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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NFI5
number of stems
diameter at breast height (main diameter classes) · main tree species
regional demarcation: economic region
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (main diameter classes)	main tree species	1000 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	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2356	18	607	38	448	35	2691	20	1621	17	1607	21	3901	16	1579	25	2699	16	1207	22	862	28	4123	12	7459	10	2939	19	34099	5
	fir	3316	12	191	39	429	34	1697	18	548	24	587	25	2301	18	731	25	299	34	387	27	80	59	291	31	49	58	428	30	11333	7
	pine	19	100	0	.	39	100	56	58	20	100	0	.	135	71	0	.	75	76	94	84	22	100	635	38	1431	29	116	65	2642	19
	larch	0	.	74	80	215	73	17	100	78	61	44	100	15	100	49	100	147	57	0	.	93	52	1355	20	880	25	1302	21	4269	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	401	37	776	40	32	70	1210	29
	other conifers	156	70	69	61	0	.	88	45	226	57	0	.	35	71	39	71	132	58	69	100	33	71	121	74	432	35	20	100	1420	19
	beech	3710	13	1837	19	1042	37	1670	20	2347	16	388	30	1637	16	990	21	644	24	266	33	545	26	712	29	248	33	2981	14	19015	6
	maple	1614	19	526	31	362	44	342	32	814	18	215	45	573	29	501	26	903	24	213	31	176	38	929	35	249	41	723	32	8141	8
	ash	532	35	292	40	190	35	203	44	462	25	145	55	224	41	81	50	262	36	275	51	107	63	506	36	175	54	949	27	4402	11
	oak	582	58	96	52	73	50	59	74	149	50	21	100	0	0	0	.	35	100	18	100	35	100	454	41	0	.	472	39	1994	22
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	2018	25	2037	25
	other broadleaves	1371	15	388	30	778	27	936	28	1448	20	679	29	1091	23	527	25	1369	28	408	34	401	35	2574	15	1773	22	5850	12	19592	6
	indeterminable	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	13657	7	4080	14	3576	17	7757	11	7713	9	3687	14	9912	9	4496	13	6565	12	2937	17	2354	16	12120	8	13471	8	17830	7	110154	3
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3232	12	698	33	1148	38	2706	18	3189	17	2478	20	5034	14	2451	17	2989	14	1936	21	1459	23	5509	12	10654	9	3378	14	46861	4
	fir	4509	10	245	36	339	33	1772	18	716	28	809	25	3018	15	737	24	398	29	679	28	154	47	394	26	142	47	339	33	14251	6
	pine	111	52	92	60	0	0	101	62	95	84	0	.	191	84	0	.	52	74	146	51	65	100	1916	27	1537	24	49	100	4355	16
	larch	0	.	0	.	274	100	17	100	326	58	88	79	35	100	0	0	123	62	0	.	53	100	2460	15	1413	20	2467	16	7255	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	465	28	1224	25	16	100	1705	19
	other conifers	245	36	52	58	20	100	36	71	292	43	0	.	15	100	95	52	155	71	75	80	34	71	52	74	248	33	0	.	1318	17
	beech	4315	11	1476	17	1034	25	1717	14	2394	13	679	31	2426	14	1688	18	969	28	613	27	854	33	787	31	409	35	4543	14	23905	5
	maple	1833	16	641	24	572	41	533	24	1139	17	465	26	1003	23	740	28	1047	22	177	46	417	37	696	24	159	37	730	32	10153	7
	ash	482	24	473	27	308	37	279	32	597	30	254	38	351	34	456	28	329	37	266	41	196	46	520	32	345	46	857	25	5711	9
	oak	426	45	59	58	463	46	56	58	72	61	0	0	0	.	21	100	89	66	0	.	0	.	423	39	15	100	673	36	2298	18
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	100	18	100	3455	17	3529	17
	other broadleaves	1245	21	380	26	293	35	982	22	1323	19	652	35	640	23	709	34	863	28	673	49	496	40	1913	16	2302	22	6358	12	18828	6
