Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI2
number of stems
diameter at breast height (main diameter classes) · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (main diameter classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4208	11	865	29	1468	22	2941	17	4725	13	2504	13	3786	11	2840	15	2147	12	1094	23	1192	18	3473	10	6211	8	1707	14	39161	4
	fir	3146	9	361	33	372	29	1116	16	662	23	666	23	2293	11	947	15	233	29	413	24	192	31	498	26	169	28	300	27	11369	5
	pine	108	42	115	51	40	50	46	72	153	38	0	.	193	45	20	100	62	100	31	74	23	71	1355	21	1374	21	38	100	3556	12
	larch	0	0	38	78	28	75	59	70	92	48	0	.	0	.	42	100	10	100	0	.	62	52	1391	15	637	21	876	17	3236	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	256	29	296	27	10	100	562	19
	other conifers	234	42	48	65	58	71	102	44	220	38	9	100	43	50	31	58	0	.	82	64	19	71	93	69	321	30	0	.	1259	16
	beech	3504	8	1415	14	1079	15	1702	14	2135	10	485	25	2182	14	1497	14	1073	24	447	25	727	24	653	27	370	26	3643	12	20913	4
	maple	1063	12	334	22	333	31	209	26	749	21	304	25	340	28	507	29	579	25	93	48	211	31	313	26	196	36	306	27	5538	7
	ash	585	20	310	28	418	33	225	40	580	24	71	43	317	28	572	27	128	32	105	45	131	48	328	26	227	40	887	18	4885	8
	oak	485	30	168	53	447	37	62	41	189	43	13	100	9	100	0	.	74	43	20	70	0	.	286	29	0	0	688	28	2442	13
	sweet chestnut	0	.	0	.	20	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	77	0	.	3435	13	3495	13
	other broadleaves	1662	13	484	25	588	25	575	26	1141	17	529	21	957	20	943	19	834	19	556	39	509	28	2346	14	1746	17	5668	10	18538	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	14995	5	4138	11	4849	11	7039	10	10646	7	4582	10	10119	7	7399	9	5141	10	2841	15	3066	14	11032	7	11548	6	17559	5	114954	2
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4745	10	704	24	1666	18	3417	16	5048	11	4157	13	5122	10	4149	12	3217	12	1599	18	1695	19	4907	9	8029	8	2543	12	50997	3
	fir	3435	9	223	30	648	24	1606	14	899	15	889	18	2411	11	1068	14	394	23	447	27	292	34	519	25	235	28	386	26	13453	4
	pine	261	30	237	49	137	66	64	61	244	39	21	100	243	56	9	100	51	53	194	46	114	60	2338	17	1748	17	49	67	5709	10
	larch	59	71	9	100	71	64	39	100	166	56	0	0	0	.	14	100	51	53	0	.	49	72	2027	13	964	15	1431	15	4882	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	32	100	10	100	0	.	11	100	0	.	520	22	759	24	0	.	1333	16
	other conifers	137	52	105	53	108	71	63	46	158	41	0	.	30	76	42	60	10	100	22	71	9	100	72	100	221	47	0	.	977	20
	beech	5854	7	2101	12	1486	14	2302	11	2903	10	586	26	2920	11	1989	14	880	20	627	23	784	23	812	25	665	22	4348	11	28258	3
	maple	1320	12	388	21	344	41	299	27	866	21	542	20	619	17	900	17	711	20	208	32	394	30	323	28	161	38	246	28	7321	6
	ash	947	20	470	28	523	23	288	27	822	17	308	34	538	24	800	22	205	35	197	30	274	47	298	30	282	41	759	22	6712	7
	oak	750	30	192	37	718	34	162	42	355	33	92	49	28	73	12	100	191	51	20	70	0	0	339	29	59	63	791	23	3710	12
	sweet chestnut	0	.	0	.	29	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	78	0	.	4518	12	4625	11
	other broadleaves	1348	14	419	23	540	25	426	27	1341	13	398	23	680	24	797	27	823	21	295	27	415	28	1643	14	1344	25	5638	8	16106	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18857	4	4849	10	6270	10	8665	8	12803	6	6993	10	12622	6	9791	8	6532	9	3620	13	4026	13	13877	6	14467	6	20709	4	144082	2
