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
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 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
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	101808	36	10913	59	52516	65	138149	50	101067	30	45473	53	107794	29	64663	32	73468	30	8324	41	3779	73	46199	24	129957	44	4253	57	888363	13
	fir	107094	21	27497	71	11111	39	194376	25	168348	21	26150	45	63860	25	60608	38	18033	44	0	.	8585	86	9896	52	2595	71	5616	50	703771	10
	pine	4491	100	0	.	1302	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4158	100	12617	53	0	.	22569	41
	larch	0	.	0	.	0	.	0	.	4612	100	0	.	0	.	0	.	0	.	0	.	0	.	5014	77	5577	61	0	.	15204	46
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13731	55	26265	66	0	.	39996	47
	other conifers	0	.	0	.	0	.	0	.	2604	100	0	.	1403	100	0	.	0	.	0	.	0	.	0	0	0	.	0	.	4007	74
	beech	1586847	20	469805	26	618014	63	290365	31	631225	19	81308	54	136411	28	343651	28	304818	77	50493	55	18139	59	220449	71	14984	38	19509	37	4786018	13
	maple	665877	19	321004	52	131479	64	253182	30	1572093	21	111005	61	77363	26	461184	29	76547	30	33583	53	11513	49	15635	42	18337	48	27831	54	3776632	11
	ash	626864	18	255945	30	236320	32	300860	29	1079406	17	79327	33	246906	42	426643	25	152354	44	81176	58	130844	31	194612	44	64068	41	377261	28	4252585	8
	oak	284795	62	2726	70	194890	86	22099	41	48968	70	3036	71	1403	100	1403	100	0	.	0	.	0	.	39080	54	6837	61	32592	63	637828	39
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22402	34	22402	34
	other broadleaves	133233	22	23631	38	43094	45	30305	50	106326	23	36301	31	58916	33	41489	26	73407	32	25977	40	12161	37	74487	32	94603	28	68370	24	822299	9
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3511008	13	1111522	28	1288726	36	1229335	17	3714649	14	382601	26	694056	18	1399640	18	698626	38	199552	35	185021	26	623263	34	375840	23	557835	21	15971674	6
height 40-129 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	32535	36	1952	61	10817	54	25241	21	40555	31	23212	33	56901	25	18351	30	34449	28	10541	32	10498	36	36597	32	85881	26	7912	33	395443	9
	fir	37247	33	16264	88	2505	52	49849	27	32971	28	3459	38	28757	37	6233	61	1987	61	1022	71	0	.	6234	54	1469	74	2002	79	189999	15
	pine	539	100	0	.	0	.	1022	71	1107	100	0	.	0	.	0	.	0	.	0	.	0	.	1478	58	10476	47	0	.	14623	35
	larch	0	.	0	.	0	.	0	.	2214	100	0	.	620	100	0	.	0	.	0	.	0	.	1996	79	21085	56	969	71	26883	45
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2458	60	7450	40	0	.	9908	34
	other conifers	0	0	0	.	0	.	976	100	553	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	1529	73
	beech	420087	14	239945	20	76705	31	127539	25	473133	21	96745	61	101699	30	118901	31	21992	46	24810	93	18001	38	3626	47	20485	44	2622	44	1746290	9
	maple	61316	22	35972	32	36822	58	40994	28	226959	21	18245	39	20111	34	55443	36	16106	45	5166	57	2884	63	6506	41	4496	53	7438	64	538457	11
	ash	73702	23	71022	31	23705	37	51545	34	284327	29	25461	34	51805	33	96707	29	16559	33	15245	53	19489	41	21280	31	9340	55	33780	39	793968	12
	oak	4405	66	1970	61	3474	55	5018	34	5383	37	0	.	588	100	0	.	0	.	523	100	0	.	6427	56	0	.	0	0	27788	21
	sweet chestnut	0	.	0	.	1107	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4638	45	5745	41
	other broadleaves	43088	22	13938	44	9076	38	12813	50	92814	21	13433	35	21908	24	23502	28	25715	24	14787	36	7325	38	21648	25	36549	34	23822	22	360417	8
