Selection from the list of topics

back

growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

diameter at breast height (10 classes) #1317
Diameter at breast height (dbh) of the trees and shrubs ≥12 cm dbh – in ten classes. Reference: Field Survey (MID 60: Brusthöhendurchmesser, MID 62: Umfang)

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). Reference: Field Survey (MID 50: Baumart)

production region #49
Demarcation of Switzerland into five regions (Jura, Plateau, Pre-Alps, Alps and Southern Alps) with relatively uniform growth and timber production conditions. The production regions were established by the Federal Office of Forestry long before the first National Forest Inventory (NFI1, 1983-1985). With a small exception along Lake Geneva, the boundaries of the production regions still follow the municipal boundaries of the time. Unlike the NFI, the Forestry Statistics of the Federal Statistical Office don't use the production regions as demarcation but rather the forestry zones, whose boundaries are somewhat different.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI3
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: production region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/07
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1293	16	2035	12	2003	10	3323	7	311	17	8965	5
	fir	816	12	716	20	902	14	288	19	51	41	2774	8
	pine	51	33	77	48	35	66	574	18	5	100	742	16
	larch	3	100	24	59	22	73	408	14	188	24	645	12
	Arolla pine	0	.	0	.	0	.	196	36	1	100	197	36
	other conifers	37	34	46	36	16	48	45	31	0	.	144	18
	beech	1487	10	1193	11	1062	13	718	24	941	17	5400	6
	maple	356	16	462	20	351	19	354	15	65	34	1588	9
	ash	305	26	471	24	266	20	266	20	159	36	1467	11
	oak	159	40	187	29	13	56	79	33	172	32	609	17
	sweet chestnut	0	.	8	100	0	.	0	0	898	21	907	21
	other broadleaves	371	14	448	15	397	15	1142	12	1173	15	3530	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	4877	6	5668	6	5067	6	7393	5	3963	8	26969	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2830	12	4076	11	5512	9	7994	7	997	18	21409	4
	fir	1762	13	1671	15	1830	12	750	18	152	39	6167	7
	pine	307	32	205	36	30	100	1185	16	56	62	1783	13
	larch	11	100	129	44	8	100	966	14	533	19	1647	11
	Arolla pine	0	.	0	.	0	.	470	20	3	100	472	19
	other conifers	77	43	100	39	4	100	8	300	0	.	189	30
	beech	3856	9	3097	9	2503	11	1496	14	1403	19	12355	5
	maple	869	14	771	16	1081	16	622	19	105	40	3448	8
	ash	571	22	989	17	1059	18	648	33	165	36	3431	10
	oak	461	27	610	25	64	51	81	50	278	31	1495	15
	sweet chestnut	0	.	0	.	0	.	24	78	1339	17	1362	17
	other broadleaves	337	21	655	18	492	25	741	16	1356	15	3581	8
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	11082	5	12304	5	12583	5	14985	5	6386	7	57339	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3661	10	5070	10	7948	7	12236	6	1914	18	30829	4
	fir	2726	12	2203	13	2948	10	1011	17	179	36	9067	6
	pine	751	21	796	20	140	50	1462	17	35	80	3185	11
	larch	57	54	170	36	11	100	1959	14	928	24	3124	11
	Arolla pine	0	.	0	.	0	.	456	21	0	.	456	21
	other conifers	72	76	56	64	0	.	0	.	0	.	127	51
	beech	6159	8	4192	8	3834	11	1422	14	796	18	16403	5
	maple	869	15	632	20	732	17	605	17	56	54	2894	8
	ash	625	21	1859	16	894	24	441	29	86	50	3906	10
	oak	496	26	665	22	64	50	53	51	120	42	1398	15
	sweet chestnut	0	.	0	.	0	.	24	60	783	20	807	20
	other broadleaves	467	26	763	21	184	33	431	22	661	19	2506	10
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	15882	5	16406	5	16756	5	20100	4	5558	8	74702	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4666	9	7134	8	10681	7	14738	6	2206	15	39426	4
	fir	3744	10	3567	11	4051	10	1350	16	353	31	13064	5
	pine	777	21	1512	20	286	64	991	19	77	63	3643	12
	larch	47	71	239	32	15	100	3170	11	1006	18	4477	9
	Arolla pine	0	.	0	.	0	.	365	21	0	.	365	21
	other conifers	92	73	162	49	0	.	0	.	16	100	271	39
	beech	5631	7	5210	8	3875	12	1528	16	722	18	16966	5
	maple	776	16	409	28	616	20	348	20	18	100	2167	10
	ash	459	27	1875	15	411	26	573	43	43	138	3362	12
	oak	395	28	998	20	45	75	97	46	205	53	1741	15
	sweet chestnut	0	.	0	.	0	.	12	71	537	28	549	28
	other broadleaves	220	31	575	25	110	54	147	46	310	24	1361	14
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	16807	4	21681	4	20091	5	23319	4	5494	8	87392	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4518	9	6970	10	10605	7	11837	6	1964	16	35895	4
	fir	2616	13	3684	11	4028	10	1837	17	329	33	12494	6
	pine	258	30	915	19	144	46	634	24	20	100	1970	13
	larch	15	100	409	40	78	74	3649	11	917	17	5068	9
