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
volume of bolewood (HG 2010)
timber assortment classes (HG 2010) · ownership (2 categories)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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
timber assortment classes (HG 2010)	ownership (2 categories)	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	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	.
	public	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	private	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
smallwood	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	.
	public	19	27	48	32	32	32	190	31	21	75	49	47	78	25	298	22	190	23	294	20	10	47	19	34	13	24	2	.	62	41	79	47	177	32	146	35	1	58	61	22	69	18	44	15	50	14	126	10	217	11	650	15	37	68	33	49	11	29	72	37	120	38	119	41	78	32	160	29	11	35	25	22	39	22	50	45	45	68	55	26	42	51	60	75	58	36	26	31	47	47	10	28	14	24	25	21	77	36	25	36	25	63	27	57	48	46	8	24	39	19	20	21	14	28	6	28	9	25	31	13	15	19	18	21	11	30	5	37	18	26	39	36	10	25	12	27	32	49	4	45	164	38	49	49	79	30	278	32	87	36	81	31	84	36	8	48	44	50	13	49	40	54	105	10	107	30	147	20	50	55	15	34	13	36	10	58	15	29	7	30	19	51	40	57	0	.	6029	4
	private	29	69	3	41	2	62	4	39	44	59	109	36	178	15	169	21	155	25	73	40	5	56	2	51	14	62	0	.	2	52	24	56	41	48	87	49	2	58	5	32	17	26	9	32	24	29	17	24	6	37	305	23	27	25	165	27	50	34	45	88	228	28	12	66	49	52	303	26	6	34	2	.	9	39	142	35	8	51	23	54	15	47	73	31	5	45	2	47	10	70	1	68	21	41	7	33	9	40	3	34	83	45	37	67	30	45	3	54	0	.	3	47	2	53	10	20	15	19	3	39	5	35	7	29	4	41	13	59	3	57	8	39	4	48	20	32	5	40	1	72	70	47	15	67	0	.	66	54	20	64	3	59	32	66	2	55	2	60	0	.	22	69	29	19	33	33	18	28	7	39	11	62	24	46	55	32	61	43	19	80	24	80	22	92	0	.	3335	6
	total	49	43	52	30	34	31	195	30	64	47	158	29	256	13	467	16	344	17	367	18	15	37	21	32	27	35	2	.	64	40	103	38	218	28	233	28	2	43	66	20	86	15	53	13	74	13	144	9	223	11	955	12	64	41	198	24	62	28	117	41	348	22	132	37	127	28	462	20	17	26	27	22	48	20	192	29	53	58	77	24	56	40	133	38	63	34	28	29	58	41	11	27	36	26	32	18	87	32	28	32	108	38	64	46	77	33	11	22	39	19	23	19	16	25	16	16	24	15	35	13	20	17	25	17	15	24	19	44	21	24	47	30	14	22	32	22	37	42	6	39	234	30	63	41	79	30	344	28	107	32	85	30	117	32	10	39	46	48	13	49	61	43	134	9	140	24	166	18	57	48	26	33	37	32	65	29	77	35	25	60	43	50	62	49	0	.	9364	3
classes 1 and 2	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	.
