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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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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		production region
		Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
diameter at breast height (10 classes)	main tree species	1000 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	1115	13	1850	13	2154	9	3930	7	532	15	9582	4
	fir	873	11	878	17	944	13	418	20	56	41	3169	7
	pine	34	42	48	87	12	263	650	19	7	100	751	18
	larch	0	0	49	63	33	59	564	13	265	22	911	11
	Arolla pine	0	.	0	.	0	.	276	26	2	100	277	26
	other conifers	13	188	55	27	1	1729	68	27	0	.	138	27
	beech	1355	10	1032	10	940	13	763	17	929	15	5019	6
	maple	454	15	478	21	431	18	452	15	126	28	1941	8
	ash	292	21	410	23	267	19	450	23	214	31	1633	11
	oak	102	37	134	35	10	65	83	30	150	34	479	17
	sweet chestnut	0	.	2	100	0	.	19	100	836	19	857	18
	other broadleaves	347	13	519	13	400	19	1489	11	1298	14	4054	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	4585	5	5456	6	5193	6	9163	4	4416	7	28812	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2605	12	3831	11	5421	8	8293	7	1229	16	21379	4
	fir	1675	14	1166	15	1697	12	783	17	158	41	5480	7
	pine	304	37	173	27	62	74	1109	16	45	61	1692	13
	larch	6	100	106	50	21	100	1237	13	818	17	2188	10
	Arolla pine	0	.	0	.	0	.	527	19	3	100	531	19
	other conifers	50	45	113	40	10	72	44	55	0	.	217	26
	beech	3425	9	2844	10	2552	10	1445	14	1641	17	11907	5
	maple	776	14	913	16	1146	16	794	17	127	39	3755	8
	ash	471	20	904	16	1013	19	585	18	251	29	3224	9
	oak	485	29	388	22	47	46	112	31	259	26	1289	14
	sweet chestnut	0	.	0	.	0	.	27	64	1454	16	1481	16
	other broadleaves	355	19	629	18	425	23	975	14	1592	14	3975	8
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0
	total	10152	5	11067	5	12394	5	15931	4	7576	6	57120	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3729	11	5036	10	8618	7	13387	5	2126	16	32896	4
	fir	2773	12	1979	13	3165	10	1053	17	254	35	9224	6
	pine	665	22	625	22	137	46	1467	16	39	67	2932	11
	larch	63	49	203	35	22	71	2284	12	1160	17	3732	9
	Arolla pine	0	.	0	.	0	.	542	19	0	.	542	19
	other conifers	66	61	84	57	0	.	-14	145	0	.	137	48
	beech	5538	8	3735	10	3655	11	1464	13	996	16	15389	5
	maple	792	15	606	20	1004	15	741	18	64	53	3207	8
	ash	707	17	1636	14	949	21	498	25	95	40	3885	9
	oak	408	26	616	19	33	59	67	42	231	29	1355	13
	sweet chestnut	0	.	6	100	0	.	43	64	997	20	1045	19
	other broadleaves	383	22	815	22	214	28	441	19	1050	16	2903	10
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	15124	4	15342	5	17797	4	21973	4	7011	7	77247	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4827	9	5703	9	10685	6	15796	5	2389	15	39400	3
	fir	3851	10	3031	11	3990	10	1744	15	296	30	12911	5
	pine	682	22	916	19	282	69	925	18	45	53	2851	12
	larch	15	100	230	36	16	100	3433	11	955	16	4649	9
	Arolla pine	0	.	0	.	0	.	451	20	0	.	451	20
	other conifers	64	72	175	52	0	.	0	.	20	100	259	40
	beech	5993	7	5094	8	3964	11	1886	16	788	18	17726	4
	maple	721	17	238	29	604	19	372	22	9	100	1944	10
	ash	560	23	1854	14	588	23	369	31	109	50	3480	10
	oak	501	25	904	19	72	59	111	46	231	40	1819	13
	sweet chestnut	0	.	0	.	0	.	22	59	644	21	666	20
	other broadleaves	167	32	684	23	125	39	231	39	380	23	1587	13
	indeterminable	0	0	0	.	0	0	0	0	0	.	0	0
	total	17381	4	18828	4	20327	4	25340	4	5867	7	87743	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4367	10	5865	10	11025	7	13141	6	2140	16	36538	4
	fir	3098	12	3636	11	4409	10	1700	16	518	30	13361	6
	pine	286	29	853	19	136	54	621	24	38	100	1935	13
	larch	25	82	319	36	79	74	4090	10	1085	14	5598	8
	Arolla pine	0	.	0	.	0	.	210	27	0	.	210	27
	other conifers	70	100	177	60	0	.	0	.	30	100	278	47
	beech	4314	10	4860	10	2687	14	1376	17	568	24	13806	6
	maple	278	28	284	35	218	38	248	38	20	100	1049	17
