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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NFI2
growing stock (stemwood)
diameter at breast height (main diameter classes) · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (main diameter classes)	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	±%
n/a	n/a	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	.
	fir	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	.
	larch	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	.
	other conifers	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	.
	maple	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	.
	oak	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	.
	other broadleaves	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	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	461	12	73	30	155	22	376	21	675	18	321	21	288	11	252	17	230	27	94	22	122	23	278	13	489	11	191	41	4005	5
	fir	377	16	38	42	42	46	138	22	71	49	57	31	225	15	84	27	32	59	38	43	12	32	54	59	12	27	16	28	1195	8
	pine	12	44	5	95	4	51	4	72	15	39	0	.	16	46	1	100	4	100	3	80	2	71	136	25	123	23	3	100	329	14
	larch	0	0	1	81	3	74	5	74	23	73	0	.	0	.	4	100	0	100	0	.	4	53	86	22	42	21	100	61	269	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	31	14	27	1	100	26	20
	other conifers	18	42	4	70	24	85	9	44	17	37	0	100	3	51	3	58	0	.	4	60	2	72	4	61	13	31	0	.	100	23
	beech	345	12	186	17	115	20	162	18	207	16	31	26	240	23	122	16	83	37	37	48	78	53	38	30	20	28	379	15	2044	6
	maple	114	13	27	69	42	35	24	29	48	71	29	25	36	28	47	27	53	23	40	85	19	31	18	65	19	36	15	63	530	12
	ash	81	27	38	29	27	39	12	39	113	46	7	47	24	50	75	27	14	34	12	45	15	45	32	26	23	41	96	19	568	12
	oak	42	31	17	54	61	45	0	1719	16	42	1	100	0	100	0	.	6	42	2	74	0	.	17	28	0	0	87	38	249	19
	sweet chestnut	0	.	0	.	2	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	80	0	.	359	19	366	18
	other broadleaves	145	13	40	23	51	26	54	28	104	19	51	21	98	21	99	21	80	20	50	43	30	67	170	20	158	17	397	10	1527	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1593	6	430	12	525	13	784	12	1288	12	499	15	929	9	688	10	503	17	280	21	284	23	848	9	912	8	1644	9	11207	3
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1307	12	228	25	534	18	1109	17	1493	13	1112	14	1261	10	1021	12	730	13	418	21	366	22	1115	10	1894	8	545	13	13132	3
	fir	903	11	76	34	207	26	553	17	192	22	230	20	712	12	282	17	85	23	118	36	102	53	112	35	47	31	84	27	3703	5
	pine	72	49	73	49	42	78	14	58	71	38	6	100	45	65	2	100	9	51	46	46	15	93	463	18	347	17	10	63	1216	10
	larch	41	83	4	100	17	62	9	100	41	60	0	0	0	.	3	100	10	56	0	.	10	72	402	14	221	22	323	19	1081	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	3	100	2	100	0	.	1	100	0	.	91	27	115	24	0	.	212	17
	other conifers	24	52	30	49	22	76	16	46	30	41	0	.	6	83	7	59	3	100	4	75	1	100	8	100	26	47	0	.	175	19
	beech	1704	8	638	14	467	16	661	13	811	11	164	36	710	12	502	17	211	24	119	27	152	24	165	27	142	30	930	13	7376	4
	maple	381	14	95	21	78	48	87	28	269	21	149	22	171	23	235	17	141	28	40	37	108	37	67	29	32	38	56	29	1909	7
	ash	292	23	122	27	223	27	74	30	325	26	75	30	132	27	181	32	51	36	50	34	83	47	72	32	56	51	170	23	1905	9
	oak	196	30	55	35	196	40	54	40	86	31	19	52	7	81	2	100	26	51	6	72	0	0	49	30	10	64	188	29	893	14
	sweet chestnut	0	.	0	.	7	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	84	0	.	1020	12	1046	12
