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 NFI2-NFI5
grid: 1.4 km grid, subgrids 1-5
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	75340	45	3819	51	42094	79	169618	25	109254	21	23056	49	74719	21	41023	40	43277	35	11290	41	15973	68	62401	22	52820	20	20915	52	745599	10
	fir	87648	18	7757	49	21606	42	150320	18	123227	23	12496	36	77095	22	24047	26	3382	71	1763	75	551	100	4658	50	1763	75	6896	47	523209	9
	pine	606	100	0	.	532	100	1098	100	0	.	0	.	0	.	0	.	0	.	551	100	0	.	2831	82	3856	47	0	.	9474	35
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1140	71	14629	49	5066	43	5885	64	26720	31
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9459	60	7815	41	0	.	17274	38
	other conifers	0	0	0	.	4981	100	549	100	532	100	0	.	0	.	1323	100	0	0	0	.	0	.	0	0	0	0	0	0	7385	71
	beech	1104084	16	694518	21	116071	32	172245	23	564014	15	56078	57	176970	37	239528	36	44301	39	22372	84	58399	44	19911	86	54077	45	7688	38	3330255	8
	maple	661626	16	542474	27	62151	48	127192	24	1153785	22	66101	54	121093	37	372011	24	36886	30	11183	39	25291	37	22946	44	17230	39	13428	30	3233397	10
	ash	498173	21	345253	18	320128	48	244909	33	782034	14	89425	48	201013	29	282350	23	69709	42	36245	33	50535	29	48232	35	20139	39	197285	30	3185431	8
	oak	36524	72	19484	68	16441	49	5412	37	28830	32	0	.	1144	72	568	100	0	.	589	100	0	.	15029	47	1663	74	26850	70	152534	25
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87478	26	87478	26
	other broadleaves	98832	17	42035	25	23283	51	22880	31	262338	50	27996	41	64605	25	42497	22	37451	31	36678	37	19931	31	67455	21	70646	24	114624	21	931251	15
	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	2562833	12	1655340	18	607288	30	894222	14	3024013	12	275153	36	716639	17	1003347	19	235006	23	120671	26	171819	27	267551	16	235074	16	481051	18	12250007	5
height 40-129 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	41843	31	6159	48	15489	30	47124	19	38671	24	13500	21	54712	17	24796	38	35397	21	12631	27	15059	36	52572	20	80100	16	22155	21	460209	7
	fir	47736	17	616	53	10777	36	23683	26	14712	34	3885	33	18728	27	8590	32	758	47	2045	41	234	71	8337	40	1544	44	7949	41	149593	9
	pine	540	100	0	.	117	100	1075	81	383	73	0	.	0	.	0	.	123	100	123	100	0	.	2985	41	7377	40	485	71	13208	26
	larch	0	.	0	.	0	.	0	.	0	.	155	100	0	.	0	.	245	100	0	.	2503	86	7100	29	11760	45	2559	39	24322	25
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2724	36	5686	35	115	100	8524	26
	other conifers	120	100	0	.	122	100	728	71	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	970	56
	beech	279389	12	222380	27	43529	39	72292	23	150709	18	4228	35	54538	30	40089	40	10708	39	2661	49	14989	53	1749	38	7854	47	9405	30	914520	9
	maple	89286	15	76424	25	7235	32	38822	35	171159	18	14214	81	20890	48	29695	32	7345	27	4086	60	3782	43	6644	41	4304	39	2661	32	476547	9
	ash	88001	23	56771	27	35255	35	68286	35	197921	19	14284	41	63924	57	31265	22	14294	44	7611	58	11367	37	10102	37	8406	54	31194	37	638680	10
	oak	2612	48	1355	59	476	50	1100	58	2882	44	0	.	270	71	0	0	0	.	0	.	0	.	4364	33	250	100	1145	71	14455	18
	sweet chestnut	123	100	0	.	277	100	0	.	122	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11136	49	11659	47
	other broadleaves	49187	17	19385	31	9626	24	18138	34	103021	46	14004	32	21302	17	26420	28	18602	27	9751	39	12158	37	39071	17	24921	21	60872	22	426458	12
	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	598837	9	383090	20	122902	21	271250	15	679580	13	64269	25	234365	21	160855	18	87471	16	38908	25	60093	26	135648	12	152202	12	149676	14	3139146	5
