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
growing stock (stemwood)
site quality · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
site quality	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	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	.
low	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	.
	spruce	32	73	232	60	0	.	4184	15	38	87	849	37	0	.	1483	28	5150	12	115	38	651	40	304	42	338	44	2089	19	1257	27	0	.	71	48	1027	27	0	.	421	34	962	32	1931	19	1847	24	0	.	213	71	23195	6
	fir	37	78	0	.	0	.	799	45	30	62	66	97	0	.	116	67	17	100	418	33	47	76	247	43	33	64	151	42	175	53	0	.	120	44	54	89	111	100	0	.	31	92	656	31	54	60	0	.	0	.	3162	16
	pine	71	64	2	100	0	.	239	39	162	53	0	.	0	.	0	.	341	26	156	40	39	69	14	100	0	.	135	70	19	78	0	.	189	41	92	74	0	.	15	100	56	75	0	.	87	44	0	.	16	100	1635	14
	larch	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	2981	13	0	.	0	.	0	.	0	.	17	100	50	100	0	.	0	.	0	.	0	.	309	27	0	.	2	100	2305	15	0	.	0	.	5665	9
	Arolla pine	0	.	0	.	0	.	39	79	0	.	0	.	0	.	0	.	1282	16	0	.	0	.	0	.	0	.	48	100	22	100	0	.	0	.	0	.	0	.	5	100	20	100	0	.	806	18	0	.	0	.	2221	11
	other conifers	18	62	4	100	0	.	48	76	0	.	0	.	0	.	1	100	40	34	0	.	0	.	4	100	6	100	2	100	0	.	0	.	20	59	1	100	0	.	0	.	4	89	8	73	0	.	0	.	0	.	156	28
	beech	366	42	53	77	0	.	1678	20	965	31	343	51	0	.	263	48	0	.	1152	23	170	75	359	37	75	71	329	58	401	50	83	100	1099	25	105	59	0	.	0	.	66	94	653	28	112	57	0	.	113	86	8382	9
	maple	36	46	0	.	0	.	242	27	29	55	49	51	0	.	96	39	0	.	93	62	4	100	33	55	21	66	112	71	23	69	0	.	43	52	62	48	0	.	0	.	3	146	149	25	55	45	0	.	11	100	1062	14
	ash	25	52	0	.	0	.	72	53	66	59	37	100	0	.	43	83	0	.	98	45	0	.	0	.	-4	100	10	78	47	55	0	.	54	50	2	100	0	.	0	.	1	100	87	52	59	68	0	.	35	60	633	18
	oak	68	51	0	.	0	.	43	53	44	73	1	100	0	.	0	.	0	.	0	.	40	100	0	.	3	100	0	.	0	100	0	.	61	58	1	100	0	.	0	.	21	100	51	54	59	47	0	.	0	.	390	22
	sweet chestnut	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	60	0	.	0	.	40	58
	other broadleaves	97	57	5	100	0	.	89	37	39	52	14	54	0	.	5	100	3	76	42	43	6	74	9	60	13	92	11	43	14	65	0	.	50	53	33	52	0	.	1	71	6	74	76	49	132	51	0	.	12	100	655	17
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	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	750	32	296	54	0	.	7432	12	1372	28	1358	29	0	.	2007	25	9814	9	2074	21	957	37	971	29	485	37	2903	18	2010	22	83	100	1707	23	1377	25	111	100	752	24	1170	29	3612	16	5554	12	0	.	401	58	47197	4
moderate	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	.
