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 NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is 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 NFI4/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	8384	11	12214	10	18768	8	36864	5	4570	14	80801	4
	fir	7553	9	5075	13	7501	10	3219	14	502	32	23849	5
	pine	289	31	394	58	330	68	6289	16	81	100	7383	14
	larch	17	100	492	69	293	58	5993	11	3450	15	10245	9
	Arolla pine	0	.	0	.	0	.	2363	18	32	100	2396	17
	other conifers	524	35	593	34	146	35	1336	28	0	.	2599	18
	beech	9934	7	8086	7	7595	10	5791	12	6544	14	37950	4
	maple	3276	14	2824	15	2824	16	3966	13	1133	29	14024	7
	ash	1838	20	2676	14	1744	18	2762	15	1603	24	10622	8
	oak	1258	36	774	34	96	45	977	28	1396	35	4500	17
	sweet chestnut	0	.	36	71	0	.	247	86	8663	15	8947	15
	other broadleaves	3397	12	4251	12	4614	13	14125	10	12298	11	38686	6
	indeterminable	124	48	67	50	215	32	414	23	151	48	971	16
	total	36594	4	37483	5	44126	5	84346	3	40424	5	242973	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5090	11	7020	10	13038	8	22325	6	3187	13	50660	4
	fir	3770	12	2278	14	3735	11	1818	13	427	33	12028	6
	pine	800	28	390	25	226	75	3678	14	120	54	5214	12
	larch	34	71	223	52	51	100	3801	12	2550	14	6659	9
	Arolla pine	0	.	0	.	0	.	1722	19	16	100	1738	19
	other conifers	120	43	257	34	35	71	192	43	0	.	604	22
	beech	6845	8	5369	8	5394	9	3571	12	4010	16	25189	5
	maple	1710	12	1930	16	2379	13	1801	15	245	37	8067	7
	ash	882	19	1422	14	2016	18	1513	18	656	28	6489	9
	oak	976	28	899	22	168	43	378	29	726	23	3146	13
	sweet chestnut	0	.	0	.	0	.	73	62	4656	14	4729	14
	other broadleaves	875	18	1601	15	1370	22	3322	12	5023	12	12190	7
	indeterminable	19	100	0	.	107	41	102	41	20	100	248	27
	total	21123	5	21388	5	28518	5	44297	4	21637	5	136962	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3383	10	4133	9	8586	6	14595	5	2214	15	32910	3
	fir	2422	11	1590	13	2598	9	1047	16	272	35	7929	6
	pine	780	20	595	22	169	58	1914	15	79	54	3537	10
	larch	62	48	175	34	14	71	2918	11	1422	15	4591	8
	Arolla pine	0	.	0	.	0	.	821	19	0	.	821	19
	other conifers	45	64	82	53	0	.	22	76	0	.	149	36
	beech	5209	7	3109	9	3414	11	1625	12	1226	17	14583	5
	maple	807	16	693	16	959	15	848	18	74	43	3381	8
	ash	590	18	1416	15	861	22	756	32	98	36	3721	10
	oak	429	25	540	19	38	63	77	43	304	27	1388	13
	sweet chestnut	0	.	7	100	0	.	71	66	1518	18	1595	17
	other broadleaves	430	22	719	19	254	27	646	18	1486	15	3536	9
	indeterminable	35	100	17	100	13	71	55	49	26	77	145	36
	total	14193	4	13075	4	16906	4	25394	3	8718	6	78286	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2411	8	2684	8	5814	6	9029	5	1413	14	21351	3
	fir	1926	10	1302	11	1825	9	878	15	161	29	6092	5
	pine	435	20	425	17	143	63	686	16	35	53	1724	11
	larch	7	100	110	34	7	100	2237	10	709	15	3069	8
	Arolla pine	0	.	0	.	0	.	373	19	0	.	373	19
	other conifers	28	71	81	45	0	.	0	.	8	100	117	36
	beech	2956	7	2352	7	2012	11	1058	13	584	18	8962	4
	maple	383	15	179	23	317	18	276	20	7	100	1162	9
	ash	267	22	855	13	303	22	214	26	62	48	1702	9
	oak	250	24	413	18	44	53	70	41	153	37	931	13
	sweet chestnut	0	.	0	.	0	.	21	58	601	19	622	18
	other broadleaves	156	38	365	20	75	39	195	24	335	20	1125	11
	indeterminable	15	71	0	.	30	51	76	35	0	.	121	27
	total	8834	4	8765	4	10569	4	15114	3	4068	7	47350	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1454	9	1734	9	3667	7	4962	5	826	15	12642	3
	fir	964	12	1077	11	1334	10	533	15	163	30	4071	6
	pine	104	29	258	19	58	55	278	23	21	74	719	13
	larch	14	70	84	35	21	74	1630	10	502	14	2251	8
	Arolla pine	0	.	0	.	0	.	135	25	0	.	135	25
	other conifers	21	100	47	55	0	.	0	.	8	100	76	45
	beech	1337	10	1418	9	846	13	488	16	284	19	4372	5
	maple	112	25	90	34	107	28	124	42	8	100	441	17