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	672919	11	381062	18	164211	23	314997	16	1160016	14	180556	38	282389	15	319137	19	116808	19	72093	37	58198	26	108250	18	197231	17	83185	20	4111052	5
DBH 0-3.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13751	31	1413	73	2298	39	10352	28	12122	25	12462	33	20741	19	9419	38	15566	23	3046	42	4712	32	15770	22	32781	29	5026	23	159457	9
	fir	12687	18	2307	100	2075	56	17790	30	5210	51	1887	41	11479	31	4698	44	1162	50	375	71	0	.	5331	42	732	50	1832	40	67565	12
	pine	388	100	0	.	0	.	0	.	996	82	0	.	177	100	365	100	0	.	176	100	0	.	3217	57	8285	56	0	0	13603	37
	larch	0	.	0	.	199	100	0	.	199	100	223	100	0	.	0	.	176	100	0	.	0	.	4169	35	11133	39	863	45	16962	27
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	356	71	2141	37	330	100	2827	32
	other conifers	177	100	0	.	0	.	399	70	176	100	0	.	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	752	50
	beech	197438	15	115631	25	22011	38	103619	29	184984	20	18269	49	75689	33	31157	29	11448	30	2464	51	6109	44	1106	58	7321	36	28842	70	806089	9
	maple	36636	19	12281	39	6893	68	17700	28	66391	27	4487	34	12535	30	17106	55	7796	36	2972	66	532	58	1946	39	3175	50	1452	43	191901	12
	ash	48484	24	22510	28	15902	30	24903	41	72325	19	6164	38	44127	46	21710	44	5833	35	4807	54	10318	53	9600	33	1356	56	5212	35	293253	11
	oak	194	100	194	100	199	100	399	70	886	52	0	.	0	.	212	100	0	.	0	.	0	.	353	71	0	.	0	0	2436	29
	sweet chestnut	0	.	0	.	199	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	188	100	0	.	2037	43	2424	38
	other broadleaves	19755	20	7837	34	7566	29	8799	21	32426	23	22584	28	13186	22	17818	42	18203	28	4686	38	4604	35	24188	47	13340	55	17956	21	212949	9
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	329511	12	162173	21	57343	28	183960	20	375715	13	66076	20	177933	20	102485	22	60185	16	18526	29	26274	30	66222	22	80264	18	63549	34	1770217	5
DBH 4-7.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	5495	24	1440	44	1690	31	7157	23	6663	24	4689	26	8863	17	3495	27	7534	28	2613	28	2797	36	9031	16	18220	18	5612	20	85301	7
	fir	9421	14	483	51	1570	60	5298	29	1410	32	936	39	4775	25	2842	42	543	57	898	35	122	70	1929	38	345	53	1703	43	32276	10
	pine	76	100	0	.	0	.	0	.	311	61	0	.	138	100	71	100	0	.	69	100	0	.	1807	38	3181	48	451	100	6105	29
	larch	0	.	137	71	0	.	69	100	144	71	0	.	0	.	0	.	0	.	0	.	0	.	3305	27	2599	36	1477	34	7730	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1112	34	1180	43	0	.	2293	27
	other conifers	0	.	0	.	78	100	274	100	78	100	0	.	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	.	430	69
	beech	14092	16	6299	24	3954	44	8744	19	9678	21	300	50	4810	22	3775	26	1666	28	842	58	1601	34	354	84	2889	50	4251	26	63256	7
	maple	4800	29	2188	36	1634	46	978	34	3756	30	323	54	1169	29	859	43	1328	30	208	58	211	58	847	31	579	78	1096	30	19976	11
	ash	2467	33	2246	39	1718	40	1575	62	6032	29	845	74	1451	40	1081	59	1031	59	358	72	904	53	421	62	216	75	1186	32	21529	13
	oak	282	50	227	100	224	76	233	74	916	78	0	.	0	.	0	.	0	.	0	.	0	.	350	60	0	.	223	74	2455	34
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	100	0	.	595	40	669	37
	other broadleaves	4551	19	963	53	1389	37	2400	27	4152	26	2300	37	2131	23	1108	29	3716	24	1123	44	1047	43	4583	21	2619	57	8579	19	40661	8
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	41184	9	13984	19	12256	22	26729	13	33139	12	9393	19	23338	11	13231	17	15819	17	6110	24	6682	22	23812	11	31829	14	25174	11	282680	4
DBH 8-11.9 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	4151	22	816	44	652	40	4974	24	3915	21	1821	32	4659	17	2005	23	3845	22	1122	35	1522	34	5046	16	9897	14	3427	21	47851	6
