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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NFI5
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, subgrids 1-5
state 2018/26
		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	16	29	14	33	11	38	113	48	30	87	52	70	48	26	64	37	206	27	578	23	7	54	14	45	56	75	1	.	73	73	94	55	112	40	220	45	1	79	27	23	47	19	46	22	47	18	127	12	172	17	697	19	13	37	45	79	13	47	107	48	191	41	182	39	88	37	177	37	13	50	41	36	31	31	47	63	13	42	34	28	6	35	18	44	78	51	69	70	11	41	14	41	12	38	24	27	19	33	9	32	13	50	8	43	18	71	10	33	54	31	27	30	27	41	4	34	10	33	38	18	19	22	17	26	16	37	5	44	23	31	27	39	8	30	11	59	78	49	7	84	306	39	89	54	54	39	196	47	263	39	78	42	113	57	9	44	99	43	16	71	40	73	94	13	79	21	274	30	7	47	6	50	7	43	24	82	17	47	4	49	27	57	5	40	0	.	6352	6
	private	6	47	3	60	0	.	9	41	30	77	43	68	118	11	90	18	162	33	167	36	1	81	4	71	6	34	0	.	2	75	20	35	29	70	50	44	8	79	5	42	22	30	12	42	20	26	15	32	8	41	196	30	26	31	84	28	50	21	72	86	296	43	9	71	75	68	288	37	16	44	4	.	91	75	64	57	15	55	14	38	5	41	98	41	5	49	13	89	0	.	2	71	6	43	4	59	5	44	5	41	71	50	23	54	27	61	2	.	1	85	5	69	2	57	13	29	18	25	2	56	3	61	10	37	4	46	5	53	6	57	14	52	29	93	19	47	10	67	13	91	118	57	1	82	0	.	34	93	22	90	2	74	16	90	3	56	25	98	0	.	85	68	32	26	45	44	41	33	3	56	5	60	7	46	56	56	29	57	2	51	1	70	17	77	0	.	3096	8
	total	22	25	16	29	11	38	122	44	61	58	95	49	165	11	154	18	368	21	745	19	7	50	18	38	62	68	1	.	75	71	114	46	141	35	270	37	9	70	32	20	69	16	58	19	67	15	142	11	180	16	893	16	38	24	128	33	63	19	179	45	487	30	191	38	164	37	465	26	29	33	45	34	122	57	111	42	28	35	48	22	11	27	116	36	84	48	82	60	11	39	15	37	18	29	28	25	24	28	15	25	85	42	31	42	45	46	11	31	54	30	32	28	29	38	17	24	27	19	40	17	22	21	27	21	20	31	10	34	30	28	41	31	37	72	31	37	88	44	21	66	424	33	90	53	54	39	231	42	286	37	79	41	129	51	12	36	124	40	16	71	125	52	125	12	124	21	315	26	10	36	11	38	13	31	81	46	46	40	6	36	27	56	22	59	0	.	9448	4
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	785	23	704	29	610	28	563	28	363	38	717	38	1558	20	1308	19	2005	15	2275	16	322	36	347	35	649	30	38	.	335	39	615	30	1214	23	587	32	85	74	1306	20	2019	17	1528	17	2057	16	5188	10	4705	10	1754	16	517	29	606	33	532	34	193	36	262	37	265	38	218	40	179	46	556	46	906	28	1382	25	361	40	556	34	1245	21	666	39	561	33	974	23	774	28	667	29	491	30	541	31	1039	24	937	26	506	30	482	39	311	45	268	34	491	28	1767	27	815	24	1041	24	364	37	630	31	1874	16	1122	22	988	23	694	38	331	38	859	29	611	28	511	26	374	41	353	42	111	54	660	29	202	55	273	44	601	27	358	30	561	30	406	32	299	43	157	48	185	65	48	85	3131	12	2620	17	3418	14	450	34	371	36	268	38	314	46	434	39	368	36	289	36	383	36	0	.	78365	2
