Selection from the list of topics

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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NFI1
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		biogeographical region
		Jura		Plateau		Northern Alps		Western Central-Alps		Eastern Central-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	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-20 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1132	11	2428	9	3581	5	889	10	1504	8	391	14	9925	3
	fir	996	8	1078	10	1129	8	107	31	73	25	51	31	3435	5
	pine	80	29	72	30	78	28	393	18	311	17	17	45	951	10
	larch	9	64	61	39	14	41	283	17	141	17	187	21	695	10
	Arolla pine	0	.	0	.	6	90	57	29	73	22	2	100	138	17
	other conifers	25	41	63	30	22	37	0	.	21	54	2	100	134	19
	beech	2023	7	2143	6	1675	8	82	41	150	21	774	13	6848	4
	maple	378	11	431	14	622	9	23	34	37	45	48	28	1539	6
	ash	289	15	708	12	570	13	32	34	53	43	134	21	1786	7
	oak	151	25	374	20	40	33	33	32	7	72	156	24	761	12
	sweet chestnut	0	.	9	87	2	100	8	73	0	.	953	11	972	11
	other broadleaves	340	11	688	10	865	10	264	17	168	18	1053	9	3378	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5424	4	8057	4	8604	4	2171	7	2539	6	3768	5	30562	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2852	9	5821	7	9542	5	2178	10	4031	8	998	13	25421	3
	fir	2321	9	2933	9	2491	8	112	31	111	26	83	38	8051	5
	pine	348	21	337	20	253	22	610	17	615	17	109	43	2272	8
	larch	48	45	161	35	27	53	726	14	457	15	478	18	1896	8
	Arolla pine	0	.	0	.	19	66	190	28	201	23	1	100	411	17
	other conifers	7	100	67	37	2	204	0	.	5	73	0	.	80	33
	beech	4924	6	4325	6	3263	7	85	46	329	25	801	13	13727	4
	maple	826	10	827	11	1044	9	7	95	81	35	57	35	2841	6
	ash	626	15	1221	10	740	14	29	58	56	46	72	40	2744	7
	oak	395	20	704	14	95	36	23	49	28	73	211	27	1457	10
	sweet chestnut	0	.	28	88	2	100	4	100	0	.	794	13	828	13
	other broadleaves	438	14	1036	12	596	13	123	30	128	32	884	12	3205	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	12784	4	17460	3	18073	4	4086	7	6040	6	4489	5	62933	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4675	7	12292	6	16371	4	2532	10	6172	7	1665	13	43708	3
	fir	3564	8	4571	8	4208	8	155	30	242	23	234	35	12974	4
	pine	912	14	1551	12	274	25	466	18	832	16	161	53	4196	7
	larch	42	50	318	22	93	36	1233	12	1214	14	819	14	3719	7
	Arolla pine	0	.	0	.	13	58	158	23	323	21	4	100	498	15
	other conifers	0	.	92	47	0	.	0	.	0	.	6	100	98	45
	beech	5745	6	5817	6	4070	8	38	57	292	29	647	13	16610	4
	maple	607	14	558	16	622	13	13	100	43	49	16	66	1859	8
	ash	431	17	1571	10	593	18	23	71	10	100	33	64	2662	8
	oak	318	21	964	12	45	49	0	.	41	75	184	31	1552	10
	sweet chestnut	0	.	0	.	5	100	13	72	0	.	387	16	405	16
	other broadleaves	282	21	638	15	377	20	47	39	79	48	383	19	1805	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	16576	3	28373	3	26671	3	4679	7	9249	6	4538	6	90085	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5653	6	12258	5	17443	4	1986	10	6192	7	1822	11	45353	2
	fir	3310	8	4405	8	4828	7	167	36	372	26	306	25	13388	4
	pine	737	17	1755	11	339	33	309	26	609	16	42	68	3790	8
	larch	27	74	309	24	171	33	1436	12	1250	13	864	12	4057	6
	Arolla pine	0	.	0	.	0	.	110	28	275	25	-1	100	384	20
	other conifers	67	100	92	60	0	.	0	.	0	.	0	.	159	55
	beech	3958	7	5131	7	3289	9	23	73	336	28	371	16	13108	4
	maple	245	18	301	21	272	18	0	.	34	41	5	100	858	11
	ash	200	23	854	13	217	23	0	.	12	71	22	75	1305	10
	oak	244	27	948	13	28	54	14	75	31	59	58	36	1323	11
	sweet chestnut	0	.	4	100	4	100	0	.	0	.	419	18	427	18
	other broadleaves	110	33	336	21	235	23	25	64	5	100	93	37	803	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14551	4	26393	3	26826	3	4069	8	9116	6	4000	7	84955	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3713	8	7801	6	12333	5	1389	12	4583	8	1412	13	31232	3
	fir	1888	11	2463	11	3277	9	172	38	317	31	187	33	8304	6
	pine	345	24	897	16	75	60	181	56	312	27	38	50	1847	12
	larch	26	71	216	24	345	34	1178	12	991	15	759	15	3514	8
	Arolla pine	0	.	0	.	7	100	30	54	203	27	0	.	240	24
	other conifers	14	100	73	57	0	.	0	.	0	.	0	.	87	50
	beech	1486	13	2961	9	1818	12	20	111	146	44	288	28	6719	6
	maple	71	56	136	37	101	34	0	.	8	100	0	.	316	23
	ash	95	35	486	20	52	46	0	.	15	100	0	.	649	17
	oak	213	43	711	15	21	71	0	.	11	100	22	101	979	15
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	196	22	202	22
	other broadleaves	23	71	232	27	121	37	5	100	0	.	42	42	424	19
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	7874	6	15978	4	18156	4	2974	9	6586	7	2944	8	54512	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1450	13	2748	10	5253	8	464	26	2166	12	781	21	12862	5
