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 (main diameter classes) · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		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 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	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	.	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	308	17	165	42	98	34	176	23	370	21	141	27	328	16	143	33	174	21	40	54	56	29	283	18	512	15	113	18	2909	6
	fir	257	13	15	42	51	30	153	21	100	45	55	35	227	19	68	49	25	31	41	54	17	40	18	39	9	43	13	34	1048	9
	pine	4	71	6	78	0	0	22	93	0	0	0	.	5	71	0	.	4	71	3	100	0	.	95	27	86	29	3	100	228	18
	larch	0	.	0	.	3	100	2	100	4	100	0	.	5	100	3	100	-13	137	0	.	1	100	108	31	20	27	60	33	193	22
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	74	46	67	1	100	84	49
	other conifers	14	52	2	100	0	.	4	61	13	38	0	.	0	.	1	100	0	.	5	100	1	100	2	73	13	52	0	.	56	23
	beech	392	13	131	22	53	27	108	15	217	25	30	33	191	25	110	20	153	76	18	33	46	35	31	37	15	33	306	17	1803	9
	maple	97	31	35	35	24	41	37	32	101	33	16	35	55	50	38	31	38	35	8	53	24	36	18	58	12	48	25	36	527	12
	ash	68	39	37	53	64	55	17	59	99	51	9	63	39	32	32	62	20	42	22	47	15	64	27	54	32	41	75	37	555	15
	oak	51	46	9	75	17	49	0	.	9	47	0	.	1	100	0	.	4	100	0	.	0	.	20	43	0	.	54	30	166	20
	sweet chestnut	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	316	30	320	29
	other broadleaves	135	18	31	27	59	40	53	28	53	31	38	39	72	24	69	23	82	26	43	41	66	39	214	23	152	28	468	20	1534	8
	indeterminable	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0
	total	1327	8	431	19	372	18	572	12	967	15	289	17	922	11	466	17	487	27	180	27	227	21	854	11	897	12	1433	10	9424	4
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1238	17	381	39	247	32	814	20	1295	16	478	22	1132	16	988	20	428	17	250	32	359	34	1185	15	1891	11	397	18	11083	5
	fir	890	13	94	58	154	46	423	19	211	50	242	33	518	19	269	24	102	40	133	39	12	58	102	36	50	33	69	39	3270	8
	pine	42	41	32	69	10	80	39	61	46	41	0	.	29	64	0	.	0	0	24	77	0	.	449	26	266	24	7	100	943	15
	larch	0	0	3	100	5	100	2	100	19	58	0	.	3	100	18	100	46	65	0	.	10	71	302	19	125	27	265	26	798	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	37	94	33	3	100	200	24
	other conifers	25	46	14	60	11	100	34	49	29	56	0	.	3	100	17	51	0	.	0	.	5	71	6	59	15	42	0	.	158	20
	beech	1363	12	414	19	415	22	613	16	674	18	144	46	727	16	466	20	274	31	88	47	179	26	133	38	68	33	1079	19	6635	6
	maple	317	20	93	32	65	38	125	35	340	23	131	29	159	29	191	29	139	30	43	51	86	34	108	32	25	108	47	37	1870	8
	ash	201	31	94	42	88	29	96	30	372	29	74	37	106	39	291	26	56	38	69	36	121	94	61	52	72	62	145	39	1848	12
	oak	157	41	34	68	215	33	27	43	31	44	7	71	4	100	5	100	7	95	0	.	0	.	57	51	8	100	173	32	724	16
	sweet chestnut	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	966	18	972	17
	other broadleaves	208	23	92	39	76	48	142	45	228	20	96	32	119	35	132	44	129	27	54	46	87	67	297	20	293	24	1090	13	3044	7
	indeterminable	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	4442	7	1250	16	1291	15	2315	11	3244	10	1172	17	2800	10	2376	12	1181	14	662	21	860	25	2803	10	2908	9	4240	7	31543	3
