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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NFI1
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 1983/85
		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	.	234	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	64	0	.	0	.	133	39	0	.	0	.	382	20
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	382	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	77	0	.	0	.	336	33	0	.	0	.	747	19
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	790	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	72	0	.	0	.	322	31	0	.	0	.	1155	18
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	467	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	58	0	.	0	.	236	29	0	.	0	.	736	19
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	504	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	40	0	.	0	.	628	26
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	264	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	59	0	.	0	.	376	31
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	52	0	.	0	.	208	35
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	122	51
	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	.	34	100	0	.	0	.	34	100
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	112	59
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2951	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	57	0	.	0	.	1429	22	0	.	0	.	4500	14
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	.	198	22	0	.	0	.	0	.	0	.	691	15	0	.	2	100	0	.	52	87	90	60	55	52	0	.	0	.	2	100	0	.	84	29	25	56	17	55	414	20	0	.	0	.	1630	10
	21-30 cm	0	.	0	.	0	.	810	24	0	.	0	.	0	.	0	.	1690	16	0	.	0	.	0	.	46	100	154	53	146	58	0	.	0	.	13	100	0	.	281	32	62	59	45	85	999	21	0	.	0	.	4246	10
	31-40 cm	0	.	0	.	0	.	1132	27	0	.	0	.	0	.	42	72	2962	14	0	.	0	.	0	.	30	79	135	61	116	61	0	.	0	.	45	80	0	.	381	28	139	74	57	55	1507	19	0	.	0	.	6546	9
	41-50 cm	0	.	0	.	0	.	914	27	0	.	0	.	0	.	105	66	2787	13	0	.	0	.	0	.	33	100	109	69	126	80	0	.	0	.	24	100	0	.	524	26	138	61	110	45	1750	18	0	.	0	.	6620	9
	51-60 cm	0	.	0	.	0	.	318	32	0	.	0	.	0	.	48	71	1850	17	0	.	0	.	0	.	37	100	36	75	77	86	0	.	0	.	77	100	0	.	425	37	151	68	48	77	1280	18	0	.	0	.	4347	11
	61-70 cm	0	.	0	.	0	.	197	41	0	.	0	.	0	.	28	100	723	23	0	.	0	.	0	.	28	100	56	71	64	59	0	.	0	.	58	100	0	.	147	48	48	71	35	100	593	35	0	.	0	.	1977	15
	71-80 cm	0	.	0	.	0	.	50	71	0	.	0	.	0	.	31	100	386	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	71	59	71	0	.	445	33	0	.	0	.	1046	20
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	104	58	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	20	100	0	.	33	100	83	71	0	.	0	.	292	34
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	72	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	100	0	.	0	.	180	52
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	100	0	0	0	.	0	.	65	100