	fir	805	16	879	17	1539	14	13	100	75	67	142	39	3453	9
	pine	40	73	219	32	48	61	7	100	104	50	0	.	417	23
	larch	0	.	130	42	61	58	555	19	456	21	360	24	1562	11
	Arolla pine	0	.	0	.	0	.	24	58	76	42	0	.	99	35
	other conifers	0	.	82	51	0	.	0	.	0	.	0	.	82	51
	beech	523	22	1540	14	409	26	14	73	93	51	119	29	2699	10
	maple	19	100	89	51	6	100	0	.	0	.	0	.	114	43
	ash	23	71	131	43	0	.	0	.	0	.	0	.	155	38
	oak	18	100	377	27	32	72	0	.	0	.	12	100	439	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	110	32	110	32
	other broadleaves	0	.	52	58	54	58	0	.	0	.	13	73	118	38
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2878	9	6248	7	7402	6	1076	16	2970	10	1536	13	22110	4
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	573	25	966	18	2372	13	263	30	1006	18	491	25	5671	8
	fir	551	23	482	26	684	22	0	.	98	58	105	60	1919	13
	pine	0	.	20	100	0	.	0	.	20	100	0	.	40	71
	larch	19	100	21	100	0	.	348	26	184	33	232	30	804	16
	Arolla pine	0	.	0	.	0	.	53	46	58	48	8	100	118	32
	other conifers	0	.	30	100	0	.	0	.	0	.	0	.	30	100
	beech	51	71	518	24	103	46	19	100	0	.	110	46	802	18
	maple	0	.	34	100	30	58	0	.	9	100	9	100	82	49
	ash	0	.	94	53	0	.	0	.	0	.	0	.	94	53
	oak	0	.	259	30	11	100	0	.	0	.	0	.	271	29
	sweet chestnut	0	.	0	.	0	.	11	100	0	.	211	35	222	34
	other broadleaves	0	.	18	100	0	.	0	.	0	.	0	.	18	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1194	18	2441	12	3200	11	694	18	1374	15	1168	16	10071	6
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	137	45	207	38	512	22	110	45	248	36	159	37	1372	14
	fir	58	71	161	45	373	31	0	.	30	100	67	73	688	22
	pine	0	.	39	100	0	.	0	.	0	.	0	.	39	100
	larch	0	.	28	100	0	.	117	42	99	46	95	55	339	26
	Arolla pine	0	.	0	.	0	.	13	100	16	100	0	.	28	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	59	71	339	32	32	72	0	.	28	78	0	0	459	26
	maple	0	.	0	.	22	71	0	.	0	.	0	.	22	71
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	203	39	0	.	0	.	0	.	0	.	203	39
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	85	49	85	49
	other broadleaves	0	.	27	100	0	.	0	.	0	.	0	.	27	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	254	34	1003	19	938	18	240	29	420	25	405	25	3261	10
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	56	71	127	58	198	38	0	.	151	55	64	60	596	24
	fir	69	72	39	100	163	51	0	.	49	100	0	.	320	36
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	44	71	0	.	63	71	106	51
	Arolla pine	0	.	0	.	0	.	18	100	0	.	0	.	18	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	58	74	0	.	0	.	0	.	0	.	58	74
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	52	72	0	.	0	.	0	.	0	.	52	72
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	145	38	145	38
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	125	51	276	37	360	31	62	58	201	48	272	30	1296	16
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	193	50	0	0	28	100	105	72	326	39
	fir	37	100	0	.	0	0	0	.	0	.	118	61	155	52
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	31	100	17	100	92	58	140	46
	Arolla pine	0	.	0	.	0	.	16	100	17	100	0	.	34	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	48	100	48	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	36	100	0	.	0	.	0	.	0	.	36	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	487	40	487	40
	other broadleaves	54	100	0	.	0	.	0	.	0	.	13	100	67	83
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	90	72	36	100	193	50	47	74	62	59	863	27	1292	21
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20241	5	44648	4	67797	3	9811	8	26080	5	7888	9	176466	2
	fir	13598	6	17012	6	18692	5	725	26	1366	18	1293	20	52687	3
	pine	2463	12	4889	9	1066	21	1966	15	2804	13	365	36	13553	6
	larch	171	31	1245	16	710	27	5950	8	4809	9	3949	10	16833	5
	Arolla pine	0	.	0	.	45	58	670	17	1241	16	14	81	1969	12
	other conifers	114	73	498	28	24	43	0	.	26	47	9	77	670	24
	beech	18771	4	22832	5	14660	6	281	32	1375	22	3158	9	61077	3
	maple	2146	8	2377	9	2718	7	42	39	211	34	136	28	7630	4
	ash	1664	11	5066	7	2173	10	85	35	146	37	261	20	9394	5
	oak	1339	15	4629	8	273	24	70	31	119	55	644	21	7073	6
	sweet chestnut	0	.	41	89	19	65	35	59	0	.	3787	10	3883	10
	other broadleaves	1245	11	3027	10	2247	9	463	19	380	20	2480	9	9843	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	61751	2	106265	2	110424	2	20098	5	38557	4	23983	4	361078	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. 1217881. Birmensdorf, Swiss Federal Research Institute WSL

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