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4798	8	719	23	1822	16	2156	13	3565	10	3626	12	5807	8	4211	11	3362	12	1531	17	1739	23	5080	9	8513	7	2610	12	49540	3
	fir	3431	8	279	30	385	25	1581	15	956	15	931	19	2036	11	1205	14	298	29	353	25	152	32	308	27	179	27	252	38	12347	4
	pine	661	24	199	26	98	37	112	42	361	23	0	.	205	39	19	71	49	65	307	37	136	53	1314	16	1165	18	240	51	4866	8
	larch	29	75	86	40	82	60	78	56	157	35	0	.	0	.	0	.	40	61	0	.	108	44	2386	12	1292	14	1461	14	5720	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	53	100	10	100	0	.	23	100	0	.	617	23	812	19	0	.	1515	15
	other conifers	20	100	79	89	70	87	60	58	70	55	0	.	0	.	10	100	0	.	0	.	9	100	0	.	21	71	0	.	338	32
	beech	6694	7	2244	12	1567	14	2092	12	2658	10	885	26	2347	13	2324	12	1033	21	373	23	755	23	412	28	406	29	2183	12	25974	3
	maple	1145	12	475	20	178	30	347	24	937	14	181	33	500	17	696	17	453	20	133	30	359	27	92	37	99	35	222	28	5819	5
	ash	804	17	385	23	527	22	469	30	1024	14	279	39	277	28	714	20	168	37	94	64	125	52	176	31	91	53	321	24	5453	7
	oak	681	23	317	30	407	25	228	28	477	23	12	100	85	69	68	56	120	54	10	100	0	.	59	47	30	100	547	23	3040	10
	sweet chestnut	0	.	0	.	20	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	48	9	100	2226	13	2349	12
	other broadleaves	596	17	328	27	295	36	266	28	756	18	38	50	349	39	428	21	328	23	117	31	157	30	450	22	371	29	2337	12	6817	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	18859	4	5110	9	5451	9	7390	7	10959	5	5952	10	11660	6	9684	7	5852	9	2941	13	3542	14	10987	6	12988	5	12400	5	123776	1
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3957	6	665	16	1965	13	2447	9	3415	8	2687	10	4996	7	3569	8	2856	10	958	16	1181	14	2709	8	5775	6	1884	10	39064	2
	fir	2719	7	256	25	355	19	1298	11	1176	11	615	15	1913	9	1122	12	309	21	161	28	178	28	243	27	186	21	258	23	10789	3
	pine	683	16	273	22	268	36	171	24	791	13	0	.	159	42	61	39	45	49	99	40	37	60	525	18	759	15	107	42	3979	7
	larch	4	100	34	71	93	40	44	43	115	24	12	100	20	60	30	63	47	37	0	.	72	49	1872	10	1361	10	985	11	4689	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	8	71	5	100	0	.	245	22	428	18	4	100	701	13
	other conifers	0	.	8	100	4	100	53	51	29	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	98	35
	beech	3542	6	1558	11	995	14	1479	10	1826	8	385	21	1308	11	1445	12	491	20	274	26	452	18	75	42	233	25	840	11	14905	3
	maple	358	13	151	23	63	35	78	29	227	20	76	29	139	26	287	16	109	24	50	33	121	23	23	59	29	47	33	47	1745	6
	ash	316	19	158	22	188	23	273	27	678	13	52	41	100	26	199	19	46	43	30	58	89	34	20	53	28	52	46	37	2224	7
	oak	238	20	139	26	357	22	236	18	302	17	8	71	29	55	9	71	19	71	13	75	0	.	20	65	60	56	141	32	1571	8
	sweet chestnut	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	71	7	100	630	15	662	15
	other broadleaves	155	23	119	41	66	38	107	33	268	18	4	100	54	44	72	32	110	44	11	58	42	43	94	38	49	33	346	15	1500	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	11974	3	3362	8	4372	8	6187	6	8826	5	3840	9	8730	5	6795	6	4040	8	1601	13	2171	11	5835	6	8916	5	5277	5	81926	1
