Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
growing stock (stemwood)
diameter at breast height (main diameter classes) · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
diameter at breast height (main diameter classes)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
12-15 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	281	15	93	30	119	53	241	20	382	23	105	23	315	17	148	30	214	20	64	29	50	32	356	20	603	15	136	18	3107	6
	fir	298	16	24	41	52	31	132	34	94	29	56	46	183	25	72	43	48	52	34	44	24	53	18	38	7	62	33	51	1076	9
	pine	2	100	0	1676	0	0	11	100	5	100	0	.	7	85	0	.	5	72	1	100	0	.	116	42	64	30	4	100	214	25
	larch	0	.	0	.	24	100	1	100	3	72	1	100	3	100	5	100	8	64	0	.	1	100	89	30	26	25	55	44	216	20
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	67	68	68	2	100	97	52
	other conifers	18	53	6	71	1	100	8	47	8	64	0	.	2	71	5	60	0	.	6	100	1	100	4	62	16	43	0	.	75	21
	beech	312	12	111	37	59	32	103	21	159	17	18	33	191	32	102	28	65	45	14	33	56	36	83	75	25	54	294	16	1592	8
	maple	94	35	44	27	21	45	50	38	77	88	24	34	46	34	44	56	52	35	11	63	15	46	31	30	26	40	26	36	562	16
	ash	59	25	27	65	74	72	63	79	66	47	12	55	46	37	38	35	20	54	46	52	4	74	12	37	26	48	97	27	589	16
	oak	52	47	0	0	11	60	2	71	4	59	1	100	1	100	2	100	5	100	1	100	0	.	30	37	0	.	46	32	156	20
	sweet chestnut	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	237	26	239	25
	other broadleaves	120	19	36	28	54	51	51	30	110	23	28	35	59	31	46	47	90	24	34	41	39	32	179	18	170	28	454	22	1469	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	0
	total	1235	8	340	19	418	24	663	15	907	14	244	16	852	12	462	17	508	14	211	22	191	20	944	13	1031	12	1384	10	9391	4
16-23 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	1057	16	252	35	344	33	744	20	1024	18	555	23	1005	14	762	18	447	18	284	26	276	31	1061	17	1831	11	559	20	10199	5
	fir	895	13	110	41	125	35	500	23	285	37	236	28	662	16	194	27	79	40	152	36	17	47	105	49	54	37	86	38	3500	7
	pine	25	51	38	53	0	.	63	79	23	62	0	.	25	108	0	.	0	0	21	65	0	.	437	26	262	27	7	100	901	17
	larch	0	0	6	100	23	79	8	77	23	59	9	100	11	100	25	100	14	51	0	.	16	75	381	21	121	27	216	32	853	14
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	121	46	91	29	3	100	216	29
	other conifers	-10	233	10	72	8	100	18	77	57	38	0	.	4	100	1	2270	0	.	0	.	5	71	11	67	19	41	0	.	122	34
	beech	1383	11	288	24	402	30	396	17	641	20	189	34	604	16	505	21	238	29	152	33	170	31	146	37	64	41	1048	17	6225	6
	maple	360	18	107	31	80	37	84	35	469	25	127	33	146	25	196	31	139	35	32	57	89	31	111	31	28	47	74	33	2042	9
	ash	163	27	170	28	163	37	49	34	201	28	64	39	100	33	214	28	59	47	59	39	40	52	97	42	81	55	172	37	1632	10
	oak	101	36	28	74	135	42	22	51	39	45	0	0	5	100	6	100	19	61	0	.	0	.	56	44	2	100	138	33	553	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	1087	17	1087	17
	other broadleaves	206	20	54	30	72	34	185	32	212	20	77	38	102	24	113	43	123	30	89	43	101	37	357	19	345	31	1075	15	3111	8
	indeterminable	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0
	total	4179	7	1063	14	1351	18	2069	12	2974	10	1257	15	2663	9	2016	12	1117	13	789	18	713	19	2884	10	2898	9	4465	7	30439	3
