NFI Results

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 NFI4/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	60	61	50	44	629	12	24	67	322	26	0	.	73	28	269	7	261	27	192	31	588	19	13	47	56	25	306	20	48	56	29	69	118	38	166	28	53	23	48	31	866	20	202	17	50	70	255	25	4835	6
	fir	85	40	14	64	54	54	577	16	7	36	159	35	0	.	6	72	13	25	431	21	207	27	509	19	1	47	12	38	150	26	4	66	34	39	37	42	87	43	9	40	13	32	360	17	17	34	31	79	84	30	2900	7
	pine	4	57	0	.	0	100	58	37	6	67	36	49	0	70	0	.	50	17	21	54	7	96	5	77	0	.	4	84	4	52	1	59	5	39	3	59	12	66	4	61	2	56	10	58	49	19	0	.	13	29	294	12
	larch	5	53	0	.	0	.	9	45	0	100	5	88	0	.	0	100	46	13	0	100	1	71	2	100	0	.	1	100	10	47	0	69	0	100	0	100	2	74	42	19	0	0	34	53	107	19	0	0	3	67	266	11
	Arolla pine	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	13	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	17	35	0	.	0	.	30	22
	other conifers	2	54	0	.	2	84	7	38	4	100	0	.	0	.	0	100	2	40	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	110	46
	beech	16	17	1	68	2	37	46	11	13	21	10	28	0	.	4	38	9	27	11	19	13	22	8	22	2	45	6	35	20	16	7	28	21	17	6	25	4	24	31	15	4	40	24	14	4	41	1	53	11	19	274	4
	maple	1	40	0	72	1	63	8	19	1	42	1	44	0	.	1	44	4	29	2	38	2	36	2	41	1	74	2	57	6	26	0	79	2	27	1	37	2	54	2	47	1	56	5	23	2	39	1	65	3	28	50	8
	ash	1	31	0	.	1	61	4	35	1	47	4	31	0	100	1	61	2	52	2	35	4	38	0	72	2	73	0	66	4	30	1	48	2	39	1	45	2	31	1	36	1	47	5	27	1	48	0	100	5	28	46	9
	oak	2	35	0	.	0	.	2	33	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	100	2	30	35	12
	sweet chestnut	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	14	0	.	0	100	2	56	0	.	0	.	40	13
	other broadleaves	1	29	0	80	0	76	6	23	1	44	1	44	0	0	1	60	9	21	2	32	1	47	1	70	0	92	0	100	1	39	1	75	1	42	1	55	1	36	22	16	0	69	4	34	8	28	0	100	2	31	66	8
	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	76	60	111	38	1347	11	59	30	538	22	3	57	87	25	422	6	732	17	446	21	1117	15	20	36	81	20	545	19	63	45	96	26	167	29	277	25	205	8	70	24	1332	14	411	11	83	58	382	19	8946	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	913	23	659	30	790	29	9315	7	183	41	2706	15	0	.	1011	27	11035	6	566	30	2745	14	712	26	676	39	1355	20	2567	15	611	31	557	26	1686	17	1290	25	1715	15	1491	22	3700	12	5456	10	307	39	2209	16	54255	3
	fir	360	35	107	45	266	35	3612	10	211	34	529	30	0	.	68	49	357	22	724	22	1533	15	402	25	64	49	346	33	554	21	47	72	483	24	441	24	486	31	280	34	451	32	1405	15	388	26	110	44	336	22	13560	5
	pine	81	40	0	.	0	0	434	31	97	36	161	50	74	84	0	.	1021	19	120	54	57	66	70	59	0	.	120	89	93	84	163	44	130	33	54	66	69	34	82	53	106	54	111	56	1227	18	0	.	320	24	4590	9
	larch	83	58	0	.	0	.	88	41	7	71	37	81	0	.	4	100	2147	10	3	100	12	63	0	0	0	.	9	100	110	42	18	64	3	100	5	100	36	86	1292	14	0	0	147	37	2485	11	4	100	65	44	6554	6
	Arolla pine	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	635	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	346	24	0	.	0	.	990	15
