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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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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 (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 1993/95
		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	1650	14	3027	12	2843	9	3285	8	289	17	11094	5
	fir	891	12	749	17	1058	11	362	21	67	38	3128	7
	pine	94	51	126	48	33	80	743	20	9	100	1005	17
	larch	4	88	32	44	12	100	365	17	187	25	600	13
	Arolla pine	0	.	0	.	0	.	143	29	1	100	144	29
	other conifers	24	36	49	37	15	44	37	49	0	.	124	22
	beech	1907	10	1537	9	1141	13	724	18	933	19	6242	6
	maple	373	14	272	27	436	19	310	23	36	32	1427	10
	ash	291	23	377	25	430	18	263	24	141	27	1501	10
	oak	207	33	298	33	29	45	70	30	206	43	809	19
	sweet chestnut	0	.	1	100	0	.	2	100	1034	17	1038	17
	other broadleaves	369	16	520	20	476	16	892	13	993	12	3250	7
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	5810	6	6988	7	6473	6	7195	5	3896	7	30362	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2888	11	4473	10	6720	8	8547	7	1050	18	23679	4
	fir	2131	12	2085	14	1975	12	558	18	95	45	6844	7
	pine	307	33	174	30	46	100	1102	15	41	69	1670	13
	larch	29	58	174	37	0	.	1294	15	531	21	2028	11
	Arolla pine	0	.	0	.	0	.	387	24	0	.	387	24
	other conifers	56	54	87	51	0	.	-16	145	0	.	127	46
	beech	4407	8	3326	9	2969	13	1186	13	1010	19	12898	5
	maple	909	14	764	16	873	16	611	23	65	42	3221	8
	ash	700	21	981	13	884	20	483	26	100	37	3148	9
	oak	373	25	661	22	97	56	74	38	210	33	1416	14
	sweet chestnut	0	.	0	.	0	.	40	65	1158	17	1198	17
	other broadleaves	391	19	757	20	373	21	497	16	1038	14	3056	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	12191	5	13482	5	13938	5	14763	5	5298	7	59671	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4509	10	8856	8	10210	7	13279	6	1738	16	38591	4
	fir	3323	11	3247	11	3486	11	974	18	177	37	11207	6
	pine	886	20	1135	19	135	45	1290	15	81	67	3526	10
	larch	80	64	256	38	10	100	1922	12	724	22	2992	10
	Arolla pine	0	.	0	.	0	.	510	20	0	.	510	20
	other conifers	46	100	88	50	0	.	0	.	12	100	147	45
	beech	6355	8	4785	8	3771	11	1564	16	741	18	17217	5
	maple	851	16	392	19	813	17	582	19	17	100	2654	9
	ash	914	24	1735	19	530	21	341	32	28	73	3549	12
	oak	502	22	813	22	108	79	25	71	162	43	1609	15
	sweet chestnut	0	.	0	.	0	.	33	63	570	24	603	23
	other broadleaves	402	24	741	20	139	35	430	22	533	21	2245	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	17866	4	22048	4	19201	5	20950	4	4784	8	84848	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5618	9	11395	7	12826	7	14185	6	2046	16	46069	3
	fir	3238	10	4225	10	5114	10	1416	16	457	32	14451	5
	pine	662	19	1656	16	352	53	1006	20	30	57	3706	11
	larch	32	72	321	28	16	100	3206	10	752	19	4327	9
	Arolla pine	0	.	0	.	0	.	420	22	0	.	420	22
	other conifers	16	100	181	44	0	.	0	.	0	.	197	41
	beech	5079	9	5648	8	3594	12	1482	16	516	22	16319	5
	maple	491	19	197	31	424	24	227	25	16	100	1354	12
	ash	443	22	1618	17	304	26	224	30	-43	90	2547	12
	oak	283	30	1013	18	0	.	96	65	136	65	1528	15
	sweet chestnut	0	.	0	.	0	.	-5	100	410	25	406	25
	other broadleaves	152	35	304	26	144	39	226	46	147	25	975	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	16014	5	26559	4	22775	5	22484	4	4466	9	92298	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5124	10	8370	9	9976	7	10557	7	1765	15	35792	4
	fir	1990	14	3241	12	4056	11	1492	17	130	47	10908	7
	pine	281	30	856	21	142	57	575	28	36	71	1891	14
	larch	3	100	235	39	92	61	3014	12	735	20	4078	10
	Arolla pine	0	.	0	.	0	.	230	32	0	.	230	32
	other conifers	0	.	224	54	0	.	0	.	0	.	224	54
	beech	2793	12	3412	11	2216	18	990	20	325	36	9736	7
	maple	105	44	269	34	75	69	90	41	0	.	538	22
	ash	159	46	605	23	154	46	56	70	39	100	1014	18
	oak	346	33	725	24	0	.	13	100	17	100	1102	19
	sweet chestnut	0	.	0	.	0	.	19	72	259	31	278	29
	other broadleaves	122	54	50	74	10	100	299	36	13	100	494	27
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	10922	6	17988	5	16721	6	17333	5	3318	10	66283	3