	total	0	.	0	.	0	.	3618	22	0	.	0	.	0	.	285	62	11292	11	0	.	2	100	0	.	225	71	580	46	628	47	0	.	0	.	219	74	0	.	1937	22	622	52	410	42	7128	14	0	.	0	.	26948	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	.	301	19	0	.	169	44	0	.	24	71	1343	13	0	.	115	52	7	100	1	100	125	39	44	48	0	.	0	.	129	74	0	.	256	21	212	39	139	62	1246	16	3	100	0	.	4114	8
	21-30 cm	0	.	0	.	0	.	787	21	0	.	336	49	0	.	70	83	3599	12	0	.	187	56	24	100	29	100	268	39	150	41	0	.	0	.	228	54	0	.	499	28	525	34	271	34	1966	14	18	100	0	.	8959	7
	31-40 cm	0	.	0	.	0	.	1822	20	0	.	401	39	0	.	78	61	5207	11	0	.	140	58	0	0	17	100	409	38	183	75	0	.	0	.	298	42	0	.	833	25	649	36	830	33	2300	14	8	100	0	.	13174	7
	41-50 cm	0	.	0	.	0	.	1953	19	0	.	231	35	0	.	63	72	5266	11	0	.	110	60	0	.	40	100	306	43	440	49	0	.	0	.	226	38	0	.	731	23	395	33	640	31	2079	16	81	100	0	.	12563	7
	51-60 cm	0	.	0	.	0	.	1567	22	0	.	85	51	0	.	18	100	3587	13	0	.	33	73	0	.	0	.	225	38	428	52	0	.	0	.	204	58	0	.	630	30	172	49	394	35	1426	18	0	.	0	.	8767	8
	61-70 cm	0	.	0	.	0	.	344	43	0	.	46	100	0	.	270	72	1748	19	0	.	0	.	0	.	0	.	181	44	104	52	0	.	0	.	0	.	0	.	428	37	58	71	97	50	544	27	0	.	0	.	3820	13
	71-80 cm	0	.	0	.	0	.	255	49	0	.	0	.	0	.	92	100	880	24	0	.	0	.	0	.	0	.	31	100	229	63	0	.	0	.	34	100	0	.	180	48	0	.	60	71	373	33	0	.	0	.	2133	16
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	59	136	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	100	33	100	27	100	117	59	0	.	0	.	513	30
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	49	100	0	.	0	.	0	.	0	.	0	.	69	100	0	.	0	.	0	.	0	.	58	100	0	.	0	.	37	100	0	.	0	.	252	46
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	73	0	.	37	100	0	0	0	.	0	.	284	53
	total	0	.	0	.	0	.	7029	16	0	.	1268	36	0	.	773	57	21895	9	0	.	584	43	31	100	87	100	1544	32	1649	37	0	.	0	.	1120	36	0	.	3863	18	2045	30	2496	26	10088	11	110	100	0	.	54581	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	128	34	149	44	389	30	3064	9	137	41	1004	19	0	.	368	30	344	20	767	24	992	16	718	18	218	36	341	30	1155	16	230	42	402	23	527	22	160	40	955	17	159	35	1285	13	259	29	63	49	459	24	14277	4
	21-30 cm	300	34	567	32	1318	30	5898	9	261	36	1905	18	0	.	897	30	707	23	1479	19	1672	16	1253	16	436	37	722	27	2846	14	351	38	1266	22	1138	22	269	33	1007	18	365	32	3012	11	509	29	69	57	1674	19	29919	4
	31-40 cm	524	37	807	30	1330	25	9420	9	400	38	2663	20	0	.	1101	25	757	23	2192	17	3188	16	1846	17	497	37	1125	26	3596	15	400	43	1585	19	1752	19	436	37	723	21	332	35	4045	13	437	29	320	49	2480	16	41956	4
	41-50 cm	267	39	745	32	1306	29	9931	9	549	34	2007	18	0	.	1032	24	870	22	1696	18	2838	16	2044	15	311	43	1058	30	3706	14	188	58	1856	19	1751	20	449	34	669	20	374	36	3455	12	411	30	256	43	2196	19	39966	4
	51-60 cm	130	56	304	42	719	34	6149	10	258	46	1045	27	0	.	576	34	920	24	813	22	2115	20	1390	21	177	42	973	29	2501	17	115	52	594	32	1333	25	218	60	583	29	307	47	2993	15	171	42	188	56	852	22	25424	5
	61-70 cm	102	73	106	78	34	100	2291	15	138	64	601	31	0	.	210	47	387	38	391	32	582	30	353	31	26	100	338	55	1142	23	44	100	243	61	488	35	145	52	172	36	194	69	860	25	60	71	65	71	368	35	9341	7
	71-80 cm	0	.	82	72	0	.	1157	23	0	.	133	53	0	.	142	54	196	61	75	73	116	70	483	33	0	.	58	100	612	41	46	100	0	.	232	50	0	.	235	69	0	.	620	35	52	75	42	100	173	50	4454	12
