	26	240	54	96	43	137	33	567	19	47	40	336	25	302	25	772	38	4585	8	202	31	1555	18	1572	13	25	57	654	18	17316	4
	indeterminable	9	52	0	.	7	85	182	23	10	80	6	76	0	.	9	74	157	31	12	88	24	55	10	71	0	.	14	68	44	46	0	.	30	62	19	59	1	100	32	35	22	66	51	35	92	28	4	100	18	69	752	11
	total	16573	8	2925	21	6352	17	74118	4	7422	12	19374	8	589	40	6784	14	61447	4	14494	8	19460	8	12157	9	2366	21	7986	12	25609	7	4981	14	12106	9	12185	10	9164	11	31448	3	6130	13	38025	5	36631	5	2453	20	19576	7	450357	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. 1308145. Birmensdorf, Swiss Federal Research Institute WSL

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