	other broadleaves	317	16	108	24	122	27	89	25	347	18	77	25	155	24	178	27	161	22	53	32	94	40	308	14	225	28	1063	9	3296	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5236	5	1427	10	1915	10	2667	9	3665	7	1833	11	3202	6	2416	8	1426	10	854	15	931	16	2868	7	3115	7	4389	5	35944	2
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3678	8	599	24	1740	17	1922	13	3230	10	2623	12	4280	9	3308	12	2271	13	1131	19	1163	26	3371	10	5778	8	1507	14	36602	3
	fir	2828	9	261	33	345	25	1511	15	870	16	754	20	1627	12	1076	14	207	29	274	28	129	41	193	29	103	29	164	46	10343	5
	pine	426	24	152	26	66	41	82	54	314	24	0	.	105	41	10	71	20	58	195	38	81	55	632	17	676	19	172	52	2931	9
	larch	22	71	81	43	65	70	64	58	137	34	0	.	0	.	0	.	28	76	0	.	70	45	1326	13	830	14	801	16	3424	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	19	100	3	100	0	.	7	100	0	.	252	26	383	21	0	.	664	16
	other conifers	9	100	42	91	43	76	42	63	48	54	0	.	0	.	6	100	0	.	0	.	-13	100	0	.	6	71	0	.	182	35
	beech	5125	7	1674	12	1242	15	1672	13	2120	11	625	23	1716	13	1820	13	550	22	210	26	519	25	181	35	237	34	1110	13	18803	4
	maple	735	12	358	21	112	31	225	27	685	15	108	40	348	20	462	17	306	22	75	32	218	27	23	83	33	53	74	38	3762	6
	ash	651	18	305	24	345	23	380	33	846	15	215	37	214	28	524	20	106	37	87	70	64	58	108	33	39	58	183	26	4068	7
	oak	437	22	199	27	308	27	146	33	422	25	22	100	48	73	43	56	51	53	7	100	0	.	26	47	20	100	290	23	2019	10
	sweet chestnut	0	.	0	.	12	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	47	3	100	1097	13	1160	13
	other broadleaves	357	19	215	31	182	39	192	33	505	18	20	56	182	42	216	22	164	25	64	34	107	35	218	26	146	31	1190	13	3759	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14269	4	3886	9	4460	9	6237	8	9179	6	4369	10	8540	6	7468	7	3703	10	2050	14	2337	16	6379	7	8255	6	6586	5	87715	2
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7471	6	1422	18	3934	13	5367	10	7365	8	4910	11	9618	7	6558	9	5044	10	1743	18	2154	15	4189	8	9943	6	2995	11	72714	2
	fir	5104	7	530	23	711	20	2763	11	2622	12	1184	17	4173	10	2481	13	617	23	294	30	369	30	365	27	412	25	480	24	22105	4
	pine	1001	16	440	22	478	35	357	24	1489	13	0	.	239	49	122	38	74	62	103	46	54	60	651	20	1136	16	123	44	6268	7
	larch	11	100	60	71	187	40	79	42	263	26	25	100	58	67	48	68	86	41	0	.	160	49	2817	10	2004	10	1327	12	7124	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	7	71	1	100	0	.	252	23	462	18	6	100	738	14
	other conifers	0	.	8	100	16	100	119	51	63	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	212	36
	beech	6177	7	2807	11	1952	14	3028	11	3582	8	671	22	2549	12	2560	12	813	21	429	32	815	20	81	49	352	30	1061	12	26876	3
	maple	589	13	255	23	85	34	106	29	402	22	99	28	200	27	447	18	152	24	70	35	132	28	29	62	23	60	35	48	2624	7
	ash	523	20	314	24	342	24	543	28	1290	13	78	44	167	28	346	20	61	42	63	62	114	44	21	60	36	49	20	111	3918	8
	oak	360	22	254	26	666	22	491	19	543	16	7	71	28	57	10	73	24	73	17	76	0	.	16	61	108	56	187	34	2710	9
	sweet chestnut	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	155	8	100	699	17	730	16
	other broadleaves	186	24	199	43	99	39	153	31	379	19	3	100	83	39	129	34	185	47	14	60	62	44	100	44	36	63	352	17	1979	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	21422	4	6290	8	8489	9	13007	6	17997	5	6976	9	17125	6	12700	7	7063	9	2733	15	3859	12	8527	6	14521	5	7291	6	148000	1
