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–NFI5
increment (gross increment)
site quality · main tree species
regional demarcation: canton
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
change 2009/17–2018/26
		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
site quality	main tree species	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
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	.
low	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	2	82	0	.	71	26	2	100	19	47	0	.	21	38	119	20	2	80	12	52	5	91	8	52	35	40	36	47	0	.	1	76	6	57	0	.	17	44	12	79	39	37	23	33	0	.	4	82	433	10
	fir	1	100	0	.	0	.	22	39	3	97	0	84	0	.	3	107	0	.	13	40	1	63	13	44	-1	102	2	84	1	100	0	.	1	70	1	94	3	100	0	.	3	111	22	47	2	76	0	.	0	.	90	19
	pine	2	100	0	.	0	.	5	95	1	63	0	.	0	.	0	.	10	41	0	592	1	52	2	100	0	100	4	91	0	712	0	.	2	72	2	76	0	.	0	100	0	100	0	.	0	79	0	.	0	100	29	28
	larch	0	100	0	.	0	.	0	0	0	.	0	.	0	.	0	.	84	18	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	22	44	0	.	0	100	29	38	0	.	0	.	135	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	38	0	.	0	.	27	32
	other conifers	0	100	0	.	0	.	0	85	0	.	0	.	0	.	0	100	1	43	0	.	0	100	0	100	0	.	0	100	0	.	0	.	1	79	0	0	0	.	0	.	0	100	0	79	0	0	0	.	0	.	3	35
	beech	7	124	-1	100	0	.	39	36	8	57	5	145	0	.	5	56	0	.	18	41	10	86	2	62	3	100	-1	567	6	60	-3	100	14	38	1	160	0	.	0	.	5	95	8	73	4	72	0	.	5	108	135	19
	maple	1	63	0	.	0	.	7	54	1	71	1	43	0	.	4	55	0	.	1	78	0	100	0	64	1	80	-1	141	0	86	0	.	1	142	1	59	0	100	0	.	-1	117	3	52	4	63	0	.	0	100	25	24
	ash	0	100	0	.	0	.	-1	209	0	.	7	100	0	.	0	104	0	.	0	287	0	0	0	.	2	100	1	100	1	60	0	.	1	81	0	.	0	.	0	.	0	100	1	69	0	77	0	.	1	69	13	59
	oak	0	103	0	.	0	.	4	92	0	100	0	100	0	.	0	.	0	.	0	.	-1	100	0	.	0	100	0	.	0	100	0	.	3	90	0	.	0	.	0	.	1	100	1	103	1	77	0	.	0	.	9	53
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	76	0	.	0	.	1	76
	other broadleaves	0	731	0	.	0	.	5	46	0	120	1	90	0	.	0	.	0	68	-1	102	0	74	0	71	0	71	1	66	0	122	0	.	1	96	0	93	0	.	0	74	0	95	1	63	8	53	0	.	0	100	17	30
	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
	total	11	88	1	71	0	.	152	19	15	49	33	39	0	.	33	34	228	13	33	35	24	47	22	35	13	52	41	41	44	40	-3	100	25	31	12	43	3	100	39	39	20	58	75	26	84	20	0	.	11	70	916	7
moderate	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	.	9	52	5	100	212	15	0	.	52	27	0	.	21	48	536	9	-2	108	10	76	21	35	1	173	10	61	50	38	0	.	-1	91	44	33	0	.	102	17	30	45	106	21	288	12	7	78	0	.	1501	5
	fir	0	.	3	86	0	.	43	26	0	.	18	51	0	.	1	100	23	47	7	69	7	66	41	38	5	79	15	51	3	53	0	.	5	70	18	42	0	.	25	30	1	474	57	27	25	33	5	86	0	.	304	11
	pine	0	.	0	.	0	.	1	69	0	.	0	.	0	.	0	.	40	30	0	.	0	.	0	.	0	.	0	.	-1	100	0	.	0	0	0	100	0	.	-2	262	2	100	0	0	20	32	0	.	0	.	60	24
	larch	0	.	0	.	0	.	2	81	0	.	1	100	0	.	0	.	103	17	0	.	0	100	0	.	0	.	0	.	0	135	0	.	0	.	0	.	0	.	66	25	0	.	8	59	97	20	0	.	0	.	276	11
	Arolla pine	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	24	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	3	104	0	.	0	.	29	32
	other conifers	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	93	0	.	0	.	3	83
	beech	0	.	9	65	0	100	52	26	0	.	6	59	0	.	7	73	21	41	-2	112	0	72	5	74	1	332	1	67	19	39	0	.	2	84	10	62	0	.	48	33	3	81	15	53	1	462	0	.	0	100	200	14
	maple	0	.	1	99	0	.	4	156	0	.	4	70	0	.	9	58	11	52	0	307	-1	135	2	79	1	75	3	96	2	47	0	.	0	89	0	1039	0	.	6	55	2	85	12	60	12	43	0	.	0	.	69	21
