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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NFI4
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 NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		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	283	15	93	30	126	51	241	20	382	23	141	26	325	17	174	26	253	18	85	24	69	29	376	19	717	13	169	17	3434	6
	fir	302	16	24	41	52	31	132	34	94	29	56	46	183	25	72	43	48	52	37	40	24	53	18	38	7	62	33	51	1083	9
	pine	2	100	0	1676	0	0	11	100	5	100	0	.	7	85	0	.	5	72	9	78	2	100	117	41	84	26	4	100	245	23
	larch	0	.	0	.	24	100	1	100	3	72	1	100	3	100	5	100	8	64	0	.	6	61	106	26	53	27	88	31	298	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	65	79	59	2	100	108	46
	other conifers	18	53	6	71	1	100	8	47	8	64	0	.	2	71	5	60	3	82	6	100	1	100	4	62	20	36	0	.	82	20
	beech	312	12	120	35	59	32	103	21	164	16	19	32	191	32	102	28	65	45	17	30	57	35	85	73	25	54	303	16	1623	8
	maple	94	35	44	27	21	45	50	38	83	81	24	34	55	33	68	48	54	34	12	56	23	46	53	28	32	38	48	31	663	14
	ash	59	25	27	65	75	72	63	79	69	46	12	55	46	37	40	34	32	47	52	47	7	53	23	36	30	43	108	24	643	15
	oak	52	47	0	0	11	60	2	71	4	59	1	100	1	100	2	100	5	100	1	100	1	100	31	36	0	.	46	32	158	20
	sweet chestnut	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	237	26	247	25
	other broadleaves	123	18	36	28	62	46	52	29	110	23	52	35	80	27	22	147	120	24	75	42	42	31	202	17	220	24	525	20	1721	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	1245	7	349	18	434	23	664	15	921	14	306	17	892	12	490	17	593	14	294	20	232	18	1052	12	1268	10	1564	9	10305	3
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1070	16	252	35	361	32	744	20	1024	18	715	20	1091	14	849	17	511	16	322	25	292	30	1166	16	2122	10	681	17	11199	5
	fir	897	13	110	41	125	35	500	23	285	37	236	28	662	16	194	27	79	40	164	34	17	47	105	49	54	37	96	36	3524	7
	pine	25	51	38	53	0	.	63	79	23	62	0	.	25	108	0	.	4	100	23	60	14	100	447	26	286	25	7	100	954	16
	larch	0	0	6	100	23	79	8	77	23	59	9	100	11	100	25	100	14	51	0	.	24	60	466	20	201	29	329	24	1139	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	45	146	24	3	100	275	24
	other conifers	-10	233	10	72	8	100	18	77	57	38	0	.	4	100	1	2270	11	100	0	.	5	71	11	67	21	38	0	.	135	32
	beech	1398	11	310	23	402	30	396	17	652	20	189	34	604	16	505	21	243	29	168	31	172	30	147	36	78	38	1100	17	6364	6
	maple	369	18	123	29	80	37	84	35	469	25	130	32	170	24	201	30	144	34	38	51	92	31	129	27	28	47	109	28	2167	8
	ash	163	27	175	27	163	37	49	34	214	27	64	39	100	33	260	29	62	45	82	35	66	42	220	42	87	51	190	33	1895	10
	oak	101	36	28	74	135	42	22	51	39	45	0	0	17	75	6	100	19	61	0	.	0	.	70	38	2	100	156	30	594	15
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	1163	17	1174	17
	other broadleaves	222	20	75	30	72	34	203	30	220	19	120	39	102	24	184	43	135	28	142	39	109	35	398	18	422	27	1266	14	3671	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	4235	6	1128	14	1368	18	2087	12	3007	10	1463	14	2784	8	2225	11	1222	12	939	16	790	18	3294	10	3447	8	5101	7	33091	2