	private	168	46	114	46	0	.	387	39	499	44	687	29	6008	10	2837	14	2724	16	473	34	21	82	137	76	310	41	0	.	113	71	764	36	735	34	588	31	59	72	276	40	826	23	541	41	917	27	689	31	372	37	823	26	878	27	1294	22	2028	18	81	64	195	40	16	71	36	75	242	34	796	35	120	.	322	40	569	32	625	45	571	40	370	43	990	21	365	45	95	73	6	.	94	72	389	43	169	50	273	42	225	47	673	35	403	34	578	33	47	.	61	72	164	64	204	55	839	25	1474	22	269	67	185	61	842	29	472	46	355	52	226	49	261	41	246	52	837	41	347	54	115	83	248	49	14	71	0	.	182	47	143	73	94	76	222	55	221	53	40	72	0	.	115	65	1261	23	1110	22	1047	28	186	52	204	51	329	40	1002	30	614	28	127	61	50	81	297	44	0	.	47928	3
	total	953	21	817	25	610	28	950	23	862	30	1404	24	7566	9	4145	11	4729	11	2749	15	344	34	484	33	960	24	38	.	448	34	1379	24	1949	19	1176	22	143	52	1582	18	2844	14	2069	17	2974	14	5877	10	5077	10	2577	14	1394	20	1899	18	2560	16	274	31	457	27	281	36	254	35	421	27	1353	28	1027	27	1704	21	930	25	1182	29	1816	19	1036	29	1551	18	1339	20	869	26	673	29	585	27	930	25	1208	22	1210	22	731	25	1155	26	714	27	846	25	539	27	1828	26	979	22	1245	22	1203	21	2104	17	2143	16	1307	20	1830	18	1166	29	686	33	1086	25	873	23	756	24	1211	31	701	34	226	50	909	25	216	52	273	44	783	23	501	30	655	28	628	28	521	33	197	41	185	65	164	52	4392	11	3730	13	4464	12	637	28	575	29	597	28	1316	25	1048	23	495	31	339	33	680	28	0	.	126293	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	849	26	535	28	825	31	1331	25	860	39	1036	38	2087	22	2192	19	3156	15	3840	14	303	43	639	34	740	37	139	.	644	40	845	30	1355	25	1275	34	80	74	2402	21	2584	17	2262	18	2327	19	5096	11	4928	11	4090	16	839	32	1089	31	509	40	288	52	609	38	632	40	494	42	663	33	662	42	759	32	1518	24	682	40	803	35	1516	22	1002	32	873	34	1889	21	1148	27	828	29	705	33	787	30	1622	25	949	31	1235	28	237	64	319	42	619	36	577	31	1897	35	1126	26	463	26	210	39	285	34	1225	20	765	26	865	26	521	42	357	45	832	31	842	37	1072	29	417	45	436	43	117	74	1629	25	216	82	346	40	928	24	793	30	1141	26	828	33	411	46	449	45	217	62	114	100	3143	14	2250	17	3831	14	550	37	678	36	397	36	244	48	450	35	357	38	358	37	530	35	0	.	101562	2
	private	682	39	277	46	0	.	681	40	996	39	1276	33	8540	11	5197	13	3988	16	1110	31	33	82	186	96	453	41	0	.	157	77	1055	33	1133	33	1148	29	131	71	452	43	1577	25	412	38	1004	30	873	29	355	45	1838	24	1555	27	2029	23	2884	19	223	56	729	35	127	74	267	66	1157	30	620	36	48	.	607	50	1100	34	904	42	606	33	889	44	2991	20	306	46	233	67	49	.	202	73	617	37	184	75	579	43	442	53	954	30	591	36	584	31	24	.	30	71	156	68	37	61	570	30	779	25	99	52	127	54	354	38	508	45	504	51	178	66	366	45	355	44	788	35	481	54	194	67	268	48	89	71	0	.	325	45	133	77	187	75	381	51	238	59	164	76	0	.	341	59	1278	27	909	28	985	32	399	57	353	54	593	43	1341	25	1354	28	256	50	134	72	169	56	0	.	70480	3
