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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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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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
grid: 1.4-km grid
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	9079	9	11466	7	18233	6	37691	4	5458	10	81927	3
	fir	7325	6	5091	11	7643	7	3256	12	696	27	24011	4
	pine	389	32	291	43	414	41	7200	11	106	55	8400	10
	larch	82	50	356	53	247	49	7179	9	3426	12	11289	7
	Arolla pine	0	.	0	.	30	74	2311	13	38	61	2380	12
	other conifers	505	30	631	29	247	31	1388	22	40	79	2810	14
	beech	9644	6	8605	6	7348	7	5835	10	7165	10	38597	3
	maple	3388	11	3353	10	2925	12	3962	9	1302	19	14930	5
	ash	1687	15	3275	10	1727	14	2437	12	1837	15	10964	6
	oak	1079	27	937	23	65	41	1238	22	1213	25	4532	12
	sweet chestnut	0	.	50	52	0	.	172	71	7802	12	8024	11
	other broadleaves	3800	10	4703	9	4356	10	14527	7	12797	7	40183	4
	indeterminable	191	27	47	45	168	27	385	18	164	31	955	12
	total	37169	3	38804	4	43403	3	87580	3	42045	4	249002	1
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4980	8	7130	8	12243	6	23539	4	3254	10	51145	3
	fir	3635	8	2371	11	3864	8	1922	11	432	24	12224	4
	pine	679	22	353	22	293	42	3921	11	105	43	5351	9
	larch	68	52	227	32	39	78	4319	8	2361	11	7013	6
	Arolla pine	0	.	0	.	52	73	1893	13	10	100	1954	13
	other conifers	168	41	294	24	51	45	217	31	0	.	730	17
	beech	6530	7	5122	6	4900	7	3454	10	3851	11	23858	4
	maple	1797	9	2033	12	2288	10	1854	12	280	25	8252	5
	ash	1020	15	1581	11	1508	15	1237	14	1020	21	6366	7
	oak	1025	21	821	16	115	40	419	22	661	19	3041	10
	sweet chestnut	0	.	29	100	0	.	60	47	4041	11	4130	10
	other broadleaves	1167	16	1764	11	1204	16	3500	9	4755	9	12389	5
	indeterminable	40	50	19	71	80	35	217	22	20	71	377	17
	total	21109	4	21744	3	26636	4	46552	3	20790	4	136830	1
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3278	7	4141	7	8162	5	14896	4	2109	11	32586	2
	fir	2470	8	1631	9	2564	7	911	12	261	29	7837	4
	pine	779	17	580	16	212	37	1928	11	184	51	3683	8
	larch	50	36	217	26	16	50	3111	8	1436	11	4831	6
	Arolla pine	0	.	0	.	44	91	954	13	10	100	1009	13
	other conifers	75	52	130	38	10	100	13	76	0	.	229	28
	beech	5270	6	3172	7	3307	8	1665	10	1387	12	14801	3
	maple	902	11	766	13	786	13	750	14	163	32	3368	6
	ash	632	14	1276	12	699	17	573	25	293	24	3474	8
	oak	502	20	481	15	71	37	111	35	367	19	1532	10
	sweet chestnut	0	.	25	86	0	.	38	66	1446	13	1509	12
	other broadleaves	540	16	641	14	248	22	798	14	1577	10	3803	6
	indeterminable	29	74	17	63	12	58	119	32	19	66	195	24
	total	14526	3	13078	3	16132	3	25870	3	9251	4	78857	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2621	6	2513	6	5929	4	9018	3	1359	10	21440	2
	fir	1836	7	1182	9	2025	6	784	11	171	22	5998	4
	pine	470	15	400	13	142	40	812	11	73	42	1897	7
	larch	34	61	104	25	29	51	2319	8	813	12	3299	6
	Arolla pine	0	.	0	.	13	100	435	13	0	.	448	13
	other conifers	45	52	63	37	0	.	0	.	4	100	113	30
	beech	3175	5	2322	6	1971	8	947	10	711	12	9126	3
	maple	376	12	246	15	258	14	276	15	29	65	1186	7
	ash	335	15	776	10	327	16	194	20	94	27	1726	7
	oak	253	18	449	14	30	47	95	29	131	28	958	10
	sweet chestnut	0	.	8	100	0	.	16	50	565	14	588	13
	other broadleaves	196	23	291	18	81	27	163	19	372	13	1103	8
	indeterminable	8	71	8	71	17	50	70	27	4	100	106	21
	total	9348	3	8362	3	10820	3	15130	2	4326	5	47987	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1513	7	1611	7	3743	5	5045	4	791	11	12703	3
	fir	1055	8	994	9	1476	7	518	11	136	26	4178	4
	pine	152	25	260	16	46	48	366	15	23	52	847	10
	larch	19	45	110	23	40	45	1693	8	547	11	2410	6
	Arolla pine	0	.	0	.	0	.	187	17	4	100	191	17
	other conifers	12	100	44	46	0	.	0	.	4	100	60	40
	beech	1389	7	1435	7	913	9	504	15	333	14	4574	4
	maple	110	21	95	25	103	22	84	36	9	71	399	13
	ash	130	20	359	15	84	27	60	29	16	60	650	10
	oak	119	30	316	16	13	58	44	37	31	42	523	12
	sweet chestnut	0	.	15	100	0	.	4	100	306	17	325	17
	other broadleaves	70	48	99	27	27	41	154	33	58	30	407	17
	indeterminable	0	.	5	100	4	100	28	38	0	.	37	33
	total	4569	4	5342	3	6450	3	8686	3	2258	6	27305	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	604	10	566	11	1545	7	1934	6	342	14	4991	4
