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

NFI4
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 2009/17
		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	1105	13	1839	13	1940	9	3416	7	432	18	8732	5
	fir	867	11	878	17	944	13	410	21	56	41	3155	7
	pine	34	42	48	87	12	263	586	20	7	100	688	19
	larch	0	0	49	63	33	59	425	15	168	29	676	13
	Arolla pine	0	.	0	.	0	.	222	31	2	100	224	30
	other conifers	13	188	55	27	1	1729	52	30	0	.	122	30
	beech	1337	10	1016	10	939	13	745	17	884	16	4920	6
	maple	443	15	471	21	373	18	391	16	69	30	1747	9
	ash	287	21	399	24	238	18	287	22	195	33	1407	11
	oak	102	37	134	35	10	65	78	31	140	36	464	18
	sweet chestnut	0	.	2	100	0	.	0	0	813	19	814	19
	other broadleaves	326	14	504	14	302	16	1205	12	1150	16	3488	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	4515	5	5396	6	4792	6	7818	5	3915	8	26435	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2568	12	3777	11	4859	9	7496	7	1081	17	19780	4
	fir	1667	14	1166	15	1677	13	765	18	148	43	5423	7
	pine	304	37	173	27	62	74	1059	17	45	61	1642	13
	larch	6	100	106	50	21	100	960	15	602	21	1695	12
	Arolla pine	0	.	0	.	0	.	436	20	3	100	440	20
	other conifers	50	45	113	40	10	72	22	48	0	.	195	27
	beech	3397	9	2837	10	2515	10	1345	14	1580	18	11674	5
	maple	731	14	913	16	1102	17	643	17	120	41	3509	8
	ash	471	20	899	16	944	19	442	19	210	33	2966	9
	oak	485	29	369	23	35	52	93	36	235	27	1216	15
	sweet chestnut	0	.	0	.	0	.	20	80	1435	16	1455	15
	other broadleaves	336	19	598	19	361	26	846	15	1326	14	3468	8
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	10015	5	10950	5	11587	6	14126	5	6785	7	53463	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3643	11	5036	10	7887	7	12556	6	2033	17	31155	4
	fir	2760	12	1979	13	3141	10	1030	18	245	36	9155	6
	pine	665	22	625	22	137	46	1414	17	39	67	2879	11
	larch	63	49	203	35	22	71	2015	13	962	20	3265	10
	Arolla pine	0	.	0	.	0	.	474	20	0	.	474	20
	other conifers	66	61	84	57	0	.	-19	100	0	.	131	50
	beech	5572	8	3719	10	3474	11	1433	13	996	16	15193	5
	maple	782	15	606	20	933	16	627	18	64	53	3011	8
	ash	653	18	1624	15	949	21	450	27	95	40	3770	9
	oak	408	26	616	19	23	73	61	45	208	31	1316	13
	sweet chestnut	0	.	6	100	0	.	19	71	997	20	1022	20
	other broadleaves	359	23	804	22	214	28	360	21	895	17	2632	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	14970	4	15303	5	16779	4	20418	4	6533	7	74003	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4729	9	5674	9	9823	7	14974	5	2322	16	37523	3
	fir	3840	10	3010	11	3873	10	1664	16	282	31	12669	6
	pine	682	22	916	19	282	69	821	19	36	61	2737	12
	larch	15	100	230	36	16	100	3068	12	825	17	4154	9
	Arolla pine	0	.	0	.	0	.	406	21	0	.	406	21
	other conifers	64	72	175	52	0	.	0	.	20	100	259	40
	beech	5903	7	5094	8	3822	11	1782	16	764	18	17365	4
	maple	699	17	238	29	604	19	326	23	9	100	1876	10
	ash	560	23	1800	14	588	23	369	31	109	50	3426	10
	oak	501	25	877	20	72	59	92	52	226	41	1768	14
	sweet chestnut	0	.	0	.	0	.	22	59	644	21	666	20
	other broadleaves	167	32	631	24	125	39	185	41	353	24	1461	14
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	17160	4	18644	4	19207	4	23710	4	5589	8	84310	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4294	10	5865	10	10294	8	12727	6	2077	17	35258	4
	fir	3041	12	3571	11	4409	10	1636	17	518	30	13176	6
	pine	286	29	853	19	136	54	596	25	38	100	1910	13
	larch	25	82	319	36	79	74	3797	11	919	15	5138	9
	Arolla pine	0	.	0	.	0	.	200	28	0	.	200	28
	other conifers	70	100	177	60	0	.	0	.	30	100	278	47
	beech	4024	11	4865	10	2574	15	1354	17	549	24	13366	6
	maple	274	28	284	35	216	38	248	38	20	100	1042	17
	ash	379	28	1716	18	248	40	158	43	0	.	2501	14
	oak	169	46	855	22	0	.	87	51	39	71	1150	18
	sweet chestnut	0	.	0	.	0	.	8	100	455	28	463	28
	other broadleaves	111	53	247	48	66	59	389	49	69	60	882	27
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	12674	5	18752	5	18021	5	21200	4	4716	9	75363	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2504	14	2753	14	6136	10	6644	8	1400	19	19437	5
