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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NFI1
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 1983/85
	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	188	57	69	64	63	72	181	71	7	.	0	.	965	27	671	37	66	81	100	57	60	89	108	72	164	66	0	.	0	.	138	56	455	52	1	.	0	.	259	53	74	86	71	64	220	84	45	84	174	52	321	36	79	71	149	71	45	80	142	.	0	.	0	.	0	.	127	74	0	.	286	57	31	73	43	.	0	.	20	71	112	77	147	70	208	59	49	79	183	56	0	.	0	.	149	48	27	79	-1	.	245	65	0	.	29	.	0	.	20	73	10	69	40	62	0	.	24	70	4	.	95	83	0	.	18	100	0	.	0	.	0	.	28	.	56	.	0	.	0	.	91	71	0	.	46	.	13	.	15	.	0	.	24	.	38	.	51	84	40	.	144	71	167	50	85	57	180	62	1	.	136	.	0	.	125	93	0	.	101	100	180	71	0	.	0	.	8201	9
temporarily unstocked forest area	0	.	0	.	0	.	0	.	0	.	0	.	27	75	0	.	31	100	14	.	56	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	74	0	.	12	.	91	98	132	99	0	.	2	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	152	.	106	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	.	0	.	0	.	0	.	0	.	0	.	0	.	54	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	.	0	.	0	.	14	100	9	100	86	.	50	.	48	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1052	27
aisle, embankment	0	.	0	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	17	.	18	100	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	47	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	0	.	0	.	42	100	24	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	254	35
permanently open stand	0	.	0	.	0	.	0	.	20	.	37	100	597	25	165	53	5	.	371	41	0	.	0	.	9	.	0	.	0	.	65	89	178	69	44	69	0	.	340	43	635	30	391	54	178	46	727	24	635	26	230	40	10	.	1	.	86	61	19	.	369	50	0	.	40	100	328	58	144	59	53	66	120	60	0	.	48	78	267	47	93	.	149	71	0	.	44	.	0	.	0	.	0	.	141	59	196	43	36	.	0	.	0	.	0	.	157	59	205	43	148	63	0	.	0	.	24	84	208	61	59	68	126	88	41	.	0	.	47	83	281	44	223	39	157	52	0	.	0	.	59	69	0	.	14	.	359	39	50	85	136	75	49	.	0	.	0	.	8	.	0	.	495	32	293	33	551	40	0	.	15	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10474	7
(nut) orchard, plantation	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	122	58	0	.	0	.	0	.	133	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	109	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	67	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	477	32
coppice forest	137	82	20	.	0	.	0	.	0	.	0	.	0	.	31	.	62	.	0	.	0	.	53	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	51	30	71	105	.	0	.	0	.	0	.	25	.	0	.	0	.	0	.	0	.	47	.	0	.	0	.	0	.	47	.	0	.	0	.	0	.	0	.	0	.	82	73	0	.	0	.	0	.	0	.	0	.	14	.	0	.	0	.	10	71	0	.	0	.	275	35	255	44	800	20	38	.	48	74	628	29	0	.	0	.	5	.	0	.	36	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	.	38	83	0	.	0	.	0	.	0	.	61	79	91	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3137	12
