Selection from the list of topics

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total number of stems #3
Number of stems of all living and dead trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm. The total number of stems is the sum of the number of stems and the number of deadwood stems.

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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NFI4
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	8314	11	12116	10	16801	8	31460	6	3533	15	72225	4
	fir	7458	9	5075	13	7501	10	3117	14	502	32	23653	5
	pine	289	31	394	58	330	68	5415	18	81	100	6510	15
	larch	17	100	492	69	293	58	4357	12	1824	22	6984	11
	Arolla pine	0	.	0	.	0	.	1560	19	32	100	1592	19
	other conifers	524	35	593	34	146	35	1036	33	0	.	2300	19
	beech	9811	8	7956	7	7577	10	5506	13	6243	15	37093	4
	maple	3221	14	2769	15	2494	17	3339	14	628	32	12451	7
	ash	1815	20	2550	14	1598	18	2071	18	1498	26	9532	8
	oak	1258	36	774	34	96	45	925	30	1342	36	4394	17
	sweet chestnut	0	.	36	71	0	.	37	100	8405	15	8478	15
	other broadleaves	3215	12	4091	12	3625	13	11461	10	10274	13	32665	6
	indeterminable	124	48	67	50	198	34	359	24	99	66	846	17
	total	36046	4	36914	5	40658	5	70643	4	34463	6	218723	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5019	11	6903	11	11701	8	19751	6	2715	14	46089	4
	fir	3750	12	2278	14	3701	11	1781	14	409	35	11920	6
	pine	800	28	390	25	226	75	3425	15	120	54	4961	12
	larch	34	71	223	52	51	100	2837	13	1757	17	4902	10
	Arolla pine	0	.	0	.	0	.	1358	19	16	100	1374	19
	other conifers	120	43	257	34	35	71	105	41	0	.	517	22
	beech	6733	8	5351	8	5316	9	3330	13	3851	16	24580	5
	maple	1573	12	1930	16	2214	13	1508	15	229	38	7455	7
	ash	882	19	1403	14	1829	17	1198	20	549	32	5862	9
	oak	976	28	865	22	123	46	322	33	637	25	2924	13
	sweet chestnut	0	.	0	.	0	.	52	76	4402	14	4454	14
	other broadleaves	822	19	1515	16	1124	25	2921	13	4154	12	10536	7
	indeterminable	19	100	0	.	107	41	102	41	20	100	248	27
	total	20730	5	21115	5	26427	5	38691	4	18860	5	125822	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3307	11	4133	9	7785	7	13473	5	2075	16	30773	3
	fir	2414	11	1590	13	2585	9	972	17	254	37	7815	6
	pine	780	20	595	22	169	58	1814	15	79	54	3438	10
	larch	62	48	175	34	14	71	2422	12	1092	19	3765	10
	Arolla pine	0	.	0	.	0	.	699	21	0	.	699	21
	other conifers	45	64	82	53	0	.	15	100	0	.	142	38
	beech	5188	7	3091	9	3268	11	1584	13	1226	17	14358	5
	maple	800	16	693	16	896	16	729	19	74	43	3191	8
	ash	540	18	1408	15	861	22	680	35	98	36	3586	11
	oak	429	25	540	19	29	77	70	47	271	29	1339	13
	sweet chestnut	0	.	7	100	0	.	29	73	1518	18	1553	17
	other broadleaves	403	23	713	19	254	27	518	20	1259	15	3146	9
	indeterminable	35	100	17	100	13	71	48	54	7	100	119	40
	total	14003	4	13041	5	15875	4	23053	4	7952	6	73924	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2346	8	2674	8	5372	6	8446	5	1363	14	20201	3
	fir	1918	10	1294	11	1773	9	829	15	154	30	5968	5
	pine	435	20	425	17	143	63	602	18	28	62	1633	11
	larch	7	100	110	34	7	100	1984	11	600	17	2708	9
	Arolla pine	0	.	0	.	0	.	334	20	0	.	334	20
	other conifers	28	71	81	45	0	.	0	.	8	100	117	36
	beech	2919	7	2352	7	1933	11	988	14	562	18	8754	4
	maple	369	16	179	23	317	18	253	21	7	100	1125	9
	ash	267	22	831	14	303	22	214	26	62	48	1678	9
	oak	250	24	403	19	44	53	62	44	138	39	897	13
	sweet chestnut	0	.	0	.	0	.	21	58	601	19	622	18
	other broadleaves	156	38	339	21	75	39	171	23	306	21	1047	12
	indeterminable	15	71	0	.	30	51	69	37	0	.	114	28
	total	8711	4	8687	4	9996	4	13974	3	3830	7	45198	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1424	9	1726	9	3374	7	4762	5	790	16	12076	4
	fir	949	12	1060	11	1334	10	510	15	163	30	4016	6
	pine	104	29	258	19	58	55	264	24	14	100	698	13
	larch	14	70	84	35	21	74	1490	10	397	15	2005	8
	Arolla pine	0	.	0	.	0	.	121	27	0	.	121	27
	other conifers	21	100	47	55	0	.	0	.	8	100	76	45
	beech	1246	10	1404	9	808	14	482	16	275	19	4215	5
	maple	105	25	90	34	94	29	124	42	8	100	421	17
	ash	103	26	469	18	73	39	69	36	0	.	714	14
	oak	58	43	261	22	0	.	34	53	18	72	371	18
	sweet chestnut	0	.	0	.	0	.	6	100	259	25	265	24