	spruce	0	.	352	46	95	59	11369	10	0	.	1816	22	0	.	964	33	24964	6	417	43	669	40	1065	23	212	50	927	31	2104	22	0	.	98	70	1594	25	0	.	4381	13	1246	30	6648	12	12556	7	201	63	0	.	71675	3
	fir	0	.	108	53	0	.	2401	20	0	.	474	39	0	.	275	51	991	24	332	44	76	49	1286	24	167	62	355	52	503	54	0	.	100	61	576	31	0	.	1099	23	252	44	2006	18	1202	23	78	59	0	.	12279	8
	pine	0	.	0	.	0	.	40	46	0	.	0	.	0	.	0	.	2457	14	0	.	0	.	0	.	0	.	0	.	91	71	0	.	0	0	10	71	0	.	242	59	59	91	0	0	1996	15	0	.	0	.	4895	10
	larch	0	.	0	.	0	.	45	60	0	.	0	.	0	.	6	100	5232	11	0	.	0	.	0	.	0	.	0	.	87	60	0	.	0	.	0	.	0	.	3514	11	0	.	104	42	7319	9	0	.	0	.	16306	6
	Arolla pine	0	.	0	.	0	.	-4	100	0	.	0	.	0	.	0	.	344	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	91	0	.	0	.	296	32	0	.	0	.	640	20
	other conifers	0	.	0	.	0	.	23	94	0	.	0	.	0	.	0	.	24	57	0	.	0	.	0	.	0	.	0	.	-7	123	0	.	0	.	0	.	0	.	1	100	3	100	3	100	9	58	0	.	0	.	55	50
	beech	0	.	95	66	53	71	1378	20	0	.	204	45	0	.	205	43	972	20	273	46	42	56	524	28	98	72	161	50	808	26	0	.	91	58	110	50	0	.	2119	16	63	65	1431	22	397	33	1	100	59	100	9085	7
	maple	0	.	20	60	0	.	516	20	0	.	70	44	0	.	248	38	222	27	11	66	35	62	125	42	38	53	36	60	179	37	0	.	10	63	95	55	0	.	90	37	34	63	436	21	178	26	0	.	0	.	2343	9
	ash	0	.	11	100	0	.	63	33	0	.	62	51	0	.	90	57	204	27	7	77	0	.	18	71	35	64	13	70	62	52	0	.	5	100	76	76	0	.	111	40	56	91	133	51	237	28	0	.	22	100	1204	13
	oak	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	127	60	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	159	33	0	.	5	100	133	34	0	.	0	.	440	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	670	29	0	.	0	.	5	100	0	.	0	.	719	27
	other broadleaves	0	.	0	0	6	100	339	36	0	.	14	45	0	.	63	45	759	17	15	71	0	.	6	73	26	76	10	48	35	42	0	.	1	100	9	132	0	.	1020	17	21	53	87	35	949	14	0	.	6	100	3365	8
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	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	.	585	40	153	58	16173	8	0	.	2641	19	0	.	1850	26	36340	5	1055	35	822	35	3024	18	576	44	1501	30	3877	19	0	.	305	45	2469	21	0	.	13410	7	1733	26	10852	11	25275	6	280	61	87	100	123008	2
good	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	.
	spruce	693	32	1152	35	2835	22	9205	10	139	44	3821	16	0	.	942	35	6423	12	681	28	4091	16	2653	17	370	49	1367	27	5226	15	655	33	565	31	3274	18	556	39	1079	27	1336	28	5057	13	355	47	762	31	1664	21	54903	4
	fir	533	39	359	39	1143	30	7917	11	171	49	1817	22	0	.	119	84	1165	23	1218	22	3207	16	2061	19	36	57	201	43	2391	18	14	97	498	31	986	25	152	47	257	46	392	38	2563	16	251	46	402	37	1001	24	28853	5
	pine	119	61	20	100	10	100	485	45	255	43	97	85	0	.	0	.	51	100	265	49	128	49	135	52	0	.	0	.	99	72	144	48	180	36	0	.	154	45	112	76	46	70	60	53	0	.	0	.	210	33	2571	14
	larch	155	54	6	100	67	100	67	53	5	100	26	100	0	.	34	100	521	36	1	100	131	99	0	.	0	.	0	.	208	68	79	74	2	100	26	98	47	66	258	34	0	.	93	51	175	91	0	.	74	52	1976	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	52	54	0	.	1	100	125	60	0	.	0	.	0	.	0	.	0	.	-4	100	152	74	0	.	0	.	0	.	-2	593	31	100	4	71	0	.	0	.	27	100	4	100	182	66	3	100	3	100	21	51	599	31
	beech	1964	21	119	47	797	33	5199	12	1237	25	1459	21	0	.	273	42	700	33	1502	22	1771	20	623	23	214	40	626	41	2428	14	784	25	1794	19	991	21	579	31	3525	13	293	37	2884	14	309	38	296	43	1726	17	32091	4
	maple	151	41	42	124	156	47	755	16	143	34	302	30	0	.	66	42	152	60	216	29	337	21	191	39	58	38	147	54	711	19	62	50	236	25	190	31	222	48	272	25	92	40	438	21	99	39	56	42	300	30	5393	7