coppice with standards	885	30	596	39	0	.	223	56	0	.	0	.	110	68	73	.	93	.	0	.	155	58	147	74	217	62	0	.	0	.	0	.	0	.	0	.	186	45	120	63	30	.	117	67	162	59	246	42	8	80	497	43	93	.	67	.	0	.	0	.	0	.	28	.	0	.	0	.	0	.	0	.	0	.	0	.	210	62	29	.	0	.	0	.	580	32	0	.	63	.	103	71	639	34	89	.	11	.	86	79	26	.	293	49	131	79	60	71	173	68	161	57	202	42	730	25	880	24	115	60	357	32	27	61	0	.	0	.	55	.	133	61	159	71	210	57	0	.	3	.	0	.	0	.	0	.	0	.	156	51	83	.	36	.	111	61	0	.	0	.	41	.	56	72	52	75	232	46	0	.	0	.	115	77	31	.	158	71	284	57	238	52	407	51	0	.	11579	7
selection type high forest	0	.	0	.	0	.	0	.	0	.	100	83	2578	20	3349	17	668	37	396	39	0	.	0	.	0	.	0	.	226	58	103	55	201	50	41	.	0	.	235	53	1095	25	640	33	1392	28	1570	21	2880	15	237	51	131	61	318	52	564	43	0	.	661	32	137	71	82	72	987	29	185	71	481	42	415	38	0	.	18	.	498	35	501	43	142	55	0	.	35	.	0	.	0	.	0	.	0	.	211	62	154	70	0	.	0	.	41	.	203	44	272	45	139	50	75	70	11	.	0	.	247	43	30	.	53	71	0	.	59	.	343	45	221	72	45	.	196	76	0	.	0	.	135	56	0	.	48	.	575	31	47	.	138	58	0	.	0	.	0	.	0	.	74	.	1320	22	1695	19	766	26	147	.	0	.	0	.	86	.	48	71	0	.	0	.	0	.	0	.	28247	5
non-uniform high forest	159	64	461	37	117	73	0	.	93	72	280	73	1210	26	1166	27	891	31	570	33	144	69	13	.	130	74	0	.	363	48	1045	33	478	52	540	44	0	.	385	50	618	36	339	47	492	40	1589	22	1377	23	354	40	257	56	314	46	289	48	282	44	404	41	20	.	278	46	398	52	163	77	365	48	471	39	124	73	465	43	565	39	539	41	672	39	0	.	64	.	524	37	354	43	0	.	239	52	307	58	25	.	473	40	116	.	47	.	33	72	148	56	151	52	58	67	101	85	32	77	784	31	260	43	166	44	178	72	0	.	97	71	230	52	386	42	469	40	0	.	101	.	125	71	0	.	0	.	255	44	900	30	601	40	0	.	19	.	59	.	0	.	77	.	1944	19	1422	25	714	28	282	53	0	.	80	.	200	59	18	.	83	.	0	.	195	60	0	.	30737	5
young growth/ thicket	98	68	76	69	64	54	111	71	0	.	129	97	510	33	81	61	204	40	126	77	0	.	120	73	8	.	0	.	0	100	45	71	18	81	72	63	0	.	43	57	74	58	12	.	109	58	215	54	135	52	50	95	32	87	78	65	59	60	34	.	23	100	0	.	0	.	0	.	2	100	104	.	2	100	63	64	30	70	73	100	96	80	17	.	68	61	63	61	0	.	8	91	54	85	196	84	127	61	13	.	53	61	114	92	47	67	13	69	37	65	3	100	13	93	7	.	15	.	9	78	19	100	31	68	0	.	1	.	4	73	14	.	27	96	0	.	0	.	3	100	16	94	0	.	0	.	0	.	8	64	0	.	0	.	0	.	0	.	0	.	0	.	128	47	53	48	217	44	51	84	0	.	47	69	16	61	1	100	43	92	1	.	64	75	0	.	4494	10
poletimber	473	32	160	46	797	31	376	39	144	72	248	54	663	30	402	38	1348	21	1032	25	0	.	207	48	182	54	73	.	57	72	252	53	503	35	349	45	26	.	525	41	177	46	237	42	82	47	937	24	288	32	628	28	241	64	208	60	289	44	29	71	55	.	74	82	0	.	34	.	161	47	325	48	271	47	404	41	118	65	386	41	200	56	195	42	638	32	32	93	64	64	275	41	364	43	338	35	273	53	124	58	232	43	174	65	60	71	28	72	119	38	41	67	141	44	28	60	76	.	467	27	316	43	292	41	278	41	110	55	245	68	92	64	0	.	19	.	601	45	21	.	85	64	235	54	213	52	181	42	63	71	572	31	282	42	63	96	170	60	0	.	193	72	885	23	502	27	524	33	166	51	167	67	180	53	329	45	311	44	105	62	45	71	172	46	0	.	24048	5
