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)
degree of mixture (terrestrial) · ownership (2 categories)
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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
degree of mixture (terrestrial)	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	.	63	80	48	.	193	100	14	.	0	.	139	72	0	.	323	82	84	76	188	71	79	.	0	.	0	.	0	.	42	.	322	100	0	.	0	.	97	.	0	.	125	65	0	.	67	95	270	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	.	0	.	155	93	34	100	0	.	0	.	0	.	0	.	0	.	262	76	0	.	69	.	0	.	0	.	11	100	0	.	0	.	246	.	0	.	52	.	0	.	0	.	9	.	0	.	0	.	0	.	7	.	19	100	0	.	33	100	0	.	0	.	0	.	52	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	26	.	0	.	0	.	67	.	16	.	75	.	124	.	81	100	0	.	295	63	0	.	79	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3868	18
	private	176	72	0	.	0	.	138	.	0	.	0	.	427	41	532	52	0	.	40	74	0	.	120	.	0	.	0	.	0	.	80	81	455	51	0	.	0	.	168	.	0	.	0	.	72	.	0	.	3	.	15	100	99	96	256	71	57	.	263	.	0	.	0	.	31	.	154	.	0	.	0	.	0	.	0	.	272	.	34	.	2	.	129	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	100	68	.	0	.	0	.	243	.	0	.	18	.	0	.	0	.	127	.	0	.	0	.	4339	18
	total	176	72	63	80	48	.	331	71	14	.	0	.	566	36	532	52	323	82	123	56	188	71	200	72	0	.	0	.	0	.	122	63	778	51	0	.	0	.	265	73	0	.	125	65	72	.	67	95	272	56	15	100	99	96	256	71	57	.	263	.	0	.	0	.	31	100	235	74	0	.	155	93	34	100	0	.	272	.	34	.	2	100	129	.	262	76	0	.	69	.	0	.	0	.	11	100	0	.	0	.	386	73	0	.	52	.	0	.	0	.	9	.	0	.	0	.	26	.	7	.	19	100	0	.	33	100	0	.	0	.	0	.	52	.	92	.	0	.	0	.	0	.	0	.	0	.	23	.	26	.	0	.	0	.	67	.	16	.	75	.	124	.	183	71	68	.	295	63	0	.	322	79	0	.	18	.	0	.	0	.	127	.	0	.	0	.	8207	13
pure conifer forest	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	650	62	1048	47	481	58	2772	28	1222	44	1681	41	8072	17	4053	23	3912	23	2225	30	225	.	247	84	236	.	128	.	195	70	712	42	2747	33	1382	44	0	.	2778	28	4009	21	2997	23	5225	20	10277	13	10577	11	2222	29	1027	42	653	64	1281	37	435	56	207	71	425	66	681	52	991	42	386	61	1788	35	1182	36	198	71	1474	47	1918	32	1118	42	676	64	1547	36	1046	44	369	74	1104	48	0	.	1208	33	2255	30	1617	37	272	78	142	.	234	92	1004	34	1899	26	1365	30	62	97	0	.	0	.	532	42	365	54	472	60	832	48	590	54	1587	34	627	54	938	45	285	57	946	51	0	.	1194	36	302	73	472	70	816	36	566	58	368	61	1166	39	0	.	616	62	0	.	381	.	6442	14	4822	16	5035	17	485	50	224	.	217	84	361	85	333	73	313	75	972	51	667	49	0	.	131558	3
	private	168	73	621	74	0	.	201	.	511	63	1373	43	7194	17	6782	18	6209	19	1049	42	0	.	0	.	322	95	0	.	0	.	1716	40	174	82	1092	45	0	.	123	.	1677	35	384	55	1039	38	692	47	328	60	635	48	1761	38	4175	23	3188	25	24	.	1074	33	228	.	123	100	1475	36	549	55	172	.	940	72	847	53	51	72	303	72	338	.	2031	39	14	.	0	.	0	.	0	.	0	.	95	80	761	51	255	77	284	77	772	51	346	53	0	.	104	.	96	.	0	.	0	.	0	.	0	.	0	.	0	.	70	.	574	65	75	.	296	71	400	62	584	57	0	.	313	.	40	.	0	.	0	.	272	62	0	.	0	.	106	.	0	.	234	71	0	.	547	78	976	29	667	38	859	40	195	.	0	.	763	51	1359	36	1024	38	411	63	181	.	424	65	0	.	62668	5