	public	891	22	874	22	829	19	906	18	319	31	433	36	1859	14	1106	14	2033	12	2414	12	204	32	472	24	745	20	13	.	371	32	742	25	1103	19	851	24	112	55	1410	17	1823	13	1798	12	2186	12	5038	8	4484	8	2533	11	440	25	593	29	482	30	303	25	127	35	290	30	214	31	198	28	572	30	981	21	1299	18	233	37	693	24	1503	16	693	27	385	29	1196	16	750	20	694	22	515	22	618	22	1012	19	738	22	599	23	335	36	326	29	407	29	445	21	1443	14	856	18	890	17	489	24	579	24	1774	12	924	18	1046	17	484	32	223	34	723	23	556	22	530	23	527	26	441	31	145	45	701	23	257	33	341	31	535	20	476	28	603	23	592	24	263	40	206	39	184	48	172	44	3399	9	2514	12	3752	10	413	26	416	32	384	27	213	38	468	27	406	27	290	32	540	25	0	.	80948	2
	private	447	24	157	36	156	67	222	43	538	28	993	21	5515	8	3126	10	2286	12	785	23	77	58	103	60	265	38	0	.	173	42	693	27	776	23	550	26	79	53	320	31	737	22	414	33	966	20	765	22	311	34	930	17	1006	20	954	19	1958	14	63	62	343	32	69	74	50	60	311	31	356	34	67	.	268	35	875	21	403	39	397	31	352	31	1267	19	236	39	74	64	192	45	52	72	416	31	379	30	410	28	232	38	749	24	374	33	618	28	129	53	24	.	160	41	213	40	727	20	1333	16	307	37	352	33	565	25	312	39	312	40	129	50	262	33	237	43	908	25	263	43	66	72	373	41	117	62	39	.	235	40	167	44	128	48	237	39	141	47	144	68	0	.	177	40	1206	17	900	19	785	20	260	36	193	40	288	29	877	22	642	23	222	42	166	38	87	48	0	.	47540	2
	total	1338	16	1032	19	986	19	1128	17	857	21	1426	18	7374	7	4231	8	4319	8	3199	11	280	28	574	23	1010	18	13	.	544	25	1435	18	1879	14	1401	18	191	39	1730	15	2560	11	2212	12	3152	10	5803	7	4796	7	3463	9	1446	16	1547	16	2440	13	366	23	470	25	360	28	264	27	508	22	928	22	1048	21	1567	16	1108	19	1096	21	1900	14	1045	21	1652	16	1432	15	824	19	886	20	568	21	1034	18	1391	16	1148	17	830	20	1084	20	700	22	1025	20	575	20	1467	14	1016	16	1103	16	1216	15	1912	13	2080	12	1276	16	1611	14	796	25	535	27	852	21	818	18	767	21	1435	19	704	25	211	38	1074	20	374	30	380	30	770	18	643	24	731	20	829	20	404	31	350	36	184	48	349	30	4605	8	3414	10	4538	9	673	21	609	25	673	20	1090	19	1110	17	628	23	456	24	627	22	0	.	128488	1
classes 3 and 4	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	.
	public	1051	19	879	24	1046	20	1864	17	523	35	741	34	2467	16	2201	16	2932	12	3412	12	464	31	734	29	1005	22	67	.	557	34	892	25	1733	20	1203	24	68	61	2151	16	2412	14	2512	13	2439	14	5373	8	4874	10	4337	11	630	26	700	31	357	30	595	27	411	34	538	30	498	30	829	24	446	32	916	23	1392	18	410	37	852	26	1889	17	1178	23	821	29	1716	16	1158	21	1085	22	796	24	955	20	1376	19	929	24	1190	22	157	52	397	34	695	28	442	23	1378	16	1010	19	457	20	266	30	237	26	1253	14	489	24	730	20	383	34	297	39	671	25	745	29	857	23	762	29	466	31	134	55	1250	23	464	37	532	28	953	19	868	26	1183	20	977	22	357	44	435	37	357	41	416	36	3435	10	2281	13	3668	11	689	32	660	28	581	27	207	45	648	25	404	35	467	34	624	26	0	.	102887	2