	total	1531	22	813	24	825	31	2012	21	1856	27	2313	25	10627	10	7389	11	7144	11	4949	13	337	39	825	34	1193	27	139	.	801	36	1899	22	2488	20	2423	23	211	52	2855	19	4161	14	2675	16	3331	16	5970	10	5283	11	5928	13	2395	20	3118	18	3392	17	511	38	1338	25	759	35	761	36	1820	23	1282	28	806	31	2125	22	1783	26	1708	28	2122	18	1890	26	3864	17	2195	19	1381	25	877	28	907	30	1404	23	1805	24	1528	25	1677	25	1190	27	910	28	1203	24	601	30	1927	34	1282	24	499	25	780	24	1064	20	1324	19	892	24	1219	21	1029	30	860	35	1010	28	1207	29	1426	24	1205	28	917	35	312	50	1897	22	305	61	346	40	1253	21	926	28	1329	25	1209	28	650	36	612	39	217	62	455	51	4421	12	3159	14	4816	13	949	32	1032	30	991	29	1585	23	1804	23	613	30	491	33	699	30	0	.	172042	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	262	35	360	47	437	38	660	30	208	64	166	48	796	32	1035	26	1315	20	815	26	135	57	178	59	207	55	91	.	107	55	269	54	276	39	311	42	0	0	1182	27	855	28	1017	26	1090	23	2201	14	1128	17	1075	25	383	43	331	47	123	48	213	64	266	56	52	78	170	63	292	54	111	78	165	45	482	30	425	48	227	41	489	33	648	38	567	41	508	36	632	45	85	83	225	47	147	44	754	32	204	40	471	44	42	61	156	58	497	43	394	42	938	40	341	33	167	36	50	61	101	62	420	26	168	35	302	41	156	57	158	55	382	49	278	41	606	39	229	51	144	54	27	100	365	36	72	71	46	73	231	52	224	62	182	45	264	46	64	66	136	59	44	67	106	100	1168	20	497	24	1284	20	103	43	473	42	201	46	142	64	164	42	131	48	156	56	148	47	0	.	35904	4
	private	252	44	87	53	0	.	410	42	245	44	260	41	2831	14	2301	18	1597	20	284	39	25	100	0	0	206	46	0	.	23	100	344	47	600	36	302	33	65	100	103	45	653	29	87	60	439	35	257	34	112	56	516	33	531	36	837	28	910	26	116	78	115	64	28	100	0	0	308	34	203	42	26	.	328	63	412	62	263	78	410	43	259	47	1227	24	97	63	224	64	0	.	62	85	143	64	21	100	213	61	193	71	372	43	474	65	274	47	13	.	5	100	73	64	8	100	189	37	141	42	210	88	28	100	254	46	148	56	96	62	47	71	131	86	264	60	334	43	206	70	0	0	16	74	104	100	0	.	9	100	30	100	25	83	63	54	29	63	47	100	0	.	166	67	333	36	285	32	295	40	150	57	78	67	112	66	657	33	180	56	184	73	71	77	108	61	0	.	25136	5
	total	513	28	448	39	437	38	1070	24	453	38	425	31	3627	13	3336	14	2911	14	1099	22	160	50	178	59	413	36	91	.	130	49	613	35	876	27	613	27	65	100	1286	25	1508	20	1104	24	1529	19	2458	13	1240	17	1591	20	914	27	1168	24	1032	23	329	49	381	43	80	61	170	63	600	31	315	39	191	41	810	31	837	39	490	46	900	26	907	30	1794	21	605	32	856	37	85	83	287	41	290	38	775	31	417	37	663	37	414	39	630	51	771	32	407	40	943	40	413	29	176	35	238	32	242	35	631	34	196	33	555	31	305	40	254	41	430	44	409	39	870	33	562	33	350	47	27	100	381	35	177	66	46	73	240	50	255	55	207	41	327	39	93	49	183	50	44	67	272	56	1501	17	782	19	1579	18	254	38	551	38	313	38	800	29	344	35	314	47	226	45	256	38	0	.	