	ash	239	34	58	90	245	41	700	26	101	41	421	30	0	.	79	65	228	48	379	34	463	55	34	46	235	63	31	67	604	22	82	49	161	44	361	39	242	33	812	19	48	45	457	33	26	64	42	62	340	26	6388	8
	oak	257	39	0	.	13	100	212	28	175	64	49	56	0	.	0	.	106	52	85	44	102	56	99	62	0	.	19	100	3	100	230	45	93	49	34	77	101	37	715	19	22	79	472	29	8	85	28	81	281	33	3104	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	293	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4352	11	0	.	4	100	14	100	0	.	0	.	4662	11
	other broadleaves	215	33	2	100	27	67	408	23	91	33	82	36	0	.	79	60	893	19	143	33	114	37	189	68	49	66	66	50	273	27	21	52	72	49	155	37	10	71	2961	10	129	42	551	28	82	37	16	78	137	40	6768	6
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	0	0	0	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	4378	16	1758	30	5295	19	25074	7	2318	19	8074	13	0	.	1592	29	10530	10	4486	15	10498	12	5984	13	962	33	2456	23	11940	11	2102	20	3605	16	6018	14	2064	24	14370	6	2363	21	12760	9	1322	28	1605	26	5754	13	147308	2
very good	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	.
	spruce	2881	17	98	58	154	61	6958	11	534	32	1822	24	19	100	288	50	174	100	2024	20	1679	20	826	38	138	100	127	71	2090	23	991	27	1513	23	263	45	2420	21	0	.	103	63	1562	20	109	69	65	68	4675	13	31514	5
	fir	1607	22	55	96	281	68	5975	12	494	40	335	37	0	.	125	86	0	.	1490	22	2260	18	633	36	0	.	109	72	1215	21	101	52	920	25	331	47	815	30	0	.	14	97	1172	22	15	67	70	61	1358	21	19373	6
	pine	342	31	0	.	0	.	354	27	42	58	197	52	62	79	0	.	0	.	65	53	0	.	0	.	0	.	0	.	136	60	105	41	82	46	47	87	239	36	0	.	29	100	161	40	0	.	0	.	830	21	2690	11
	larch	168	43	3	100	0	.	200	39	8	100	25	70	0	.	0	.	0	.	26	71	8	73	24	100	0	.	8	100	117	46	6	100	96	54	30	100	23	73	0	.	0	.	344	40	10	100	32	100	213	34	1344	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	88	46	0	.	4	100	95	71	166	72	56	100	0	.	0	.	0	.	4	64	16	82	0	.	0	.	0	.	179	98	34	100	-11	100	2	100	54	83	0	.	0	.	163	60	0	.	0	.	46	57	895	29
	beech	3055	14	11	100	103	86	4874	12	1204	25	2016	22	0	.	353	44	0	.	1375	20	1143	23	333	36	40	100	223	53	1331	24	1190	22	2463	15	609	41	1046	20	113	73	142	91	2028	18	108	51	58	66	2950	16	26769	5
	maple	473	17	11	90	0	.	589	20	270	32	119	38	9	78	37	45	0	.	329	24	114	38	44	54	8	100	25	60	218	31	19	71	296	30	53	42	303	34	3	100	18	100	313	34	12	59	43	65	616	20	3923	7
	ash	864	19	16	89	24	100	693	23	318	37	672	27	16	100	32	73	0	.	503	26	290	28	45	67	11	100	45	76	644	25	73	51	233	34	96	51	746	25	54	64	13	92	988	22	82	54	158	52	1085	18	7702	7
	oak	612	26	0	.	0	.	487	25	159	58	192	38	396	42	0	.	0	.	178	46	90	71	8	100	0	.	0	.	199	56	161	61	161	38	0	.	463	45	26	85	11	100	1057	24	43	59	0	.	347	25	4590	10
	sweet chestnut	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	71	99	60	95	0	.	0	.	139	65
	other broadleaves	695	37	-3	100	26	100	499	27	149	45	316	54	37	49	3	71	7	100	191	35	88	49	25	54	3	100	22	62	178	40	13	51	200	34	22	53	748	39	21	58	26	69	703	31	192	49	5	100	434	22	4601	12
	indeterminable	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	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	10787	9	192	64	592	55	20724	7	3344	18	5750	15	539	39	838	39	181	100	6187	13	5688	14	1938	28	200	75	560	41	6308	14	2692	20	5954	13	1452	30	6857	13	221	49	356	53	8563	11	631	37	432	43	12555	9	103541	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	.
