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
volume of bolewood (HG 2010)
timber assortment classes (HG 2010) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
timber assortment classes (HG 2010)	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	±%	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	.	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	.	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	.	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	.	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	.	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	.	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	.	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	.	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	.	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	.	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	.	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	.	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	.	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
smallwood	n/a	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	.
	spruce	157	35	59	62	50	44	610	13	24	67	314	27	0	.	68	30	230	8	261	27	191	31	588	19	13	50	48	26	277	21	48	56	29	69	115	39	166	28	47	26	44	34	847	20	186	18	13	76	255	25	4639	6
	fir	85	40	14	64	54	54	557	16	7	36	159	35	0	.	6	72	12	25	431	21	207	27	509	19	1	47	12	38	147	27	4	66	34	39	36	42	87	43	9	41	13	34	360	17	16	35	31	79	84	30	2874	7
	pine	4	57	0	.	0	100	56	38	6	67	36	49	0	70	0	.	46	18	21	54	7	96	5	77	0	.	4	84	1	81	1	59	5	39	2	85	12	66	3	76	1	65	10	58	48	19	0	.	13	29	282	13
	larch	5	53	0	.	0	.	9	46	0	100	5	88	0	.	0	100	33	15	0	100	1	71	2	100	0	.	1	100	6	42	0	69	0	100	0	100	2	74	31	24	0	0	34	53	93	21	0	0	3	67	224	13
	Arolla pine	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	9	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	14	40	0	.	0	.	23	26
	other conifers	2	54	0	.	2	84	6	41	4	100	0	.	0	.	0	100	2	42	1	100	17	98	0	100	0	.	0	100	44	98	0	100	1	48	1	100	0	90	0	0	0	100	22	85	1	68	0	.	4	51	108	46
	beech	16	17	1	68	2	37	43	10	11	21	10	28	0	.	4	38	9	27	11	19	13	22	8	22	2	45	6	35	18	17	7	28	20	17	6	26	4	24	31	15	4	40	24	14	4	41	1	53	11	19	266	4
	maple	1	40	0	72	1	63	7	20	1	42	1	44	0	.	1	54	4	30	2	38	2	36	2	41	1	74	2	57	6	26	0	79	2	28	1	38	2	54	2	49	0	59	4	24	2	41	1	65	3	28	48	8
	ash	1	31	0	.	1	61	3	29	1	53	4	31	0	100	1	61	2	52	2	35	4	38	0	72	2	73	0	66	3	31	1	48	2	39	1	45	2	31	1	38	1	47	5	28	1	60	0	100	5	28	44	9
	oak	2	36	0	.	0	.	2	34	1	50	1	48	2	53	0	.	0	88	1	50	2	58	1	87	0	0	0	.	1	64	1	51	2	53	0	0	2	33	8	34	1	75	4	30	2	56	0	.	2	31	34	12
	sweet chestnut	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	15	0	.	0	100	1	67	0	.	0	.	38	13
	other broadleaves	1	29	0	80	0	76	5	23	1	48	1	44	0	0	1	60	8	22	2	32	1	47	1	70	0	92	0	100	1	39	1	75	1	42	0	65	1	38	18	18	0	81	4	36	6	32	0	100	2	31	57	9
	indeterminable	0	0	0	.	0	100	0	81	0	100	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	100	0	100	0	100	0	0	0	0	0	100	0	0	0	.	0	0	1	43
	total	275	24	74	61	111	38	1298	11	57	31	530	22	3	57	82	26	360	6	732	17	445	21	1117	15	19	37	73	21	505	20	63	45	96	26	162	30	277	25	181	9	64	25	1313	14	374	12	46	75	382	19	8638	4
classes 1 and 2	n/a	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	.
