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
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 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 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	6956	12	11737	10	24936	7	8611	12	14217	9	4415	16	70873	4
	fir	7662	10	6151	11	7696	10	453	33	243	31	715	27	22920	6
	pine	345	31	187	43	597	42	2584	27	2700	27	152	71	6564	16
	larch	17	100	913	47	510	38	2481	17	866	20	1784	22	6571	11
	Arolla pine	0	.	0	.	0	.	699	33	912	42	51	73	1662	27
	other conifers	507	37	390	23	502	49	156	62	784	42	37	71	2376	20
	beech	9511	8	10682	9	9973	9	1010	33	946	23	6150	14	38272	5
	maple	3636	15	3694	13	4806	12	691	31	331	38	811	30	13970	7
	ash	1587	18	2619	15	2516	15	493	33	368	37	1517	24	9100	8
	oak	1158	42	1009	25	196	58	788	36	0	.	1167	38	4318	18
	sweet chestnut	0	.	0	.	0	.	37	100	18	100	7888	15	7942	15
	other broadleaves	3059	12	5624	11	6125	11	3761	16	3231	22	10612	12	32413	6
	indeterminable	123	48	85	45	245	27	56	58	124	43	59	57	692	17
	total	34561	5	43092	5	58102	5	21819	8	24741	8	35359	6	217675	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3825	11	6750	10	15825	7	4837	14	8726	9	3177	14	43140	4
	fir	4369	10	3510	12	4367	10	239	35	224	32	371	32	13080	6
	pine	511	31	435	32	459	51	2114	20	1229	23	123	63	4871	12
	larch	0	.	362	37	170	49	1690	17	781	23	1962	18	4965	10
	Arolla pine	0	.	0	.	0	.	561	28	774	28	53	76	1388	19
	other conifers	120	47	213	29	52	58	0	.	35	71	17	100	437	21
	beech	5959	9	5225	8	6167	9	433	44	699	31	3751	17	22234	5
	maple	1909	12	1932	16	3460	11	170	44	261	43	257	31	7990	7
	ash	724	19	1207	16	1839	15	216	42	244	73	650	34	4881	10
	oak	511	30	960	25	134	38	252	42	54	100	765	24	2674	13
	sweet chestnut	0	.	0	.	0	.	18	100	0	.	4419	14	4438	14
	other broadleaves	846	21	2053	12	2053	16	1161	20	1092	26	4537	13	11742	7
	indeterminable	54	58	17	100	122	38	18	100	58	75	18	100	287	27
	total	18828	5	22664	5	34648	4	11712	9	14176	7	20101	6	122129	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3126	11	3807	10	11446	6	3177	12	5480	9	2210	14	29246	4
	fir	2198	11	1982	13	2914	10	183	40	156	29	286	32	7720	6
	pine	749	23	401	24	427	36	833	20	653	24	155	49	3218	11
	larch	15	71	96	44	127	49	1396	17	927	18	1102	18	3663	10
	Arolla pine	0	.	0	.	10	100	293	34	468	26	15	100	785	20
	other conifers	37	68	101	46	15	100	17	100	0	.	0	.	170	34
	beech	4522	8	3358	8	4041	9	223	37	444	23	1698	15	14287	5
	maple	913	16	843	14	1693	12	0	.	187	37	104	56	3741	8
	ash	540	25	1260	15	1080	21	99	58	110	48	144	35	3233	10
	oak	301	31	499	20	24	57	82	51	14	71	288	28	1208	13
	sweet chestnut	0	.	0	.	0	.	49	79	0	.	1637	18	1686	18
	other broadleaves	351	24	645	19	455	20	63	44	149	32	1586	18	3249	10
	indeterminable	34	71	32	62	91	37	7	100	34	71	0	.	198	26
	total	12786	5	13025	5	22324	4	6424	9	8621	7	9224	7	72404	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2163	9	2618	8	7678	6	1760	12	3682	8	1472	14	19373	3
	fir	1633	10	1491	10	2081	9	117	41	118	29	184	29	5624	5