	total	818	52	1669	40	481	58	2972	27	1732	36	3054	30	15266	12	10835	14	10121	14	3275	24	225	.	247	84	557	69	128	.	195	70	2428	31	2920	31	2474	31	0	.	2901	27	5686	18	3380	21	6264	17	10969	12	10905	11	2857	25	2788	28	4828	21	4468	21	460	53	1281	29	653	55	805	47	2466	27	935	41	1960	33	2122	38	1045	45	1525	45	2221	29	1455	40	2707	33	1561	35	1046	44	369	74	1104	48	0	.	1304	31	3016	26	1872	33	556	55	914	46	580	49	1004	34	2003	25	1461	29	62	97	0	.	0	.	532	42	365	54	472	60	901	45	1164	42	1663	33	923	43	1338	37	869	42	946	51	313	100	1233	35	302	73	472	70	1088	31	566	58	368	61	1272	37	0	.	850	49	0	.	928	61	7417	13	5489	15	5894	16	681	46	224	100	980	44	1720	33	1357	34	725	48	1154	46	1091	39	0	.	194226	3
mixed conifer forest	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	365	92	399	61	313	65	1027	45	181	73	614	74	1115	42	1793	39	1460	34	2599	22	0	.	0	.	163	71	0	.	426	66	251	71	617	51	483	67	46	.	828	55	364	45	803	42	257	63	140	51	8	71	2079	28	294	65	315	58	86	.	386	74	332	57	540	47	353	58	337	64	295	94	91	.	788	45	363	72	283	71	489	55	562	71	676	53	568	54	488	51	638	50	138	.	414	62	480	66	18	.	276	78	72	80	204	75	838	45	10	.	18	75	168	.	78	.	4	.	223	.	469	52	265	66	61	83	99	.	0	.	137	.	396	57	39	100	221	71	0	.	0	.	751	48	72	.	545	58	707	31	538	53	913	37	489	54	229	98	184	72	369	73	0	.	203	63	50	59	335	47	389	71	413	60	392	66	273	.	899	57	612	58	229	.	339	68	0	.	37768	6
	private	0	.	416	62	0	.	346	75	833	58	977	54	714	49	3273	25	3212	25	808	44	223	.	0	.	0	.	0	.	433	78	0	.	199	102	1108	47	0	.	531	71	295	71	76	.	44	.	38	.	0	.	731	54	252	65	824	46	1709	40	86	.	238	72	203	.	92	.	131	.	206	.	0	.	89	.	966	43	107	.	347	57	544	59	1052	43	212	70	391	.	0	.	0	.	132	.	13	.	417	80	223	82	629	57	682	50	414	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	354	67	433	87	5	.	141	.	219	.	0	.	292	74	117	.	0	.	23	.	0	.	0	.	0	.	0	.	49	.	91	70	363	54	0	.	114	.	592	65	1498	35	459	58	208	.	216	.	153	.	0	.	29543	8
	total	365	92	815	43	313	65	1373	39	1014	49	1590	44	1829	32	5066	21	4672	20	3407	20	223	.	0	.	163	71	0	.	859	51	251	71	815	46	1591	38	46	.	1359	43	659	40	878	40	301	56	178	45	8	71	2810	25	546	46	1139	37	1796	39	472	63	570	44	744	44	444	50	468	54	501	69	91	.	877	41	1329	37	390	58	836	40	1106	46	1728	33	779	44	879	53	638	50	138	.	545	53	493	64	434	76	499	57	701	52	886	42	1252	37	10	.	18	75	168	.	78	.	4	.	223	.	469	52	265	66	61	83	99	.	0	.	137	.	396	57	39	100	575	50	433	87	5	.	892	44	292	79	545	58	999	31	655	47	913	37	512	52	229	98	184	72	369	73	0	.	251	54	141	50	698	36	389	71	528	52	984	47	1771	33	1357	43	820	50	445	71	493	56	0	.	67311	5
