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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

diameter at breast height (10 classes) #1317
Diameter at breast height (dbh) of the trees and shrubs ≥12 cm dbh – in ten classes. Reference: Field Survey (MID 60: Brusthöhendurchmesser, MID 62: Umfang)

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). Reference: Field Survey (MID 50: Baumart)

production region #49
Demarcation of Switzerland into five regions (Jura, Plateau, Pre-Alps, Alps and Southern Alps) with relatively uniform growth and timber production conditions. The production regions were established by the Federal Office of Forestry long before the first National Forest Inventory (NFI1, 1983-1985). With a small exception along Lake Geneva, the boundaries of the production regions still follow the municipal boundaries of the time. Unlike the NFI, the Forestry Statistics of the Federal Statistical Office don't use the production regions as demarcation but rather the forestry zones, whose boundaries are somewhat different.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI5
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	956	15	1826	14	1842	12	3796	7	477	18	8898	5
	fir	892	12	798	15	931	15	326	21	64	36	3011	7
	pine	-10	300	28	53	28	75	422	21	8	100	477	21
	larch	7	88	122	51	29	51	437	22	161	34	755	17
	Arolla pine	0	.	0	.	0	.	203	37	3	100	206	37
	other conifers	58	36	75	37	23	42	52	29	1	100	209	19
	beech	1418	9	1177	10	921	14	634	19	894	15	5045	6
	maple	511	18	494	18	327	18	419	15	109	38	1862	9
	ash	232	20	352	24	174	25	273	27	197	26	1228	11
	oak	132	40	95	38	1	100	81	31	93	43	401	20
	sweet chestnut	0	.	0	.	0	.	5	100	664	20	669	20
	other broadleaves	309	14	666	13	330	15	1024	12	1026	14	3355	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	4506	6	5634	6	4607	7	7672	5	3697	7	26115	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1688	12	3433	11	4144	9	7715	7	1098	17	18078	4
	fir	1996	12	1495	16	2094	13	670	17	105	36	6360	7
	pine	299	37	112	33	99	73	1142	17	5	100	1657	14
	larch	14	100	164	42	42	82	915	15	690	23	1825	12
	Arolla pine	0	.	0	.	0	.	453	20	5	100	458	20
	other conifers	60	44	76	35	24	58	22	56	0	.	182	23
	beech	2863	10	2199	11	2399	11	1320	15	1643	18	10424	5
	maple	852	12	989	17	1050	14	709	19	80	42	3681	8
	ash	397	24	465	20	701	23	352	29	300	35	2215	12
	oak	339	34	375	28	36	53	99	36	270	26	1119	16
	sweet chestnut	0	.	0	.	0	.	13	74	1376	16	1389	16
	other broadleaves	342	20	768	15	426	25	914	14	1365	17	3814	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	8849	5	10077	6	11016	6	14325	5	6937	7	51203	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3056	12	3893	11	7747	7	11395	6	2097	19	28188	4
	fir	2492	12	1826	15	2908	12	1111	17	357	36	8695	7
	pine	659	24	358	26	160	50	1226	16	42	106	2444	12
	larch	5	201	115	49	10	100	2024	13	986	21	3140	11
	Arolla pine	0	.	0	.	0	.	542	20	0	.	542	20
	other conifers	18	71	120	44	0	.	-25	100	0	.	114	52
	beech	4988	8	3552	10	3341	10	1630	13	1407	16	14918	5
	maple	1018	15	833	16	1132	15	737	19	94	62	3813	8
	ash	457	20	1384	15	850	28	520	25	102	39	3314	11
	oak	412	24	356	27	42	52	77	39	251	27	1138	14
	sweet chestnut	0	.	0	.	0	.	33	74	897	19	931	19
	other broadleaves	232	28	610	18	203	27	256	24	1094	19	2396	11
	indeterminable	0	0	0	0	0	0	0	0	0	.	0	0
	total	13336	5	13046	5	16394	5	19527	4	7329	8	69632	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3775	9	4827	10	8942	7	14874	6	2532	17	34950	4
	fir	3488	10	2697	12	3401	9	1690	16	300	32	11577	6
	pine	534	21	755	19	250	61	1007	18	57	63	2603	12
	larch	41	58	217	36	13	100	3049	12	1041	16	4361	9
	Arolla pine	0	.	0	.	0	.	413	21	0	.	413	21
	other conifers	57	58	118	50	0	.	0	.	16	100	191	36
	beech	5616	8	4286	9	4053	10	1986	14	949	16	16890	5
	maple	800	15	378	22	593	18	483	22	52	49	2307	9
	ash	551	21	1335	16	662	24	292	32	106	61	2946	11
	oak	558	25	722	20	115	56	87	45	192	34	1674	14
	sweet chestnut	0	.	0	.	0	.	21	60	838	23	859	22
	other broadleaves	332	28	517	21	81	40	222	31	663	21	1815	12
	indeterminable	0	0	0	.	0	.	0	0	0	.	0	0
	total	15751	4	15853	5	18111	5	24124	4	6746	7	80585	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3665	11	3720	11	9278	7	12723	6	2375	19	31761	4
	fir	3282	11	3300	12	5310	10	1855	16	593	29	14340	6
	pine	376	25	741	23	172	49	683	23	27	100	1999	13
	larch	49	71	288	31	57	100	3613	11	1049	16	5057	9
	Arolla pine	0	.	0	.	0	.	315	26	0	.	315	26
	other conifers	142	68	266	51	0	.	0	.	28	100	436	39
	beech	3786	11	5242	8	2951	13	1418	18	686	21	14082	5
