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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NFI3
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (10 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		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
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (10 classes)	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	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	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	.
upper subalpine	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	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	244	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	59	0	.	0	.	224	43	0	.	0	.	492	23
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	507	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	59	0	.	0	.	299	35	0	.	0	.	871	18
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	643	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	376	0	.	0	.	416	32	0	.	0	.	1066	19
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	663	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	64	0	.	0	.	382	30	0	.	0	.	1087	17
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	695	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	154	39	0	.	0	.	863	23
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	158	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	42	0	.	0	.	296	32
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	270	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	141	42	0	.	0	.	412	31
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	160	53
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	0	.	84	71
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	119	59
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3442	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	53	0	.	0	.	1856	22	0	.	0	.	5449	13
subalpine	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	.
	12-20 cm	0	.	0	.	0	.	207	28	0	.	0	.	0	.	20	100	621	19	0	.	10	100	0	.	53	94	86	65	31	76	0	.	0	.	7	100	0	.	140	29	37	86	14	62	443	19	0	.	0	.	1667	11
	21-30 cm	0	.	0	.	0	.	542	23	0	.	0	.	0	.	14	100	1696	15	0	.	10	100	0	.	96	98	155	55	123	69	0	.	0	.	0	.	0	.	322	29	81	52	57	94	1018	21	0	.	0	.	4114	9
	31-40 cm	0	.	0	.	0	.	1011	23	0	.	0	.	0	.	8	100	2617	15	0	.	0	.	0	.	41	74	202	63	47	68	0	.	0	.	80	78	0	.	528	26	58	70	109	52	1354	18	0	.	0	.	6056	9
	41-50 cm	0	.	0	.	0	.	968	24	0	.	0	.	0	.	44	82	3549	13	0	.	0	.	0	.	12	100	201	55	135	76	0	.	0	.	52	100	0	.	602	26	91	78	116	49	1754	18	0	.	0	.	7525	8
	51-60 cm	0	.	0	.	0	.	682	26	0	.	0	.	0	.	73	74	2652	14	0	.	0	.	0	.	53	100	64	78	76	64	0	.	0	.	73	76	0	.	658	25	154	54	84	62	1727	19	0	.	0	.	6296	9
	61-70 cm	0	.	0	.	0	.	227	45	0	.	0	.	0	.	20	100	1421	18	0	.	0	.	0	.	41	100	0	.	26	100	0	.	0	.	59	100	0	.	254	36	24	100	160	68	987	27	0	.	0	.	3218	13
	71-80 cm	0	.	0	.	0	.	155	52	0	.	0	.	0	.	41	100	616	30	0	.	0	.	0	.	0	.	0	.	51	71	0	.	0	.	38	100	0	.	147	45	33	100	32	100	559	33	0	.	0	.	1671	17
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	72	275	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	46	100	378	38	0	.	0	.	761	26
	91-100 cm	0	.	0	.	0	.	33	100	0	.	0	.	0	.	0	.	99	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	133	61
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	45	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	97	71
	total	0	.	0	.	0	.	3825	20	0	.	0	.	0	.	281	67	13597	11	0	.	20	100	0	.	296	72	707	49	534	52	0	.	0	.	310	71	0	.	2651	21	476	51	619	47	8220	14	0	.	0	.	31536	7
high-montane	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	.