	ash	379	28	1716	18	248	40	158	43	33	100	2534	14
	oak	169	46	906	21	0	.	87	51	39	71	1201	18
	sweet chestnut	0	.	0	.	0	.	8	100	455	28	463	28
	other broadleaves	239	59	275	45	66	59	542	44	103	52	1225	25
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	0
	total	13226	5	18892	5	18869	5	22181	4	5030	9	78198	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2548	14	2753	14	6632	10	7016	8	1422	18	20372	5
	fir	2013	16	2970	14	3028	15	1187	22	99	59	9296	8
	pine	53	72	200	42	114	61	199	39	18	100	584	24
	larch	0	.	126	51	0	.	2075	16	734	24	2935	13
	Arolla pine	0	.	0	.	0	.	119	38	0	.	119	38
	other conifers	0	.	296	42	0	.	0	.	0	.	296	42
	beech	1545	18	2210	15	1221	22	617	30	406	31	5997	9
	maple	35	100	133	51	93	79	100	54	0	.	360	33
	ash	208	39	407	43	182	45	83	71	46	100	926	24
	oak	123	58	460	28	0	.	0	.	46	71	628	24
	sweet chestnut	0	.	0	.	0	.	50	76	143	35	193	32
	other broadleaves	96	58	101	59	72	100	112	50	16	100	397	31
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	0
	total	6620	9	9654	7	11342	7	11556	6	2930	12	42102	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	934	23	1657	19	3243	14	4140	12	990	25	10964	8
	fir	838	25	930	26	1918	17	824	25	178	51	4689	11
	pine	29	100	43	100	0	.	55	71	0	.	127	51
	larch	43	100	0	.	73	72	1459	18	448	32	2023	15
	Arolla pine	0	.	0	.	0	.	143	38	0	.	143	38
	other conifers	44	100	303	56	0	.	0	.	0	.	346	50
	beech	644	32	1004	26	516	37	298	39	158	41	2621	16
	maple	60	100	0	.	13	100	64	71	0	.	137	56
	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	.	19	100	162	36	181	34
	other broadleaves	0	.	130	62	0	.	34	100	21	100	184	48
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	2795	14	4447	12	5904	11	7148	9	1957	15	22251	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	275	53	635	30	1104	32	1963	18	553	31	4531	13
	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	.	649	29	302	44	951	24
	Arolla pine	0	.	0	.	0	.	114	45	0	.	114	45
	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	50	100	0	.	0	.	0	.	92	71
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	738	30	2045	18	1796	23	3296	13	1138	20	9013	9
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	65	100	60	100	392	39	395	39	84	71	996	24
	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	.	212	75	0	.	0	.	0	.	212	75
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	290	50	657	40	522	35	706	29	331	40	2506	17
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	67	100	141	71	112	72	446	36	204	71	970	27
	fir	0	.	215	58	126	100	0	.	0	.	341	52
	pine	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	107	71	163	100	270	67
	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	.	417	49	417	49
	other broadleaves	0	.	400	100	0	.	0	.	0	.	400	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.
	total	67	100	756	57	238	63	626	30	784	38	2470	23
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	20531	6	27533	6	49387	5	68507	4	11670	10	177626	2
	fir	15767	8	15342	8	19812	7	8070	11	1771	25	60763	4
	pine	2052	17	2906	14	745	47	5079	12	192	45	10974	8
	larch	153	38	1033	21	244	40	16116	8	5930	11	23475	6
	Arolla pine	0	.	0	.	0	.	2484	13	5	100	2489	13
	other conifers	308	62	1275	34	82	89	98	46	50	100	1813	27
	beech	22815	5	21202	6	15688	7	8013	10	5569	12	73286	3
	maple	3116	9	2651	12	3509	10	2794	12	347	25	12416	5
	ash	2714	13	7302	10	3426	14	2217	15	748	25	16407	6
	oak	1893	18	4079	14	189	33	498	27	957	20	7615	9
	sweet chestnut	0	.	8	82	0	.	219	53	5342	12	5569	12
	other broadleaves	1629	15	3814	17	1302	16	3824	12	4460	11	15029	7
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0
	total	70977	2	87144	2	94382	3	117918	2	37041	4	407462	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. 1218013. Birmensdorf, Swiss Federal Research Institute WSL

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