Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2
growing stock (stemwood)
forest type (17 classes)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
	forest district (2024)
	AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
forest type (17 classes)	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	±%	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	±%	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	±%	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
inaccessible forest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
shrub forest	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
permanently unstocked forest area	254	61	4	.	42	.	122	.	18	.	57	100	1182	48	737	39	73	85	162	54	135	76	21	.	261	74	0	.	0	.	179	60	495	64	77	89	0	.	210	74	18	.	151	89	37	100	23	73	206	52	222	50	541	72	0	.	59	71	51	.	0	.	0	.	0	.	0	.	0	.	213	71	0	.	190	.	41	.	0	.	28	.	58	60	278	55	13	.	186	52	0	.	0	.	487	67	128	88	28	.	306	71	0	.	0	.	0	.	41	81	0	.	48	72	158	100	0	100	0	.	80	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	0	.	0	.	1	.	20	.	139	80	0	.	32	.	38	.	59	83	56	.	402	71	408	63	294	71	135	49	55	.	120	.	0	.	99	88	53	77	23	96	0	.	0	.	0	.	9558	12
temporarily unstocked forest area	0	.	0	.	0	.	0	.	0	.	32	100	96	85	0	.	19	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	210	72	2	.	57	74	204	64	17	100	0	.	32	100	37	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	113	75	0	.	0	.	0	0	0	0	0	.	54	100	0	.	0	.	0	.	0	.	0	.	20	73	0	.	46	.	0	.	0	.	0	.	0	.	0	.	14	.	13	.	0	.	24	.	0	.	0	.	0	.	0	.	0	.	11	74	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	8	92	165	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1240	23
aisle, embankment	0	.	0	.	18	.	13	.	0	.	0	.	73	74	98	.	21	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	.	15	.	0	.	0	.	0	.	28	100	62	99	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	0	.	0	.	0	.	0	.	37	.	0	.	0	.	52	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	0	.	36	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	59	0	.	28	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	686	25
permanently open stand	0	.	0	.	0	.	0	.	28	.	33	.	675	28	51	86	6	.	726	38	122	.	0	.	9	.	0	.	0	.	21	100	61	66	108	64	0	.	159	57	679	25	188	40	657	30	603	28	1329	19	375	39	0	.	0	.	77	77	0	.	155	45	0	.	74	74	189	91	73	54	111	50	174	54	0	.	136	78	511	33	0	.	34	.	0	.	0	.	0	.	0	.	0	.	148	71	212	47	0	.	33	.	0	.	0	.	337	41	31	77	182	60	14	89	0	.	105	85	171	47	111	47	0	.	115	62	72	76	28	.	92	65	176	48	196	47	274	.	0	.	106	58	0	.	18	.	347	34	108	67	0	.	102	64	0	.	0	.	11	.	0	.	819	23	184	37	256	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11613	7
(nut) orchard, plantation	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	.	0	.	0	.	0	.	275	52	0	.	0	.	0	.	177	60	162	80	0	.	360	51	0	.	0	.	0	.	0	.	72	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1213	26