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0
	total	16397	6	4115	13	4452	17	8200	10	10143	9	5424	14	12713	8	6896	11	7014	11	4565	16	3729	17	15191	8	18466	7	22865	6	140169	2
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3309	12	538	33	984	27	1819	21	2773	15	2845	19	4635	13	3396	14	3055	14	1590	19	1324	24	5141	12	8961	9	3204	16	43575	4
	fir	3146	11	328	37	277	36	1419	19	750	22	831	30	2074	14	781	26	471	28	439	31	80	52	377	34	126	43	321	37	11420	6
	pine	422	40	155	53	51	58	151	55	68	50	0	.	296	69	0	.	53	100	182	55	61	77	1480	22	882	27	83	81	3885	13
	larch	0	.	17	100	118	77	17	100	71	50	0	.	17	100	49	100	141	50	0	.	71	79	2390	18	1501	19	2646	14	7038	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	576	27	1269	23	16	100	1860	18
	other conifers	17	100	69	61	0	.	36	71	86	66	0	.	17	100	36	71	69	79	0	.	15	100	0	0	17	100	0	.	363	28
	beech	4720	10	1327	18	849	21	1261	15	2218	13	643	28	2651	13	1610	19	877	23	816	28	537	34	564	32	522	30	3720	15	22315	5
	maple	1314	14	394	28	86	53	340	28	1232	19	439	28	815	23	985	21	563	29	317	35	486	31	303	32	234	31	297	33	7805	7
	ash	420	30	177	42	385	33	243	28	485	25	316	39	321	61	491	27	228	39	164	55	139	40	388	35	222	55	474	36	4451	10
	oak	575	34	174	40	304	35	176	49	156	33	39	71	91	61	0	0	93	68	0	0	0	.	199	37	15	100	717	26	2540	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	71	15	100	3392	16	3520	16
	other broadleaves	507	24	225	35	280	36	365	30	753	19	133	39	287	32	497	30	310	31	147	43	106	48	633	24	634	32	3784	13	8661	7
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	14430	6	3402	13	3335	14	5828	10	8591	8	5246	14	11205	8	7845	10	5859	10	3654	15	2819	17	12163	8	14400	7	18656	6	117433	2
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	2957	9	284	26	769	23	1156	17	1855	12	2281	14	3377	10	3155	11	2792	12	1155	21	1042	22	3058	10	6210	8	2509	13	32600	3
	fir	2457	10	194	39	282	28	980	16	649	16	449	23	1393	11	874	15	292	28	419	28	216	30	291	34	123	34	273	31	8894	5
	pine	509	26	221	29	116	49	95	38	337	21	0	.	200	49	13	71	28	50	108	44	23	73	563	21	698	22	69	48	2979	10
	larch	7	100	28	50	50	47	21	74	75	43	9	100	0	.	7	100	57	62	0	.	85	74	1903	15	1491	13	1450	14	5181	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	277	27	493	19	0	.	780	15
	other conifers	0	.	42	58	28	100	76	39	13	71	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	8	100	173	28
	beech	3690	8	1285	15	852	19	1426	12	1278	11	329	34	1474	16	1645	13	593	22	275	29	465	24	196	45	371	29	1270	13	15150	4
	maple	624	13	167	30	74	35	181	24	371	17	97	43	253	24	353	19	215	25	67	37	227	26	69	54	91	55	66	50	2854	7
	ash	350	21	152	30	315	29	257	32	526	17	109	55	205	45	321	26	128	33	76	60	89	42	87	49	30	50	107	34	2753	9
	oak	290	27	160	36	208	34	130	28	212	24	28	76	34	61	23	58	16	71	32	76	0	.	58	57	19	73	260	28	1472	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	20	72	6	100	1036	18	1069	17
	other broadleaves	275	34	84	35	85	42	67	37	383	24	27	60	61	35	30	50	109	43	27	50	51	42	163	31	97	40	988	14	2445	9
	indeterminable	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0
	total	11159	4	2617	11	2779	12	4388	9	5697	7	3328	12	6998	7	6421	8	4254	10	2159	15	2198	16	6685	8	9630	6	8036	5	76349	2