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1769	17	3656	12	5093	11	5140	10	1063	24	16721	6
	fir	960	21	1098	23	1875	17	720	23	151	51	4806	10
	pine	103	59	157	45	75	71	97	50	0	.	433	27
	larch	0	.	64	71	131	59	1349	16	409	26	1954	13
	Arolla pine	0	.	0	.	0	.	88	45	0	.	88	45
	other conifers	0	.	137	63	0	.	0	.	0	.	137	63
	beech	958	24	1352	22	545	30	336	34	224	37	3414	13
	maple	37	100	22	100	36	71	0	.	0	.	95	53
	ash	126	66	247	38	42	100	74	72	0	.	489	29
	oak	76	74	162	46	48	100	0	.	0	.	286	37
	sweet chestnut	0	.	0	.	0	.	0	.	121	36	121	36
	other broadleaves	27	100	115	60	0	.	49	71	26	100	217	40
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	4057	11	7010	9	7845	9	7854	8	1995	15	28760	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	644	28	801	24	1465	20	2380	13	736	30	6027	9
	fir	568	32	557	29	917	27	243	46	52	100	2338	16
	pine	0	.	40	100	0	.	65	72	0	.	106	59
	larch	38	100	44	100	0	.	957	21	182	51	1221	19
	Arolla pine	0	.	0	.	0	.	141	39	0	.	141	39
	other conifers	0	.	0	.	59	100	0	.	0	.	59	100
	beech	332	47	849	28	232	52	58	100	66	58	1537	21
	maple	0	.	0	.	0	.	52	73	0	.	52	73
	ash	0	.	39	100	51	100	0	.	0	.	90	71
	oak	0	.	254	41	0	.	25	100	0	.	279	39
	sweet chestnut	0	.	0	.	0	.	27	100	197	36	224	34
	other broadleaves	0	.	35	100	0	.	0	.	20	100	55	73
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	1582	19	2619	14	2724	15	3949	10	1253	21	12128	7
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	164	58	381	38	318	38	636	25	27	100	1527	17
	fir	109	71	222	50	364	41	203	60	124	72	1021	25
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	221	41	37	100	258	38
	Arolla pine	0	.	0	.	0	.	24	100	0	.	24	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	254	63	37	100	137	52	0	.	428	42
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	109	71	0	.	0	.	0	.	109	71
	oak	0	.	185	63	0	.	0	.	0	.	185	63
	sweet chestnut	0	.	0	.	0	.	0	.	226	46	226	46
	other broadleaves	0	.	148	72	0	.	0	.	0	.	148	72
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	273	45	1300	23	718	27	1222	19	414	35	3926	12
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	57	100	154	71	200	58	348	38	85	100	844	27
	fir	57	100	0	.	142	71	0	.	0	.	198	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	87	71	60	100	147	59
	Arolla pine	0	.	0	.	0	.	35	100	0	.	35	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	87	100	141	71	0	.	0	.	0	.	228	58
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	60	100	0	.	0	.	0	.	60	100
	sweet chestnut	0	.	0	.	0	.	0	.	121	53	121	53
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	201	59	355	45	342	45	470	32	266	46	1633	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	55	100	178	59	71	100	304	46
	fir	0	.	0	.	0	.	0	.	96	100	96	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	51	100	69	100	120	71
	Arolla pine	0	.	0	.	0	.	70	71	0	.	70	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	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	485	49	485	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	55	100	299	43	721	38	1075	29
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22423	6	41112	5	49705	5	58534	4	8870	12	180646	2
	fir	13267	8	15425	8	18988	7	5970	12	1348	28	54997	4
	pine	2332	15	4146	13	783	41	4879	12	196	47	12336	8
	larch	185	38	1125	24	260	42	12467	8	3687	14	17724	6
	Arolla pine	0	.	0	.	0	.	2049	15	1	100	2050	15
	other conifers	141	63	766	34	73	80	21	126	12	100	1013	28
	beech	21918	5	21304	6	14504	9	6477	10	3816	13	68018	3
	maple	2765	10	1914	12	2657	11	1871	14	133	34	9341	6
	ash	2633	17	5713	12	2395	14	1440	21	265	29	12447	7
	oak	1787	18	4172	12	282	41	302	31	731	25	7274	9
	sweet chestnut	0	.	1	100	0	.	117	51	4580	13	4698	13
	other broadleaves	1464	15	2670	14	1143	14	2393	13	2770	11	10440	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	68916	3	98348	2	90791	3	96519	3	26410	5	380985	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. 1217903. Birmensdorf, Swiss Federal Research Institute WSL

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