	private	1049	25	256	36	186	50	502	36	903	29	2067	21	7509	9	5990	10	3769	13	1387	25	187	54	258	53	465	34	0	.	279	48	945	28	1218	27	961	27	163	56	530	32	1037	22	369	36	819	25	805	22	227	35	2057	16	1622	22	1849	19	2909	15	146	54	879	25	199	63	168	54	1138	25	402	33	24	.	459	43	1312	22	556	37	449	28	691	31	2834	17	307	40	242	47	296	57	115	74	718	28	567	35	600	33	310	43	1147	26	454	36	540	31	50	52	14	.	104	51	82	59	535	23	601	20	170	36	242	40	257	29	302	39	286	44	126	55	476	35	212	38	1147	24	343	42	95	55	461	41	333	44	2	.	331	36	193	52	348	51	376	40	232	48	251	51	0	.	297	46	1269	19	690	23	632	23	487	36	457	41	501	34	1344	21	980	24	401	40	356	44	343	48	0	.	70196	3
	total	2100	15	1135	20	1233	19	2366	15	1426	22	2807	18	9977	7	8191	9	6701	9	4799	11	651	27	992	25	1470	18	67	.	835	28	1837	19	2951	16	2164	18	231	43	2681	14	3449	12	2881	13	3258	12	6178	8	5101	10	6395	9	2252	17	2549	16	3266	14	742	24	1291	20	738	28	666	26	1967	17	848	23	939	22	1850	17	1722	19	1408	21	2338	15	1869	19	3656	14	2023	15	1400	19	1381	21	911	23	1673	16	1942	17	1529	20	1500	20	1303	23	851	25	1235	21	492	21	1392	16	1114	18	539	19	801	18	837	16	1423	13	732	21	987	16	685	25	582	29	797	22	1221	22	1069	20	1909	18	809	25	229	39	1711	20	796	28	534	28	1284	17	1061	23	1531	19	1354	19	589	33	686	30	357	41	713	28	4704	9	2971	11	4301	10	1176	24	1118	23	1082	21	1551	19	1627	17	805	26	824	27	967	24	0	.	173084	1
classes 5 and 6	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	.
	public	422	28	431	29	360	26	624	25	123	54	113	40	815	24	875	23	1273	17	776	21	154	44	301	40	293	42	20	.	186	45	173	36	348	27	502	37	13	100	904	23	758	24	1011	20	1185	17	1965	13	1231	16	817	19	318	34	183	46	57	47	208	39	96	54	94	44	125	42	144	41	50	56	277	30	553	27	200	41	203	30	517	24	484	32	520	34	404	26	613	26	107	37	249	31	199	34	571	27	157	38	588	32	3	100	112	55	438	52	346	31	768	22	409	27	208	57	82	55	117	44	431	19	203	43	281	32	100	46	232	47	302	43	225	36	477	30	226	34	219	47	27	79	285	33	205	49	82	54	154	35	255	36	224	29	226	39	14	77	143	45	154	56	154	48	1282	16	533	20	981	20	148	41	389	32	374	47	137	40	229	43	141	48	105	47	213	35	0	.	34220	3
	private	254	29	126	44	28	67	260	43	138	37	690	31	2543	12	2109	13	1251	17	207	28	144	62	47	71	212	46	0	.	63	69	254	38	390	34	196	36	32	88	194	41	365	25	228	70	225	30	327	34	59	48	402	24	514	30	398	28	844	20	73	75	112	48	26	90	99	85	343	37	132	38	14	.	204	52	342	35	144	63	228	38	217	37	1227	26	94	63	171	54	18	82	23	79	158	53	238	53	156	38	91	77	208	37	238	58	133	46	21	83	25	.	40	59	22	83	285	62	98	42	278	48	54	54	165	41	112	48	15	93	27	59	197	44	119	62	439	33	138	53	3	100	8	100	77	57	0	.	5	100	73	62	43	74	60	49	20	64	123	56	0	.	125	57	428	26	196	31	230	30	165	45	113	52	179	39	365	31	191	44	115	62	163	50	141	53	0	.	22047	4