DBH 0-3.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	15309	22	3757	38	6003	29	20498	22	14751	21	7443	24	22058	18	7985	26	14102	20	4936	26	7452	37	18510	18	39291	16	13750	20	195844	6
	fir	25277	15	366	58	5028	33	13753	28	8243	54	5661	56	15643	24	8540	33	479	50	967	43	0	0	4052	46	1047	88	4568	58	93625	10
	pine	0	.	0	.	237	100	1065	100	1022	64	0	.	123	100	0	.	123	100	0	.	0	.	5180	56	7083	42	1314	72	16148	27
	larch	240	70	0	.	0	.	237	100	235	100	0	.	369	100	0	.	0	0	0	.	258	100	11683	27	5675	46	1875	41	20572	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1614	46	2952	72	0	.	4565	49
	other conifers	123	100	0	.	122	100	1602	85	118	100	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1966	70
	beech	127807	14	32829	21	10194	39	30321	27	49275	21	4001	42	19132	23	13021	31	4930	29	5117	75	9602	37	3025	43	2899	50	16406	41	328558	8
	maple	29722	21	32811	35	5404	57	13335	43	66022	21	8421	57	6315	43	4424	35	3137	35	1287	82	1125	46	2247	43	2469	55	2016	38	178734	12
	ash	17707	22	30671	36	11304	36	12848	29	57464	21	8916	58	17491	43	7049	37	3878	44	3324	50	6557	48	2545	40	2450	42	8745	37	190949	11
	oak	727	47	120	100	355	74	481	61	355	57	0	.	147	100	0	.	0	.	0	.	0	.	2364	36	131	100	679	52	5359	20
	sweet chestnut	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	100	0	.	4656	33	4787	32
	other broadleaves	22700	16	5892	36	5264	33	6558	30	17325	20	9070	28	15099	17	12630	46	14079	35	3646	34	4810	49	24416	19	10122	25	36578	22	188188	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	0	0	0	0	0	0	0	0	0	0
	total	239613	9	106446	22	43911	19	100698	14	214810	12	43513	23	96376	13	53647	18	40727	17	19276	27	29803	25	75767	12	74119	13	90588	15	1229294	4
DBH 4-7.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5448	21	1939	50	1226	45	11576	22	12569	17	4930	24	10155	19	4809	26	5568	24	2767	36	3903	44	8617	17	12739	18	4809	28	91055	6
	fir	10600	15	771	48	717	47	3776	25	1913	42	1790	51	8415	19	2868	38	807	42	733	63	348	58	2028	35	392	75	1016	39	36175	9
	pine	0	0	0	.	118	100	0	.	359	74	0	.	0	.	0	.	0	.	258	100	0	.	4063	40	2496	39	485	71	7778	25
	larch	0	.	0	.	0	.	0	.	0	.	0	.	123	100	0	.	375	74	0	.	258	100	5268	29	1113	53	2029	38	9166	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1254	51	873	43	0	.	2127	35
	other conifers	384	74	120	100	0	.	0	.	481	61	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	.	985	43
	beech	10926	17	2999	30	1791	38	4591	27	6019	20	1123	50	4325	22	3230	35	1063	40	740	48	1859	41	1367	74	1772	39	3493	30	45297	8
	maple	2719	28	995	43	591	66	1216	49	3374	27	370	59	337	58	404	58	492	50	380	58	497	61	1490	56	0	.	639	52	13505	13
	ash	670	67	1827	54	855	51	1212	51	3953	36	341	73	804	56	249	71	637	72	131	100	125	100	490	62	232	71	784	47	12310	17
	oak	1096	65	0	0	118	100	122	100	122	100	0	.	0	.	0	.	125	100	131	100	0	.	2602	52	0	.	777	57	5094	32
	sweet chestnut	0	.	0	.	352	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2055	35	2407	33
	other broadleaves	4399	20	732	41	1473	36	1760	32	2571	31	1687	36	2647	30	1511	40	2261	34	1004	43	2091	42	5509	22	2860	33	8517	18	39023	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	0	.	0	0	0	0
	total	36242	9	9383	21	7241	19	24252	16	31363	11	10241	19	26806	11	13072	17	11328	17	6144	26	9081	26	32688	12	22476	13	24604	11	264923	4
DBH 8-11.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4948	21	1351	50	2700	32	4728	22	10529	20	2187	34	3739	25	3416	28	3736	24	873	43	1008	47	5293	18	6981	21	1994	28	53483	7