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3199	13	565	31	964	27	1474	19	2829	15	2005	19	3348	12	2836	14	2154	16	1079	22	1075	30	3103	12	5150	10	1915	16	31694	4
	fir	2353	14	238	37	226	36	1022	20	605	25	543	33	1566	16	691	21	271	32	333	40	99	51	265	36	74	49	207	40	8493	7
	pine	350	41	96	67	109	58	55	77	171	34	0	.	86	83	0	.	19	100	107	51	64	87	706	23	635	27	51	64	2448	13
	larch	12	100	9	100	75	73	0	.	121	47	0	.	0	.	0	.	87	50	0	.	43	72	1234	15	773	18	1187	16	3539	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	254	31	447	22	0	.	700	18
	other conifers	7	100	52	52	54	100	50	52	36	71	0	.	0	.	0	.	26	76	0	.	-19	100	0	0	4	100	0	.	211	36
	beech	3815	11	1229	19	740	20	1246	19	1923	14	432	37	1887	15	1270	18	498	24	381	32	345	30	377	32	255	34	1694	16	16093	5
	maple	819	16	196	34	24	74	199	29	739	18	210	34	658	23	551	25	346	33	182	33	268	34	83	37	129	59	128	40	4530	8
	ash	431	24	222	39	447	31	355	24	638	23	304	40	325	49	646	23	195	50	126	51	92	47	112	40	59	43	203	31	4154	9
	oak	411	32	193	42	247	37	156	43	168	30	33	77	11	100	0	0	11	100	10	100	0	.	48	63	6	100	369	27	1662	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	71	5	100	1397	17	1459	17
	other broadleaves	330	23	159	36	159	56	245	35	443	26	30	52	82	40	277	29	250	39	57	45	39	46	217	29	177	32	1693	15	4157	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
	total	11727	6	2959	13	3045	14	4802	11	7672	8	3556	15	7964	8	6271	9	3855	12	2275	17	2005	20	6455	8	7712	8	8843	6	79141	2
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6334	9	905	24	1750	21	2947	16	4277	11	3843	14	6881	10	5592	11	4744	14	2095	21	2120	19	4799	11	10393	8	3692	14	60372	3
	fir	5271	10	393	34	695	29	2208	16	1520	16	895	22	2888	12	2337	16	571	26	712	29	380	33	530	34	362	30	529	27	19291	5
	pine	795	23	429	31	386	45	286	33	799	20	0	.	337	64	56	52	36	54	126	53	25	75	612	24	888	24	73	43	4848	10
	larch	30	75	48	61	73	53	116	62	145	51	0	.	16	100	22	71	80	66	0	.	73	64	2652	15	2067	14	1737	15	7057	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	262	28	507	21	0	.	763	17
	other conifers	0	.	155	73	41	100	100	79	46	73	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	20	100	367	40
	beech	6927	8	2845	15	1764	19	2759	12	3112	11	625	31	2709	15	2818	15	858	28	500	29	719	26	175	46	517	37	1321	15	27649	4
	maple	890	14	289	31	30	62	75	68	432	21	189	41	418	26	587	19	306	35	130	39	225	31	57	86	155	59	41	64	3824	8
	ash	654	24	320	27	589	26	771	45	1619	14	74	67	265	31	683	23	248	47	147	56	186	43	67	71	39	50	167	40	5829	9
	oak	493	26	279	39	526	34	355	32	539	21	9	100	57	69	25	72	16	71	58	63	0	.	36	63	60	75	301	34	2753	12
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	7	100	0	.	0	.	18	70	5	100	1157	19	1194	18
	other broadleaves	265	36	103	38	295	40	264	42	578	24	6	100	129	36	80	54	176	43	28	62	89	56	128	58	128	35	870	17	3138	10
	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	21659	4	5766	11	6148	12	9881	9	13073	7	5641	12	13702	7	12199	8	7042	11	3796	16	3817	15	9336	8	15120	7	9908	6	137086	2
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6549	10	1281	27	1605	27	4003	17	4808	15	4309	17	8810	11	8468	13	5263	14	2737	21	3038	21	4803	15	10041	10	5213	14	70927	4