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3123	13	449	36	971	24	1866	19	2721	16	1706	19	3542	13	2780	15	1704	15	1123	26	1332	36	3098	13	4785	10	1712	17	30913	4
	fir	2389	13	230	33	294	45	1322	21	604	24	549	29	1488	16	579	23	266	31	261	41	160	52	184	42	94	43	210	34	8631	7
	pine	343	35	239	49	118	73	88	73	219	35	0	.	105	60	0	.	33	71	149	52	0	.	639	23	781	25	74	54	2789	12
	larch	11	100	27	78	42	84	41	71	135	55	0	.	0	.	0	.	43	62	0	.	30	100	1014	17	631	21	875	23	2849	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	241	34	389	26	0	.	630	20
	other conifers	0	.	121	56	19	100	23	57	32	72	0	.	0	.	0	.	6	100	0	.	-19	100	15	76	4	100	0	.	202	39
	beech	4190	10	1570	19	779	24	1614	17	1919	13	528	33	1814	18	1505	18	457	25	264	33	410	33	324	34	285	27	1377	18	17036	5
	maple	928	16	211	31	35	84	167	32	727	20	154	38	555	25	561	23	296	32	103	42	306	33	35	64	25	85	128	39	4232	8
	ash	565	25	255	39	458	35	339	25	751	22	301	51	397	51	615	23	167	48	148	69	194	44	100	57	66	52	145	39	4501	10
	oak	381	30	180	39	393	40	190	39	195	31	45	60	10	100	25	100	10	100	29	76	0	.	47	66	0	.	296	31	1798	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	6	100	1297	18	1331	18
	other broadleaves	256	25	196	40	206	51	251	31	451	27	11	71	149	46	302	30	241	38	76	41	40	45	181	30	135	38	1393	15	3889	9
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	12186	6	3478	14	3315	13	5902	10	7753	8	3295	15	8060	9	6366	10	3224	11	2154	18	2452	23	5905	9	7202	8	7508	7	78801	2
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6088	9	998	21	1924	22	3785	13	4730	11	3633	14	6880	11	5408	12	4410	15	2004	22	1918	21	4461	11	9789	9	3338	14	59365	3
	fir	5087	10	394	38	655	32	2694	15	1862	15	933	23	2935	12	2323	17	582	24	429	33	341	38	439	36	268	32	475	29	19418	5
	pine	796	23	436	29	511	42	397	27	1325	22	0	.	291	69	62	53	70	81	61	51	0	.	564	26	1050	22	89	73	5654	10
	larch	29	75	48	71	87	54	68	60	153	42	0	.	15	100	11	100	88	64	0	.	17	153	2405	15	2120	14	1585	17	6625	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	179	31	406	23	0	.	597	18
	other conifers	0	.	101	74	0	.	155	55	66	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	338	37
	beech	6560	8	2863	14	1657	20	2770	14	3338	11	481	33	2704	16	3023	17	747	28	395	35	844	26	114	56	483	34	1133	17	27112	4
	maple	896	15	308	27	116	77	129	43	410	19	192	36	303	28	466	23	247	27	76	45	215	28	75	64	68	56	45	53	3547	8
	ash	551	29	244	30	465	30	926	42	1652	15	58	53	167	39	611	26	183	37	135	63	382	61	51	71	37	50	88	72	5549	11
	oak	410	27	399	53	486	34	346	35	655	21	0	.	72	76	18	72	4	100	21	73	0	.	56	68	48	71	253	44	2769	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	15	71	5	100	1026	24	1051	24
	other broadleaves	333	37	175	33	215	43	173	52	533	24	0	.	78	40	118	52	123	51	13	100	83	58	76	75	104	39	535	20	2558	11
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	20750	5	5965	12	6116	13	11443	9	14725	7	5298	12	13446	7	12040	9	6472	12	3135	17	3800	17	8435	9	14377	7	8582	7	134585	2
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6690	10	1249	26	2317	26	4293	16	5034	14	3347	18	7960	11	7715	14	4181	15	2297	21	2501	23	3873	16	8760	10	4461	15	64677	4