mixed broadleaf forest	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	203	75	65	.	387	50	403	62	0	.	0	.	774	48	398	52	1717	31	1239	33	86	.	139	72	568	49	0	.	0	.	215	.	63	.	447	71	0	.	292	72	0	.	0	.	0	.	144	72	0	.	1536	29	349	59	0	.	0	.	92	.	0	.	111	.	69	80	27	.	0	.	72	.	502	64	508	71	0	.	669	58	195	75	502	68	550	46	711	45	822	43	420	60	656	43	43	.	0	.	335	76	0	.	175	.	88	.	52	.	64	.	99	.	64	.	0	.	120	.	82	74	194	78	278	70	0	.	0	.	13	75	132	.	7	.	137	.	232	.	0	.	275	62	539	59	201	75	0	.	305	50	243	74	255	71	119	.	0	.	103	.	236	71	70	100	31	100	264	58	0	.	241	73	116	.	0	.	822	46	138	.	269	74	215	88	0	.	21489	8
	private	417	59	232	71	0	.	190	.	0	.	174	100	891	45	327	72	387	66	244	61	0	.	29	.	357	73	0	.	0	.	462	68	362	61	305	75	0	.	0	.	0	.	126	88	50	100	0	.	48	.	823	43	285	71	259	71	368	59	0	.	0	.	0	.	0	.	0	.	32	.	0	.	132	.	255	71	0	.	80	.	71	.	417	54	90	.	225	.	113	71	0	.	117	.	41	.	0	.	0	.	453	60	245	.	339	83	0	.	0	.	0	.	0	.	0	.	39	.	0	.	0	.	0	.	253	73	0	.	98	.	155	66	172	72	8	.	184	.	0	.	155	76	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	.	125	.	244	69	0	.	337	71	91	.	0	.	0	.	0	.	0	.	10903	11
	total	620	46	297	60	387	50	593	53	0	.	174	100	1665	33	725	43	2104	28	1483	29	86	.	168	62	925	41	0	.	0	.	677	56	426	54	752	52	0	.	292	72	0	.	126	88	50	100	144	72	48	.	2359	24	635	46	259	71	368	59	92	.	0	.	111	.	69	80	27	.	32	.	72	.	634	54	763	53	0	.	750	53	266	61	919	44	640	42	936	42	935	38	420	60	772	40	85	71	0	.	335	76	453	60	420	72	427	69	52	.	64	.	99	.	64	.	0	.	159	80	82	74	194	78	278	70	253	73	0	.	111	88	287	58	179	69	145	95	416	71	0	.	430	48	539	59	201	75	0	.	305	50	243	74	255	71	119	.	0	.	103	.	236	71	70	100	31	100	363	50	125	100	485	50	116	.	337	71	913	43	138	.	269	74	215	88	0	.	32392	6
pure broadleaf forest	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	2057	27	872	43	405	59	354	53	0	.	0	.	133	79	396	51	2442	29	591	42	655	53	613	46	785	40	0	.	269	86	90	72	328	48	0	.	80	.	354	58	345	59	0	.	132	58	552	39	94	62	2043	27	455	66	222	.	0	.	27	.	41	.	25	.	0	.	0	.	162	71	0	.	955	50	106	.	443	62	361	55	390	55	0	.	850	37	58	.	467	47	507	47	404	52	255	59	249	.	177	.	131	83	140	87	187	75	17	71	288	67	147	75	468	35	261	48	452	37	900	33	652	32	325	46	228	59	45	.	103	.	202	80	59	71	69	.	263	71	37	.	212	80	0	.	156	63	0	.	217	71	290	67	297	71	395	56	15	.	0	.	0	.	3	100	13	71	667	35	85	.	405	71	443	58	13	.	167	78	64	100	211	62	261	71	0	.	28635	6
	private	375	71	146	72	0	.	476	61	0	.	278	73	1142	37	864	51	785	39	0	.	101	.	179	.	0	.	0	.	177	.	263	71	0	.	389	61	310	66	214	61	413	78	0	.	152	.	216	52	12	.	1013	41	333	71	136	.	218	85	163	70	0	.	0	.	0	.	88	.	347	70	0	.	0	.	229	.	641	59	165	.	0	.	467	66	83	.	355	.	0	.	240	71	710	46	166	89	0	.	170	72	245	68	194	74	407	60	0	.	19	.	0	.	214	49	1347	26	1626	23	127	60	103	.	1215	29	124	.	0	.	0	.	0	.	169	.	0	.	156	.	72	.	36	.	0	.	0	.	0	.	0	.	353	59	125	.	176	66	148	.	0	.	34	.	130	61	100	69	20	.	44	.	0	.	17	.	58	.	389	58	477	54	42	.	132	.	0	.	20317	8
