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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NFI3
growing stock (stemwood)
diameter at breast height (10 classes) · main tree species
regional demarcation: biogeographical region
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2004/07
		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	1174	12	2433	8	2960	6	946	11	1779	8	536	14	9828	4
	fir	794	8	858	13	998	9	67	37	41	22	122	45	2879	6
	pine	51	28	55	37	83	29	286	20	304	17	7	59	786	11
	larch	1	100	48	27	29	50	367	13	179	19	308	15	933	8
	Arolla pine	0	.	0	.	7	80	81	42	81	28	2	78	170	24
	other conifers	43	32	61	34	47	32	3	65	41	34	2	81	197	16
	beech	1292	8	1445	8	1442	10	81	44	92	18	1061	11	5414	4
	maple	418	15	496	13	717	10	61	25	50	30	98	22	1840	6
	ash	258	19	628	15	400	12	68	29	77	29	279	20	1710	8
	oak	139	31	179	24	33	32	71	26	5	58	175	24	600	13
	sweet chestnut	5	100	6	100	13	72	0	0	0	.	804	15	828	14
	other broadleaves	387	12	623	12	816	10	404	16	371	19	1363	9	3963	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	4562	5	6829	4	7545	4	2434	7	3020	6	4757	5	29147	2
21-30 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2469	9	4835	8	8069	6	2065	11	3602	7	1199	12	22240	3
	fir	1625	9	1830	10	2316	9	154	34	125	27	144	27	6194	5
	pine	242	23	235	26	179	28	577	17	497	19	64	43	1794	9
	larch	24	78	215	30	59	39	792	13	417	17	672	14	2180	8
	Arolla pine	0	.	0	.	32	58	216	21	291	19	10	62	550	14
	other conifers	105	42	145	27	16	110	6	100	18	42	3	100	293	21
	beech	3808	7	3309	7	3265	8	107	36	298	24	1337	13	12123	4
	maple	842	11	919	11	1531	9	36	44	78	40	139	29	3545	6
	ash	595	16	1211	12	1205	15	70	56	91	35	275	26	3447	8
	oak	365	22	599	18	125	33	53	43	17	86	303	23	1461	11
	sweet chestnut	0	.	8	100	3	100	9	100	0	.	1106	13	1127	13
	other broadleaves	409	14	742	13	869	12	270	19	209	26	1532	10	4032	6
	indeterminable	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	10487	4	14047	4	17669	4	4356	7	5643	6	6783	5	58986	2
31-40 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3657	8	6209	7	12815	5	2897	10	5746	7	1802	13	33126	3
	fir	2952	9	2745	9	3420	8	174	31	243	30	245	33	9778	5
	pine	720	18	893	15	340	24	599	17	749	18	180	59	3480	8
	larch	45	48	221	25	56	64	1283	12	1111	14	1065	14	3781	7
	Arolla pine	0	.	0	.	32	71	222	19	310	22	7	100	571	15
	other conifers	8	94	146	38	5	100	0	.	0	.	0	.	160	35
	beech	6276	6	5113	7	4155	7	88	41	332	26	976	14	16940	3
	maple	966	11	823	13	995	11	23	56	58	41	51	118	2916	7
	ash	661	16	1745	11	993	16	38	82	71	54	182	25	3689	8
	oak	407	20	948	16	84	40	24	49	49	92	222	25	1736	11
	sweet chestnut	0	.	16	73	26	71	10	71	0	.	816	13	869	13
	other broadleaves	424	21	837	15	468	20	131	29	97	36	901	12	2859	8
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	0	0	0
	total	16117	3	19695	4	23389	3	5490	7	8766	6	6447	5	79903	2
41-50 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4983	7	8274	6	16194	4	2601	10	6691	7	2030	12	40773	3
	fir	3948	8	3849	8	4690	7	214	28	313	24	286	24	13299	4
	pine	718	17	1403	15	326	36	268	26	713	17	112	39	3540	8
	larch	60	51	364	27	183	38	1798	11	1507	11	1162	13	5074	6