24-35 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	3154	13	565	31	932	28	1474	19	2818	15	1855	20	3164	12	2545	14	1842	16	959	24	1058	31	2963	13	4807	10	1757	17	29893	4
	fir	2345	14	238	37	226	36	1022	20	605	25	543	33	1546	16	691	21	271	32	298	45	99	51	265	36	74	49	198	41	8421	7
	pine	350	41	96	67	109	58	55	77	171	34	0	.	86	83	0	.	19	100	107	51	0	.	706	23	635	27	51	64	2384	13
	larch	12	100	9	100	75	73	0	.	121	47	0	.	0	.	0	.	80	53	0	.	17	100	1090	16	648	20	930	20	2982	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	229	34	371	24	0	.	601	19
	other conifers	7	100	52	52	54	100	50	52	36	71	0	.	0	.	0	.	8	100	0	.	-19	100	0	0	4	100	0	.	193	38
	beech	3815	11	1231	18	740	20	1246	19	1899	15	432	37	1752	14	1248	18	484	25	365	33	290	29	377	32	255	34	1648	17	15781	5
	maple	819	16	167	34	24	74	199	29	739	18	210	34	626	24	529	26	325	35	135	35	260	35	64	43	64	62	120	42	4280	8
	ash	431	24	198	42	447	31	355	24	638	23	304	40	325	49	596	23	130	56	126	51	92	47	71	47	40	53	173	35	3927	9
	oak	411	32	193	42	247	37	137	47	168	30	33	77	11	100	0	0	11	100	10	100	0	.	40	74	6	100	334	28	1600	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	5	100	1431	17	1462	17
	other broadleaves	318	24	159	36	149	60	245	35	443	26	30	52	82	40	252	31	240	40	48	50	34	50	161	31	172	33	1458	14	3788	8
	indeterminable	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0
	total	11661	6	2908	13	3003	14	4783	11	7639	8	3407	16	7592	8	5860	9	3409	12	2048	18	1829	21	5992	9	7080	8	8101	6	75312	2
36-51 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6186	9	882	25	1750	21	2947	16	4248	11	3641	14	6252	11	5049	12	4262	15	1992	22	1932	20	4671	11	9926	8	3597	14	57335	3
	fir	5247	10	393	34	695	29	2208	16	1499	17	881	23	2888	12	2211	17	571	26	645	31	367	34	530	34	362	30	515	28	19013	5
	pine	795	23	429	31	386	45	286	33	799	20	0	.	337	64	56	52	15	72	72	55	0	.	599	24	870	24	64	47	4707	10
	larch	30	75	48	61	73	53	116	62	145	51	0	.	16	100	22	71	80	66	0	.	30	64	2389	15	1825	15	1477	16	6250	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	214	30	457	22	0	.	665	18
	other conifers	0	.	155	73	41	100	100	79	46	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	362	41
	beech	6894	8	2822	15	1764	19	2759	12	3112	11	617	32	2583	15	2749	15	858	28	445	32	683	26	143	51	517	37	1296	15	27242	4
	maple	857	15	289	31	30	62	75	68	432	21	189	41	418	26	533	19	293	36	109	42	196	32	57	86	75	60	41	64	3596	8
	ash	654	24	289	28	589	26	771	45	1552	14	74	67	265	31	683	23	235	49	147	56	186	43	67	71	39	50	167	40	5719	9
	oak	493	26	279	39	526	34	355	32	512	22	9	100	57	69	15	100	16	71	38	81	0	.	29	73	60	75	290	36	2680	12
	sweet chestnut	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	7	100	0	.	0	.	18	70	5	100	1157	19	1194	18
	other broadleaves	202	35	92	40	268	43	264	42	541	25	6	100	129	36	80	54	118	42	28	62	89	56	89	78	107	39	738	18	2751	10
	indeterminable	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	21359	4	5677	11	6122	12	9881	9	12892	7	5417	12	12947	7	11397	8	6450	12	3476	17	3484	16	8807	9	14244	7	9362	6	131513	2
=52 cm	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	6389	10	1281	27	1605	27	4003	17	4808	15	3925	18	8220	11	7849	14	4829	15	2737	21	2985	22	4603	15	9284	11	4917	14	67434	4
