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 of young forest plants
size classe (young forest; 5 classes) · main tree species in young forest (counting)
regional demarcation: economic region
unit: 1000 n
evaluation area: stockable and 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
size classe (young forest; 5 classes)	main tree species in young forest (counting)	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	.
	species not assessed in all inventories	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	.
height 10-39 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	59124	33	6842	40	46713	47	134700	21	111205	20	14122	41	92678	20	41187	27	30681	29	12022	32	12587	52	54066	18	61296	14	23338	31	700563	8
	fir	117299	13	7506	35	24341	27	139709	20	115047	18	11196	32	70570	19	34877	19	1863	70	1263	62	1584	53	6066	32	1596	53	5730	38	538648	8
	pine	341	100	298	100	283	100	925	74	0	.	0	.	1016	100	339	100	2295	87	328	100	0	.	4719	53	8162	30	0	.	18705	23
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	663	100	0	.	0	.	951	58	9069	43	8253	45	7665	39	26600	23
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12288	35	7503	34	304	100	20094	25
	other conifers	0	0	0	.	2726	100	308	100	2127	64	0	.	0	0	1352	63	0	0	0	.	0	.	0	0	0	0	0	0	6513	49
	beech	1242703	11	691650	15	207382	22	185719	19	588835	11	34118	53	145455	27	221042	25	50843	35	21960	57	57192	31	13649	69	34328	40	15874	41	3510749	6
	maple	743490	13	663695	19	109786	31	152217	29	1068291	15	45816	46	128553	23	345818	19	51578	23	24163	51	30467	28	26984	34	13576	32	22238	36	3426671	7
	ash	569805	15	478828	16	299967	31	233656	24	883625	10	76188	35	206773	20	354044	21	49141	34	46534	27	52226	24	46201	27	27353	27	203068	19	3527407	6
	oak	39046	41	24754	55	17754	36	4927	29	31653	22	0	.	1316	50	2026	57	320	100	960	74	0	.	19519	31	2800	53	22556	48	167631	16
	sweet chestnut	0	.	0	.	308	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83232	19	83540	19
	other broadleaves	117533	12	83163	26	20306	38	19539	25	180334	40	27477	26	72633	16	52398	15	59519	19	29336	27	27551	28	76467	15	80804	18	127016	14	974076	9
	species not assessed in all inventories	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	2889341	8	1956733	13	729566	18	871700	12	2981117	9	208917	28	718993	11	1053745	15	246240	16	136567	21	182559	20	269029	11	245669	11	511019	12	13001196	4
height 40-129 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	34578	23	4745	37	18552	21	38720	15	36404	17	17840	16	64041	18	25175	25	32341	16	12846	21	14333	25	57620	15	80745	11	26743	16	464682	5
	fir	44357	12	692	37	12701	25	19065	20	9878	29	3892	25	16749	18	10567	21	418	47	2178	29	205	58	9102	33	1349	39	5302	34	136453	7
	pine	758	72	0	.	71	100	592	80	276	61	0	.	590	67	151	100	2771	97	73	100	0	0	6445	32	9384	24	1087	61	22199	19
	larch	0	.	0	.	0	.	0	.	340	71	75	100	0	.	0	.	361	60	0	.	1366	85	6911	20	9522	33	4949	34	23524	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	100	0	.	0	.	0	.	2726	28	5246	26	135	71	8177	19
	other conifers	70	100	152	100	216	58	485	62	213	74	0	0	75	100	0	0	0	0	0	.	0	0	0	0	0	0	0	0	1211	33
	beech	283167	10	221191	22	57132	25	71364	19	174682	13	6424	39	47122	24	42535	29	15828	44	3183	41	16947	34	2806	34	6717	34	9566	24	958665	7
	maple	84931	12	103006	20	17161	27	37646	25	224473	15	8676	74	22539	29	33357	28	6658	22	4461	35	5211	30	9646	48	3410	33	5029	24	566204	8
	ash	76284	19	63891	20	39384	22	65242	25	198111	14	14936	36	46493	45	36814	18	10880	36	6126	43	14686	31	9440	30	10902	42	36944	23	630134	7
	oak	2406	35	1120	44	543	35	1434	44	2192	35	0	.	313	50	148	100	0	.	0	.	0	.	3923	26	413	47	1379	40	13870	13
	sweet chestnut	70	100	0	.	152	100	0	.	69	100	63	100	0	.	0	.	0	.	0	.	0	.	142	100	0	.	7820	39	8315	37