	Arolla pine	0	.	0	.	9	71	175	24	295	19	0	.	479	14
	other conifers	0	.	157	41	0	.	0	.	0	.	9	100	166	39
	beech	5810	6	6256	6	4227	8	51	70	321	27	927	13	17592	3
	maple	666	15	582	14	670	13	11	73	41	51	39	79	2009	8
	ash	498	18	1861	11	771	22	13	100	19	81	60	63	3222	9
	oak	282	23	1173	13	99	36	11	100	49	66	187	37	1801	10
	sweet chestnut	0	.	15	100	7	71	0	.	0	.	546	20	567	20
	other broadleaves	122	31	631	20	257	23	-12	170	31	53	334	16	1364	11
	indeterminable	0	.	0	0	0	0	0	0	0	0	0	.	0	0
	total	17087	3	24565	3	27431	3	5132	7	9979	5	5691	6	89885	2
51-60 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5109	7	7523	7	15792	5	1717	12	5422	8	1909	11	37471	3
	fir	3382	11	4515	9	5195	8	175	36	459	24	338	27	14064	5
	pine	441	27	947	16	126	35	239	31	478	21	36	58	2266	10
	larch	26	71	396	28	273	32	1828	12	1616	12	1305	11	5443	6
	Arolla pine	0	.	0	.	0	0	46	38	181	25	7	100	233	21
	other conifers	24	100	192	39	0	.	0	.	0	.	14	100	230	35
	beech	3388	9	5507	7	3357	10	33	71	272	29	580	17	13137	5
	maple	164	26	260	30	238	26	0	.	34	62	6	100	703	16
	ash	270	25	1212	15	277	29	7	100	38	53	30	62	1833	11
	oak	180	39	1149	14	57	46	6	100	21	59	109	37	1522	12
	sweet chestnut	0	.	6	100	17	100	0	.	0	.	467	20	490	19
	other broadleaves	86	36	275	31	452	39	77	45	21	71	82	47	993	21
	indeterminable	0	.	0	.	0	0	0	0	0	0	0	.	0	0
	total	13070	5	21982	4	25784	4	4127	8	8541	6	4883	6	78387	2
61-70 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2418	12	4606	9	7810	6	1036	21	3099	10	1226	15	20194	4
	fir	1892	12	2391	11	3107	11	189	38	175	36	158	36	7912	6
	pine	124	41	359	25	54	61	18	71	213	33	11	100	779	17
	larch	0	.	155	34	208	45	1045	16	590	18	689	17	2688	9
	Arolla pine	0	.	0	.	23	60	52	41	139	32	0	.	214	24
	other conifers	25	100	210	52	0	.	0	.	0	.	0	.	236	48
	beech	1004	19	2437	11	1410	16	10	100	33	71	303	26	5197	8
	maple	47	79	74	50	70	48	0	.	0	.	0	.	191	33
	ash	115	39	483	26	108	47	0	.	14	100	25	100	745	19
	oak	160	50	602	25	38	71	0	.	19	72	19	82	838	21
	sweet chestnut	0	.	0	.	18	71	0	.	0	.	198	26	216	25
	other broadleaves	52	58	81	52	70	51	0	.	0	.	38	59	241	28
	indeterminable	0	.	0	.	0	0	0	.	0	.	0	.	0	0
	total	5838	7	11399	6	12915	5	2351	12	4282	9	2668	9	39452	3
71-80 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1000	18	1660	15	4613	9	441	25	1587	15	974	21	10275	6
	fir	963	19	1151	16	1523	14	37	71	227	37	150	41	4050	9
	pine	0	.	41	71	0	.	0	.	0	.	0	.	41	71
	larch	24	100	50	71	46	73	590	22	296	29	474	21	1480	13
	Arolla pine	0	.	0	.	0	.	74	43	53	45	17	100	144	30
	other conifers	28	100	97	59	0	.	0	.	0	.	0	.	125	51
	beech	288	29	1277	18	216	37	0	.	93	58	119	36	1993	14
	maple	0	.	35	100	29	73	0	.	12	100	0	.	76	56
	ash	0	.	277	35	17	100	0	.	0	.	0	.	294	33
	oak	23	100	446	29	105	54	0	.	0	.	0	.	574	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	204	28	204	28
	other broadleaves	0	.	107	55	24	100	0	.	0	.	25	71	156	42
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	2326	12	5142	9	6573	7	1142	16	2267	12	1962	12	19412	4
81-90 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	535	44	433	30	1539	18	215	40	479	27	410	25	3610	12