	other broadleaves	36	52	76	45	25	58	132	42	39	45	308	23
	indeterminable	0	.	0	.	0	.	29	50	0	.	29	50
	total	4060	5	5476	4	5788	5	8022	4	1971	8	25316	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	550	14	571	13	1454	9	1705	8	373	18	4652	5
	fir	419	16	589	14	615	15	262	20	29	50	1914	8
	pine	15	71	41	41	25	59	65	40	0	.	145	25
	larch	0	.	34	45	0	.	600	15	194	23	828	12
	Arolla pine	0	.	0	.	0	.	43	41	0	.	43	41
	other conifers	0	.	54	43	0	.	0	.	0	.	54	43
	beech	290	19	447	14	255	20	128	27	121	31	1242	9
	maple	7	100	28	50	24	79	24	59	0	.	83	34
	ash	52	38	88	40	37	45	6	100	10	100	194	23
	oak	28	59	95	28	0	.	0	.	15	71	138	24
	sweet chestnut	0	.	0	.	0	.	7	100	83	30	90	28
	other broadleaves	23	58	20	58	18	100	26	50	8	100	95	31
	indeterminable	0	.	0	.	6	100	7	100	0	.	13	71
	total	1384	9	1967	7	2434	7	2873	6	833	11	9491	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	160	24	277	19	573	15	736	12	203	23	1949	8
	fir	138	25	143	25	285	17	134	24	30	49	730	11
	pine	7	100	7	100	0	.	14	71	0	.	28	50
	larch	7	100	0	.	16	71	302	18	71	34	396	15
	Arolla pine	0	.	0	.	0	.	50	37	0	.	50	37
	other conifers	7	100	42	57	0	.	0	.	0	.	49	51
	beech	64	37	152	25	82	34	53	39	44	41	395	15
	maple	9	100	0	.	6	100	8	100	0	.	23	58
	ash	14	71	34	50	18	72	14	71	0	.	79	32
	oak	18	70	28	50	8	100	15	71	0	.	69	33
	sweet chestnut	0	.	0	.	0	.	0	.	92	37	92	37
	other broadleaves	0	.	21	58	0	.	6	100	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	424	14	704	11	987	11	1331	9	447	14	3893	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	54	80	31	140	35	284	19	77	32	617	14
	fir	51	38	61	37	49	43	43	41	8	100	212	19
	pine	0	.	7	100	0	.	7	100	0	.	14	71
	larch	0	.	0	.	0	.	100	30	43	52	144	26
	Arolla pine	0	.	0	.	0	.	22	58	0	.	22	58
	other conifers	0	.	8	100	7	100	0	.	0	.	15	71
	beech	7	100	49	42	16	71	22	58	22	58	115	26
	maple	0	.	0	.	0	.	6	100	0	.	6	100
	ash	0	.	17	72	7	100	0	.	0	.	24	58
	oak	0	.	43	41	0	.	0	.	0	.	43	41
	sweet chestnut	0	.	0	.	0	.	8	100	57	42	65	39
	other broadleaves	7	100	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	101	29	264	18	220	25	492	14	207	21	1284	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	7	100	34	45	67	33	7	100	123	24
	fir	29	50	13	71	8	100	7	100	18	72	75	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	30	50	0	.	30	50
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	7	100	7	100	0	.	0	.	14	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	7	100	0	.	0	.	0	.	7	100
	oak	0	.	16	100	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	37	52	37	52
	other broadleaves	0	.	23	100	0	.	0	.	0	.	23	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	36	45	73	44	50	38	119	25	62	39	340	16
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	14	71	14	71	59	35	19	72	112	26
	fir	0	.	14	71	7	100	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	7	100	14	100	22	74
	Arolla pine	0	.	0	.	0	.	14	71	0	.	14	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	.	51	49	51	49
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	28	50	21	58	80	30	83	38	220	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21169	7	28501	7	47248	5	80741	4	11156	11	188816	3
	fir	17126	7	12117	8	17859	7	7657	11	1567	25	56325	4
	pine	2431	17	2117	18	951	54	11607	13	321	57	17427	10
	larch	141	43	1116	34	403	51	14130	8	5992	14	21782	7
	Arolla pine	0	.	0	.	0	.	4214	15	49	100	4263	15
	other conifers	745	29	1164	24	188	30	1156	31	16	100	3270	15
	beech	26259	5	20809	5	19262	7	12092	10	12345	13	90766	3
	maple	6084	9	5688	11	6045	11	5992	11	946	27	24756	5
	ash	3674	14	6806	9	4726	13	4252	16	2218	25	21677	6
	oak	3017	23	3025	16	300	29	1427	27	2422	26	10191	11
	sweet chestnut	0	.	43	62	0	.	161	61	15504	12	15707	12
	other broadleaves	4661	11	6799	10	5120	13	15235	10	16046	10	47861	5
	indeterminable	193	37	84	44	354	25	614	18	126	59	1370	13
	total	85501	3	88270	3	102455	3	159278	3	68707	4	504211	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. 1296680. Birmensdorf, Swiss Federal Research Institute WSL

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