	12-20 cm	0	.	0	.	0	.	289	21	0	.	101	56	0	.	28	65	1569	12	0	.	100	46	0	.	6	100	127	39	33	61	0	.	0	.	55	59	0	.	358	26	143	38	149	37	1219	15	0	.	0	.	4179	7
	21-30 cm	0	.	0	.	0	.	647	21	0	.	304	47	0	.	89	80	2851	12	0	.	241	57	0	.	8	100	378	40	54	59	0	.	0	.	109	70	0	.	521	21	462	33	305	53	2320	15	0	.	0	.	8289	7
	31-40 cm	0	.	0	.	0	.	921	23	0	.	264	50	0	.	44	73	4813	12	0	.	110	48	0	.	39	100	341	37	134	57	0	.	0	.	298	43	0	.	822	25	717	37	514	42	2307	15	0	0	0	.	11323	7
	41-50 cm	0	.	0	.	0	.	1528	20	0	.	506	41	0	.	120	75	5490	12	0	.	257	49	0	.	8	100	259	36	221	59	0	.	0	.	255	42	0	.	1025	25	568	36	728	36	2652	14	0	0	0	.	13618	7
	51-60 cm	0	.	0	.	0	.	1931	23	0	.	198	45	0	.	122	62	4732	12	0	.	152	61	0	.	0	0	352	39	78	66	0	.	0	.	240	48	0	.	801	25	381	38	690	32	2114	17	0	.	0	.	11790	7
	61-70 cm	0	.	0	.	0	.	911	29	0	.	121	66	0	.	141	67	2021	18	0	.	47	71	0	.	0	.	124	45	46	76	0	.	0	.	92	66	0	.	791	27	78	58	406	45	1031	22	0	.	0	.	5810	10
	71-80 cm	0	.	0	.	0	.	391	31	0	.	96	100	0	.	246	65	1321	23	0	.	0	.	0	.	0	.	129	66	123	71	0	.	0	.	90	60	0	.	409	36	110	59	44	100	715	28	0	.	0	.	3675	13
	81-90 cm	0	.	0	.	0	.	270	52	0	.	0	.	0	.	89	71	653	29	0	.	0	.	0	.	0	.	0	.	57	100	0	.	0	.	0	.	0	.	202	52	0	.	81	73	299	47	0	.	0	.	1652	19
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	113	71	53	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	77	100	47	100	0	.	92	71	0	.	0	.	382	38
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	160	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	100	0	.	38	100	48	100	0	.	0	.	380	49
	total	0	.	0	.	0	.	6888	17	0	.	1590	35	0	.	992	55	23664	9	0	.	906	42	0	.	62	100	1711	31	747	47	0	.	0	.	1138	35	0	.	5140	17	2506	30	2955	27	12797	11	0	0	0	.	61096	5
lower and upper montane	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	.
	12-20 cm	145	57	54	75	201	39	2234	12	93	35	636	20	0	.	360	28	319	23	665	23	791	16	588	16	194	42	234	30	707	20	157	56	411	21	481	21	269	38	1073	16	177	40	1145	14	268	24	127	50	313	42	11638	5
	21-30 cm	136	40	323	39	733	30	5102	9	297	38	1489	19	0	.	1190	37	738	21	958	19	2095	17	1231	18	519	35	575	29	2546	15	437	44	897	22	928	23	542	37	1771	17	407	37	2799	13	511	28	169	41	1070	21	27466	4
	31-40 cm	219	38	769	31	1221	26	7255	9	443	36	2186	17	0	.	1116	30	1062	20	2133	19	2329	17	1780	15	621	36	600	29	3691	13	496	35	1430	21	1404	20	589	32	903	18	507	35	3661	11	568	27	184	46	2093	18	37261	3
	41-50 cm	548	34	798	30	1438	25	9545	8	475	40	2114	19	0	.	1426	29	1124	22	2241	17	3355	15	2154	16	322	36	914	29	4030	15	262	49	1523	19	1947	19	517	36	1057	18	285	38	4032	12	596	29	299	46	2219	17	43220	3
	51-60 cm	393	44	792	32	1169	28	7419	10	404	48	1874	19	0	.	1101	27	973	25	1280	20	2657	18	1903	17	305	47	609	34	3528	15	179	53	1272	24	1744	21	405	42	910	22	328	40	4151	14	381	35	142	52	1973	20	35893	4
	61-70 cm	98	60	205	42	593	30	3970	12	151	60	778	30	0	.	169	41	679	25	674	31	1795	21	968	24	88	58	585	41	2471	20	28	100	389	40	919	27	197	55	343	28	284	46	2113	20	75	61	133	62	836	29	18543	6
	71-80 cm	0	.	124	77	0	.	2100	17	0	.	318	45	0	.	341	44	524	32	411	37	591	34	598	30	0	.	109	71	1097	28	0	.	148	58	499	37	194	61	160	43	166	71	803	30	45	71	0	.	316	38	8545	8
	81-90 cm	63	100	49	100	0	.	908	26	0	.	110	71	0	.	20	100	38	100	47	100	0	.	348	41	0	.	0	.	491	61	0	.	0	.	222	50	0	.	88	80	47	100	246	50	57	73	53	100	126	58	2912	16
	91-100 cm	114	71	0	.	0	.	61	100	0	.	0	.	0	.	0	0	57	100	68	100	45	100	0	.	0	.	0	.	70	100	0	.	0	.	82	100	0	.	91	100	0	.	0	.	0	.	0	.	0	.	587	34
	>100 cm	0	.	0	.	0	.	91	100	0	.	0	.	0	.	0	.	0	.	62	100	0	.	0	.	0	.	0	.	91	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	244	59
	total	1717	31	3114	28	5355	22	38685	7	1863	32	9507	14	0	.	5724	24	5514	16	8537	15	13660	13	9571	12	2049	30	3625	26	18722	11	1559	35	6071	16	8226	16	2714	28	6395	13	2200	27	18950	10	2501	24	1107	37	8947	14	186309	2
submontane	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	.