	spruce	909	23	600	32	790	29	8648	7	178	41	2449	16	0	.	933	29	10009	7	566	30	2593	15	712	26	641	41	1258	21	2355	16	611	31	554	26	1502	19	1290	25	1536	16	1378	23	3388	13	5078	10	169	38	2197	16	50344	3
	fir	360	35	107	45	266	35	3581	10	209	34	529	30	0	.	68	49	350	23	724	22	1533	15	402	25	64	49	346	33	528	22	47	72	483	24	402	26	486	31	270	35	418	34	1405	15	385	27	104	46	336	22	13402	5
	pine	81	40	0	.	0	0	425	31	97	36	161	50	74	84	0	.	980	20	120	54	57	66	70	59	0	.	120	89	2	100	163	44	130	33	39	85	69	34	78	56	96	59	111	56	1207	18	0	.	320	24	4399	9
	larch	83	58	0	.	0	.	83	43	7	71	37	81	0	.	4	100	1623	12	3	100	12	63	0	0	0	.	9	100	77	44	18	64	3	100	5	100	36	86	995	17	0	0	147	37	2136	12	4	100	65	44	5346	7
	Arolla pine	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	476	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	307	26	0	.	0	.	793	16
	other conifers	57	47	0	.	4	100	92	39	24	100	0	.	0	.	1	100	58	40	3	100	7	67	5	100	0	.	3	100	23	58	21	100	25	53	1	100	2	74	5	100	6	100	73	68	21	59	0	.	44	55	477	18
	beech	1115	17	62	55	138	44	4008	9	754	24	975	19	0	.	264	27	591	23	789	20	1001	17	452	17	199	42	409	33	1548	17	646	26	1252	18	363	22	479	20	2300	15	354	36	1789	12	521	29	64	50	1050	13	21121	4
	maple	232	22	5	72	170	51	916	14	160	27	257	26	0	.	149	38	179	26	171	24	257	24	266	31	82	35	105	64	677	19	57	53	227	21	126	29	177	27	133	37	82	46	612	20	184	30	15	73	679	22	5918	6
	ash	248	28	0	.	178	46	510	20	151	39	473	28	27	100	111	39	241	44	275	33	222	29	51	40	174	62	28	56	552	23	67	50	170	32	163	38	308	29	361	30	109	51	582	21	304	50	38	60	394	24	5737	7
	oak	94	68	0	.	0	.	148	35	71	54	48	44	136	56	0	.	13	66	57	59	26	50	31	91	4	100	0	.	27	58	63	47	85	51	0	100	112	32	406	23	14	62	493	27	112	40	0	.	67	38	2007	11
	sweet chestnut	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	253	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2630	13	0	.	3	59	28	77	0	.	0	.	2915	12
	other broadleaves	264	23	2	76	33	64	607	15	91	33	137	30	3	75	78	40	1060	17	194	29	138	29	79	35	33	72	50	55	335	29	23	46	182	37	152	42	172	31	2078	12	87	38	525	22	570	16	11	75	272	25	7180	5
	indeterminable	3	100	0	.	1	100	18	65	3	93	0	0	0	.	7	100	1	78	1	100	2	100	0	0	0	.	0	0	2	100	0	.	21	84	0	0	1	100	15	67	0	0	10	68	1	100	0	.	0	0	85	30
	total	3446	12	776	29	1581	23	19040	5	1747	18	5066	12	240	51	1615	21	15832	5	2903	13	5849	11	2068	13	1197	28	2328	17	6127	11	1718	19	3133	13	2753	14	3132	15	10811	5	2544	18	9137	9	10852	7	406	31	5423	10	119725	2
classes 3 and 4	n/a	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	.
	spruce	1397	22	1234	30	1380	28	13769	7	325	38	3592	16	0	.	1638	22	12593	7	1217	22	3708	16	2100	17	482	38	1401	22	4245	14	693	28	1007	29	2578	18	1073	22	2369	16	1649	22	5337	11	5629	10	263	42	3093	15	72772	3
	fir	967	28	294	39	797	38	6770	9	189	44	1176	25	0	.	253	57	802	23	1917	23	2880	16	1970	19	150	63	434	35	1583	18	61	66	509	25	838	25	355	46	484	32	294	36	3102	13	578	32	30	75	1055	21	27488	4
	pine	88	51	0	.	16	100	695	36	130	43	266	48	41	71	0	.	950	20	168	40	80	50	95	81	0	.	35	93	49	71	125	41	217	29	11	100	275	38	58	68	45	71	75	45	571	27	0	.	503	24	4492	10
	larch	113	42	0	.	0	.	129	46	11	76	34	64	0	.	11	76	2622	11	11	100	0	0	28	100	0	.	0	0	45	63	10	76	9	100	35	100	8	100	1081	16	0	0	120	42	3045	13	24	100	124	44	7458	7
	Arolla pine	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	438	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	224	32	0	.	0	.	667	17
	other conifers	46	86	0	.	0	0	151	56	118	100	0	.	0	.	0	0	0	0	0	0	103	66	0	0	0	.	0	0	85	100	39	100	0	0	0	0	0	0	32	100	0	0	34	100	0	0	0	.	0	0	608	32