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1590	7	224	21	557	14	1105	11	1184	9	968	13	1995	8	1673	10	1089	11	488	18	524	17	1082	12	2425	7	942	11	15845	3
	fir	983	10	74	29	179	24	567	13	471	14	273	19	1029	10	443	14	149	22	87	36	80	31	99	30	125	23	132	27	4690	4
	pine	120	25	35	43	42	35	56	41	213	19	0	.	35	57	17	61	4	100	4	100	11	100	101	35	174	23	23	47	836	10
	larch	8	71	8	71	36	47	12	58	60	33	4	100	21	66	38	45	51	35	4	100	27	47	1034	10	620	12	486	12	2408	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	0	0	.	0	.	79	27	178	20	0	.	260	16
	other conifers	0	.	4	100	16	62	33	43	25	71	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	31
	beech	576	11	501	15	228	21	472	15	674	11	111	29	377	19	358	15	213	34	68	36	94	31	16	50	61	42	272	17	4022	5
	maple	14	58	18	51	16	50	14	58	55	31	4	100	17	51	28	49	30	43	0	.	27	41	0	.	8	71	4	100	235	15
	ash	45	35	20	60	80	34	43	42	139	21	5	100	22	52	23	46	4	100	4	100	11	74	0	.	7	100	6	100	409	13
	oak	63	38	90	31	137	29	60	29	194	19	12	72	16	58	27	46	4	100	13	58	0	.	10	72	8	71	27	59	660	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	69	4	100	543	16	571	15
	other broadleaves	10	71	52	37	13	58	26	61	51	37	0	.	5	100	0	.	36	48	8	71	13	76	12	74	0	.	50	37	275	16
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3407	5	1026	11	1304	10	2387	8	3067	6	1376	11	3521	6	2611	8	1581	10	675	15	788	13	2456	8	3609	6	2484	6	30293	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	19298	6	3177	15	7477	11	12065	10	17937	8	13942	10	21706	7	16442	8	12671	9	5670	14	6331	14	17251	7	30953	5	9686	8	194606	2
	fir	13714	6	1192	19	1939	18	6168	10	4164	10	3374	15	9681	7	4785	10	1384	18	1461	20	895	22	1667	21	893	18	1329	19	52647	3
	pine	1832	16	859	23	585	30	449	26	1762	13	21	100	834	37	127	31	210	46	636	33	321	54	5633	14	5219	13	457	42	18945	7
	larch	101	58	175	35	311	38	232	45	590	28	17	100	41	59	124	52	199	28	4	100	318	35	8710	9	4875	9	5239	10	20936	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	98	100	24	100	8	71	38	100	0	.	1717	18	2473	16	13	77	4371	12
	other conifers	390	42	243	49	257	62	310	29	501	30	9	100	77	49	84	39	10	100	104	53	38	78	165	82	562	35	4	100	2754	14
	beech	20171	5	7819	9	5355	11	8048	8	10196	7	2453	19	9136	9	7613	10	3691	19	1790	20	2812	18	1969	22	1735	19	11285	10	94072	2
	maple	3901	8	1367	14	934	28	947	18	2834	13	1106	18	1615	13	2418	14	1882	16	484	24	1111	22	752	21	493	27	812	21	20658	4
	ash	2696	14	1343	19	1736	17	1299	19	3243	10	715	25	1254	19	2308	16	551	26	431	30	631	36	821	21	636	37	2018	16	19683	5
	oak	2217	21	906	25	2067	22	749	19	1516	16	137	43	167	46	115	40	408	42	76	43	0	0	713	23	157	60	2194	18	11422	8
	sweet chestnut	0	.	0	.	84	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	244	50	21	83	11352	10	11702	9
	other broadleaves	3771	10	1403	18	1501	19	1401	20	3557	11	969	19	2044	18	2240	18	2131	16	986	28	1135	23	4546	13	3510	17	14039	7	43235	4
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	68091	3	18485	7	22246	7	31668	6	46301	4	22743	8	46653	5	36280	6	23145	7	11680	11	13593	11	44187	5	51528	4	58430	3	495030	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. 1349041. Birmensdorf, Swiss Federal Research Institute WSL

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