young timber	1315	26	537	42	320	46	1237	27	589	47	1073	36	3043	19	1606	25	1530	24	1722	24	82	72	145	72	623	36	0	.	262	71	619	45	1649	27	1284	31	54	.	1025	35	266	56	570	39	115	.	1045	32	1526	26	2564	19	362	47	630	35	1854	24	127	58	179	75	600	46	66	87	588	50	472	43	229	73	728	35	549	39	405	60	716	39	497	45	967	38	1261	30	552	47	681	38	221	67	1247	26	303	48	486	51	416	51	291	59	174	59	507	44	0	.	284	45	220	67	163	50	223	43	186	58	165	45	93	88	323	53	306	50	190	91	624	39	307	43	176	.	461	41	650	39	318	65	397	39	329	53	246	52	502	39	248	50	745	34	762	30	358	48	175	71	129	71	207	63	1068	29	529	40	1519	24	205	71	406	51	230	71	823	38	432	41	333	53	540	46	102	.	0	.	54883	4
medium timber	946	35	1081	30	780	35	2412	23	1403	32	2184	27	6402	14	7047	14	7882	13	2992	20	358	52	660	39	946	31	0	.	1193	36	942	38	1150	36	1442	32	103	.	1269	32	1697	28	1535	23	2123	21	3565	17	1895	22	4369	15	2754	22	3571	20	2489	24	546	42	482	46	506	47	176	58	606	39	1011	35	583	46	1354	37	1961	27	588	43	1991	27	678	41	1785	28	457	41	1221	29	680	34	926	34	627	36	1157	30	1394	31	570	40	1359	31	910	40	800	34	381	48	310	41	522	32	41	.	285	54	0	.	365	40	261	52	199	71	356	62	240	61	283	60	420	56	292	56	1062	33	1081	36	110	.	495	39	587	43	982	34	202	64	277	59	206	73	819	34	245	61	349	70	191	58	1221	34	1458	24	979	29	1733	21	808	37	1191	34	1896	24	1699	27	2127	24	683	38	747	41	1106	36	0	.	113770	3
old timber	622	42	921	40	335	53	1066	34	273	75	883	42	4572	17	3715	20	6550	14	1542	27	658	42	402	51	278	60	0	.	141	.	398	59	1281	37	988	40	0	.	885	41	2045	26	1592	28	2009	25	2851	19	1810	24	2829	20	1099	36	696	39	638	48	143	78	181	76	193	71	467	48	484	53	0	.	286	80	524	46	942	40	656	58	904	42	452	58	2214	28	426	47	1621	30	166	73	153	.	97	.	484	51	773	42	1482	31	199	.	492	51	1065	31	167	62	864	32	273	56	23	.	0	.	119	.	267	49	57	.	403	46	127	71	385	58	189	75	109	.	217	72	269	76	264	73	89	.	734	39	475	46	95	.	47	.	0	.	127	.	392	50	0	.	330	65	127	.	77	.	1865	22	66	.	1607	27	563	52	313	58	623	52	434	52	1021	36	463	52	602	46	318	59	0	.	69585	4
incomplete survey	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	141	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	141	.
total	4822	13	3921	16	2486	18	5607	14	2528	23	4933	17	20678	7	18306	8	19472	7	8868	10	1512	25	1855	21	2558	18	73	.	2243	24	3627	18	5912	14	4759	16	369	39	5089	15	6830	12	5523	13	6892	12	13012	8	10916	8	12184	8	5058	15	6036	14	6314	13	1346	24	2355	18	1558	26	1127	26	3553	17	2184	21	2711	20	3917	17	4086	17	2742	22	5555	14	3168	18	6287	15	3638	15	3681	18	2443	18	2040	21	3039	16	3102	17	3805	17	2905	20	2891	19	2273	23	2727	18	1052	24	2553	15	1668	19	1032	18	1640	18	2287	14	2676	14	1623	17	2248	17	1352	25	985	33	1892	22	1808	21	1697	22	2953	18	2597	22	645	42	2161	19	1625	25	1643	24	2134	16	1765	20	2609	18	2434	18	874	28	1192	31	495	39	2035	24	9441	9	5770	11	8287	10	2274	22	2276	23	3172	19	3743	17	4115	16	2095	22	2354	22	2363	22	0	.	361078	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. 2293600. Birmensdorf, Swiss Federal Research Institute WSL

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