	12-20 cm	861	20	0	.	0	.	1134	15	253	32	294	27	0	.	0	.	74	44	209	29	404	25	69	56	0	.	47	71	288	37	359	36	206	29	29	95	761	23	0	.	47	75	630	20	91	53	18	74	654	20	6427	6
	21-30 cm	1451	17	0	.	0	.	2572	14	640	26	651	25	0	.	0	.	89	75	635	28	829	23	89	42	0	.	64	100	500	31	690	29	677	26	53	62	1197	23	0	.	238	63	1305	19	80	81	37	75	1380	18	13174	6
	31-40 cm	2226	20	0	.	0	.	2885	13	1123	23	974	25	0	.	0	.	118	46	771	26	1453	25	299	42	0	.	4	100	483	40	563	30	944	22	152	68	1137	20	0	.	181	52	1625	19	16	100	57	71	1705	18	16715	5
	41-50 cm	2919	15	0	.	0	.	4016	13	828	29	1131	25	0	.	0	.	208	55	1067	26	1600	28	292	42	0	.	15	100	551	37	985	28	1142	24	131	59	1367	21	0	.	7	716	1822	20	50	87	87	87	2254	15	20470	5
	51-60 cm	2976	16	0	.	0	.	4084	14	496	27	978	27	0	.	0	.	145	45	881	26	1315	26	236	50	0	.	22	100	349	42	576	33	914	34	116	76	1304	25	0	.	50	100	1041	26	0	.	68	100	2505	17	18056	6
	61-70 cm	1531	20	0	.	0	.	2074	19	639	34	117	51	0	.	0	.	22	100	170	49	631	35	0	.	0	.	0	.	104	71	344	51	237	47	25	100	534	34	0	.	21	100	379	38	15	100	34	100	762	27	7638	9
	71-80 cm	718	31	0	.	0	.	632	34	32	100	33	100	0	.	0	.	0	.	34	100	111	72	83	71	0	.	0	.	168	50	36	100	209	64	0	.	614	38	0	.	45	73	346	43	0	.	0	.	274	38	3334	14
	81-90 cm	146	58	0	.	0	.	231	51	63	100	0	.	0	.	0	.	58	100	0	.	52	100	0	.	0	.	0	.	82	100	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	199	54	877	26
	91-100 cm	113	73	0	.	0	.	235	71	88	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	128	100	0	.	0	.	58	100	0	.	0	.	0	.	623	40
	>100 cm	0	.	0	.	0	.	65	100	0	.	73	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	71
	total	12941	12	0	.	0	.	17927	10	4163	20	4250	20	0	.	0	.	713	40	3766	22	6395	20	1067	35	0	.	152	84	2524	28	3552	21	4329	21	505	57	7087	16	0	.	588	52	7205	15	253	47	302	79	9733	12	87452	4
hyperinsubric and colline	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	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	73	55	0	.	189	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	91	100	1919	13	0	.	63	79	231	27	0	.	0	.	2567	11
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	268	51	0	.	287	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	2463	12	0	.	89	60	296	35	0	.	-1	100	3424	11
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	209	53	0	.	189	38	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	1420	15	0	.	53	68	323	33	0	.	87	100	2281	12
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	136	83	0	.	105	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	1056	22	0	.	0	.	125	55	0	.	38	72	1474	18
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	45	100	0	.	114	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	453	27	0	.	0	.	122	70	0	.	0	.	756	22
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	29	100	0	.	47	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	305	30	0	.	0	.	33	100	0	.	0	.	414	25
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	126	44	0	.	0	.	0	.	0	.	0	.	126	44
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	46	0	.	0	.	21	100	0	.	0	.	171	42
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	60	0	.	0	.	0	.	0	.	0	.	150	60
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	379	57	0	.	0	.	0	.	0	.	0	.	379	57
	total	0	.	0	.	0	.	0	.	0	.	0	.	760	48	0	.	931	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	100	8421	9	0	.	206	59	1152	28	0	.	123	84	11741	8
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	.
