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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NFI5
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 NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	905	15	2014	13	2787	9	894	16	1806	11	493	18	8898	5
	fir	928	12	1012	13	924	15	51	40	32	35	64	36	3011	7
	pine	27	47	-9	341	40	56	212	34	190	29	16	71	477	21
	larch	1	100	133	48	44	41	325	28	80	29	172	32	755	17
	Arolla pine	0	.	0	.	0	.	114	59	87	41	4	72	206	37
	other conifers	58	36	57	28	55	47	7	66	28	39	3	72	209	19
	beech	1280	10	1409	9	1285	13	67	35	109	31	894	15	5045	6
	maple	485	19	531	17	583	13	101	34	49	36	112	37	1862	9
	ash	160	21	424	21	324	24	67	36	56	37	197	26	1228	11
	oak	109	44	118	35	17	71	61	35	0	.	96	42	401	20
	sweet chestnut	0	.	0	.	0	.	0	0	5	100	664	20	669	20
	other broadleaves	266	15	743	13	564	12	368	19	352	24	1062	14	3355	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	4219	6	6432	6	6624	6	2268	11	2794	9	3778	7	26115	3
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1746	12	3627	11	6103	7	1949	17	3493	11	1161	17	18078	4
	fir	2040	12	1919	14	2106	13	131	41	59	35	105	36	6360	7
	pine	241	42	170	35	167	50	599	25	446	26	33	87	1657	14
	larch	0	.	178	40	103	54	597	19	221	28	726	22	1825	12
	Arolla pine	0	.	0	.	0	.	169	31	270	28	19	77	458	20
	other conifers	46	48	100	31	24	60	0	.	8	73	4	100	182	23
	beech	2878	10	2579	9	2860	10	200	46	263	34	1643	18	10424	5
	maple	794	13	1018	17	1615	12	71	44	103	38	80	42	3681	8
	ash	321	26	644	20	773	20	78	42	99	83	300	35	2215	12
	oak	175	32	550	26	42	45	75	44	0	0	276	26	1119	16
	sweet chestnut	0	.	0	.	0	.	9	100	0	.	1380	16	1389	16
	other broadleaves	310	22	855	14	622	19	324	21	266	30	1436	17	3814	8
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	8552	6	11641	6	14416	5	4202	10	5228	9	7164	7	51203	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3084	12	4599	10	10663	6	2624	13	5085	10	2133	19	28188	4
	fir	2539	12	2310	13	3127	12	189	41	172	31	357	36	8695	7
	pine	555	27	463	22	318	34	538	22	519	28	52	88	2444	12
	larch	8	100	111	51	82	52	1150	20	793	18	996	21	3140	11
	Arolla pine	0	.	0	.	12	100	174	34	341	25	14	100	542	20
	other conifers	41	65	97	49	-25	100	0	0	0	.	0	.	114	52
	beech	4943	8	4006	9	4077	9	159	38	326	25	1407	16	14918	5
	maple	990	15	891	15	1659	13	0	.	180	39	94	62	3813	8
	ash	441	21	1530	14	1136	23	61	81	44	57	102	39	3314	11
	oak	241	29	552	22	30	58	51	51	12	71	251	27	1138	14
	sweet chestnut	0	.	0	.	0	.	33	74	0	.	897	19	931	19
	other broadleaves	237	27	619	18	307	24	38	49	85	34	1111	19	2396	11
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	13079	5	15179	5	21385	4	5018	10	7557	8	7415	8	69632	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3695	10	5662	9	13722	6	2815	14	6506	9	2550	17	34950	4
	fir	3478	10	3206	11	4135	9	218	42	240	30	300	32	11577	6
	pine	506	22	837	19	252	57	333	26	618	26	57	63	2603	12
	larch	67	53	204	37	70	84	1665	18	1246	16	1109	15	4361	9
	Arolla pine	0	.	0	.	0	.	126	36	287	26	0	.	413	21