	fir	5698	12	482	31	1041	31	3894	16	2942	17	1330	27	5143	14	3016	16	1484	24	717	37	481	46	393	36	844	29	906	29	28372	5
	pine	297	32	21	100	162	54	285	40	553	27	0	.	177	60	57	71	0	.	20	100	0	.	138	54	714	26	57	75	2479	13
	larch	43	100	5	100	25	100	60	73	337	35	0	.	0	.	152	58	131	51	0	.	129	64	4461	14	3510	15	2588	16	11440	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	290	33	354	30	0	.	643	22
	other conifers	0	.	70	100	185	100	374	55	288	95	0	.	71	100	0	.	0	.	0	.	0	.	0	.	0	.	30	100	1018	40
	beech	3342	13	2515	21	1045	23	2793	17	4044	14	348	41	2301	22	1687	22	761	31	345	37	621	38	173	56	430	40	1151	19	21557	6
	maple	129	41	163	49	40	75	0	.	324	38	66	52	48	71	133	60	124	48	14	100	109	54	0	.	62	63	20	100	1232	17
	ash	344	32	320	35	584	39	602	37	1061	24	36	100	188	62	385	45	126	54	59	100	57	65	15	100	30	100	79	72	3885	13
	oak	130	49	206	61	246	54	376	35	1252	31	0	.	0	.	27	100	0	.	42	72	0	.	21	100	62	73	85	60	2448	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	64	8	100	1388	23	1496	22
	other broadleaves	86	80	228	43	521	48	162	89	430	93	0	.	20	100	118	87	491	55	32	76	66	61	43	73	39	62	106	46	2342	25
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	16617	6	5291	14	5454	14	12549	10	16039	8	6089	15	16756	9	14043	10	8381	12	3966	18	4501	17	10435	10	16093	8	11624	8	147839	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17435	6	3096	19	4805	17	9409	12	13319	9	11013	12	20455	8	17918	10	12925	12	6318	17	6593	17	14247	9	28423	7	11670	10	177626	2
	fir	14521	8	1246	25	2139	24	7756	13	5447	13	3060	21	10441	10	6310	13	2453	21	1963	26	1002	33	1311	28	1341	24	1771	25	60763	4
	pine	1468	21	584	28	657	37	699	26	1550	18	0	.	632	55	113	51	63	47	285	46	105	72	2020	19	2606	18	192	45	10974	8
	larch	85	59	68	46	219	39	185	49	629	28	10	100	30	71	204	46	320	36	0	.	275	49	8917	11	6604	12	5930	11	23475	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	958	22	1532	18	5	100	2489	13
	other conifers	16	170	292	65	290	73	549	44	436	68	0	.	77	93	5	306	45	83	6	100	-13	118	15	58	45	35	50	100	1813	27
	beech	15794	6	7020	12	4010	17	7298	11	9894	9	1614	23	7692	12	6382	13	2426	20	1412	25	1914	21	958	29	1304	27	5569	12	73286	3
	maple	2301	10	815	21	196	29	408	22	2047	15	620	24	1349	17	1541	16	974	20	376	27	716	24	322	27	406	43	347	25	12416	5
	ash	1651	17	1063	22	1859	22	1841	29	3602	12	489	36	924	31	2014	19	662	31	465	36	407	33	437	31	244	37	748	25	16407	6
	oak	1187	23	707	33	1165	28	911	22	2003	22	43	63	86	55	60	55	51	57	111	48	1	100	205	37	130	71	957	20	7615	9
	sweet chestnut	0	.	0	.	2	100	0	.	6	100	0	.	0	.	0	.	7	100	0	.	0	.	194	59	18	100	5342	12	5569	12
	other broadleaves	1027	20	602	24	1108	33	925	26	1781	28	208	28	413	20	680	27	1173	32	333	31	345	30	987	18	986	21	4460	11	15029	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	0	0	0	0
	total	55484	3	15493	9	16449	10	29982	7	40712	5	17056	10	42099	6	35228	7	21093	9	11269	13	11345	13	30572	7	43639	5	37041	4	407462	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. 1280569. Birmensdorf, Swiss Federal Research Institute WSL

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