	beech	2233	17	175	49	323	37	5946	9	1359	22	1671	21	0	.	503	32	737	26	1746	18	1610	19	880	20	193	57	624	35	2752	17	953	23	2280	15	776	29	706	22	1485	14	315	43	3341	13	207	41	140	52	1867	14	32823	3
	maple	229	30	9	71	46	53	713	18	148	41	96	41	0	.	124	34	125	38	197	30	97	33	119	35	39	45	38	43	551	19	11	73	266	27	127	37	165	50	37	46	43	51	253	23	78	69	59	54	273	23	3842	7
	ash	534	31	0	.	90	53	476	24	112	43	411	32	0	0	177	45	45	48	416	33	474	60	40	66	143	52	15	100	496	24	75	51	192	27	231	48	424	26	126	47	25	63	624	24	53	76	86	76	657	20	5919	8
	oak	151	45	0	.	0	.	265	24	47	85	54	57	206	66	0	.	72	71	153	47	182	49	38	73	0	0	0	.	32	62	99	66	43	49	8	100	271	33	254	31	33	71	316	26	30	60	0	.	292	26	2545	11
	sweet chestnut	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	922	16	0	.	5	100	24	52	0	.	0	.	1052	15
	other broadleaves	175	34	0	0	15	100	334	29	62	49	136	61	0	0	57	77	237	24	130	74	64	65	5	100	5	100	0	0	160	39	7	100	57	80	39	62	105	39	465	22	5	71	365	43	128	37	0	0	240	36	2791	11
	indeterminable	0	0	0	.	0	0	8	71	11	100	0	0	0	.	0	0	10	100	0	0	0	0	0	0	0	.	0	0	0	0	0	.	8	100	12	100	0	0	0	0	0	0	18	100	0	0	0	.	0	0	66	42
	total	5936	12	1713	28	2665	23	29262	5	2511	18	7437	12	247	59	2762	20	18728	6	5956	13	9199	11	5275	12	1012	29	2548	19	9998	10	2074	20	4588	13	4656	14	3381	16	7311	7	2408	19	13588	8	10566	8	602	36	8103	10	162523	1
classes 5 and 6	n/a	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	.
	spruce	411	39	237	46	277	44	4431	10	197	64	1026	22	0	.	793	36	4037	10	417	42	1127	23	483	26	103	54	343	32	2204	23	211	38	225	42	875	27	349	39	1263	24	564	30	1341	17	1693	17	40	71	598	26	23242	5
	fir	336	30	68	63	152	46	2554	14	42	71	355	34	0	.	109	51	500	28	252	41	682	23	511	29	47	60	222	50	578	28	6	100	187	40	363	30	108	100	169	41	81	66	840	25	195	42	7	100	337	27	8702	7
	pine	13	100	0	.	0	0	138	45	15	100	7	100	0	0	0	.	221	31	36	86	6	100	10	100	0	.	0	0	6	100	5	100	8	77	0	0	37	71	0	0	6	100	0	0	29	51	0	.	64	67	602	19
	larch	10	100	0	.	0	.	16	100	0	0	0	0	0	.	22	100	972	18	0	0	0	0	0	0	0	.	0	0	39	76	0	0	0	0	0	0	0	0	405	33	0	0	17	74	921	17	18	100	15	74	2435	11
	Arolla pine	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	159	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	125	38	0	.	0	.	284	24
	other conifers	0	0	0	.	0	0	69	63	35	100	0	.	0	.	0	0	0	0	0	0	93	73	0	0	0	.	0	0	141	100	0	0	0	0	0	0	0	0	7	100	0	0	125	100	0	0	0	.	0	0	468	44
	beech	1138	22	34	60	34	64	1810	18	393	31	210	30	0	.	119	49	215	36	480	29	475	29	101	91	0	0	195	45	628	28	216	45	734	26	249	53	366	32	305	24	118	53	840	20	73	57	24	100	815	24	9572	6
	maple	32	77	0	0	0	0	34	45	63	78	12	71	0	.	17	73	12	71	8	100	0	0	0	0	0	0	0	0	78	51	0	0	9	100	0	0	40	83	0	0	4	100	14	72	0	0	7	100	107	57	438	23
	ash	125	40	0	.	7	100	149	49	46	62	47	47	0	0	18	79	19	100	68	51	40	64	0	0	39	87	0	0	98	59	18	100	7	100	45	71	86	50	30	100	0	0	130	50	0	0	31	100	105	54	1107	15
	oak	139	46	0	.	0	.	92	54	22	100	33	100	50	100	0	.	12	71	10	100	28	72	0	0	0	0	0	.	26	100	27	100	36	100	5	100	303	72	16	71	11	100	78	79	0	0	0	.	173	43	1062	25
	sweet chestnut	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	718	29	0	.	0	0	23	75	0	.	0	.	755	28
	other broadleaves	62	52	0	0	0	0	158	77	0	0	80	74	0	0	8	100	12	71	0	0	0	0	0	0	0	0	0	0	39	100	0	0	7	100	21	74	0	0	8	100	0	0	77	90	12	71	0	0	13	100	497	33
	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	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	2265	16	339	39	470	34	9451	8	813	28	1770	18	50	100	1085	28	6174	8	1272	20	2452	16	1105	20	189	38	760	25	3835	16	484	30	1213	21	1558	21	1288	27	2919	13	784	27	3461	13	3071	12	128	53	2227	16	49163	3
total	n/a	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	.