	other conifers	24	77	150	43	0	.	0	.	0	.	16	100	191	36
	beech	5249	8	5368	8	4660	10	141	60	523	29	949	16	16890	5
	maple	810	15	526	20	801	15	58	100	60	45	52	49	2307	9
	ash	510	23	1528	15	783	22	9	100	11	100	106	61	2946	11
	oak	337	33	1011	18	91	54	20	76	0	.	214	32	1674	14
	sweet chestnut	0	.	0	.	16	72	0	.	0	.	844	22	859	22
	other broadleaves	295	31	558	20	242	30	40	60	17	71	663	21	1815	12
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	14971	4	19049	5	24772	4	5424	10	9508	7	6860	7	80585	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3748	11	4420	10	13499	6	2364	14	5348	10	2383	19	31761	4
	fir	3347	11	4286	12	5463	10	256	52	395	30	593	29	14340	6
	pine	329	27	794	22	206	45	145	42	498	28	27	100	1999	13
	larch	68	58	270	33	145	54	1905	16	1542	17	1128	16	5057	9
	Arolla pine	0	.	0	.	0	.	69	68	246	27	0	.	315	26
	other conifers	23	71	385	43	0	.	0	.	0	.	28	100	436	39
	beech	3719	11	5844	8	3542	12	15	100	276	39	686	21	14082	5
	maple	405	24	347	27	571	22	0	.	143	37	0	.	1465	13
	ash	321	32	1551	18	430	28	15	100	22	100	73	74	2412	13
	oak	105	51	1134	17	41	71	0	.	48	100	121	42	1450	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	545	27	545	27
	other broadleaves	88	72	489	31	310	38	34	75	20	100	79	51	1020	21
	indeterminable	0	.	0	.	0	0	0	0	0	.	0	.	0	0
	total	12153	6	19521	5	24208	4	4802	11	8538	8	5662	10	74883	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2857	14	3316	13	9402	8	993	19	2939	14	1399	17	20906	5
	fir	2113	17	2677	14	3813	13	32	100	329	37	138	50	9101	8
	pine	47	71	357	33	44	100	17	100	219	35	0	.	684	22
	larch	0	.	167	45	23	100	1426	21	1320	20	747	21	3683	12
	Arolla pine	0	.	0	.	0	.	20	100	67	59	0	.	88	51
	other conifers	0	.	341	46	0	.	0	.	0	.	36	100	377	43
	beech	1301	18	3133	12	1569	20	22	100	196	38	475	30	6697	8
	maple	55	71	203	47	196	51	0	.	0	.	0	.	454	32
	ash	225	42	676	24	146	52	0	.	0	.	55	100	1103	19
	oak	94	72	561	32	0	0	0	.	0	.	66	71	721	27
	sweet chestnut	0	.	0	.	16	100	0	.	0	.	178	33	194	31
	other broadleaves	72	100	156	47	233	63	26	100	20	100	47	73	553	33
	indeterminable	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	6764	9	11588	7	15444	7	2535	14	5089	11	3139	10	44559	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1239	21	1845	18	5490	11	611	39	1557	19	984	30	11727	7
	fir	1146	20	1863	17	2537	16	184	61	314	42	195	50	6238	10
	pine	41	100	161	50	0	.	0	.	62	71	0	.	264	38
	larch	41	100	84	71	112	59	616	30	505	27	506	28	1864	15
	Arolla pine	0	.	0	.	0	.	87	50	65	58	0	.	151	38
	other conifers	0	.	277	60	0	.	0	.	0	.	0	.	277	60
	beech	266	47	1531	20	851	27	0	.	118	70	170	41	2936	14
	maple	72	72	34	100	19	100	0	.	0	.	0	.	126	52
	ash	79	71	148	59	81	71	0	.	33	100	0	.	341	36
	oak	0	.	373	36	0	.	0	.	10	100	0	.	383	35
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	168	38	168	38
	other broadleaves	0	.	216	41	38	100	0	.	0	.	27	100	281	36
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2885	14	6532	9	9129	9	1497	23	2664	14	2050	17	24755	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	345	48	909	26	1407	20	242	47	763	31	333	48	4000	13