	fir	4648	19	243	71	1108	48	2268	26	963	47	107	100	2281	33	990	40	109	100	505	61	109	100	880	48	387	75	0	.	14598	11
	pine	0	.	240	100	0	.	118	100	122	100	0	.	0	.	0	.	0	.	0	.	0	.	2970	29	1488	62	0	.	4939	26
	larch	0	.	120	100	240	71	0	.	240	71	0	.	123	100	0	.	260	100	0	.	129	100	1760	35	503	61	1111	48	4487	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	609	53	514	50	0	.	1123	37
	other conifers	0	.	123	100	122	100	256	71	353	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	854	38
	beech	5357	19	862	43	1350	35	2046	30	2422	23	360	58	1600	33	1199	56	698	53	436	79	724	47	500	61	1281	40	2356	28	21190	9
	maple	867	43	246	70	718	53	476	61	607	44	834	42	380	75	155	100	506	50	0	.	245	100	359	58	0	.	0	.	5392	17
	ash	515	50	0	.	122	100	366	57	1558	35	126	100	147	100	274	71	250	71	380	74	109	100	760	74	0	.	1234	51	5842	20
	oak	616	52	123	100	0	0	0	.	244	100	0	.	0	.	0	.	109	100	131	100	0	.	1475	70	0	.	524	50	3222	36
	sweet chestnut	0	.	0	.	117	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	100	0	.	1020	50	1268	42
	other broadleaves	2042	32	240	70	733	47	500	50	949	35	1417	35	1052	45	337	58	609	45	1123	54	738	62	2493	34	2127	38	7223	19	21584	10
	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
	total	18992	10	3549	26	7210	20	10759	15	17988	14	5032	21	9322	15	6371	20	6277	19	3448	34	3062	29	17231	13	13280	16	15462	13	137984	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	142888	28	17025	31	67511	54	253545	20	185773	15	51116	25	165383	15	82029	30	102081	22	32496	25	43395	39	147393	15	191932	13	63623	25	1546190	6
	fir	175909	12	9754	42	39236	33	193800	17	149059	21	23939	32	122163	17	45035	22	5534	49	6013	38	1242	54	19954	35	5133	45	20429	37	817199	7
	pine	1146	100	240	100	1004	89	3357	57	1886	53	0	.	123	100	0	.	245	71	931	77	0	.	18029	35	22300	32	2285	71	51546	19
	larch	240	70	120	100	240	71	237	100	475	61	155	100	614	100	0	.	881	51	0	.	4287	72	40441	26	24116	33	13458	33	85267	17
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15660	44	17839	32	115	100	33614	26
	other conifers	628	53	244	71	5348	93	3135	77	1484	51	0	0	0	0	1323	100	0	0	0	0	0	0	0	0	0	0	0	0	12161	47
	beech	1527562	13	953588	19	172934	29	281495	19	772440	13	65790	50	256564	30	297066	34	61700	33	31326	62	85573	39	26552	67	67883	39	39348	26	4639820	7
	maple	784220	14	652949	24	76100	41	181042	22	1394946	19	89940	44	149015	33	406689	24	48365	27	16937	34	30940	35	33685	33	24002	34	18744	26	3907574	9
	ash	605066	20	434522	17	367664	44	327620	30	1042930	13	113093	43	283380	28	321187	21	88767	37	47691	32	68693	28	62129	33	31227	39	239243	28	4033212	7
	oak	41575	66	21082	64	17391	47	7115	31	32434	29	0	.	1561	56	568	100	234	71	851	75	0	.	25834	35	2044	62	29975	63	180664	22
	sweet chestnut	123	100	0	.	746	73	0	.	122	100	0	.	0	.	0	.	0	.	0	.	0	.	262	70	0	.	106346	26	107600	25
	other broadleaves	177160	13	68284	23	40378	33	49836	24	386204	46	54174	27	104705	18	83395	20	73002	26	52203	33	39728	28	138945	14	110677	19	227814	16	1606505	12
	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	3456518	10	2157807	16	788553	26	1301181	13	3967753	11	398208	27	1083508	15	1237293	18	380809	18	188447	21	273857	24	528885	11	497152	11	761381	14	17021353	4

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. 1196466. Birmensdorf, Swiss Federal Research Institute WSL

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