	fir	5831	17	207	58	876	35	2279	25	994	37	438	52	2946	22	1222	46	330	61	263	63	281	62	942	55	73	100	355	53	17037	10
	pine	0	.	76	100	0	.	66	100	389	82	0	.	207	100	0	.	69	100	145	100	0	.	1038	32	1453	37	466	76	3910	21
	larch	68	100	0	.	233	100	78	100	0	.	0	.	0	.	0	.	207	58	0	.	0	.	1637	29	1632	34	803	35	4657	18
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	213	74	766	41	0	.	979	36
	other conifers	69	100	0	.	0	.	66	100	66	100	0	.	71	100	142	100	0	.	0	.	0	0	0	0	0	0	0	.	415	47
	beech	4977	17	2733	30	702	40	4190	24	3097	21	356	45	2824	24	988	31	1027	32	330	45	882	53	288	50	718	51	2930	27	26045	8
	maple	1296	29	821	46	346	54	610	40	753	35	60	100	330	45	513	60	563	35	73	100	142	71	1055	32	0	.	428	47	6990	12
	ash	428	48	683	45	412	52	270	50	2016	24	322	63	295	50	735	34	354	82	213	74	207	74	290	50	138	100	817	38	7178	13
	oak	753	82	0	.	0	.	78	100	389	100	0	.	0	.	0	.	0	0	74	100	0	.	767	52	0	.	216	58	2275	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	796	35	796	35
	other broadleaves	2018	23	832	37	639	40	1162	37	1684	30	1198	42	1103	30	555	54	931	32	410	54	489	62	2677	23	1045	34	4910	18	19654	8
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	19590	10	6168	21	3861	19	13774	14	13303	12	4196	20	12434	11	6160	16	7327	16	2630	25	3521	24	13953	12	15721	11	15146	11	137786	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	157740	27	16535	41	67973	52	185874	38	164322	21	87658	34	198957	22	97934	24	134863	22	25644	29	23307	27	112643	20	276737	27	26230	21	1576416	9
	fir	172280	19	46759	77	18137	33	269592	23	208933	20	32871	37	111816	24	75603	31	22055	39	2557	38	8988	82	24332	42	5214	46	11509	36	1010647	9
	pine	5494	100	76	100	1302	100	1089	67	2804	75	0	.	522	74	436	100	69	100	390	73	0	.	11699	47	36013	35	917	86	60810	25
	larch	68	100	137	71	433	71	146	71	7170	95	223	100	620	100	0	.	383	69	0	.	0	.	16120	30	42026	45	4111	29	71435	29
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17871	48	37802	50	330	100	56002	37
	other conifers	247	77	0	.	78	100	1715	61	3477	77	0	.	1474	95	142	100	0	0	0	.	0	0	0	0	0	0	0	0	7133	45
	beech	2223442	16	834413	19	721387	54	534458	22	1302118	15	196978	53	321433	24	498472	24	340951	69	78939	63	44733	33	225824	70	46396	33	58154	38	7427699	9
	maple	769925	17	372265	46	177175	58	313463	25	1869951	19	134121	55	111509	22	535104	27	102339	25	42001	45	15282	39	25989	33	26587	41	38245	50	4533956	10
	ash	751945	16	352406	24	278056	29	379153	27	1444106	16	112119	28	344585	33	546875	23	176132	39	101800	55	161761	28	226203	39	75117	38	418256	26	5368513	7
	oak	290428	61	5117	50	198788	84	27827	34	56541	61	3036	71	1991	76	1615	88	0	0	597	88	0	.	46975	48	6837	61	33031	62	672782	37
	sweet chestnut	0	.	0	.	1306	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	262	100	0	.	30469	28	32037	27
	other broadleaves	202644	18	47202	30	61764	33	55479	34	237402	18	75816	23	97244	23	84472	20	121972	24	46983	30	25626	27	127583	24	148155	22	123637	17	1455979	6
	species not assessed in all inventories	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4574212	11	1674909	21	1526398	31	1768796	14	5296823	11	642821	25	1190151	14	1840653	17	898765	30	298911	31	279697	21	835501	26	700884	16	744889	17	22273410	5

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. 1196491. Birmensdorf, Swiss Federal Research Institute WSL

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