	ash	0	.	0	.	0	.	1	230	0	.	2	64	0	.	5	73	4	60	0	100	0	.	1	72	3	58	0	73	0	1457	0	.	0	100	2	90	0	.	3	56	4	91	-1	318	14	45	0	.	0	.	39	26
	oak	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	70	0	.	0	100	5	48	0	.	0	.	7	37
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	88	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	67	0	.	0	.	0	100	0	.	0	.	14	54
	other broadleaves	0	.	0	0	0	.	6	35	0	.	2	58	0	.	1	75	51	33	0	100	0	0	0	67	-2	108	0	74	0	193	0	.	0	100	5	92	0	.	18	68	1	70	2	46	35	25	0	.	0	.	119	19
	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
	total	0	.	22	54	5	100	324	13	0	.	85	23	0	.	45	36	818	8	3	144	17	61	71	30	9	76	30	44	75	30	0	.	6	83	79	29	0	.	279	12	43	37	198	18	499	10	12	71	0	100	2622	4
good	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	41	36	37	48	51	35	170	16	6	64	84	24	0	.	11	117	189	17	11	44	75	22	51	25	14	52	14	67	107	19	10	40	10	44	61	29	13	42	34	55	47	35	116	22	17	50	29	47	48	32	1245	6
	fir	12	60	14	48	26	49	230	14	6	55	30	67	0	.	2	77	14	47	41	39	125	23	63	28	3	64	5	49	59	24	1	92	12	36	37	37	2	230	3	75	0	856	91	19	14	65	11	48	31	26	834	7
	pine	-4	100	0	.	0	100	10	63	8	50	2	97	0	.	0	.	0	.	1	203	5	78	2	71	0	.	0	.	1	100	6	53	-1	100	0	.	3	78	0	.	1	68	0	310	0	.	0	.	5	65	40	27
	larch	-2	120	0	.	0	.	1	74	0	100	2	100	0	.	-1	100	7	106	0	.	0	100	0	.	0	.	0	.	1	100	1	86	0	.	0	.	1	74	4	85	0	0	1	74	0	.	0	.	3	64	20	45
	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	3	100	0	.	0	100	10	53	0	.	0	.	0	.	0	.	0	.	2	100	7	78	0	.	0	.	0	.	1	145	4	100	0	79	0	.	0	.	2	92	0	0	3	64	0	.	0	.	1	51	33	30
	beech	35	32	3	59	8	65	129	19	28	32	36	30	0	.	1	460	16	43	21	30	40	28	16	39	2	240	1	925	91	29	14	40	25	34	10	57	14	54	77	23	13	43	65	21	7	60	2	107	37	31	689	7
	maple	4	86	0	0	9	59	27	25	3	112	4	95	0	.	-1	364	6	49	0	608	6	71	11	56	-2	210	0	997	32	26	1	86	4	95	9	40	5	49	13	40	2	70	19	35	4	72	1	103	26	34	185	12
	ash	5	47	0	.	6	91	11	43	3	38	9	44	0	.	3	72	9	60	5	52	9	43	1	54	3	105	0	100	29	49	1	194	-4	127	8	61	0	2594	22	51	3	44	24	54	1	66	2	96	9	57	160	17
	oak	2	60	0	.	0	.	7	52	0	61	4	61	0	.	0	.	3	71	1	115	4	84	0	4613	0	.	0	.	0	100	4	62	0	71	0	100	1	52	14	36	1	84	9	54	2	100	0	100	8	41	61	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	24	0	.	0	100	0	100	0	.	0	.	89	22
	other broadleaves	4	49	0	100	1	62	13	41	2	42	12	44	0	.	2	55	34	40	3	40	6	46	1	56	3	63	0	94	7	39	1	60	5	54	9	40	0	100	109	20	2	173	9	49	1	73	1	87	6	43	230	12
	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
	total	101	23	54	38	102	32	608	10	56	29	182	20	0	.	17	83	285	14	85	24	278	15	144	19	23	50	20	63	327	16	43	29	52	28	135	20	40	32	363	11	70	27	337	14	47	35	47	41	175	18	3586	4
very good	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	95	25	0	.	0	71	226	14	13	45	38	41	0	75	4	63	0	.	35	29	66	27	19	47	11	100	17	73	50	27	18	34	37	34	3	61	81	24	0	.	1	100	48	30	0	.	1	90	149	16	911	7
	fir	35	32	0	.	8	71	176	16	7	52	15	50	0	100	-1	125	0	.	44	34	100	22	14	40	0	.	10	73	53	38	5	71	34	34	3	103	41	42	0	.	2	93	53	28	0	.	2	100	30	27	635	8
	pine	1	50	0	.	0	.	1	229	1	85	1	183	1	87	0	.	0	.	7	90	0	.	0	.	0	.	0	.	1	100	6	97	1	68	0	.	3	71	0	.	0	100	3	68	0	.	0	.	9	38	37	30
	larch	8	88	0	.	0	.	4	79	0	100	1	100	0	.	0	.	0	.	0	100	1	71	0	100	0	.	1	100	3	67	0	.	0	100	1	100	3	100	0	.	0	.	14	50	0	.	1	100	3	44	39	28
	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	6	60	0	.	0	100	9	60	7	100	0	.	0	.	0	.	0	.	0	100	2	100	0	.	0	.	0	.	5	100	0	.	3	100	0	100	0	86	0	.	0	.	5	100	0	.	0	.	3	62	41	30