coppice forest	0	.	0	.	0	.	0	.	29	.	0	.	162	100	80	92	80	.	42	.	0	.	13	.	0	.	0	.	0	.	0	.	0	.	9	.	124	72	0	.	2	.	0	.	10	81	91	77	0	.	0	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	62	.	33	.	38	.	0	.	0	.	0	.	0	.	0	.	0	.	16	81	0	.	1	.	345	40	310	42	856	23	21	.	115	44	546	33	0	.	21	.	4	.	0	.	0	.	2	.	0	.	0	.	0	.	18	.	0	.	0	.	123	71	0	.	31	.	33	.	0	.	0	.	0	.	115	60	38	73	323	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3791	12
coppice with standards	0	.	272	43	149	71	266	63	0	.	0	.	76	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	0	.	319	50	0	.	17	.	0	.	0	.	317	43	55	71	93	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	108	.	0	.	0	.	0	.	159	73	0	.	0	.	0	.	0	.	50	81	116	71	83	59	148	63	285	51	147	36	245	47	950	21	227	53	155	58	493	34	0	.	0	.	0	.	0	.	0	.	12	.	0	.	0	.	0	.	0	.	0	.	0	.	35	.	136	.	72	.	49	.	0	.	0	.	62	.	0	.	21	.	40	.	0	.	0	.	0	.	0	.	19	.	125	73	205	58	97	.	0	.	5622	10
selection type high forest	0	.	0	.	0	.	0	.	0	.	0	.	1561	21	1430	26	217	71	46	.	0	.	0	.	0	.	0	.	0	.	60	71	203	67	0	.	0	.	91	71	582	34	261	42	729	37	458	34	514	31	197	65	0	.	162	71	223	51	67	.	97	75	47	.	128	59	258	55	54	.	0	.	0	.	0	.	0	.	494	40	311	61	0	.	0	.	0	.	0	.	0	.	0	.	67	63	54	.	208	56	0	.	0	.	0	.	203	49	497	37	403	36	13	92	0	.	0	.	108	67	0	.	0	.	0	.	0	.	60	71	156	77	0	.	380	53	0	.	0	.	51	.	0	.	0	.	430	37	14	.	208	65	0	.	0	.	0	.	0	.	0	.	180	44	983	25	848	25	255	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13307	7
non-uniform high forest	0	.	335	63	64	.	77	.	404	54	855	42	2325	22	2679	20	1027	31	704	32	0	.	61	.	154	77	0	.	0	.	605	50	1028	32	440	57	0	.	424	41	203	45	467	34	807	36	1167	27	703	26	1190	27	244	64	468	55	1074	36	140	59	216	60	82	.	345	49	205	75	352	46	207	55	307	43	118	.	133	.	612	39	477	40	553	48	0	.	66	.	273	51	36	.	0	.	377	52	264	51	56	.	229	64	0	.	0	.	0	.	380	39	382	52	177	52	735	31	123	55	498	30	80	84	146	85	39	.	0	.	181	72	102	73	428	43	330	47	60	.	0	.	101	73	0	.	0	.	186	64	0	.	0	.	0	.	0	.	126	.	0	.	38	.	1627	24	1088	27	985	27	37	.	63	.	110	80	282	64	171	50	82	.	0	.	3	.	0	.	31109	5
young growth/ thicket	53	97	57	67	116	49	1	100	8	.	35	98	267	43	51	54	275	42	84	67	0	.	2	72	71	.	0	.	63	81	19	.	12	100	14	74	0	.	146	50	337	40	45	74	240	43	425	33	129	57	102	71	7	88	117	76	175	63	56	79	-1	.	0	.	0	.	0	.	0	.	173	72	97	66	102	69	71	65	87	87	55	73	46	.	16	100	18	71	9	86	19	86	60	77	18	89	98	54	3	.	39	86	29	92	47	72	0	.	6	100	0	.	0	.	0	.	3	.	50	55	87	87	18	85	20	96	34	94	31	54	54	69	36	100	36	76	34	100	0	.	17	78	0	.	15	89	0	.	9	.	0	.	85	100	0	.	0	.	0	.	0	.	83	53	68	58	106	47	17	59	1	100	61	73	61	74	0	.	27	93	8	100	76	71	0	.	4934	9
poletimber	368	33	337	37	572	36	559	33	39	72	117	64	937	24	708	35	2030	20	965	27	134	90	263	49	220	51	0	.	214	52	496	39	482	40	331	42	65	.	492	49	481	40	243	44	306	30	1740	18	596	23	1297	24	440	38	482	36	559	38	118	60	91	83	190	55	0	.	171	74	333	38	568	41	330	44	587	36	417	37	414	37	343	49	438	38	457	36	122	51	234	40	207	48	147	71	246	52	387	44	191	59	582	35	275	45	386	52	104	43	365	31	38	60	567	50	266	30	74	68	1111	25	709	32	418	29	461	36	193	51	227	59	77	60	159	51	9	.	110	60	144	65	397	39	61	77	100	68	261	40	51	60	248	60	352	45	73	58	250	60	0	.	59	.	930	20	678	24	691	27	482	41	171	51	241	55	283	45	302	41	245	44	26	.	299	49	0	.	33939	4