	pine	346	20	412	17	158	49	300	27	378	23	34	60	1629	11
	larch	35	53	90	36	36	68	1077	17	760	16	797	15	2795	9
	Arolla pine	0	.	0	.	0	.	112	34	226	25	0	.	338	20
	other conifers	14	71	69	42	0	.	0	.	0	.	8	100	90	35
	beech	2673	8	2475	8	2329	9	106	52	291	26	658	16	8532	4
	maple	445	14	270	19	542	13	30	100	38	45	44	47	1369	8
	ash	261	22	781	14	372	19	15	100	14	71	66	44	1509	10
	oak	200	30	499	17	55	48	21	75	0	.	163	30	938	12
	sweet chestnut	0	.	0	.	15	70	0	.	0	.	727	18	742	18
	other broadleaves	210	36	326	19	148	29	52	43	27	50	456	20	1219	12
	indeterminable	15	71	0	.	50	48	7	100	14	71	0	.	86	33
	total	7994	4	9033	4	13464	4	3597	9	5547	7	4609	6	44244	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1339	10	1382	10	4768	6	979	12	2039	9	1004	20	11511	4
	fir	1022	11	1222	12	1631	9	102	49	131	29	190	29	4299	6
	pine	121	26	267	21	79	45	78	46	206	29	14	100	765	13
	larch	22	58	76	32	43	53	765	15	592	17	499	16	1998	9
	Arolla pine	0	.	0	.	0	.	42	71	149	27	0	.	191	26
	other conifers	14	71	102	43	0	.	0	.	0	.	8	100	124	36
	beech	1242	10	1739	8	1186	12	7	100	104	37	309	20	4588	5
	maple	151	23	123	26	212	22	0	.	64	36	0	.	549	13
	ash	96	32	436	17	153	26	7	100	7	100	33	72	733	13
	oak	42	47	359	17	14	71	0	.	21	100	63	38	499	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	343	24	343	24
	other broadleaves	28	61	174	28	113	36	16	71	14	100	38	52	383	18
	indeterminable	0	.	0	.	19	58	14	71	0	.	0	.	34	45
	total	4076	5	5880	5	8219	4	2011	10	3327	7	2503	11	26016	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	677	13	693	12	2314	8	323	18	724	13	410	16	5141	5
	fir	517	15	563	14	838	12	7	100	65	37	37	45	2027	7
	pine	15	71	75	33	10	100	7	100	65	37	0	.	171	22
	larch	0	.	34	45	7	100	416	19	349	19	216	21	1021	11
	Arolla pine	0	.	0	.	0	.	7	100	22	58	0	.	29	50
	other conifers	0	.	61	46	0	.	0	.	0	.	8	100	69	42
	beech	293	18	657	11	354	20	7	100	47	38	142	29	1500	8
	maple	14	71	41	47	57	45	0	.	0	.	0	.	112	30
	ash	53	43	140	24	36	51	0	.	0	.	10	100	240	19
	oak	20	73	115	31	7	100	0	.	0	.	21	58	163	25
	sweet chestnut	0	.	0	.	7	100	0	.	0	.	98	27	105	26
	other broadleaves	14	100	36	45	52	54	7	100	7	100	15	71	132	29
	indeterminable	0	.	0	.	0	.	7	100	0	.	0	.	7	100
	total	1602	8	2415	6	3683	6	783	14	1278	10	957	10	10717	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	224	20	303	18	1035	10	131	33	302	18	210	26	2204	7
	fir	203	19	338	16	412	16	37	60	40	41	30	49	1061	9
	pine	7	100	27	50	0	.	0	.	14	71	0	.	48	38
	larch	7	100	15	71	21	58	141	30	106	27	108	27	398	15
	Arolla pine	0	.	0	.	0	.	28	50	21	58	0	.	50	38
	other conifers	0	.	41	58	0	.	0	.	0	.	0	.	41	58
	beech	42	47	273	19	144	25	0	.	25	79	44	41	527	13
	maple	16	71	7	100	6	100	0	.	0	.	0	.	29	50
	ash	15	71	23	59	14	71	0	.	6	100	0	.	59	36
	oak	0	.	59	36	0	.	0	.	7	100	0	.	66	33
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	104	33	104	33
	other broadleaves	0	.	43	41	6	100	0	.	0	.	7	100	56	35
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	514	13	1128	9	1638	8	337	21	521	13	503	14	4641	5