	total	676	20	557	24	388	24	884	22	262	32	803	27	3359	11	2984	11	2524	12	982	18	298	37	348	36	505	31	20	.	248	38	427	27	738	22	698	28	45	69	1098	20	1123	18	1239	21	1411	15	2291	12	1291	15	1219	15	832	23	581	24	901	19	281	35	208	36	120	40	224	44	487	29	182	31	291	29	758	24	543	27	347	32	745	20	701	25	1746	21	499	24	784	23	125	34	272	29	357	30	809	24	313	27	679	30	211	36	349	43	571	42	367	29	793	21	449	25	230	52	368	50	215	30	709	22	257	36	446	25	212	33	247	45	329	39	423	28	596	27	665	25	356	35	30	72	293	32	282	39	82	54	159	34	327	31	267	27	285	32	34	49	265	35	154	56	279	37	1710	14	729	17	1211	17	313	30	501	28	553	34	502	25	420	31	257	38	268	35	354	30	0	.	56268	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	2384	17	2233	20	2268	18	3584	15	986	33	1336	32	5220	14	4480	14	6428	11	6895	11	832	29	1526	25	2055	21	102	.	1175	29	1886	24	3361	18	2702	21	194	54	4525	15	5062	13	5365	13	5861	12	12502	8	10807	8	8337	10	1424	24	1509	28	908	28	1178	26	754	31	1042	27	915	29	1330	23	1079	28	2199	20	3284	16	893	32	1792	23	3964	15	2396	22	1786	28	3374	15	2548	19	1933	20	1570	21	1786	20	2984	18	1901	21	2402	22	519	36	862	29	1588	27	1241	20	3629	14	2295	18	1570	17	843	25	942	22	3489	11	1631	18	2076	17	978	30	757	36	1714	23	1565	24	1875	22	1527	26	1157	28	311	48	2399	20	974	34	1034	25	1920	17	1685	23	2092	19	1879	20	641	38	828	31	708	40	781	34	8221	9	5434	12	8549	10	1300	28	1481	24	1353	27	566	36	1360	24	958	28	882	31	1417	23	0	.	224084	1
	private	1779	24	542	35	373	53	988	36	1623	28	3858	21	15745	8	11393	10	7460	11	2452	21	413	50	409	51	957	34	0	.	516	44	1916	26	2425	24	1794	26	275	57	1049	30	2156	20	1020	35	2034	20	1914	21	604	31	3694	16	3170	20	3366	17	5761	14	327	51	1562	22	307	64	366	48	2095	24	896	31	106	.	939	40	2671	21	1113	35	1096	27	1274	29	5400	17	642	37	488	44	517	50	191	71	1313	27	1191	34	1175	30	635	37	2187	22	1102	33	1320	25	203	50	63	.	307	43	319	40	1557	22	2047	15	758	33	653	34	994	24	730	36	626	41	285	53	944	33	572	37	2515	23	750	42	165	58	912	39	541	43	41	.	638	31	453	46	523	48	705	38	396	44	519	51	0	.	621	44	2932	17	1819	19	1666	20	919	36	773	39	992	31	2642	19	1875	23	757	38	710	36	593	45	0	.	143119	2
	total	4163	14	2775	18	2641	17	4572	14	2609	21	5194	17	20965	7	15873	8	13888	8	9347	9	1244	26	1935	23	3012	18	102	.	1691	24	3802	17	5786	14	4496	16	469	40	5575	13	7218	11	6385	12	7895	10	14417	7	11410	8	12031	8	4593	15	4876	15	6669	13	1505	23	2316	18	1349	25	1281	24	3425	17	1975	20	2305	20	4223	16	3564	17	2905	19	5060	13	3671	18	7187	14	4016	14	3036	17	2450	19	1761	20	3099	16	4174	16	3077	17	3037	19	2706	19	1964	23	2908	18	1444	18	3692	13	2602	16	1888	16	2400	16	2989	12	4247	11	2284	16	3070	13	1708	23	1383	27	1999	21	2508	19	2446	19	4041	17	1907	24	476	37	3312	18	1515	27	1075	25	2557	15	2138	20	2614	18	2585	18	1037	29	1348	27	708	40	1402	27	11152	8	7253	10	10215	9	2219	22	2254	21	2344	20	3208	17	3235	16	1715	23	1591	23	2009	21	0	.	367203	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. 2291851. Birmensdorf, Swiss Federal Research Institute WSL

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