	fir	510	28	380	29	346	31	0	.	102	58	113	58	1450	16
	pine	0	.	25	100	0	.	0	.	28	100	0	.	52	71
	larch	0	.	0	.	0	.	377	26	146	43	279	38	802	19
	Arolla pine	0	.	0	.	0	.	30	71	43	58	0	.	74	45
	other conifers	0	.	31	100	0	.	0	.	0	.	0	.	31	100
	beech	38	100	430	34	150	41	20	100	0	.	11	100	649	25
	maple	0	.	0	.	12	100	0	.	0	.	14	100	27	71
	ash	0	.	68	75	0	.	0	.	0	.	0	.	68	75
	oak	0	.	150	42	0	.	0	.	0	.	0	.	150	42
	sweet chestnut	0	.	0	.	0	.	11	100	0	.	183	31	194	30
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	1082	27	1518	16	2047	15	653	20	797	20	1010	17	7108	8
91-100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	78	71	105	58	590	25	31	100	247	36	168	43	1219	17
	fir	39	100	155	50	83	71	0	.	0	.	105	58	381	32
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	87	51	46	72	34	74	166	36
	Arolla pine	0	.	0	.	0	.	20	100	15	100	0	.	35	71
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	228	45	0	.	0	.	0	.	43	72	271	40
	maple	0	.	0	.	18	100	0	.	0	.	0	.	18	100
	ash	0	.	35	100	0	.	0	.	0	.	0	.	35	100
	oak	0	.	134	63	0	.	0	.	0	.	0	.	134	63
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	82	61	82	61
	other broadleaves	0	.	130	63	48	100	0	.	0	.	17	100	195	50
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	116	58	788	24	739	23	138	42	308	31	449	26	2538	12
>100 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	36	100	128	58	156	42	26	100	147	50	177	56	671	24
	fir	0	.	0	.	216	50	0	.	42	100	29	100	287	42
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	27	100	45	72	167	54	239	42
	Arolla pine	0	.	0	.	0	.	34	71	19	100	0	.	53	58
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	432	100	432	100
	maple	0	.	0	.	73	76	0	.	0	.	0	.	73	76
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	31	100	0	.	0	.	0	.	0	.	31	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	606	41	606	41
	other broadleaves	47	100	0	.	0	.	0	.	0	.	0	.	47	100
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	83	71	159	51	446	31	88	51	253	37	1411	36	2439	23
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	21457	5	36207	4	70537	3	11976	8	28798	5	10430	8	179406	2
	fir	16105	6	17873	6	21893	5	1009	24	1725	16	1689	18	60295	3
	pine	2297	13	3957	10	1107	21	1987	14	2982	13	409	39	12739	6
	larch	180	31	1449	16	854	29	8194	8	5952	9	6155	8	22785	4
	Arolla pine	0	.	0	.	103	52	950	16	1426	15	42	73	2522	11
	other conifers	234	42	1039	24	69	43	9	86	58	35	28	84	1437	19
	beech	21905	4	26002	4	18221	6	390	30	1441	18	5790	11	73749	2
	maple	3104	7	3189	8	4354	7	131	26	272	27	348	24	11398	4
	ash	2397	11	7521	8	3770	11	197	32	309	30	851	17	15045	5
	oak	1557	15	5411	10	540	21	165	29	160	54	1015	18	8849	7
	sweet chestnut	5	100	51	89	84	61	31	62	0	.	5013	10	5184	9
	other broadleaves	1526	11	3427	11	3004	11	870	15	730	18	4292	7	13850	5
	indeterminable	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	70768	2	106126	2	124537	2	25911	5	43855	4	36062	3	407258	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. 1217968. Birmensdorf, Swiss Federal Research Institute WSL

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