	81-90 cm	43	100	0	.	0	.	444	39	0	.	0	.	0	.	0	.	91	63	0	.	28	100	47	100	0	.	0	.	174	58	0	.	49	100	58	100	0	.	0	.	34	100	72	71	0	.	0	.	0	.	1040	23
	91-100 cm	0	.	0	.	0	.	130	72	0	.	0	.	0	.	0	.	0	.	44	100	0	.	130	71	0	.	0	.	0	.	0	.	0	.	35	100	0	.	38	100	36	100	70	100	0	.	0	.	38	100	519	33
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100
	total	1494	32	2760	29	5097	23	38483	7	1743	30	9358	15	0	.	4326	21	4272	18	7458	14	11531	13	8330	12	1665	33	4616	24	15732	11	1374	33	5995	16	7313	16	1678	31	4382	14	1801	30	16413	10	1900	23	1004	40	8239	14	166964	3
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	1023	16	0	.	0	.	1559	15	374	26	304	30	0	.	0	.	72	47	279	31	475	29	122	37	0	.	23	100	246	34	307	29	326	24	33	70	578	20	0	.	115	63	878	19	80	55	14	74	728	17	7536	6
	21-30 cm	2241	14	0	.	0	.	3101	13	816	26	943	24	0	.	0	.	204	46	763	26	1184	21	245	38	0	.	28	100	564	36	667	35	854	23	103	64	896	22	0	.	150	50	1787	19	30	60	62	81	1530	17	16166	5
	31-40 cm	3629	13	0	.	0	.	4761	12	892	26	1487	23	0	.	0	.	261	45	1111	24	1928	20	419	42	0	.	79	100	531	40	766	28	1265	24	184	63	1248	21	0	.	39	80	1748	19	18	73	96	77	2948	15	23410	5
	41-50 cm	3939	15	0	.	0	.	4857	13	594	31	1191	25	0	.	0	.	264	46	970	25	1561	20	292	47	0	.	29	100	488	39	603	29	1387	27	95	58	1584	21	0	.	7	85	1199	20	57	68	38	100	3105	15	22260	5
	51-60 cm	2524	17	0	.	0	.	2840	14	575	30	597	27	0	.	0	.	215	47	367	35	928	27	65	80	0	.	0	.	177	48	329	40	585	38	0	.	1040	22	0	.	33	100	487	25	152	55	0	.	1473	20	12388	6
	61-70 cm	818	27	0	.	0	.	1103	21	252	49	267	40	0	.	0	.	31	100	151	51	262	46	29	100	0	.	0	.	268	64	65	71	179	45	0	.	489	35	0	.	31	100	243	48	39	80	0	.	640	28	4865	10
	71-80 cm	271	42	0	.	0	.	647	28	59	100	180	50	0	.	0	.	50	100	0	.	113	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	230	41	0	.	18	100	71	71	0	.	0	.	222	45	1861	15
	81-90 cm	68	100	0	.	0	.	347	55	138	72	174	58	0	.	0	.	0	.	0	.	37	100	0	.	0	.	0	.	0	.	64	100	0	.	0	.	41	100	0	.	0	.	53	100	0	.	0	.	40	100	963	28
	91-100 cm	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	68	100	174	60
	>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	14513	11	0	.	0	.	19215	10	3699	20	5178	20	0	.	0	.	1097	38	3640	21	6487	17	1172	35	0	.	158	100	2273	33	2801	23	4596	20	415	57	6177	16	0	.	393	50	6467	16	376	48	211	72	10754	13	89623	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	.	183	52	0	.	185	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	1972	11	0	.	98	63	216	31	0	.	26	72	2685	10
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	77	49	0	.	146	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1585	13	0	.	28	59	295	31	0	.	44	92	2175	11
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	44	82	0	.	78	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	894	17	0	.	0	.	173	31	0	.	135	92	1338	16
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	85	95	0	.	84	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	100	476	21	0	.	5	100	108	69	0	.	45	73	860	19
