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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NFI2
total number of stems
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		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 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	8693	9	19091	8	28872	5	7344	9	12872	7	3955	12	80827	3
	fir	7107	7	5899	8	8122	6	729	31	486	20	686	22	23029	4
	pine	508	26	720	29	763	27	3683	17	3166	17	166	49	9008	10
	larch	81	58	414	30	153	45	3176	13	1478	16	2410	14	7711	8
	Arolla pine	0	.	0	.	43	78	552	24	897	22	20	71	1511	16
	other conifers	506	32	503	27	349	32	31	58	581	35	134	100	2105	16
	beech	10120	6	11187	5	11476	7	742	32	1163	19	7088	11	41774	3
	maple	2696	10	2500	13	4446	9	332	27	393	34	486	22	10854	6
	ash	1727	14	2802	12	2951	12	423	28	487	36	1519	18	9909	6
	oak	1119	25	2014	20	335	31	577	25	50	72	1433	23	5528	11
	sweet chestnut	10	100	39	79	86	74	10	100	0	.	8345	11	8490	11
	other broadleaves	3640	10	4834	10	8483	8	3296	15	2714	15	11639	8	34606	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	36208	4	50004	4	66079	3	20893	6	24288	6	37881	4	235353	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5032	8	8650	7	19809	5	4892	10	8743	7	2784	11	49909	3
	fir	3565	8	3382	9	4409	7	371	29	311	23	357	24	12395	4
	pine	652	21	498	19	731	25	1759	17	1589	17	164	47	5392	9
	larch	70	55	353	28	78	43	2359	12	1331	14	1747	13	5938	7
	Arolla pine	0	.	0	.	85	61	667	23	804	20	10	100	1565	14
	other conifers	88	46	268	39	64	41	0	.	60	52	0	.	479	25
	beech	8448	6	7075	6	7050	7	252	37	714	22	2719	12	26256	3
	maple	1669	10	1833	11	2724	9	53	45	195	33	287	28	6761	5
	ash	1129	16	2278	10	1853	13	105	47	183	38	434	22	5982	7
	oak	932	21	1382	16	292	33	98	42	51	72	634	20	3388	10
	sweet chestnut	0	.	20	71	32	77	51	72	0	.	3041	12	3144	12
	other broadleaves	916	13	2031	11	2156	11	582	20	505	24	3461	10	9650	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	22500	4	27767	3	39283	3	11188	7	14485	6	15638	4	130861	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3900	7	7006	6	13567	4	3101	9	5590	7	2039	11	35204	3
	fir	2873	8	2507	9	3187	7	180	35	132	25	173	37	9053	4
	pine	898	16	930	14	369	22	767	16	848	17	176	42	3988	7
	larch	57	47	184	31	93	47	1464	11	1015	13	1018	13	3831	7
	Arolla pine	0	.	0	.	32	76	346	21	475	20	0	.	853	14
	other conifers	0	.	122	38	0	.	0	.	0	.	4	100	126	37
	beech	5656	6	4587	6	3883	7	115	53	368	24	1040	13	15650	3
	maple	748	13	656	14	932	11	22	71	75	43	53	46	2487	7
	ash	594	16	1430	13	664	14	24	71	38	44	104	36	2855	8
	oak	399	18	860	14	122	58	11	100	43	91	228	26	1662	10
	sweet chestnut	0	.	17	100	9	100	23	71	0	.	825	14	874	14
	other broadleaves	332	24	864	14	510	18	112	38	128	32	888	13	2834	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	15456	3	19163	3	23369	3	6166	7	8712	5	6550	5	79416	1
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2882	6	5311	5	9006	4	1402	9	3589	6	1169	10	23359	2
	fir	1744	7	2056	7	2246	7	79	34	172	20	184	25	6481	4
	pine	417	16	694	12	229	28	240	23	428	16	50	48	2058	7
	larch	20	52	159	22	90	31	1082	11	851	11	693	11	2894	6
	Arolla pine	0	.	0	.	5	100	150	27	243	18	4	100	402	15
	other conifers	0	.	54	35	0	.	0	.	0	.	0	.	54	35
	beech	2617	6	2692	6	2103	8	27	62	190	24	437	13	8066	4
	maple	187	16	191	19	322	14	0	.	25	41	13	75	739	9
	ash	166	18	690	12	199	17	12	58	18	51	27	41	1111	8
	oak	150	24	566	13	18	50	8	71	37	63	84	38	863	11
	sweet chestnut	0	.	8	100	4	100	0	.	0	.	388	17	400	17
	other broadleaves	62	30	271	16	146	29	25	53	14	59	162	18	680	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8244	3	12692	3	14368	3	3024	7	5567	5	3210	5	47105	1
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1465	7	2621	6	4824	5	667	13	1805	7	666	11	12047	3
	fir	874	10	1138	9	1238	8	74	39	88	24	85	27	3497	5
	pine	149	21	309	15	41	50	113	37	173	23	23	47	809	10
	larch	8	71	90	27	93	27	642	12	461	13	438	12	1732	7
	Arolla pine	0	.	0	.	4	100	35	36	107	24	0	.	146	20
	other conifers	9	100	37	54	0	.	0	.	0	.	0	.	45	48
	beech	841	9	1290	8	969	11	12	58	74	32	174	20	3360	5
	maple	38	34	88	25	62	28	0	.	8	71	0	.	196	16
	ash	40	34	226	17	58	31	0	.	0	.	6	100	330	13
	oak	71	34	352	15	38	36	4	100	4	100	18	59	488	12
	sweet chestnut	0	.	0	.	25	85	0	.	0	.	246	19	271	19
	other broadleaves	21	52	56	30	82	30	12	74	0	.	36	42	206	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3515	5	6208	4	7434	4	1558	9	2721	6	1691	7	23127	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	418	12	911	8	1713	7	196	20	628	11	291	17	4156	4