	12-20 cm	1006	19	54	75	201	39	3863	9	346	25	1031	15	73	55	407	25	3016	8	873	18	1304	13	656	15	253	38	493	22	1058	17	516	30	617	17	572	20	1121	19	3514	9	404	25	2001	11	2476	10	145	44	967	19	26969	3
	21-30 cm	1587	16	323	39	733	30	8864	7	937	21	2444	15	268	51	1292	34	6169	8	1592	16	3175	14	1320	17	624	33	1172	21	3223	13	1127	24	1574	17	1090	21	1760	19	5141	8	1187	22	4556	10	4524	10	207	36	2449	13	57339	2
	31-40 cm	2444	19	769	31	1221	26	12072	7	1566	19	3424	14	209	53	1168	29	9442	7	2904	15	3892	14	2079	14	701	32	1147	22	4355	12	1059	23	2374	15	1934	17	1726	17	3681	9	1462	23	5963	9	4984	9	241	39	3885	13	74702	2
	41-50 cm	3466	14	798	30	1438	25	16057	6	1303	23	3752	14	136	83	1590	26	11138	7	3307	14	5211	13	2445	15	342	34	1388	22	4938	13	1246	24	2665	15	2385	16	1898	18	3782	10	951	26	6698	10	5560	9	386	41	4511	11	87392	2
	51-60 cm	3369	15	792	32	1169	28	14115	7	901	26	3050	15	45	100	1296	24	9310	8	2161	16	4124	14	2140	16	358	43	1047	24	4031	13	754	28	2186	20	2172	19	1729	21	2836	12	914	24	5966	11	4499	11	211	48	4478	13	73654	2
	61-70 cm	1629	20	205	42	593	30	7182	9	790	30	1016	25	29	100	331	36	4348	11	844	26	2473	18	968	24	129	51	709	35	2647	19	372	48	626	30	1095	24	732	29	1692	15	406	35	3058	16	2279	16	168	54	1598	20	35919	4
	71-80 cm	718	31	124	77	0	.	3278	14	32	100	447	39	0	.	628	36	2732	15	444	35	703	31	681	28	0	.	238	48	1439	23	36	100	357	44	626	32	808	32	842	22	354	40	1226	24	1460	19	0	.	589	27	17762	6
	81-90 cm	210	50	49	100	0	.	1409	21	63	100	110	71	0	.	171	47	1154	22	47	100	52	100	348	41	0	.	0	.	630	50	0	.	0	.	222	50	46	100	440	32	47	100	373	39	784	26	53	100	325	40	6532	10
	91-100 cm	227	51	0	.	0	.	329	55	88	100	0	.	0	.	113	71	255	46	68	100	45	100	0	.	0	.	0	.	70	100	0	.	0	.	82	100	128	100	317	47	47	100	58	100	129	58	0	.	0	.	1957	19
	>100 cm	0	.	0	.	0	.	156	72	0	.	73	100	0	.	0	.	296	47	62	100	0	.	0	.	0	.	0	.	136	75	0	.	0	.	0	.	0	.	512	50	0	.	38	100	83	72	0	.	0	.	1357	26
	total	14658	11	3114	28	5355	22	67325	5	6026	17	15347	11	760	48	6997	22	47860	5	12303	12	20980	11	10637	11	2407	27	6196	18	22527	10	5111	18	10400	13	10179	14	9948	14	22758	6	5770	18	29935	8	26778	7	1409	34	18803	9	383583	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. 1321191. Birmensdorf, Swiss Federal Research Institute WSL

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