	fir	441	39	1282	24	737	33	0	.	220	58	50	100	2731	16
	pine	0	.	49	100	0	.	0	.	0	.	0	.	49	100
	larch	0	.	0	.	0	.	281	51	303	44	387	42	972	26
	Arolla pine	0	.	0	.	0	.	46	100	35	100	34	100	116	58
	other conifers	0	.	376	46	0	.	0	.	0	.	0	.	376	46
	beech	0	.	217	50	206	51	0	0	0	.	171	45	593	29
	maple	0	.	0	.	0	.	0	.	27	100	0	.	27	100
	ash	0	.	212	58	70	100	0	.	0	.	0	.	282	50
	oak	0	.	261	45	37	100	0	.	0	.	0	.	298	42
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	153	46	153	46
	other broadleaves	0	.	70	100	0	.	0	.	0	.	0	.	70	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	786	30	3376	14	2457	16	570	33	1348	22	1130	25	9667	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	127	71	169	58	548	32	124	61	317	41	154	71	1438	20
	fir	214	58	126	71	265	50	0	.	0	.	204	58	807	29
	pine	0	.	0	.	0	.	0	.	57	100	0	.	57	100
	larch	0	.	0	.	0	.	180	50	48	100	48	100	276	41
	Arolla pine	0	.	0	.	0	.	30	100	0	.	0	.	30	100
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	281	53	55	100	0	.	0	.	0	.	337	47
	maple	0	.	0	.	25	100	0	.	0	.	0	.	25	100
	ash	0	.	119	71	0	.	0	.	0	.	0	.	119	71
	oak	0	.	89	71	0	.	0	.	0	.	0	.	89	71
	sweet chestnut	0	.	0	.	0	.	29	100	0	.	145	59	174	52
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	340	45	784	29	892	25	364	34	422	35	550	34	3352	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	81	100	227	58	361	39	64	100	224	58	184	72	1141	25
	fir	174	72	0	.	133	100	0	.	0	.	0	.	307	60
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	182	77	182	77
	Arolla pine	0	.	0	.	0	.	39	100	36	100	0	.	75	71
	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	.	194	100	0	.	0	.	0	.	0	.	194	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	374	60	374	60
	other broadleaves	0	.	66	100	0	.	0	.	0	.	0	.	66	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	255	59	486	50	494	39	103	73	260	52	740	40	2338	20
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17827	7	26787	6	63982	4	12680	10	28037	7	11774	13	161087	3
	fir	16420	7	18680	8	23240	7	1061	31	1761	22	2006	24	63168	4
	pine	1745	18	2823	14	1027	34	1844	19	2609	18	184	49	10232	8
	larch	185	41	1148	19	579	30	8144	13	6058	12	6000	12	22114	6
	Arolla pine	0	.	0	.	12	100	874	24	1435	20	72	90	2393	15
	other conifers	192	32	1785	31	55	70	7	66	36	39	88	92	2163	26
	beech	19635	5	24369	6	19106	7	606	38	1810	23	6395	12	71921	3
	maple	3610	9	3550	10	5469	8	230	41	562	27	339	30	13760	5
	ash	2058	15	6831	11	3742	14	230	43	266	43	832	32	13959	7
	oak	1062	19	4843	14	260	34	207	40	70	91	1024	21	7465	10
	sweet chestnut	0	.	0	.	32	83	71	62	5	100	5349	13	5456	12
	other broadleaves	1268	17	3772	11	2317	16	829	18	759	23	4424	12	13369	6
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	64003	3	94588	3	119820	3	26782	7	43407	6	38488	5	387089	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. 1218054. Birmensdorf, Swiss Federal Research Institute WSL

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