	Arolla pine	0	.	0	.	0	.	194	29	0	.	194	29
	other conifers	0	.	315	40	0	.	0	.	26	100	341	38
	beech	3567	11	4725	10	2198	15	1410	16	615	21	12515	6
	maple	156	37	273	42	163	41	95	44	0	.	687	22
	ash	420	26	1246	19	303	43	123	43	0	.	2092	14
	oak	196	44	803	20	0	.	50	60	78	62	1126	17
	sweet chestnut	0	.	0	.	0	.	8	100	315	30	323	29
	other broadleaves	125	42	137	71	81	64	596	42	11	204	950	29
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	11870	6	19477	5	17600	5	20433	4	4274	9	73654	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2461	16	3578	14	5530	10	5902	9	1175	19	18647	6
	fir	1504	18	2000	16	2588	15	1056	26	115	50	7263	9
	pine	111	52	192	41	80	71	237	36	0	.	619	23
	larch	0	.	94	58	28	100	1891	16	558	26	2572	13
	Arolla pine	0	.	0	.	0	.	94	45	0	.	94	45
	other conifers	31	100	362	52	0	.	0	.	0	.	392	49
	beech	852	23	1557	16	1220	22	498	35	249	31	4377	11
	maple	83	79	98	58	14	100	40	71	0	.	236	39
	ash	135	53	405	36	77	72	47	71	45	100	710	25
	oak	374	47	213	38	0	.	15	100	0	.	601	32
	sweet chestnut	0	.	0	.	0	.	15	100	167	37	182	35
	other broadleaves	85	58	26	100	0	.	95	58	19	100	226	36
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	0
	total	5636	10	8526	8	9538	8	9892	7	2328	12	35919	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	864	25	1354	21	2800	15	3489	12	909	32	9416	8
	fir	818	26	1070	23	1195	21	553	30	145	57	3781	12
	pine	0	.	37	100	0	.	0	.	0	.	37	100
	larch	42	100	0	.	43	100	1185	20	244	39	1514	17
	Arolla pine	0	.	0	.	0	.	183	37	0	.	183	37
	other conifers	0	.	51	100	69	100	0	.	0	.	120	72
	beech	193	45	907	31	239	55	249	41	164	41	1752	20
	maple	0	.	0	.	0	.	22	100	0	.	22	100
	ash	82	100	186	51	111	71	31	100	0	.	410	37
	oak	36	100	254	41	35	100	29	100	0	.	354	34
	sweet chestnut	0	.	0	.	0	.	0	.	95	49	95	49
	other broadleaves	0	.	57	71	0	.	0	.	21	100	78	59
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	2034	16	3916	13	4494	12	5740	9	1578	20	17762	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	335	46	284	45	721	44	1329	20	366	39	3034	16
	fir	410	38	193	50	527	34	195	58	144	71	1468	20
	pine	0	.	44	100	0	.	51	100	0	.	95	71
	larch	0	.	0	.	0	.	587	29	113	64	700	27
	Arolla pine	0	.	0	.	0	.	57	71	0	.	57	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	0	274	46	254	53	138	58	21	100	686	30
	maple	0	.	0	.	0	.	20	100	0	.	20	100
	ash	0	.	82	100	0	.	0	.	0	.	82	100
	oak	0	.	218	46	0	.	0	.	0	.	218	46
	sweet chestnut	0	.	0	.	0	.	21	100	150	46	171	42
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	744	29	1095	22	1502	26	2396	15	794	25	6532	10
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	291	46	312	41	91	100	694	30
	fir	68	100	125	71	0	.	0	.	133	71	326	45
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	139	58	0	.	139	58
	Arolla pine	0	.	0	.	0	.	37	100	0	.	37	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	88	100	168	73	0	.	0	.	0	.	257	59
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	58	100	0	.	0	.	0	.	58	100
	oak	0	.	128	100	0	.	0	.	0	.	128	100
	sweet chestnut	0	.	0	.	0	.	0	.	150	60	150	60
	other broadleaves	42	100	127	100	0	.	0	.	0	.	169	79
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	198	60	607	40	291	46	488	32	374	42	1957	19
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	62	100	138	71	91	100	255	46	88	100	634	33
	fir	0	.	0	.	91	100	0	.	0	.	91	100
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	50	100	134	100	184	78
	Arolla pine	0	.	0	.	0	.	69	71	0	.	69	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	.	379	57	379	57
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	62	100	138	71	182	71	374	36	601	45	1357	26
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20689	6	30640	6	46182	5	61414	4	10022	11	168948	2
	fir	14464	8	15230	8	18161	7	7040	11	1601	25	56495	4
	pine	2254	15	3778	13	715	44	5135	12	192	50	12074	8
	larch	175	37	1065	23	206	44	14005	8	4621	13	20071	7
	Arolla pine	0	.	0	.	0	.	2121	15	4	100	2124	15
	other conifers	308	55	1091	34	90	77	53	51	42	100	1584	27
	beech	21834	5	21323	6	15185	8	7460	10	4909	13	70711	3
	maple	3109	9	2646	11	2957	11	2105	12	244	32	11061	5
	ash	2597	15	7173	11	3122	15	2128	24	498	34	15518	7
	oak	2116	21	4076	13	221	36	404	29	853	22	7670	10
	sweet chestnut	0	.	8	100	0	.	103	44	4814	13	4925	13
	other broadleaves	1646	14	2788	14	1265	16	3152	14	3550	11	12401	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	69192	3	89818	3	88103	3	105120	2	31349	4	383583	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. 1217947. Birmensdorf, Swiss Federal Research Institute WSL

back