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1754	10	258	28	394	28	675	17	839	15	1202	14	1877	10	2119	11	1543	13	712	19	739	19	1551	12	2899	9	1488	14	18048	3
	fir	1520	10	63	40	277	24	865	15	614	17	412	22	1260	12	754	16	374	23	182	38	138	37	180	34	193	26	275	26	7106	5
	pine	98	32	31	50	54	56	94	35	135	26	0	.	61	49	24	76	7	100	7	100	7	100	75	41	248	25	21	74	862	12
	larch	14	70	7	100	14	71	20	58	83	35	0	.	0	.	29	61	57	50	0	.	25	78	1478	13	1119	13	906	13	3753	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	141	30	193	26	0	.	334	19
	other conifers	0	.	63	71	0	.	113	44	69	83	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	16	100	268	33
	beech	1017	12	501	19	337	22	786	15	1070	12	140	35	641	19	456	18	180	29	164	32	207	32	54	57	118	39	497	16	6167	5
	maple	109	27	43	41	14	71	7	100	105	32	51	38	46	45	121	33	66	39	20	58	29	63	0	.	44	46	0	.	657	12
	ash	83	35	59	41	101	33	172	35	278	20	8	100	29	100	81	44	38	45	25	79	50	43	7	100	6	100	46	59	984	12
	oak	75	37	55	54	93	44	134	29	281	25	0	.	0	0	8	100	0	.	16	72	0	.	8	100	41	76	57	41	768	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	63	6	100	547	21	585	20
	other broadleaves	15	71	51	47	124	41	59	44	67	41	0	.	6	100	40	51	84	68	14	71	12	70	31	49	43	62	60	39	605	17
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	4685	6	1131	13	1409	13	2925	9	3540	7	1813	12	3926	7	3631	9	2348	11	1140	16	1208	15	3556	8	4911	7	3914	7	40138	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	13608	8	2384	23	3744	20	9047	13	10277	11	10413	14	18823	10	12700	10	13078	11	6600	16	5426	18	19382	9	36184	7	13518	12	175182	3
	fir	14949	8	1020	30	1605	22	6734	13	3277	14	3088	21	10047	11	3876	15	1833	21	2106	22	667	25	1532	24	634	26	1637	24	53003	4
	pine	1160	24	499	28	260	38	497	33	655	22	0	.	883	57	37	55	215	53	538	43	179	80	4670	21	4795	19	338	53	14724	10
	larch	21	58	127	51	671	64	92	48	632	36	140	71	67	81	135	74	524	38	0	.	326	59	9587	12	6404	12	8770	11	27496	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	1860	22	3954	20	65	79	5889	15
	other conifers	419	40	293	49	47	72	349	28	686	37	0	.	75	46	170	40	364	64	144	72	82	64	173	56	696	32	44	70	3543	14
	beech	17452	7	6427	12	4115	18	6860	10	9306	9	2180	24	8829	11	6388	13	3263	19	2133	23	2608	23	2313	27	1669	23	13012	12	86553	3
	maple	5494	12	1772	19	1108	34	1404	19	3661	13	1268	23	2689	20	2700	17	2794	18	794	26	1336	23	1997	24	778	29	1817	26	29611	5
	ash	1867	17	1153	22	1300	23	1154	20	2348	14	832	32	1129	34	1430	20	985	28	804	33	581	33	1508	27	778	43	2433	22	18301	6
	oak	1948	32	544	30	1141	31	555	23	870	17	88	56	125	51	52	50	234	55	67	68	35	100	1142	32	91	72	2180	24	9071	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	238	67	45	72	10448	13	10739	13
	other broadleaves	3412	15	1128	19	1560	22	2408	20	3972	14	1490	26	2085	18	1803	23	2734	22	1269	37	1068	31	5314	14	4849	21	17040	10	50132	5
	indeterminable	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	60329	4	15346	10	15551	11	29098	7	35684	6	19498	11	44753	6	29290	8	26039	8	14455	13	12307	13	49715	6	60878	5	71300	4	484244	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1349121. Birmensdorf, Swiss Federal Research Institute WSL

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