	fir	5603	13	482	31	1041	31	3894	16	2783	18	1330	27	5143	14	2746	16	1464	25	696	38	481	46	369	38	844	29	906	29	27782	5
	pine	297	32	21	100	162	54	285	40	553	27	0	.	177	60	57	71	0	.	20	100	0	.	138	54	698	27	38	100	2444	13
	larch	43	100	5	100	25	100	60	73	337	35	0	.	0	.	152	58	132	51	0	.	116	71	4213	14	3195	16	2140	18	10418	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	232	37	354	30	0	.	586	23
	other conifers	0	.	70	100	185	100	374	55	288	95	0	.	71	100	0	.	0	.	0	.	0	.	0	.	0	.	30	100	1018	40
	beech	3081	14	2034	18	1045	23	2793	17	4019	14	348	41	2243	22	1573	23	761	31	299	40	621	38	173	56	430	40	1132	19	20553	6
	maple	124	42	163	49	40	75	0	.	324	38	66	52	48	71	131	61	51	58	14	100	109	54	0	.	62	63	20	100	1151	17
	ash	344	32	320	35	584	39	602	37	1061	24	36	100	188	62	385	45	83	63	59	100	57	65	15	100	30	100	46	100	3809	13
	oak	130	49	206	61	246	54	376	35	1152	33	0	.	0	.	27	100	0	.	42	72	0	.	21	100	62	73	85	60	2348	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	75	8	100	1357	24	1410	23
	other broadleaves	21	100	228	43	374	54	162	89	31	100	0	.	20	100	118	87	348	66	9	100	66	61	43	73	30	75	106	46	1555	24
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0
	total	16033	6	4810	13	5307	15	12549	10	15355	8	5706	15	16109	9	13037	10	7667	12	3877	19	4435	18	9851	10	14997	8	10777	8	140509	2
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	17067	7	3073	19	4750	17	9409	12	13279	9	10081	13	18956	8	16354	10	11593	12	6036	18	6301	17	13654	10	26451	7	10966	11	167969	2
	fir	14388	8	1246	25	2139	24	7756	13	5266	13	3046	21	10421	10	5914	13	2433	22	1827	27	989	33	1287	28	1341	24	1738	25	59792	4
	pine	1468	21	584	28	657	37	699	26	1550	18	0	.	632	55	113	51	39	59	221	54	0	.	1995	19	2528	19	164	50	10650	8
	larch	85	59	68	46	219	39	185	49	629	28	10	100	30	71	204	46	315	36	0	.	179	55	8161	12	5815	13	4818	13	20718	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	823	24	1342	19	5	100	2164	15
	other conifers	16	170	292	65	290	73	549	44	436	68	0	.	77	93	5	306	8	100	6	100	-13	118	15	58	39	39	50	100	1770	27
	beech	15485	6	6485	12	4010	17	7298	11	9829	9	1604	23	7372	12	6177	13	2407	20	1275	26	1820	21	922	30	1290	28	5418	12	71394	3
	maple	2255	10	770	22	196	29	408	22	2041	15	616	24	1284	18	1433	17	860	21	301	29	668	25	262	31	256	35	281	29	11631	5
	ash	1651	17	1003	23	1858	22	1841	29	3520	13	489	36	924	31	1915	19	527	35	437	38	378	35	263	34	215	41	655	28	15676	7
	oak	1187	23	707	33	1165	28	892	22	1876	23	43	63	74	61	50	63	51	57	91	54	0	.	176	42	130	71	894	21	7336	10
	sweet chestnut	0	.	0	.	2	100	0	.	6	100	0	.	0	.	0	.	7	100	0	.	0	.	90	53	18	100	5269	12	5391	12
	other broadleaves	867	18	570	25	917	35	905	26	1335	18	141	27	392	21	608	29	918	34	207	35	329	31	829	19	825	24	3831	11	12674	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	0	0	0	0
	total	54468	3	14798	9	16202	10	29944	7	39767	5	16030	11	40163	6	32773	7	19151	10	10401	14	10651	14	28478	7	40250	6	34090	4	387165	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. 1280548. Birmensdorf, Swiss Federal Research Institute WSL

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