	other conifers	57	47	0	.	4	100	124	38	24	100	0	.	0	.	1	100	66	35	3	100	7	67	5	100	0	.	3	100	27	52	21	100	25	53	1	100	2	74	5	100	6	100	73	68	21	59	0	.	44	55	521	17
	beech	1128	17	62	55	138	44	4091	9	825	22	975	19	0	.	264	27	650	22	789	20	1001	17	452	17	199	42	409	33	1618	17	646	26	1258	18	406	21	479	20	2347	14	355	36	1791	12	527	29	65	49	1075	13	21551	4
	maple	232	22	5	72	170	51	992	14	210	27	257	26	0	.	173	34	242	32	171	24	257	24	266	31	94	33	131	55	689	19	57	53	228	21	146	26	177	27	186	30	96	42	623	20	225	26	29	61	683	22	6338	6
	ash	248	28	0	.	178	46	565	21	184	35	473	28	27	100	124	36	262	41	275	33	222	29	51	40	174	62	32	51	568	23	67	50	170	32	252	39	308	29	403	27	110	51	588	21	385	41	38	60	412	23	6116	7
	oak	97	65	0	.	0	.	169	32	71	54	48	44	136	56	0	.	25	60	57	59	26	50	31	91	4	100	0	.	28	57	63	47	85	51	0	100	112	32	430	22	15	58	493	27	129	35	1	100	68	37	2091	11
	sweet chestnut	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	253	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2717	13	0	.	3	59	80	71	0	.	0	.	3055	12
	other broadleaves	264	23	2	76	33	64	692	15	111	29	141	29	23	87	78	40	1176	16	194	29	145	28	79	35	37	64	52	53	361	28	23	46	182	37	224	35	180	30	2499	11	166	38	605	20	657	16	11	75	272	25	8209	5
	indeterminable	3	100	0	.	1	100	18	65	3	93	0	0	0	.	7	100	16	91	1	100	2	100	0	0	0	.	0	0	3	73	0	.	21	84	0	0	1	100	15	63	0	0	10	68	1	100	0	.	0	0	102	29
	total	3467	12	836	28	1581	23	20107	5	1926	17	5327	11	260	48	1730	20	17884	5	2903	13	6007	10	2068	13	1249	27	2458	16	6618	10	1718	19	3143	13	3213	14	3139	15	11976	5	2796	17	9547	8	11928	7	566	30	5483	10	127932	1
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	1417	22	1331	29	1380	28	14614	7	339	36	3683	15	0	.	1778	21	13311	7	1217	22	3948	15	2100	17	520	36	1451	22	4707	13	693	28	1021	29	2664	17	1073	22	2441	15	1688	21	5716	10	5822	10	452	38	3120	14	76488	2
	fir	967	28	294	39	797	38	6857	9	189	44	1176	25	0	.	253	57	808	23	1917	23	2880	16	1970	19	150	63	434	35	1756	17	61	66	509	25	919	24	355	46	496	31	294	36	3102	13	593	31	91	72	1055	21	27924	4
	pine	88	51	0	.	16	100	709	35	130	43	266	48	41	71	0	.	976	19	168	40	80	50	95	81	0	.	35	93	80	50	125	41	217	29	14	83	275	38	79	56	88	61	75	45	578	27	0	.	503	24	4637	9
	larch	113	42	0	.	0	.	139	44	11	76	34	64	0	.	11	76	2958	10	11	100	0	0	28	100	0	.	0	0	68	54	10	76	9	100	35	100	8	100	1291	14	0	0	120	42	3333	12	24	100	124	44	8325	6
	Arolla pine	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	482	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	286	28	0	.	0	.	773	16
	other conifers	46	86	0	.	0	0	154	55	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	611	32
	beech	2233	17	175	49	323	37	6247	10	1558	21	1671	21	0	.	503	32	746	26	1746	18	1610	19	880	20	193	57	624	35	2948	16	953	23	2311	15	817	28	706	22	1499	14	334	41	3348	13	232	38	155	48	1870	14	33683	3