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6018	8	915	23	2256	14	4694	11	5063	10	3489	13	8054	8	6715	11	3972	12	1793	19	2080	17	3485	12	8863	8	3391	12	60786	3
	fir	4044	10	299	28	692	25	2390	14	1920	15	1153	21	4794	11	1867	14	638	24	361	36	404	36	322	30	631	24	635	29	20150	5
	pine	331	25	126	42	158	35	190	41	734	20	0	.	106	57	53	61	15	100	11	100	16	100	221	37	502	24	57	46	2520	10
	larch	36	72	15	90	125	45	47	59	253	34	12	100	102	72	153	45	154	44	11	100	128	48	3146	10	2061	13	1550	14	7794	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	0	0	.	0	.	206	29	411	20	0	.	627	16
	other conifers	0	.	23	100	92	60	129	45	85	73	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	362	30
	beech	2059	13	2109	16	882	24	2094	16	2797	12	464	29	1334	20	1214	18	735	34	231	34	251	36	46	56	193	45	818	31	15228	6
	maple	28	64	84	53	44	52	61	66	176	32	12	100	40	52	88	52	84	45	0	.	82	43	0	.	17	71	6	100	721	16
	ash	128	36	89	61	262	35	188	46	535	22	15	100	84	55	106	46	17	100	16	100	33	71	0	.	26	100	21	100	1520	13
	oak	213	40	366	31	490	32	247	30	837	20	33	74	33	58	99	48	7	100	38	60	0	.	23	77	15	71	45	61	2446	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	68	17	100	1761	19	1831	18
	other broadleaves	25	72	201	38	37	58	137	59	205	39	0	.	9	100	0	.	106	49	23	71	34	76	27	75	0	.	129	41	933	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12883	6	4226	11	5039	11	10175	8	12603	6	5177	11	14591	7	10306	9	5729	10	2484	16	3028	14	7529	8	12735	7	8413	7	114918	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18935	5	3237	15	8619	11	13467	8	17826	7	12454	9	23501	6	17854	7	12247	9	5179	14	5885	13	12438	7	26967	5	8630	8	187239	2
	fir	13256	6	1204	21	1998	17	7355	10	5676	10	3378	15	11531	8	5790	10	1579	18	1086	24	1015	24	1044	22	1206	19	1378	21	57496	3
	pine	1842	15	796	19	748	29	647	23	2623	12	6	100	511	38	188	37	122	45	358	32	168	53	2103	15	2785	13	365	38	13264	6
	larch	110	50	161	43	397	37	205	34	717	23	37	100	159	69	207	43	278	33	11	100	371	37	7779	8	5158	9	4101	9	19692	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	33	100	16	100	7	71	9	100	0	.	812	17	1385	15	6	91	2267	11
	other conifers	51	50	107	54	196	51	315	37	242	43	0	100	44	80	15	47	3	100	7	48	-10	110	12	85	45	40	7	100	1033	19
	beech	15412	5	7413	9	4658	12	7618	9	9516	7	1956	18	6549	10	6219	10	2392	20	1027	23	1815	18	511	24	943	23	4297	10	70327	2
	maple	1847	9	819	16	361	24	503	21	1580	12	398	21	794	15	1279	13	736	17	224	26	558	22	137	25	123	31	186	25	9546	4
	ash	1674	15	868	18	1199	17	1198	24	3109	10	390	28	621	21	1232	15	249	29	227	46	309	38	233	25	180	38	489	19	11979	5
	oak	1247	17	891	19	1722	19	938	17	1904	14	81	43	116	47	154	38	113	45	69	53	0	0	131	25	154	55	796	19	8315	7
	sweet chestnut	0	.	0	.	39	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	54	28	73	4936	10	5133	10
	other broadleaves	1029	11	764	27	491	23	625	23	1540	13	152	20	528	21	621	16	696	21	203	24	327	26	822	16	565	19	3131	8	11494	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	55404	3	16259	7	20428	7	32870	5	44733	4	18853	8	44386	5	33577	6	18423	8	8401	12	10438	10	26153	5	39539	4	28322	3	397785	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. 1280490. Birmensdorf, Swiss Federal Research Institute WSL

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