young timber	1626	22	528	39	715	35	584	38	779	37	903	41	3411	19	2141	21	2420	21	2951	18	116	77	610	39	497	44	53	.	639	40	985	36	2133	26	850	35	0	.	1252	38	983	31	1151	27	270	51	2274	19	3066	17	3716	17	945	35	526	42	869	34	144	59	249	57	352	51	123	61	117	59	671	39	466	50	1142	29	826	33	712	42	618	37	224	52	771	48	1016	29	723	34	748	35	335	47	1834	22	769	34	739	46	396	41	472	51	298	52	486	44	0	.	121	75	72	.	154	71	80	66	216	58	363	37	206	52	533	46	116	69	227	89	720	42	468	36	100	62	926	38	485	43	225	93	889	30	339	53	410	43	400	40	131	49	534	33	593	35	492	38	155	.	0	.	170	74	1630	22	681	30	1776	22	303	52	352	54	215	58	913	32	582	40	154	73	271	71	175	75	0	.	67400	4
medium timber	1581	25	813	38	689	39	2554	22	560	48	1676	29	6892	15	5301	16	6807	14	3353	19	654	38	754	38	1664	26	0	.	1093	37	905	39	1835	28	2187	26	51	.	1107	36	1182	30	1853	23	1992	23	4287	16	3187	16	4889	15	1319	32	1847	27	2768	23	311	42	995	30	667	46	165	58	1797	26	749	43	715	43	1469	35	1977	28	1234	37	1244	32	824	39	2973	24	1470	27	1535	27	664	40	1256	31	806	34	889	33	1698	31	561	44	1271	32	954	38	914	33	324	57	369	43	379	48	32	.	260	57	75	.	222	50	381	41	289	50	402	59	163	.	361	51	509	50	432	58	1055	35	1096	38	289	63	871	34	1002	35	605	41	594	37	652	39	822	34	937	31	174	75	486	51	478	47	1538	33	2085	20	2147	22	1823	23	921	36	835	40	1942	26	1465	29	1907	26	871	37	890	36	685	42	0	.	121302	3
old timber	1317	30	1226	31	397	53	1990	28	371	59	1490	34	4856	18	7028	15	7450	13	1313	34	748	41	269	61	422	54	0	.	768	47	839	37	403	72	1089	35	0	.	1321	31	2431	23	1757	27	1798	25	3179	19	1796	25	1515	28	2212	26	2618	23	1889	30	475	54	630	39	303	61	390	58	1095	31	76	.	80	.	670	49	870	43	241	.	1136	37	1131	38	2988	25	639	39	1173	40	286	59	555	46	487	48	555	49	632	45	1328	35	721	46	724	47	1154	32	232	58	751	37	294	54	109	71	61	.	168	56	507	36	48	.	234	71	362	53	593	46	350	59	718	40	529	47	459	49	650	46	96	.	230	82	487	48	453	51	27	.	779	39	847	35	317	54	153	.	510	60	152	.	340	53	2661	20	446	47	1696	26	388	77	660	42	673	45	904	39	991	35	699	39	806	40	856	40	0	.	92070	4
total	5199	13	3574	17	2760	18	6165	14	2235	22	5197	17	22512	8	20305	8	20425	7	10353	10	1910	24	1993	22	3298	18	53	.	2776	22	4116	17	6672	14	5104	16	560	36	5236	15	7141	11	6117	12	6903	12	14768	8	11641	8	13658	8	5738	16	6264	14	7704	13	1361	24	2433	18	1642	26	1226	26	3833	17	2321	20	2646	19	4214	17	4668	17	2984	21	5202	14	3393	19	7953	14	4055	15	3649	18	2513	18	2440	21	3560	16	3575	18	4213	17	2817	20	3713	18	2330	23	3103	18	1299	22	2985	15	2050	19	1617	22	2115	17	2773	13	3441	13	1972	17	3038	16	1514	24	1303	30	1975	23	2209	20	1931	22	3408	18	2738	23	755	41	2697	18	1907	24	1601	24	2266	16	2041	21	2795	18	2520	17	1011	28	1586	29	697	40	2609	24	10645	9	6637	11	8871	9	2459	22	2202	22	3242	19	4008	17	4025	17	2225	21	2205	23	2191	23	0	.	397785	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2293470. Birmensdorf, Swiss Federal Research Institute WSL

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