	maple	229	30	9	71	46	53	734	17	148	41	96	41	0	.	139	32	176	40	197	30	97	33	128	33	39	45	38	43	610	19	11	73	299	26	127	37	165	50	37	46	57	45	264	23	82	66	59	54	273	23	4062	7
	ash	534	31	0	.	90	53	476	24	125	40	411	32	0	0	177	45	45	48	416	33	474	60	40	66	143	52	15	100	543	23	75	51	192	27	231	48	424	26	142	43	25	63	641	23	53	76	86	76	657	20	6014	8
	oak	197	42	0	.	0	.	265	24	47	85	54	57	206	66	0	.	86	62	153	47	182	49	38	73	0	0	0	.	32	62	99	66	43	49	8	100	271	33	256	31	47	58	316	26	33	56	7	100	302	26	2641	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	33	47	0	.	0	.	1061	15
	other broadleaves	175	34	0	0	15	100	459	35	67	46	136	61	0	0	57	77	287	24	130	74	64	65	5	100	5	100	0	0	160	39	7	100	57	80	39	62	157	42	552	20	22	80	519	37	147	33	0	0	240	36	3302	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	6003	12	1809	27	2665	23	30668	5	2742	18	7527	12	247	59	2917	19	19984	5	5956	13	9438	11	5284	12	1050	28	2598	19	10990	10	2074	20	4668	13	4866	14	3433	15	7748	7	2556	18	14157	8	11192	7	873	33	8142	10	169587	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	263	43	277	44	4713	10	197	64	1045	21	0	.	836	34	4501	10	417	42	1178	22	483	26	125	48	373	30	2465	21	211	38	231	41	899	27	349	39	1317	23	564	30	1576	16	1778	16	40	71	608	25	24856	4
	fir	336	30	68	63	152	46	2582	14	42	71	355	34	0	.	109	51	500	28	252	41	682	23	511	29	47	60	222	50	751	27	6	100	187	40	371	30	108	100	169	41	81	66	840	25	195	42	87	92	337	27	8992	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	9	100	6	100	0	0	29	51	0	.	64	67	611	19
	larch	10	100	0	.	0	.	16	100	0	0	0	0	0	.	22	100	1093	17	0	0	0	0	0	0	0	.	0	0	39	76	0	0	0	0	0	0	0	0	534	27	0	0	17	74	959	17	18	100	15	74	2722	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	.	174	35	0	.	0	.	333	23
	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	1873	17	715	36	210	30	0	.	119	49	221	35	480	29	475	29	101	91	0	0	195	45	714	26	216	45	774	25	267	50	366	32	305	24	118	53	840	20	73	57	40	73	843	24	10149	7
	maple	32	77	0	0	0	0	51	45	63	78	12	71	0	.	17	73	12	71	8	100	0	0	6	100	0	0	0	0	78	51	0	0	9	100	0	0	40	83	0	0	4	100	27	61	0	0	7	100	107	57	473	22
	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	35	85	0	0	148	45	0	0	31	100	105	54	1130	15
	oak	172	42	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	0	183	41	1105	24
	sweet chestnut	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	759	28	0	.	0	0	54	66	0	.	0	.	827	26
	other broadleaves	62	52	0	0	0	0	193	65	0	0	80	74	0	0	8	100	57	80	0	0	0	0	0	0	0	0	0	0	39	100	0	0	7	100	21	74	328	100	8	100	0	0	129	67	12	71	0	0	13	100	958	39