	fir	4716	13	392	37	870	33	3484	16	2352	18	985	29	4553	14	2434	15	1348	25	643	40	426	43	337	38	752	31	834	31	24128	6
	pine	317	30	39	100	130	51	242	44	631	25	0	.	165	56	59	72	0	.	17	100	0	.	163	44	678	27	20	100	2461	13
	larch	42	100	15	100	23	100	58	73	423	40	0	.	0	.	150	58	241	51	22	100	128	61	3882	14	2785	16	1837	19	9605	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	246	36	367	28	0	.	613	22
	other conifers	0	.	31	100	141	100	361	51	202	100	0	.	69	100	0	.	0	.	0	.	0	.	0	.	0	.	26	100	830	38
	beech	2471	15	1880	18	809	25	2512	18	3845	14	213	45	1902	24	1347	24	831	28	232	46	504	42	168	47	396	41	1014	20	18126	6
	maple	89	48	135	56	17	100	0	.	354	36	28	71	22	100	84	56	54	59	12	100	42	71	0	.	48	72	0	.	885	20
	ash	347	32	235	45	465	42	529	39	852	26	31	100	100	60	291	49	31	100	39	73	77	60	0	.	22	100	45	100	3065	14
	oak	212	52	282	57	218	53	364	42	930	31	0	.	0	.	35	100	0	.	44	74	0	.	0	.	50	71	78	62	2213	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	72	8	100	1208	27	1251	26
	other broadleaves	22	100	200	42	173	75	148	87	26	100	0	.	20	100	62	79	358	57	27	81	183	90	77	64	18	100	64	51	1377	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0
	total	14905	7	4459	14	5163	16	11991	10	14649	8	4604	16	14792	9	12177	10	7044	12	3333	19	3862	18	8781	10	13885	8	9587	9	129231	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17447	7	3242	18	5557	18	10934	11	14149	9	9304	13	19843	8	17034	10	10897	12	5715	18	6166	19	12900	10	25736	7	10022	11	168948	2
	fir	13339	8	1125	28	2024	26	8076	13	5129	13	2766	22	9721	10	5673	13	2322	21	1509	29	957	32	1080	30	1173	25	1601	25	56495	4
	pine	1502	20	752	25	769	36	789	25	2220	18	0	.	594	52	121	50	108	69	254	54	0	.	1911	18	2862	18	192	50	12074	8
	larch	82	59	93	46	160	40	171	48	734	30	0	.	23	74	183	49	407	38	22	100	186	55	7710	12	5680	12	4621	13	20071	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	807	24	1302	20	4	100	2124	15
	other conifers	39	36	269	63	171	84	578	43	342	72	0	.	72	96	18	48	6	100	5	100	-13	118	23	67	32	42	42	100	1584	27
	beech	14976	6	6858	12	3713	18	7618	11	9993	9	1396	25	7338	14	6451	14	2463	20	998	27	1983	22	769	30	1248	26	4909	13	70711	3
	maple	2327	11	782	20	257	39	457	23	1932	14	522	24	1094	18	1341	18	775	20	241	29	674	24	237	29	179	37	244	32	11061	5
	ash	1732	20	865	26	1540	25	1907	30	3727	13	473	40	810	35	1839	20	456	33	414	49	789	54	240	38	229	45	498	34	15518	7
	oak	1212	22	904	41	1329	27	927	25	1820	20	51	56	87	67	83	59	25	61	93	56	0	.	180	42	106	71	853	22	7670	10
	sweet chestnut	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	78	52	19	100	4814	13	4925	13
	other broadleaves	953	18	694	24	729	32	767	28	1292	19	145	30	438	23	682	25	933	32	213	33	460	49	844	20	702	22	3550	11	12401	6
	indeterminable	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	53609	4	15583	10	16257	11	32224	7	41337	5	14658	11	40020	6	33425	7	18409	10	9464	15	11201	15	26778	7	39268	6	31349	4	383583	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. 1280510. Birmensdorf, Swiss Federal Research Institute WSL

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