	fir	1975	17	2937	14	2830	16	1187	22	99	59	9027	8
	pine	53	72	200	42	114	61	199	39	0	.	565	24
	larch	0	.	126	51	0	.	2057	16	685	25	2868	13
	Arolla pine	0	.	0	.	0	.	105	42	0	.	105	42
	other conifers	0	.	296	42	0	.	0	.	0	.	296	42
	beech	1334	19	2181	15	1197	22	592	31	406	31	5708	9
	maple	35	100	133	51	93	79	57	58	0	.	318	35
	ash	208	39	407	43	182	45	39	100	46	100	882	25
	oak	123	58	411	30	0	.	0	.	46	71	580	25
	sweet chestnut	0	.	0	.	0	.	15	100	143	35	158	33
	other broadleaves	96	58	101	59	72	100	91	58	16	100	376	32
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	6326	9	9544	7	10624	7	10986	6	2841	12	40321	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	890	24	1657	19	3121	15	3762	13	946	26	10376	8
	fir	838	25	930	26	1846	18	824	25	178	51	4616	11
	pine	29	100	43	100	0	.	55	71	0	.	127	51
	larch	43	100	0	.	73	72	1364	19	352	35	1832	16
	Arolla pine	0	.	0	.	0	.	143	38	0	.	143	38
	other conifers	44	100	303	56	0	.	0	.	0	.	346	50
	beech	404	36	1004	26	482	39	298	39	158	41	2346	16
	maple	60	100	0	.	13	100	32	100	0	.	106	66
	ash	96	73	178	50	113	71	75	72	0	.	462	32
	oak	106	71	202	51	27	100	38	75	0	.	373	36
	sweet chestnut	0	.	0	.	0	.	0	.	162	36	162	36
	other broadleaves	0	.	130	62	0	.	34	100	21	100	184	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	2511	14	4447	12	5674	11	6625	9	1816	16	21073	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	275	53	635	30	974	35	1690	19	433	36	4008	14
	fir	422	38	430	39	446	43	296	47	67	100	1662	20
	pine	0	.	47	100	0	.	54	100	0	.	102	71
	larch	0	.	0	.	0	.	515	32	142	63	657	28
	Arolla pine	0	.	0	.	0	.	70	58	0	.	70	58
	other conifers	0	.	72	100	71	100	0	.	0	.	142	71
	beech	0	0	368	42	110	72	165	61	82	58	726	28
	maple	0	.	0	.	0	.	23	100	0	.	23	100
	ash	0	.	141	72	65	100	0	.	0	.	207	59
	oak	0	.	301	41	0	.	0	.	0	.	301	41
	sweet chestnut	0	.	0	.	0	.	30	100	134	46	164	42
	other broadleaves	41	100	0	.	0	.	0	.	0	.	41	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	738	30	1995	18	1666	24	2845	14	859	23	8103	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	65	100	60	100	246	52	395	39	37	100	803	28
	fir	225	58	106	71	89	100	65	100	145	71	629	34
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	218	50	0	.	218	50
	Arolla pine	0	.	0	.	0	.	28	100	0	.	28	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	54	100	41	100	0	.	0	.	96	71
	maple	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	56	100	0	.	0	.	0	.	56	100
	oak	0	.	168	100	0	.	0	.	0	.	168	100
	sweet chestnut	0	.	0	.	0	.	0	.	102	62	102	62
	other broadleaves	0	.	143	100	0	.	0	.	0	.	143	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	290	50	588	43	376	43	706	29	284	44	2244	18
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	67	100	141	71	112	72	373	39	204	71	897	28
	fir	0	.	153	71	126	100	0	.	0	.	279	60
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	52	100	163	100	215	80
	Arolla pine	0	.	0	.	0	.	73	71	0	.	73	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	.	385	52	385	52
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	67	100	294	50	238	63	498	32	752	39	1849	22
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20140	6	27438	6	45391	5	64035	4	10966	11	167969	2
	fir	15634	8	15162	8	19381	7	7877	11	1738	25	59792	4
	pine	2052	17	2906	14	745	47	4783	13	164	50	10650	8
	larch	153	38	1033	21	244	40	14471	8	4818	13	20718	7
	Arolla pine	0	.	0	.	0	.	2159	15	5	100	2164	15
	other conifers	308	62	1275	34	82	89	54	47	50	100	1770	27
	beech	21971	5	21137	6	15154	7	7714	10	5418	12	71394	3
	maple	3024	9	2645	12	3334	10	2347	12	281	29	11631	5
	ash	2654	13	7219	11	3328	14	1820	16	655	28	15676	7
	oak	1893	18	3933	14	167	37	448	29	894	21	7336	10
	sweet chestnut	0	.	8	82	0	.	115	45	5269	12	5391	12
	other broadleaves	1437	14	3158	14	1140	17	3109	13	3831	11	12674	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	69266	2	85913	2	88965	3	108931	2	34090	4	387165	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. 1217991. Birmensdorf, Swiss Federal Research Institute WSL

back