	fir	268	14	264	15	482	11	32	35	43	32	40	39	1130	7
	pine	26	47	67	26	15	59	4	100	34	45	4	100	149	19
	larch	0	.	25	41	44	39	246	17	114	21	152	19	581	10
	Arolla pine	0	.	0	.	4	100	25	41	43	35	0	.	72	26
	other conifers	4	100	25	47	0	.	0	.	0	.	0	.	29	42
	beech	161	20	386	13	156	22	0	.	11	74	66	26	780	9
	maple	12	58	6	100	11	58	0	.	0	.	0	.	29	38
	ash	13	58	73	28	4	100	0	.	7	100	0	.	97	24
	oak	28	48	87	26	0	.	0	.	4	100	9	71	129	22
	sweet chestnut	0	.	0	.	5	100	0	.	0	.	82	26	87	25
	other broadleaves	0	.	41	37	8	71	0	.	0	.	9	72	59	30
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	931	8	1884	6	2442	6	502	12	885	9	652	10	7297	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	127	20	180	17	600	11	68	28	214	16	132	22	1322	7
	fir	92	23	138	19	114	19	0	.	20	45	16	50	381	11
	pine	0	.	4	100	0	.	0	.	8	71	0	.	12	58
	larch	4	100	15	62	4	100	115	23	64	28	71	25	274	14
	Arolla pine	0	.	0	.	0	.	19	45	23	41	4	100	47	29
	other conifers	0	.	12	58	0	.	0	.	0	.	0	.	12	58
	beech	34	33	164	20	29	38	0	.	0	.	37	43	265	15
	maple	0	.	6	100	7	71	0	.	6	100	4	100	22	46
	ash	0	.	12	58	0	.	0	.	0	.	0	.	12	58
	oak	0	.	51	33	15	58	0	.	0	.	0	.	66	29
	sweet chestnut	0	.	0	.	0	.	4	100	0	.	76	26	80	26
	other broadleaves	0	.	10	72	0	.	0	.	0	.	8	71	18	51
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	257	15	593	10	770	9	207	17	335	13	348	13	2510	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21	45	60	27	170	18	27	38	44	30	39	34	361	11
	fir	15	50	49	31	73	25	0	.	4	100	14	58	156	17
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	29	38	16	50	32	44	77	25
	Arolla pine	0	.	0	.	0	.	4	100	4	100	0	.	8	71
	other conifers	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	beech	7	71	48	35	17	50	4	100	4	100	8	71	88	24
	maple	0	.	0	.	12	76	0	.	0	.	0	.	12	76
	ash	0	.	10	72	0	.	0	.	0	.	0	.	10	72
	oak	0	.	36	37	0	.	0	.	0	.	0	.	36	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	65	36	65	36
	other broadleaves	0	.	13	73	0	.	0	.	0	.	4	100	18	60
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	43	30	221	16	272	14	64	26	72	24	162	20	834	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4	100	13	58	51	28	8	71	8	71	19	50	103	20
	fir	4	100	4	100	21	45	0	.	4	100	0	.	32	35
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	4	100	0	.	12	58	0	.	13	58	29	38
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	18	50	0	.	0	.	0	.	0	.	18	50
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	43	45	43	45
	other broadleaves	0	.	5	100	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	8	71	47	30	72	24	28	38	16	50	75	31	246	14
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	5	100	20	45	0	.	18	51	16	61	58	29
	fir	0	.	4	100	4	100	0	.	0	.	9	72	18	51
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	4	100	8	71	15	50	28	38
	Arolla pine	0	.	0	.	0	.	8	71	4	100	0	.	12	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	4	100	0	.	0	.	19	100	23	84
	maple	0	.	0	.	4	100	0	.	0	.	0	.	4	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	4	100	0	.	0	.	0	.	0	.	4	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	79	35	79	35
	other broadleaves	5	100	0	.	0	.	0	.	0	.	0	.	5	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	5	100	12	58	32	35	12	58	30	38	138	26	229	18
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	22541	5	43847	5	78632	3	17706	8	33512	5	11109	8	207347	2
	fir	16542	5	15441	6	19897	5	1465	25	1259	16	1566	19	56170	3
	pine	2649	13	3222	11	2148	20	6566	14	6246	13	584	38	21415	6
	larch	240	42	1244	19	556	26	9127	9	5338	9	6588	9	23094	5
	Arolla pine	0	.	0	.	172	63	1813	18	2605	16	37	70	4628	12
	other conifers	607	31	1023	22	413	31	31	58	642	35	138	97	2854	15
	beech	27884	4	27446	4	25688	5	1152	30	2523	17	11587	10	96281	2
	maple	5350	7	5280	9	8521	7	407	26	702	27	843	21	21103	4
	ash	3669	11	7521	8	5728	10	564	25	734	33	2090	16	20306	5
	oak	2699	17	5355	12	820	25	698	24	189	61	2406	18	12168	8
	sweet chestnut	10	100	84	80	161	69	88	66	0	.	13189	9	13533	9
	other broadleaves	4976	9	8126	8	11386	7	4026	15	3361	14	16207	7	48081	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	87167	2	118591	2	154121	2	43643	5	57110	4	66346	3	526978	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. 1297051. Birmensdorf, Swiss Federal Research Institute WSL

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