	ash	103	26	469	18	73	39	69	36	13	100	727	14
	oak	58	43	279	21	0	.	34	53	18	72	389	18
	sweet chestnut	0	.	0	.	0	.	6	100	259	25	265	24
	other broadleaves	86	62	83	42	25	58	212	39	55	43	461	24
	indeterminable	0	.	0	.	0	.	29	50	0	.	29	50
	total	4254	5	5539	4	6131	5	8500	4	2156	8	26579	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	564	14	578	13	1616	9	1796	8	380	17	4934	5
	fir	427	16	596	14	661	15	262	20	29	50	1974	8
	pine	15	71	41	41	25	59	65	40	7	100	152	24
	larch	0	.	34	45	0	.	607	15	215	22	856	12
	Arolla pine	0	.	0	.	0	.	49	38	0	.	49	38
	other conifers	0	.	54	43	0	.	0	.	0	.	54	43
	beech	336	18	454	14	266	20	134	26	121	31	1311	9
	maple	7	100	28	50	24	79	38	52	0	.	96	32
	ash	52	38	88	40	37	45	14	71	10	100	201	23
	oak	28	59	105	27	0	.	0	.	15	71	148	23
	sweet chestnut	0	.	0	.	0	.	36	82	83	30	119	32
	other broadleaves	23	58	20	58	18	100	37	46	8	100	107	30
	indeterminable	0	.	0	.	6	100	7	100	0	.	13	71
	total	1452	9	1999	7	2653	7	3046	6	868	10	10016	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	167	23	277	19	599	14	814	12	218	22	2075	7
	fir	138	25	143	25	296	17	134	24	30	49	741	11
	pine	7	100	7	100	0	.	14	71	0	.	28	50
	larch	7	100	0	.	16	71	328	17	94	32	444	15
	Arolla pine	0	.	0	.	0	.	50	37	0	.	50	37
	other conifers	7	100	42	57	0	.	0	.	0	.	49	51
	beech	105	33	160	24	91	32	53	39	44	41	452	15
	maple	9	100	0	.	6	100	16	71	0	.	30	50
	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	.	10	100	92	37	101	34
	other broadleaves	0	.	21	58	0	.	6	100	7	100	35	45
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	472	14	712	11	1033	10	1452	8	484	14	4153	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	54	80	31	162	32	331	18	110	27	719	13
	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	.	122	28	67	40	189	23
	Arolla pine	0	.	0	.	0	.	36	45	0	.	36	45
	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	7	100	0	.	0	.	0	.	14	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	101	29	271	17	241	24	574	13	264	18	1451	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	100	7	100	57	36	67	33	14	71	153	22
	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	.	21	58	0	.	21	58
	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	.	30	81	0	.	0	.	0	.	30	81
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	36	45	80	41	73	32	125	24	68	37	383	15
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7	100	14	71	14	71	66	33	19	72	119	25
	fir	0	.	21	58	7	100	0	.	0	.	28	50
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	15	71	14	100	29	61
	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	.	57	45	57	45
	other broadleaves	0	.	37	100	0	.	0	.	0	.	37	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	7	100	72	56	21	58	95	28	90	36	285	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21504	7	28741	7	52321	5	90848	4	12950	10	206363	3
	fir	17279	7	12156	8	18013	7	7942	11	1610	25	56999	4
	pine	2431	17	2117	18	951	54	12929	12	343	54	18771	9
	larch	141	43	1116	34	403	51	17680	7	9023	11	28363	6
	Arolla pine	0	.	0	.	0	.	5585	14	49	100	5633	14
	other conifers	745	29	1164	24	188	30	1550	27	16	100	3664	15
	beech	26728	5	21003	5	19641	7	12740	10	12835	13	92948	3
	maple	6305	9	5743	11	6616	11	7077	11	1468	26	27208	5
	ash	3748	14	6983	9	5058	14	5341	14	2442	23	23572	6
	oak	3017	23	3098	16	353	28	1551	25	2611	25	10631	11
	sweet chestnut	0	.	43	62	0	.	472	69	16023	12	16538	12
	other broadleaves	4974	11	7134	10	6356	13	18543	9	19212	10	56220	5
	indeterminable	193	37	84	44	371	25	683	17	197	44	1528	12
	total	87065	3	89381	3	110271	3	182943	2	78778	4	548438	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. 1296721. Birmensdorf, Swiss Federal Research Institute WSL

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