	other broadleaves	50495	12	25513	20	12658	20	14039	26	76295	35	11482	24	31839	15	31020	17	22734	18	13935	29	11972	28	44112	14	28071	15	55955	15	430120	7
	species not assessed in all inventories	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	577116	7	420309	15	158570	14	248587	12	722933	9	63390	17	229761	14	179837	13	91990	14	42802	18	64719	19	152872	9	155759	8	154909	9	3263554	3
DBH 0-3.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15178	15	3496	31	7026	19	19115	16	14627	14	9129	17	25666	13	9681	17	14722	15	4828	20	6595	26	21496	13	44795	11	12887	16	209239	4
	fir	23088	11	542	55	6318	24	12905	23	6991	37	4018	47	13995	16	6950	25	494	43	1728	28	279	50	4678	31	779	68	4276	42	87040	7
	pine	70	100	0	.	126	100	567	100	565	64	0	.	137	71	0	.	146	71	0	.	0	.	5934	38	8322	26	758	73	16624	20
	larch	140	71	0	.	0	.	189	74	211	75	0	.	223	100	0	0	0	0	0	.	142	100	8875	22	5895	27	2246	27	17920	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1245	35	3645	39	0	.	4891	31
	other conifers	70	100	0	.	144	71	897	86	642	80	0	0	74	100	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1828	51
	beech	114991	11	30606	18	18449	33	31610	18	61317	17	8852	36	18714	18	15111	21	8538	41	3297	64	8503	28	3247	34	3594	36	16290	27	343120	6
	maple	30447	17	36039	25	7016	31	11778	30	63260	15	7166	40	6700	27	6102	28	4536	27	1818	42	2048	39	5117	52	1997	42	3384	27	187407	8
	ash	17448	19	23039	28	19158	25	22301	23	52069	16	6897	44	12920	35	9894	30	2866	39	2442	39	8057	45	3060	34	3783	49	14504	36	198438	8
	oak	752	35	70	100	629	65	515	46	1919	50	70	100	82	100	0	.	0	0	0	.	0	.	1903	28	71	100	925	51	6937	19
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	100	0	.	5076	21	5148	21
	other broadleaves	27061	12	6740	24	6917	21	6739	21	17967	16	8193	21	21786	17	11527	29	16541	26	4979	25	4804	33	27070	14	14836	19	37453	15	212613	5
	species not assessed in all inventories	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	229246	7	100532	16	65784	14	106616	10	219566	9	44326	16	100296	9	59265	13	47843	16	19091	18	30428	22	82695	9	87717	9	97799	10	1291206	3
DBH 4-7.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	7646	16	2078	33	2529	30	10399	16	14427	13	5987	19	10742	14	7673	18	4926	18	2506	25	3837	30	9954	14	17549	13	5165	19	105417	5
	fir	10046	12	567	43	794	33	4186	18	2526	28	1528	38	7653	15	2930	28	754	35	1238	31	411	41	2301	25	558	53	1840	50	37333	7
	pine	70	100	0	.	63	100	0	.	395	47	0	.	74	100	70	100	0	.	215	74	0	.	3092	34	2808	28	347	60	7134	19
	larch	0	.	0	.	63	100	0	.	383	84	0	.	74	100	0	.	217	75	0	.	142	100	4023	22	1935	28	1856	29	8693	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	680	51	977	33	0	.	1657	28
	other conifers	216	74	340	65	0	.	132	71	704	67	0	.	74	100	295	79	0	.	73	100	0	0	0	0	0	0	0	0	1834	33
	beech	10176	12	3224	21	1589	29	4830	19	6894	16	1499	45	5058	16	3098	25	1389	28	911	34	1968	36	1261	54	2724	34	5610	23	50232	6
	maple	2446	21	833	40	1304	39	1358	32	2801	22	548	40	349	45	868	33	1259	27	350	45	610	40	1203	43	0	.	697	37	14626	9
	ash	1515	45	1698	36	1421	35	1145	36	3560	25	422	46	849	44	604	40	484	55	71	100	210	58	759	45	139	71	1175	32	14053	12
	oak	704	60	0	0	189	74	69	100	835	77	0	.	0	.	0	.	68	100	71	100	0	.	2255	37	0	.	568	46	4759	25
	sweet chestnut	0	.	0	.	213	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2066	24	2279	24
	other broadleaves	5026	16	973	29	2575	28	1587	23	1998	25	1984	26	3656	23	1555	30	2621	30	1753	30	1675	32	8062	17	5174	25	10440	12	49079	6
	species not assessed in all inventories	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	37845	7	9713	14	10740	14	23708	11	34522	9	11967	15	28530	9	17094	12	11717	13	7188	17	8854	19	33590	9	31864	9	29766	8	297097	3
