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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NFI3
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 2004/06
		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	9654	10	14282	7	18053	6	36025	4	4636	11	82649	3
	fir	7251	7	4345	10	7212	7	3126	11	748	23	22681	4
	pine	461	29	427	36	577	34	6975	11	86	60	8525	10
	larch	69	62	316	29	108	65	6568	9	3148	12	10210	7
	Arolla pine	0	.	0	.	11	100	1745	14	10	100	1765	14
	other conifers	462	30	701	30	136	36	1228	24	10	100	2538	15
	beech	9277	6	9012	6	7789	7	5669	10	7037	10	38785	3
	maple	2839	10	3287	12	2995	13	3404	10	944	20	13469	5
	ash	1736	14	2977	12	1915	14	2147	13	1852	16	10627	6
	oak	1147	26	1100	21	43	50	1092	20	1337	23	4719	11
	sweet chestnut	0	.	89	71	0	.	127	64	8161	11	8376	11
	other broadleaves	3677	10	4573	10	4230	10	13290	8	12758	8	38528	4
	indeterminable	39	50	57	41	163	27	624	17	170	27	1054	12
	total	36613	4	41166	4	43230	4	82021	3	40896	4	243926	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5356	8	7997	8	12818	6	21946	4	3077	10	51194	3
	fir	3374	8	2537	10	3985	8	1826	11	431	24	12153	4
	pine	758	19	472	23	259	49	3834	11	96	47	5420	9
	larch	68	56	288	32	14	100	3989	9	2009	12	6368	7
	Arolla pine	0	.	0	.	73	78	1706	14	20	71	1799	13
	other conifers	230	37	287	26	30	58	186	32	0	.	733	18
	beech	7389	6	5866	6	5144	7	3695	10	3550	11	25643	3
	maple	1791	9	1724	12	2133	10	1647	12	345	26	7639	5
	ash	1303	15	1648	11	1763	14	1440	20	753	24	6906	7
	oak	1078	20	933	18	175	36	382	28	784	21	3353	10
	sweet chestnut	0	.	19	100	0	.	62	54	3505	11	3586	11
	other broadleaves	1246	13	1628	12	1294	14	3036	10	4761	9	11966	5
	indeterminable	21	71	0	.	29	58	154	27	34	58	238	22
	total	22614	3	23402	4	27717	4	43902	3	19364	4	136999	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3541	7	4263	7	8845	5	14300	4	1898	11	32847	3
	fir	2503	8	1641	10	2594	7	969	12	247	31	7955	4
	pine	836	20	646	15	178	31	1977	11	194	56	3831	8
	larch	56	36	168	31	13	58	2865	8	1200	13	4302	7
	Arolla pine	0	.	0	.	34	100	807	14	0	.	841	14
	other conifers	56	58	85	49	0	.	10	100	0	.	151	36
	beech	5547	5	3707	7	3340	9	1599	10	1304	12	15495	3
	maple	899	11	670	14	660	14	609	14	155	30	2993	6
	ash	641	18	1362	12	755	18	457	22	209	24	3424	8
	oak	555	17	726	17	64	39	141	35	331	23	1817	10
	sweet chestnut	0	.	22	71	0	.	46	57	1300	12	1367	12
	other broadleaves	587	18	637	15	229	26	749	15	1295	12	3496	7
	indeterminable	0	.	0	.	13	58	119	32	0	.	132	29
	total	15220	3	13927	3	16726	4	24649	3	8133	5	78654	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2558	6	3286	6	6126	4	8467	4	1246	10	21684	2
	fir	1805	7	1362	8	2032	7	674	12	175	24	6046	4
	pine	507	15	570	14	145	38	802	12	80	40	2104	8
	larch	34	69	156	25	41	47	2197	7	728	12	3156	6
	Arolla pine	0	.	0	.	4	100	421	14	0	.	425	14
	other conifers	27	78	53	38	0	.	0	.	4	100	85	35
	beech	3021	5	2483	6	1921	8	905	11	613	12	8943	3
	maple	355	13	268	18	274	15	194	16	17	80	1108	8
	ash	295	16	731	11	266	16	223	33	46	39	1562	8
	oak	192	19	519	13	33	49	90	33	120	33	954	10
	sweet chestnut	0	.	19	100	0	.	13	58	507	15	540	15
	other broadleaves	142	26	291	18	60	33	134	22	286	15	913	9
	indeterminable	0	.	0	.	8	71	27	42	0	.	35	36
	total	8936	3	9737	3	10911	3	14147	3	3824	5	47555	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1620	7	1963	7	3880	5	4758	4	758	11	12980	3
	fir	1010	10	1147	9	1463	7	515	12	109	26	4244	5
	pine	171	23	293	15	42	46	322	16	16	61	845	10
	larch	12	58	107	28	33	44	1542	7	504	11	2197	6
	Arolla pine	0	.	0	.	0	.	152	19	4	100	156	19
	other conifers	0	.	53	37	0	.	0	.	4	100	57	35
	beech	1147	8	1396	7	823	9	537	16	256	16	4159	4
	maple	62	26	81	29	92	25	41	35	4	100	281	14
	ash	100	23	305	15	78	28	56	30	16	60	555	11
	oak	116	29	330	14	18	50	28	38	47	40	539	11
	sweet chestnut	0	.	4	100	0	.	14	78	284	17	302	16
	other broadleaves	51	29	66	38	28	48	183	30	53	35	381	17
	indeterminable	0	.	0	.	8	71	47	34	0	.	56	31
	total	4290	4	5745	4	6466	4	8195	3	2056	6	26752	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	554	12	741	10	1452	7	1770	6	305	15	4822	4