	maple	423	23	302	28	469	24	271	28	0	.	1465	13
	ash	249	35	1567	18	280	38	244	31	73	74	2412	13
	oak	282	32	957	19	0	.	112	54	99	46	1450	15
	sweet chestnut	0	.	0	.	0	.	8	100	537	27	545	27
	other broadleaves	109	61	448	33	117	45	267	42	79	51	1020	21
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	12363	5	16831	5	18635	5	21509	4	5545	10	74883	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2849	13	2694	14	6319	10	7671	8	1372	18	20906	5
	fir	1962	18	2525	15	3335	13	1143	22	138	50	9101	8
	pine	47	71	279	38	123	58	236	34	0	.	684	22
	larch	0	.	167	45	0	.	2847	14	669	22	3683	12
	Arolla pine	0	.	0	.	0	.	88	51	0	.	88	51
	other conifers	75	100	266	52	0	.	0	.	36	100	377	43
	beech	1530	18	2769	12	1113	22	809	26	475	30	6697	8
	maple	26	100	232	43	134	69	62	58	0	.	454	32
	ash	238	42	565	25	186	51	60	71	55	100	1103	19
	oak	120	60	535	33	0	0	32	100	34	100	721	27
	sweet chestnut	0	.	0	.	0	.	16	100	178	33	194	31
	other broadleaves	72	100	156	47	25	100	253	58	47	73	553	33
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	6919	9	10188	6	11235	7	13216	6	3002	11	44559	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1307	21	1429	20	3088	14	4919	11	984	30	11727	7
	fir	1146	20	1500	18	1938	17	1459	23	195	50	6238	10
	pine	41	100	161	50	0	.	62	71	0	.	264	38
	larch	41	100	0	.	84	71	1335	19	404	29	1864	15
	Arolla pine	0	.	0	.	0	.	151	38	0	.	151	38
	other conifers	0	.	277	60	0	.	0	.	0	.	277	60
	beech	347	40	1399	21	695	33	325	38	170	41	2936	14
	maple	72	72	34	100	19	100	0	.	0	.	126	52
	ash	79	71	95	72	96	71	71	71	0	.	341	36
	oak	114	71	259	42	0	.	10	100	0	.	383	35
	sweet chestnut	0	.	0	.	0	.	0	.	168	38	168	38
	other broadleaves	74	71	141	51	0	.	38	100	27	100	281	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	3223	13	5295	10	5920	10	8370	8	1948	17	24755	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	345	48	760	29	1007	24	1555	19	333	48	4000	13
	fir	392	42	1046	26	896	30	347	45	50	100	2731	16
	pine	0	.	49	100	0	.	0	.	0	.	49	100
	larch	0	.	0	.	0	.	702	31	270	51	972	26
	Arolla pine	0	.	0	.	0	.	116	58	0	.	116	58
	other conifers	61	100	235	60	81	100	0	.	0	.	376	46
	beech	48	100	169	58	109	72	96	71	171	45	593	29
	maple	0	.	0	.	0	.	27	100	0	.	27	100
	ash	66	100	76	100	140	71	0	.	0	.	282	50
	oak	0	.	215	51	45	100	37	100	0	.	298	42
	sweet chestnut	0	.	0	.	0	.	0	.	153	46	153	46
	other broadleaves	0	.	70	100	0	.	0	.	0	.	70	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	911	28	2619	16	2278	17	2879	14	978	27	9667	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	127	71	169	58	105	71	884	25	154	71	1438	20
	fir	214	58	126	71	119	71	146	71	204	58	807	29
	pine	0	.	0	.	0	.	57	100	0	.	57	100
	larch	0	.	0	.	0	.	276	41	0	.	276	41
	Arolla pine	0	.	0	.	0	.	30	100	0	.	30	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	281	53	55	100	0	.	0	.	337	47
	maple	0	.	0	.	0	.	25	100	0	.	25	100
	ash	0	.	119	71	0	.	0	.	0	.	119	71
	oak	0	.	89	71	0	.	0	.	0	.	89	71
	sweet chestnut	0	.	0	.	0	.	29	100	145	59	174	52
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	340	45	784	29	279	45	1447	19	502	36	3352	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	81	100	155	71	294	45	426	39	184	72	1141	25
	fir	174	72	0	.	133	100	0	.	0	.	307	60
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	52	100	129	100	182	77
	Arolla pine	0	.	0	.	0	.	75	71	0	.	75	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	194	100	0	.	0	.	0	.	194	100
	sweet chestnut	0	.	0	.	0	.	0	.	374	60	374	60
	other broadleaves	0	.	66	100	0	.	0	.	0	.	66	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	255	59	415	56	427	44	554	33	687	42	2338	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17851	6	22906	7	42767	5	65957	4	11606	13	161087	3
	fir	16037	8	15313	8	21065	7	8747	11	2006	24	63168	4
	pine	1946	17	2482	15	832	40	4835	12	138	56	10232	8
	larch	156	39	1074	20	236	40	15250	8	5398	12	22114	6
	Arolla pine	0	.	0	.	0	.	2385	15	8	100	2393	15
	other conifers	470	56	1434	34	128	65	50	57	82	98	2163	26
	beech	20597	5	21073	6	15637	7	8218	10	6395	12	71921	3
	maple	3701	9	3264	11	3726	10	2733	11	336	30	13760	5
	ash	2269	14	5956	11	3089	17	1813	17	832	32	13959	7
	oak	1956	18	3797	15	240	39	534	29	939	21	7465	10
	sweet chestnut	0	.	0	.	0	.	126	43	5330	13	5456	12
	other broadleaves	1469	15	3443	12	1182	14	2975	14	4300	12	13369	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	66452	3	80741	3	88902	3	113622	2	37372	5	387089	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. 1218057. Birmensdorf, Swiss Federal Research Institute WSL

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