61039	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	1913	22	1613	27	1884	27	2666	23	1461	39	1970	36	4489	20	4599	18	6681	13	7508	14	768	36	1178	31	1652	32	269	.	1159	38	1823	29	2957	22	2393	29	165	73	4918	20	5505	16	4853	17	5522	16	12612	10	10933	10	7616	15	1752	28	2071	31	1176	35	802	42	1328	36	1131	35	970	38	1311	32	1342	41	1871	29	3412	22	1515	36	1600	33	3285	20	2321	31	2019	33	3450	20	2623	25	1590	27	1434	28	1487	28	3438	23	2109	26	2221	29	775	42	794	37	1403	33	1472	28	4655	29	2310	23	1698	21	627	33	1025	31	3558	16	2074	21	2171	23	1387	37	850	39	2097	29	1758	30	2197	28	1031	37	1011	38	263	58	2960	23	580	57	718	34	1955	24	1639	29	1962	25	1611	30	784	40	841	38	462	61	308	84	7536	12	5446	15	8806	13	1110	34	1528	33	872	33	725	43	1065	31	859	33	829	33	1066	33	0	.	222182	2
	private	1108	38	481	45	0	.	1488	36	1771	39	2267	31	17496	10	10425	13	8471	14	2034	27	80	71	327	86	976	38	0	.	296	74	2183	33	2496	28	2089	27	262	72	837	40	3077	23	1053	39	2380	26	1834	28	847	39	3373	23	2990	24	4244	21	5871	18	492	52	1335	31	180	71	379	67	1995	30	1636	32	197	.	1348	47	2146	33	1808	40	1601	32	1524	42	5306	20	773	42	565	59	56	.	360	72	1155	36	378	59	1070	44	865	45	2070	30	1490	35	1463	28	86	.	97	67	397	61	251	54	1611	26	2411	20	579	53	343	51	1461	28	1133	43	961	51	458	55	772	43	893	44	1978	36	1044	54	323	69	650	42	208	79	0	.	551	43	329	63	308	67	682	50	492	52	275	71	0	.	709	55	2904	24	2350	22	2368	26	739	55	640	51	1041	40	3057	24	2178	27	569	51	255	70	591	42	0	.	146640	3
	total	3020	20	2094	23	1884	27	4153	19	3232	28	4237	23	21985	9	15024	10	15152	10	9542	12	848	34	1505	31	2628	24	269	.	1455	34	4006	22	5453	17	4482	20	428	52	5755	18	8582	13	5906	15	7902	14	14447	9	11780	9	10989	12	4741	19	6314	17	7048	16	1294	33	2663	23	1311	32	1349	33	3306	22	2978	25	2069	28	4760	21	3660	25	3408	26	4886	17	3845	25	7325	17	4223	18	3188	23	1646	27	1794	27	2642	22	3816	22	3179	23	3086	24	2845	24	2285	26	2866	21	1558	27	4752	28	2707	22	1949	20	2238	21	3437	16	4137	15	2416	19	3632	17	2520	28	1811	32	2555	26	2530	25	3090	24	3009	27	2055	33	586	46	3611	21	788	47	718	34	2506	20	1967	26	2271	23	2292	26	1275	32	1115	33	462	61	1017	46	10440	11	7795	12	11174	12	1849	30	2169	28	1914	26	3782	21	3243	21	1428	28	1084	30	1657	26	0	.	368822	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. 2276117. Birmensdorf, Swiss Federal Research Institute WSL

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