	spruce	2873	19	2130	30	2497	27	27457	7	724	36	7380	14	0	.	3432	23	26869	6	2461	21	7620	14	3883	15	1239	36	3050	20	9081	14	1564	25	1815	26	5070	16	2877	19	5214	15	3635	21	10912	10	12586	9	485	39	6143	13	150997	2
	fir	1748	24	484	37	1270	34	13462	9	447	38	2219	24	0	.	435	49	1664	22	3325	20	5302	14	3392	17	263	54	1015	31	2836	17	118	57	1212	21	1639	23	1035	30	931	30	806	32	5706	13	1173	27	172	41	1812	18	52466	4
	pine	186	40	0	.	16	100	1314	28	249	38	469	47	115	73	0	.	2198	17	345	36	150	52	180	67	0	.	159	90	57	71	295	37	360	29	52	88	393	35	138	55	148	63	195	46	1855	18	0	.	899	21	9775	8
	larch	211	39	0	.	0	.	237	38	18	73	76	54	0	.	37	83	5250	11	14	100	13	62	29	100	0	.	10	100	167	43	28	67	12	100	39	100	46	87	2512	15	0	0	317	29	6195	12	46	100	207	37	15463	6
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	1082	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	669	25	0	.	0	.	1767	15
	other conifers	106	53	0	.	6	79	318	43	180	100	0	.	0	.	2	100	59	39	5	76	219	69	6	100	0	.	3	100	293	92	61	100	26	52	2	100	3	76	43	100	6	100	253	66	22	59	0	.	48	54	1661	26
	beech	4502	15	272	44	497	33	11807	8	2517	19	2866	19	0	.	889	29	1552	22	3027	16	3099	17	1441	20	395	42	1234	30	4947	15	1822	21	4286	14	1394	27	1554	20	4121	13	790	35	5994	12	804	29	230	47	3743	13	63781	3
	maple	494	22	14	71	217	48	1671	13	372	30	367	24	0	.	290	27	320	25	378	22	357	21	387	30	122	34	145	50	1311	17	68	53	504	21	255	28	384	38	172	35	130	38	884	18	264	33	82	54	1062	18	10247	5
	ash	908	27	0	.	275	47	1138	20	311	37	935	27	27	100	307	42	306	41	761	29	740	48	92	44	358	54	43	66	1149	21	162	42	371	25	440	40	819	23	518	30	134	51	1340	21	358	46	155	69	1160	18	12807	7
	oak	385	35	0	.	0	.	507	21	141	53	135	47	394	57	0	.	97	70	221	51	238	47	70	61	4	100	0	.	87	52	191	50	166	45	13	100	688	43	683	21	59	64	891	23	144	40	0	.	535	26	5649	10
	sweet chestnut	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	370	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4302	12	0	.	8	72	76	54	0	.	0	.	4760	11
	other broadleaves	502	25	2	76	48	73	1105	21	154	32	355	47	3	75	144	45	1317	17	326	42	203	31	85	38	38	65	50	55	536	28	30	45	248	36	213	38	278	32	2569	12	93	38	970	28	717	18	11	75	527	27	10524	6
	indeterminable	3	100	0	.	1	100	26	66	14	82	0	0	0	.	7	100	11	91	1	100	2	100	0	0	0	.	0	0	2	100	0	.	29	88	12	100	1	100	15	67	0	0	28	70	1	100	0	.	0	0	152	28
	total	11922	10	2902	28	4826	22	59051	5	5127	17	14803	11	540	53	5543	19	41094	5	10863	12	17943	10	9566	11	2418	26	5709	17	20464	10	4339	18	9030	12	9129	13	8078	14	21222	5	5799	17	27499	7	24864	7	1181	33	16135	9	340049	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. 1197685. Birmensdorf, Swiss Federal Research Institute WSL

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