DBH 8-11.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6279	16	1555	38	2952	24	4300	18	9836	15	2957	21	5017	16	3021	22	3489	18	1118	33	1279	38	4732	14	8409	14	2395	23	57340	5
	fir	4226	16	203	58	1091	33	2531	18	907	35	325	52	3116	20	1461	30	134	71	617	40	132	71	836	33	349	53	270	50	16197	8
	pine	140	100	140	100	0	.	63	100	69	100	0	.	74	100	0	.	0	.	146	71	0	.	2424	25	1807	36	0	.	4863	19
	larch	0	.	70	100	257	61	126	100	269	62	0	.	74	100	0	.	142	100	0	.	71	100	1416	27	768	33	828	39	4021	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	100	0	.	0	.	0	.	625	40	494	38	0	.	1189	27
	other conifers	0	.	203	74	132	71	350	45	588	50	82	100	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	1355	28
	beech	4898	15	1428	25	1248	29	2360	20	2561	20	706	48	2665	19	1565	30	682	37	332	66	761	38	549	58	1395	33	3297	24	24447	7
	maple	987	30	269	50	488	47	471	47	954	36	770	40	512	42	509	63	1111	36	0	.	286	62	555	35	215	74	338	53	7465	12
	ash	424	41	269	50	208	58	274	50	1504	27	74	100	395	60	383	45	202	58	415	47	66	100	624	53	0	.	898	41	5735	14
	oak	489	47	132	71	71	100	63	100	412	53	0	.	0	.	0	.	66	100	142	70	0	.	1228	53	0	.	781	32	3385	23
	sweet chestnut	0	.	0	.	71	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	100	0	.	859	39	1001	35
	other broadleaves	2362	21	348	45	1306	35	353	45	1296	26	1244	31	1118	39	566	40	1058	31	1122	35	575	53	3348	20	2306	26	7452	15	24455	8
	species not assessed in all inventories	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	19807	8	4618	17	7825	15	10892	11	18396	10	6158	15	12972	10	7574	14	6883	14	3892	21	3170	22	16406	10	15743	10	17118	10	151453	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	122806	19	18715	23	77772	31	207234	16	186500	14	50035	16	198144	15	86737	19	86158	17	33320	19	38631	27	147868	12	212793	9	70528	16	1537241	5
	fir	199017	9	9508	30	45245	21	178396	17	135348	16	20960	25	112083	14	56784	15	3664	41	7024	25	2612	35	22982	26	4631	33	17419	28	815671	6
	pine	1380	59	438	75	543	88	2146	51	1304	44	0	.	1892	75	560	67	5212	90	761	60	0	0	22614	27	30484	21	2192	52	69525	15
	larch	140	71	70	100	320	66	315	72	1203	40	75	100	371	100	663	100	719	42	0	.	2672	64	30294	20	26372	24	17544	27	80759	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	141	100	0	.	0	.	0	.	17564	30	17865	23	439	72	36008	19
	other conifers	356	53	695	55	3217	85	2173	63	4275	41	82	100	224	74	1647	59	0	0	73	100	0	0	0	0	0	0	0	0	12741	29
	beech	1655936	9	948099	14	285800	21	295883	15	834289	10	51600	38	219014	22	283351	23	77279	29	29683	44	85372	27	21512	47	48757	32	50637	21	4887213	5
	maple	862301	12	803842	17	135755	28	203471	24	1359778	13	62975	37	158653	21	386654	19	65142	20	30792	42	38622	26	43504	35	19198	28	31685	27	4202372	6
	ash	665476	14	567725	15	360139	27	322618	21	1138868	9	98517	32	267430	19	401739	20	63573	30	55587	26	75246	25	60083	26	42177	29	256590	18	4375767	5
	oak	43397	39	26076	52	19187	33	7008	24	37011	20	70	100	1711	40	2175	54	454	74	1174	67	0	.	28827	26	3284	46	26209	41	196582	14
	sweet chestnut	70	100	0	.	744	60	0	.	69	100	63	100	0	.	0	.	0	.	0	.	0	.	284	61	0	.	99052	18	100283	18
	other broadleaves	202477	9	116736	21	43762	22	42257	18	277889	35	50381	18	131032	12	97066	13	102473	17	51125	23	46578	23	159058	11	131192	13	238316	11	1690343	7
	species not assessed in all inventories	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	3753355	7	2491904	11	972484	15	1261502	10	3976534	8	334758	20	1090553	10	1317515	13	404674	13	209539	17	289731	18	554591	8	536753	7	810611	9	18004506	3

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. 1196476. Birmensdorf, Swiss Federal Research Institute WSL

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