	total	2432	25	1018	38	405	59	830	41	0	.	278	73	1275	34	1260	39	3227	23	591	42	756	48	792	42	785	40	0	.	446	65	354	56	328	48	389	61	390	56	568	43	759	50	0	.	284	60	768	31	106	56	3056	22	788	48	358	73	218	85	190	62	41	.	25	.	0	.	88	.	509	53	0	.	955	50	336	75	1085	43	526	49	390	55	467	66	933	35	412	87	467	47	748	39	1113	35	421	50	249	.	347	62	376	53	334	56	594	47	17	71	307	63	147	75	682	29	1608	23	2078	19	1027	30	755	30	1540	25	352	52	45	.	103	.	202	80	228	76	69	100	419	58	109	74	247	70	0	.	156	63	0	.	217	71	643	44	422	58	570	44	164	91	0	.	34	.	133	59	112	62	687	34	130	74	405	71	461	56	71	83	556	47	541	49	252	55	392	58	0	.	48952	5
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	3275	23	2447	27	1634	28	4748	21	1417	39	2294	36	10233	14	6640	18	9855	14	6737	14	1154	38	1079	34	1751	28	128	.	891	44	1310	32	4076	25	2312	33	126	73	4349	22	4718	18	3924	19	5614	18	11179	12	10948	11	7880	14	2126	28	1190	43	1367	36	940	41	580	41	1101	36	1103	38	1436	33	843	45	2106	31	3460	23	1176	41	2200	35	3437	23	2265	29	1854	35	3777	20	2303	27	2364	25	2169	30	1473	29	1998	27	2522	29	2405	29	721	48	661	45	1398	33	1083	32	2268	24	1787	26	672	30	265	48	795	38	1990	22	1494	24	1135	33	1192	36	635	50	1841	31	1358	33	1095	40	712	38	1441	40	37	100	2432	25	914	43	1375	35	1545	24	1651	29	1813	26	2207	27	810	42	831	49	546	54	740	59	6798	14	4916	16	6595	14	959	39	1363	35	1168	36	647	64	2221	31	1127	40	1681	35	1482	32	0	.	223318	2
	private	1136	35	1416	39	0	.	1351	36	1344	43	2802	30	10369	13	11778	13	10592	14	2141	27	324	76	328	66	679	59	0	.	609	62	2521	31	1190	34	2894	27	310	66	1036	43	2385	29	586	43	1356	32	947	37	391	52	3217	23	2731	28	5650	19	5541	20	537	56	1312	30	431	71	246	63	1848	31	1134	39	172	.	1161	60	2297	30	1072	45	929	37	955	49	4096	24	398	49	971	59	113	71	240	71	958	39	316	54	1178	43	649	45	1751	31	1893	31	1506	32	0	.	123	86	96	.	214	49	1347	26	1691	22	127	60	103	.	1215	29	447	52	574	65	173	71	451	52	741	44	1038	40	773	58	390	82	371	51	219	.	0	.	564	48	117	.	353	59	254	65	176	66	382	58	0	.	581	73	1256	25	926	30	1341	31	365	65	600	53	1372	40	3269	23	1963	27	1097	38	566	55	709	48	0	.	127770	3
	total	4411	19	3863	22	1634	28	6099	18	2760	29	5097	23	20601	10	18418	10	20447	9	8878	12	1477	34	1407	30	2429	26	128	.	1500	36	3832	23	5266	21	5206	21	435	51	5385	19	7103	16	4510	18	6971	16	12126	11	11339	11	11098	11	4856	20	6841	17	6908	17	1476	33	1892	23	1533	32	1348	33	3284	22	1977	29	2278	29	4621	23	3473	24	3272	28	4367	20	3220	25	5951	19	4175	19	3274	26	2477	24	2410	28	2431	23	2314	24	3700	24	3054	25	2472	26	2554	26	2905	23	1083	32	2391	23	1883	25	886	25	1612	23	2486	19	2117	21	1598	23	2351	22	1639	30	1209	40	2014	29	1808	28	1836	29	1749	28	2214	33	427	76	2803	23	1133	39	1375	35	2109	21	1768	28	2166	24	2461	25	986	37	1214	38	546	54	1321	46	8055	12	5842	14	7936	13	1324	33	1964	29	2540	27	3917	22	4184	21	2224	28	2246	30	2191	27	0	.	351089	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. 2292596. Birmensdorf, Swiss Federal Research Institute WSL

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