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	23	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	261	24	0	.	0	.	82	61	0	.	21	100	434	22
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	44	0	.	0	.	18	100	0	.	0	.	147	37
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	254	47	0	.	0	.	19	100	0	.	0	.	273	45
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	60	0	.	0	.	0	.	0	.	0	.	118	60
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	61	0	.	0	.	0	.	0	.	0	.	80	61
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	364	58	0	.	0	.	0	.	0	.	0	.	364	58
	total	0	.	0	.	0	.	0	.	0	.	0	.	435	51	0	.	542	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	100	6106	10	0	.	131	60	912	27	0	.	272	80	8473	9
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	1152	14	149	44	389	30	5122	7	510	22	1478	15	183	52	392	28	2871	8	1046	19	1584	14	847	16	270	33	579	22	1501	14	537	24	728	16	692	22	744	18	3283	8	511	24	2418	10	2348	10	80	41	1213	13	30625	2
	21-30 cm	2541	13	567	32	1318	30	10596	6	1077	21	3183	14	77	49	967	29	6728	8	2242	15	3042	12	1522	14	511	33	1172	20	3705	12	1018	26	2120	16	1482	19	1165	18	3401	9	1103	21	5144	9	4135	9	149	45	3248	12	62212	2
	31-40 cm	4152	12	807	30	1330	25	17136	6	1293	21	4552	14	44	82	1221	23	10055	7	3302	14	5256	12	2265	16	544	34	1747	20	4425	13	1166	23	2850	15	2279	16	1697	18	2874	10	1159	24	6680	10	4758	9	425	41	5563	11	87578	2
	41-50 cm	4205	14	745	32	1306	29	17655	6	1143	23	3429	14	85	95	1201	21	9737	7	2666	14	4509	12	2335	14	385	37	1503	23	4760	12	791	26	3243	16	2096	18	2091	18	2434	11	915	22	5409	10	4640	10	376	38	5346	11	83004	2
	51-60 cm	2654	17	304	42	719	34	10873	7	833	25	1728	19	46	100	642	31	7100	9	1180	19	3076	16	1455	21	214	39	1233	24	3183	16	444	32	1179	25	1614	22	1258	21	1899	15	662	30	3923	12	3236	11	188	56	2346	15	51988	3
	61-70 cm	920	25	106	78	34	100	3935	11	390	39	914	24	0	.	508	43	3179	13	542	27	844	25	382	29	54	71	575	36	1578	20	108	58	422	40	546	33	634	29	850	22	331	44	1234	20	1366	19	65	71	1008	22	20526	5
	71-80 cm	271	42	82	72	0	.	2109	16	59	100	313	36	0	.	266	47	1641	17	75	73	229	46	483	33	0	.	89	74	842	34	46	100	0	.	265	46	230	41	743	30	77	59	751	30	968	21	42	100	395	33	9975	7
	81-90 cm	112	72	0	.	0	.	790	33	138	72	174	58	0	.	150	51	429	30	0	.	66	71	47	100	0	.	0	.	195	53	64	100	49	100	58	100	41	100	217	49	68	71	185	46	224	42	0	.	40	100	3048	14
	91-100 cm	0	.	0	.	0	.	130	72	0	.	34	100	0	.	38	100	147	58	44	100	0	.	130	71	0	.	0	.	93	79	0	.	0	.	35	100	72	100	175	48	36	100	70	100	129	59	0	.	106	74	1239	21
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	162	61	0	.	0	.	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	531	46	0	.	102	73	30	100	0	.	0	.	893	32
	total	16007	10	2760	29	5097	23	68345	5	5442	16	15804	11	435	51	5385	19	42049	6	11098	11	18604	10	9533	12	1977	29	6898	18	20282	10	4175	19	10592	12	9067	14	7930	14	16408	6	4862	18	25917	8	21833	7	1324	33	19265	9	351089	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. 1321110. Birmensdorf, Swiss Federal Research Institute WSL

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