	spruce	3606	15	1834	25	3084	20	31716	5	712	26	8308	11	19	100	3677	17	36712	5	3237	15	7090	12	4848	12	1057	27	4510	14	10676	10	1646	21	2247	17	6158	12	2976	18	5882	11	3648	17	15198	7	14867	7	1028	26	6552	11	181288	2
	fir	2177	19	521	30	1425	27	17090	7	694	31	2692	17	0	.	635	34	2173	16	3458	13	5590	12	4227	13	235	45	815	28	4285	13	114	47	1639	17	1947	18	1077	26	1356	21	689	27	6396	10	1523	20	550	29	2358	16	63668	3
	pine	531	26	22	90	10	100	1118	23	459	31	294	45	62	79	0	.	2849	13	486	31	167	41	150	48	0	.	135	70	345	37	249	33	452	24	149	53	393	28	369	45	191	42	221	33	2082	15	0	.	1056	18	11791	6
	larch	324	34	9	75	67	100	313	29	14	73	51	61	0	.	40	87	8734	8	27	69	139	93	24	100	0	.	26	75	462	36	86	69	98	53	57	70	70	50	4082	10	0	.	543	28	9809	8	32	100	287	28	25291	4
	Arolla pine	0	.	0	.	0	.	35	87	0	.	0	.	0	.	0	.	1626	14	0	.	0	.	0	.	0	.	48	100	22	100	0	.	0	.	0	.	0	.	8	70	20	100	0	.	1102	15	0	.	0	.	2861	10
	other conifers	157	32	4	100	5	79	291	37	166	72	56	100	0	.	1	100	64	30	0	2109	168	68	4	100	6	100	2	100	170	104	65	71	13	126	3	73	54	83	28	97	11	57	355	43	12	50	3	100	68	42	1705	19
	beech	5384	11	279	34	953	30	13130	7	3406	15	4022	14	0	.	1093	22	1672	18	4303	12	3126	14	1840	14	427	30	1339	26	4968	11	2057	16	5447	10	1815	18	1626	17	5757	10	563	33	6995	9	926	21	355	37	4848	11	76329	2
	maple	660	16	73	75	156	47	2102	10	442	23	540	20	9	78	447	24	374	29	649	18	491	17	393	24	125	27	319	36	1132	15	81	42	585	18	400	22	525	28	365	21	147	32	1336	13	344	19	99	37	927	16	12721	4
	ash	1128	16	85	65	269	38	1527	16	485	27	1192	19	16	100	244	34	431	28	988	19	753	36	96	37	277	54	99	42	1357	16	154	35	453	24	536	30	988	20	978	16	119	48	1665	17	405	23	201	43	1483	15	15928	5
	oak	937	20	0	.	13	100	745	19	379	39	242	32	396	42	0	.	233	40	263	34	232	41	106	58	3	100	19	100	217	52	391	36	315	26	34	75	564	37	900	16	54	54	1584	18	242	24	28	81	628	20	8524	7
	sweet chestnut	3	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	337	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5025	10	0	.	75	94	116	54	0	.	0	.	5559	9
	other broadleaves	1007	27	4	159	60	55	1334	15	280	27	426	40	37	49	150	37	1662	13	391	22	207	29	229	56	92	44	108	33	500	21	34	38	323	25	218	28	758	39	4002	8	182	32	1418	19	1355	13	22	64	590	19	15389	5
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	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	15915	8	2832	22	6041	17	69403	4	7035	12	17824	8	539	39	6288	14	56866	4	13802	8	17964	8	11917	9	2223	21	7420	12	24134	7	4876	14	11570	9	11316	10	9031	11	28752	4	5622	14	35787	5	32783	5	2317	21	18797	7	421054	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. 1195144. Birmensdorf, Swiss Federal Research Institute WSL

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