	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	2298	16	365	37	470	34	9876	8	1135	28	1789	18	50	100	1128	27	6810	8	1272	20	2502	16	1111	20	211	36	790	25	4356	15	484	30	1259	21	1608	21	1616	29	3158	13	784	27	3780	12	3275	11	222	52	2275	15	52624	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	2898	19	2314	29	2497	27	29271	7	742	35	7756	13	0	.	3699	21	29116	6	2461	21	8063	14	3883	15	1335	33	3235	19	10045	13	1564	25	1838	25	5367	16	2877	19	5526	14	3792	20	11858	9	13259	9	848	38	6192	13	160435	2
	fir	1748	24	484	37	1270	34	13629	9	449	38	2219	24	0	.	435	49	1677	21	3325	20	5302	14	3392	17	263	54	1015	31	3211	17	118	57	1212	21	1768	22	1035	30	954	29	840	31	5706	13	1192	27	319	51	1812	18	53375	4
	pine	186	40	0	.	16	100	1340	27	249	38	469	47	115	73	0	.	2269	16	345	36	150	52	180	67	0	.	159	90	182	56	295	37	360	29	70	68	393	35	173	49	203	53	195	46	1884	18	0	.	899	21	10132	8
	larch	211	39	0	.	0	.	252	36	18	73	76	54	0	.	37	83	6244	10	14	100	13	62	29	100	0	.	10	100	227	40	28	67	12	100	39	100	46	87	3158	13	0	0	317	29	6883	11	46	100	207	37	17867	6
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	1289	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	822	23	0	.	0	.	2127	14
	other conifers	106	53	0	.	6	79	354	40	180	100	0	.	0	.	2	100	67	35	5	76	219	69	6	100	0	.	3	100	297	91	61	100	26	52	2	100	3	76	43	100	6	100	253	66	22	59	0	.	48	54	1710	25
	beech	4515	15	272	44	497	33	12258	9	3111	20	2866	19	0	.	889	29	1627	21	3027	16	3099	17	1441	20	395	42	1234	30	5300	15	1822	21	4364	14	1495	25	1554	20	4182	12	810	34	6002	12	836	28	261	43	3799	13	65657	3
	maple	494	22	14	71	217	48	1784	13	422	28	367	24	0	.	330	25	434	32	378	22	357	21	401	29	134	32	171	45	1382	17	68	53	538	21	275	26	384	38	225	30	158	36	919	18	310	29	96	48	1066	18	10923	5
	ash	908	27	0	.	275	47	1194	20	357	34	935	27	27	100	319	40	327	38	761	29	740	48	92	44	358	54	47	60	1213	20	162	42	371	25	529	36	819	23	582	27	135	51	1381	21	439	39	155	69	1178	18	13306	6
	oak	469	34	0	.	0	.	529	21	141	53	135	47	394	57	0	.	123	59	221	51	238	47	70	61	4	100	0	.	87	51	191	50	166	45	13	100	688	43	710	20	74	54	891	23	164	36	8	100	555	25	5872	9
	sweet chestnut	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	370	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4431	12	0	.	8	72	170	60	0	.	0	.	4983	11
	other broadleaves	502	25	2	76	48	73	1351	23	179	29	358	46	23	87	144	45	1530	16	326	42	210	30	85	38	43	59	52	53	562	26	30	45	248	36	284	33	666	60	3082	11	189	37	1257	25	824	17	11	75	527	27	12534	6
	indeterminable	3	100	0	.	1	100	26	66	14	82	0	0	0	.	7	100	26	69	1	100	2	100	0	0	0	.	0	0	3	73	0	.	29	88	12	100	1	100	15	63	0	0	28	70	1	100	0	.	0	0	169	27
	total	12044	10	3085	27	4826	22	61998	5	5862	16	15182	11	560	52	5862	18	45100	5	10863	12	18393	10	9580	11	2530	25	5926	16	22509	9	4339	18	9166	12	9855	13	8466	14	23087	5	6206	16	28816	7	26805	6	1744	32	16282	9	359089	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. 1197708. Birmensdorf, Swiss Federal Research Institute WSL

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