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	47	47	125	25	218	19	37	45	113	30	55	43	594	12
	fir	57	39	156	24	99	32	0	.	21	58	7	100	342	16
	pine	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	larch	0	.	0	.	0	.	50	47	63	40	86	39	199	24
	Arolla pine	0	.	0	.	0	.	14	100	7	100	7	100	29	61
	other conifers	0	.	41	47	0	.	0	.	0	.	0	.	41	47
	beech	0	.	28	50	30	50	7	100	0	.	37	45	102	27
	maple	0	.	0	.	0	.	0	.	8	100	0	.	8	100
	ash	0	.	23	58	10	100	0	.	0	.	0	.	33	51
	oak	0	.	37	45	10	100	0	.	0	.	0	.	47	41
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	53	47	53	47
	other broadleaves	0	.	8	100	0	.	0	.	0	.	0	.	8	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	104	30	424	14	368	15	108	30	213	21	246	22	1462	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15	71	20	58	82	30	22	58	49	43	18	72	206	20
	fir	29	50	14	71	27	50	0	.	0	.	25	58	95	28
	pine	0	.	0	.	0	.	0	.	7	100	0	.	7	100
	larch	0	.	0	.	0	.	29	50	7	100	7	100	43	41
	Arolla pine	0	.	0	.	0	.	14	71	0	.	0	.	14	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	32	52	7	100	0	.	0	.	0	.	39	46
	maple	0	.	0	.	6	100	0	.	0	.	0	.	6	100
	ash	0	.	17	71	0	.	0	.	0	.	0	.	17	71
	oak	0	.	14	71	0	.	0	.	0	.	0	.	14	71
	sweet chestnut	0	.	0	.	0	.	7	100	0	.	45	53	53	48
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	44	41	97	28	122	24	73	31	63	37	96	33	494	13
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	14	71	21	58	50	38	7	100	23	58	18	72	133	24
	fir	15	71	0	.	7	100	0	.	0	.	0	.	22	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	22	74	22	74
	Arolla pine	0	.	0	.	0	.	7	100	7	100	0	.	14	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	.	16	100	0	.	0	.	0	.	0	.	16	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	44	57	44	57
	other broadleaves	0	.	7	100	0	.	0	.	0	.	0	.	7	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	28	50	45	48	57	35	14	71	30	50	84	39	258	19
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	18385	7	27456	7	68351	5	19886	10	35354	7	12991	12	182423	3
	fir	17704	7	15427	8	20073	7	1139	30	998	22	1847	23	57188	4
	pine	2093	19	1811	15	1731	35	5916	20	5251	20	478	51	17280	10
	larch	95	37	1586	32	914	31	8045	12	4451	12	6582	13	21675	7
	Arolla pine	0	.	0	.	10	100	1778	24	2586	25	126	73	4500	17
	other conifers	691	31	1017	19	570	47	173	65	819	41	78	59	3349	16
	beech	24242	6	24469	6	24232	7	1793	33	2556	22	12790	13	90082	3
	maple	7084	10	6911	10	10782	9	891	31	889	29	1217	26	27773	5
	ash	3277	14	6507	10	6020	12	831	31	750	46	2420	23	19805	6
	oak	2230	28	3567	16	440	34	1143	33	96	73	2468	25	9943	11
	sweet chestnut	0	.	0	.	22	74	112	83	18	100	15358	12	15510	12
	other broadleaves	4508	11	8917	9	8953	10	5060	15	4521	22	17250	11	49210	5
	indeterminable	226	33	135	34	527	18	110	39	230	32	77	50	1305	12
	total	80536	3	97803	3	142625	3	46878	7	58518	6	73681	5	500040	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. 1358381. Birmensdorf, Swiss Federal Research Institute WSL

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