	fir	493	13	560	11	726	10	235	16	36	33	2050	6
	pine	42	38	67	28	23	45	78	27	4	100	214	16
	larch	4	100	49	36	14	72	662	10	201	16	931	8
	Arolla pine	0	.	0	.	4	100	93	23	0	.	97	23
	other conifers	0	.	40	37	0	.	0	.	0	.	40	37
	beech	323	14	537	10	332	14	146	21	117	22	1455	6
	maple	4	100	24	41	13	78	21	52	0	.	63	30
	ash	43	32	103	27	30	38	12	58	10	71	197	17
	oak	54	39	118	20	9	70	5	100	13	58	198	17
	sweet chestnut	0	.	0	.	0	.	20	82	123	22	142	22
	other broadleaves	24	41	29	45	10	100	21	46	29	48	114	23
	indeterminable	0	.	4	100	4	100	4	100	0	.	12	58
	total	1591	6	2099	5	2710	5	3231	4	874	8	10504	2
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	206	17	232	15	647	10	1040	9	202	17	2329	5
	fir	172	18	176	18	298	13	125	18	41	37	812	8
	pine	4	100	4	100	0	.	13	58	0	.	21	45
	larch	4	100	0	.	17	50	303	14	89	23	413	12
	Arolla pine	0	.	0	.	0	.	46	31	0	.	46	31
	other conifers	4	100	29	52	0	.	0	.	0	.	33	47
	beech	116	21	192	16	106	22	36	36	49	37	499	10
	maple	5	100	0	.	4	100	13	58	0	.	21	45
	ash	20	53	51	34	14	59	8	71	0	.	93	24
	oak	22	45	49	41	5	100	13	58	4	100	93	26
	sweet chestnut	0	.	0	.	0	.	5	100	110	23	115	22
	other broadleaves	6	100	17	50	0	.	8	71	10	73	41	34
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	559	10	750	9	1092	7	1609	7	506	10	4515	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	45	35	81	24	161	22	311	13	103	21	700	9
	fir	43	32	65	26	93	23	40	32	13	57	253	13
	pine	0	.	4	100	0	.	4	100	0	.	8	71
	larch	0	.	4	100	0	.	103	21	59	32	166	18
	Arolla pine	0	.	0	.	0	.	36	33	0	.	36	33
	other conifers	4	100	4	100	4	100	0	.	0	.	12	58
	beech	24	41	67	28	21	52	16	50	16	50	144	18
	maple	0	.	6	100	4	100	4	100	0	.	13	59
	ash	0	.	14	58	4	100	0	.	0	.	18	50
	oak	14	59	29	38	14	58	0	.	0	.	58	28
	sweet chestnut	0	.	0	.	0	.	4	100	69	31	73	30
	other broadleaves	10	73	4	100	0	.	0	.	4	100	19	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	139	18	278	13	301	14	518	10	265	14	1500	6
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	8	71	43	32	87	25	23	40	164	17
	fir	28	43	8	71	17	50	16	50	14	58	83	23
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	56	28	10	100	66	28
	Arolla pine	0	.	0	.	0	.	16	50	0	.	16	50
	other conifers	0	.	4	100	0	.	0	.	0	.	4	100
	beech	4	100	9	71	12	58	0	.	8	71	33	36
	maple	0	.	0	.	0	.	0	.	4	100	4	100
	ash	0	.	4	100	0	.	0	.	0	.	4	100
	oak	10	73	9	100	0	.	0	.	0	.	19	61
	sweet chestnut	0	.	0	.	0	.	0	.	25	47	25	47
	other broadleaves	0	.	17	80	0	.	4	100	0	.	21	67
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	46	33	59	34	72	24	178	16	84	25	439	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	8	71	17	50	95	31	32	42	155	22
	fir	0	.	12	58	8	71	8	71	8	71	36	33
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	16	50	15	61	32	39
	Arolla pine	0	.	0	.	0	.	8	71	0	.	8	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	11	100	11	100
	maple	0	.	0	.	0	.	12	75	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	4	100	7	71	0	.	0	.	0	.	11	58
	sweet chestnut	0	.	0	.	0	.	0	.	77	38	77	38
	other broadleaves	15	74	22	100	0	.	0	.	4	100	41	61
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	23	54	50	49	25	41	139	23	147	24	383	15
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22334	6	27755	5	50723	4	93655	3	13673	8	208140	2
	fir	17056	5	12091	7	18715	5	7814	9	1808	19	57483	3
	pine	2513	14	1960	13	1130	33	14322	9	496	41	20420	7
	larch	261	34	1068	21	403	35	19761	6	8958	8	30450	4
	Arolla pine	0	.	0	.	143	75	5979	10	62	56	6184	10
	other conifers	813	27	1239	21	312	27	1618	21	49	68	4031	12
	beech	26475	4	21462	4	18910	5	12603	8	13648	9	93098	3
	maple	6581	7	6522	8	6381	9	6976	8	1787	17	28248	4
	ash	3868	10	7438	7	4394	10	4521	11	3271	14	23492	4
	oak	3082	17	3217	11	322	22	1924	19	2420	17	10965	8
	sweet chestnut	0	.	127	74	0	.	319	58	14562	9	15008	9
	other broadleaves	5828	10	7587	7	5925	9	19176	7	19606	7	58122	4
	indeterminable	268	22	99	28	284	21	824	12	207	29	1681	9
	total	89079	2	90565	2	107641	2	189492	2	80545	3	557321	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. 1358369. Birmensdorf, Swiss Federal Research Institute WSL

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