	beech	86	15	0	.	1	82	146	15	24	37	29	49	0	.	7	73	0	.	18	35	39	43	13	50	5	100	-1	121	33	44	26	34	38	26	10	65	35	23	5	87	-3	130	51	29	-3	106	6	72	78	17	641	7
	maple	17	28	0	.	1	100	30	27	4	93	2	57	0	100	0	73	0	.	7	95	5	39	13	52	1	100	0	100	17	93	1	60	16	53	0	76	7	134	0	.	1	100	8	54	0	100	0	119	23	31	156	17
	ash	15	37	0	.	0	.	21	34	1	279	21	72	1	100	4	87	0	.	11	43	3	81	1	71	0	100	5	94	17	44	8	78	8	41	3	71	25	32	1	75	0	.	18	34	1	66	4	71	22	28	188	13
	oak	17	55	0	.	0	.	7	43	0	100	4	55	11	77	0	.	0	.	1	198	1	71	0	100	0	.	0	.	3	69	2	87	4	47	0	.	12	68	1	89	0	100	10	42	1	71	0	.	4	42	78	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	100	1	100	0	.	0	.	1	88
	other broadleaves	24	27	0	.	2	100	26	29	5	43	10	43	2	56	0	100	0	.	2	130	6	63	0	263	1	100	1	59	8	49	1	72	12	59	0	100	12	94	3	93	1	100	19	55	1	293	0	100	18	34	153	14
	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
	total	304	13	0	.	12	62	646	10	62	29	120	29	17	57	13	51	0	.	125	22	222	18	61	27	18	70	33	49	190	21	67	27	154	19	20	48	219	17	10	61	2	341	230	16	-1	955	15	58	339	11	2880	4
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	136	21	49	38	56	33	680	8	20	35	193	16	0	75	57	32	844	7	45	26	163	16	96	18	33	41	76	28	242	15	29	26	47	28	115	20	94	21	153	17	89	26	308	13	328	11	38	39	202	14	4090	3
	fir	48	28	17	42	35	41	468	10	16	36	63	37	0	100	5	79	38	34	106	21	232	16	132	19	6	69	32	35	116	21	6	62	53	24	60	27	46	40	29	28	5	116	223	13	41	30	18	39	61	19	1864	5
	pine	-1	363	0	.	0	100	17	50	9	43	3	93	1	87	0	.	50	25	8	88	5	71	4	62	0	100	4	91	1	192	12	56	3	86	2	73	6	54	-2	225	4	63	3	67	21	31	0	.	15	34	166	14
	larch	6	120	0	.	0	.	7	50	0	94	3	62	0	.	-1	100	194	12	0	100	1	58	0	100	0	.	1	100	4	63	1	86	0	100	1	100	5	71	93	21	0	0	23	36	125	18	1	100	6	39	469	8
	Arolla pine	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	38	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	16	36	0	.	0	.	56	23
	other conifers	10	52	0	.	1	71	20	39	7	100	0	.	0	.	0	100	1	39	2	90	9	64	0	100	0	.	1	84	7	70	4	100	4	69	0	100	0	86	2	92	0	100	9	61	0	93	0	.	4	53	80	20
	beech	128	15	11	56	9	56	364	10	60	22	74	26	0	.	20	42	37	29	56	21	89	24	36	27	10	79	-1	1573	149	21	37	29	79	18	31	35	50	23	129	18	19	47	138	16	8	88	8	60	121	15	1665	4
	maple	22	27	1	99	10	54	67	19	8	65	11	43	0	100	12	53	17	37	8	87	11	46	27	35	2	216	2	204	52	35	3	53	22	44	10	36	12	79	19	33	5	54	43	25	20	32	1	139	50	23	435	9
	ash	20	30	0	.	6	91	31	28	5	88	39	43	1	100	11	45	13	45	16	35	12	38	3	39	9	49	6	78	47	34	8	73	5	118	12	43	25	34	27	43	8	55	43	35	16	40	6	57	32	25	401	9
	oak	19	48	0	.	0	.	17	33	0	194	8	41	11	77	0	.	4	62	2	122	5	81	0	121	0	100	0	.	4	65	6	51	6	44	0	100	13	64	17	31	1	60	20	32	8	38	0	100	12	30	155	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	93	22	0	.	0	106	2	56	0	.	0	.	105	20
	other broadleaves	28	24	0	100	2	74	50	19	7	35	25	28	2	56	3	43	85	25	4	78	13	38	1	82	2	166	3	38	15	32	2	50	17	43	14	41	12	93	130	19	4	85	31	37	44	22	1	75	24	27	518	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	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0
	total	416	11	77	30	119	28	1723	6	133	19	420	13	17	57	108	23	1330	6	247	14	540	11	299	12	62	31	124	24	637	11	107	20	236	14	246	15	263	15	690	7	136	21	841	8	629	9	74	31	526	9	10005	2

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. 1261363. Birmensdorf, Swiss Federal Research Institute WSL

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