	fir	397	12	415	13	598	11	225	18	43	32	1678	6
	pine	39	40	70	29	19	51	74	28	8	100	210	17
	larch	4	100	34	36	23	52	560	11	164	18	784	9
	Arolla pine	0	.	0	.	4	100	86	24	0	.	90	23
	other conifers	4	100	41	50	0	.	0	.	0	.	45	46
	beech	285	17	394	13	289	15	110	28	97	22	1175	8
	maple	10	72	17	50	4	100	21	52	0	.	51	31
	ash	46	37	86	26	14	58	11	58	6	100	164	19
	oak	81	35	101	28	11	71	13	59	15	72	221	19
	sweet chestnut	0	.	0	.	0	.	14	77	103	23	117	23
	other broadleaves	23	45	16	50	0	.	15	61	16	61	71	27
	indeterminable	0	.	0	.	4	100	0	.	0	.	4	100
	total	1442	7	1917	6	2416	5	2901	5	756	8	9431	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	181	17	226	15	563	10	819	9	179	19	1968	6
	fir	162	20	145	18	242	14	81	23	25	40	654	9
	pine	0	.	8	71	0	.	0	.	0	.	8	71
	larch	4	100	4	100	5	100	260	15	71	25	344	13
	Arolla pine	0	.	0	.	0	.	50	30	0	.	50	30
	other conifers	4	100	9	71	4	100	0	.	0	.	17	50
	beech	64	26	182	19	38	39	47	31	32	35	363	12
	maple	0	.	6	100	4	100	10	71	0	.	19	51
	ash	8	100	31	38	8	71	4	100	0	.	52	31
	oak	9	71	62	31	22	51	9	71	0	.	102	24
	sweet chestnut	0	.	0	.	0	.	0	.	102	25	102	25
	other broadleaves	6	100	12	58	0	.	4	100	8	71	31	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	437	11	686	9	886	8	1284	7	416	11	3710	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	82	46	39	35	142	24	234	15	77	25	574	12
	fir	69	27	24	41	54	28	33	35	15	58	195	15
	pine	0	.	4	100	0	.	4	100	0	.	8	71
	larch	0	.	0	.	0	.	113	21	42	39	155	19
	Arolla pine	0	.	0	.	0	.	20	45	0	.	20	45
	other conifers	0	.	4	100	0	.	0	.	0	.	4	100
	beech	15	61	49	37	29	42	12	58	4	100	109	23
	maple	0	.	0	.	0	.	4	100	4	100	8	71
	ash	0	.	10	72	0	.	0	.	0	.	10	72
	oak	4	100	23	46	0	.	0	.	0	.	26	42
	sweet chestnut	0	.	0	.	0	.	4	100	61	30	66	29
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	170	26	153	18	225	17	424	11	202	16	1174	7
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	8	71	4	100	65	30	81	22	27	41	185	16
	fir	8	71	8	71	17	50	0	.	18	51	51	29
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	24	41	9	72	33	36
	Arolla pine	0	.	0	.	0	.	12	58	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	4	100	18	51	0	.	0	.	8	71	31	38
	maple	0	.	0	.	4	100	0	.	0	.	4	100
	ash	0	.	4	100	0	.	0	.	0	.	4	100
	oak	4	100	13	76	0	.	0	.	0	.	16	63
	sweet chestnut	0	.	0	.	0	.	0	.	29	46	29	46
	other broadleaves	4	100	17	75	0	.	4	100	4	100	29	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	28	38	64	31	86	26	121	18	95	23	395	11
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	8	71	12	58	45	30	17	61	86	23
	fir	0	.	7	100	8	71	8	71	4	100	27	42
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	20	45	15	61	36	36
	Arolla pine	0	.	0	.	0	.	12	58	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	19	100	19	100
	maple	0	.	0	.	0	.	12	75	0	.	12	75
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	70	38	70	38
	other broadleaves	5	100	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	9	71	18	52	21	45	97	21	125	29	270	16
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	23559	6	32810	5	51956	4	88445	3	12219	8	208990	2
	fir	16578	5	11632	6	18205	5	7455	8	1814	18	55684	3
	pine	2773	13	2490	11	1220	30	13988	9	480	44	20951	7
	larch	247	38	1074	18	237	39	18138	6	7890	9	27586	5
	Arolla pine	0	.	0	.	126	83	5011	11	33	66	5170	11
	other conifers	783	28	1234	22	170	31	1425	23	18	71	3630	13
	beech	26749	4	23107	4	19374	6	12575	8	12918	9	94722	3
	maple	5956	7	6052	9	6164	9	5942	8	1469	18	25584	4
	ash	4128	11	7155	8	4799	11	4339	13	2883	15	23304	5
	oak	3185	16	3810	12	365	24	1756	19	2634	17	11751	8
	sweet chestnut	0	.	153	72	0	.	281	55	14122	9	14555	9
	other broadleaves	5741	9	7241	8	5841	10	17415	7	19181	7	55420	4
	indeterminable	60	41	57	41	226	22	971	14	204	26	1518	10
	total	89760	2	96815	2	108684	2	177741	2	75866	3	548866	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. 1296648. Birmensdorf, Swiss Federal Research Institute WSL

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