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
grid: 1.4-km grid
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	.	288	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	29	3	100	0	.	207	27	0	.	0	.	560	13
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	600	15	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	40	19	100	0	.	352	21	0	.	0	.	1043	11
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	831	16	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	32	73	2	100	0	.	500	20	0	.	0	.	1370	12
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	750	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	43	11	100	0	.	498	18	0	.	0	.	1325	11
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	723	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	42	0	.	0	.	254	24	0	.	0	.	1034	15
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	224	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	53	0	.	0	.	224	24	0	.	0	.	510	18
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	227	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	71	0	.	0	.	147	34	0	.	0	.	407	22
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	50	58	0	.	0	.	189	34
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	57	58	0	.	0	.	111	42
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	59	0	.	0	.	125	42
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3875	12	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	411	27	34	100	0	.	2351	14	0	.	0	.	6676	9
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	.	187	22	0	.	18	100	0	.	36	58	967	12	0	.	12	72	0	.	33	77	58	55	51	39	0	.	0	.	7	67	0	.	180	19	25	69	33	42	544	14	0	.	0	.	2151	7
	21-30 cm	0	.	0	.	0	.	567	18	0	.	27	100	0	.	56	66	2075	10	0	.	17	76	0	.	64	77	130	45	103	50	0	.	0	.	6	100	0	.	317	24	47	49	101	48	1281	15	0	.	0	.	4790	7
	31-40 cm	0	.	0	.	0	.	979	18	0	.	0	.	0	.	77	67	3113	10	0	.	17	100	0	.	21	72	200	44	79	57	0	.	0	.	52	68	0	.	502	20	55	58	111	38	1884	13	0	.	0	.	7090	6
	41-50 cm	0	.	0	.	0	.	843	19	0	.	0	.	0	.	132	51	3939	9	0	.	7	100	0	.	21	74	157	44	86	66	0	.	0	.	29	100	0	.	490	21	64	63	188	35	2040	12	0	.	0	.	7996	6
	51-60 cm	0	.	0	.	0	.	683	19	0	.	0	.	0	.	76	54	2992	9	0	.	0	.	0	.	41	80	52	55	78	41	0	.	0	.	40	76	0	.	555	19	86	53	170	43	1809	13	0	.	0	.	6582	6
	61-70 cm	0	.	0	.	0	.	372	27	0	.	0	.	0	.	46	59	1499	14	0	.	0	.	0	.	36	74	26	72	33	59	0	.	0	.	32	100	0	.	216	27	13	100	164	55	949	20	0	.	0	.	3386	9
	71-80 cm	0	.	0	.	0	.	191	39	0	.	0	.	0	.	22	100	631	21	0	.	0	.	0	.	0	.	24	72	28	71	0	.	0	.	21	100	0	.	266	31	20	100	95	49	512	25	0	.	0	.	1811	12
	81-90 cm	0	.	0	.	0	.	46	71	0	.	0	.	0	.	37	72	253	34	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	77	63	0	.	36	73	302	30	0	.	0	.	772	19
	91-100 cm	0	.	0	.	0	.	20	100	0	.	0	.	0	.	20	100	140	42	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	48	71	0	.	0	.	0	0	0	.	0	.	246	31
	>100 cm	0	.	0	.	0	.	20	100	0	.	0	.	0	.	33	100	51	72	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	131	46
	total	0	.	0	.	0	.	3908	15	0	.	45	100	0	.	535	44	15661	7	0	.	53	84	0	.	216	55	669	35	502	35	0	.	0	.	186	65	0	.	2652	15	309	44	898	33	9321	10	0	.	0	.	34955	5
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	.	6	100	0	.	394	17	0	.	115	34	0	.	30	47	1676	9	0	.	98	33	0	.	18	52	118	28	36	40	0	.	0	.	57	37	0	.	514	15	122	29	161	26	1313	10	0	.	0	.	4659	5
	21-30 cm	0	.	8	100	0	.	920	17	0	.	293	32	0	.	55	72	2937	9	0	.	194	44	0	.	33	63	319	34	114	37	0	.	0	.	119	44	0	.	747	15	367	26	391	30	2295	10	0	.	0	.	8792	5
	31-40 cm	0	.	0	.	0	.	1390	19	0	.	444	36	0	.	21	97	4718	9	0	.	181	33	0	.	34	74	386	27	171	63	0	.	0	.	319	29	0	.	926	18	531	30	541	28	2547	11	0	0	0	.	12209	5
	41-50 cm	0	.	5	100	0	.	1781	15	0	.	571	27	0	.	82	62	5313	8	0	.	253	34	0	.	20	64	314	27	291	38	0	.	0	.	338	28	0	.	955	17	462	28	760	23	2442	11	0	0	0	.	13587	5
	51-60 cm	0	.	0	.	0	.	2041	21	0	.	319	34	0	.	86	50	4781	9	0	.	148	44	0	.	16	100	360	28	241	41	0	.	0	.	193	44	0	.	821	17	335	29	874	20	2341	12	0	.	0	.	12557	6
	61-70 cm	0	.	0	.	0	.	759	22	0	.	106	50	0	.	154	60	2552	12	0	.	54	50	0	.	12	100	139	35	66	48	0	.	0	.	125	52	0	.	629	22	197	50	453	28	1431	17	0	.	0	.	6676	7
	71-80 cm	0	.	0	.	0	.	346	26	0	.	80	76	0	.	134	65	1622	16	0	.	19	100	0	.	29	100	104	52	118	51	0	.	0	.	49	59	0	.	386	28	274	40	221	35	901	20	0	.	0	.	4283	9
	81-90 cm	0	.	0	.	0	.	150	52	0	.	20	100	0	.	48	71	689	23	0	.	0	.	0	.	0	.	24	100	31	100	0	.	0	.	0	.	0	.	191	39	43	100	168	50	370	29	0	.	0	.	1734	14
	91-100 cm	0	.	0	.	0	.	95	74	0	.	0	.	0	.	62	71	165	45	0	.	0	.	0	.	0	.	31	100	0	.	0	.	0	.	0	.	0	.	64	74	26	100	32	100	81	58	0	.	0	.	555	25
	>100 cm	0	.	0	.	0	.	51	100	0	.	0	.	0	.	0	.	232	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	218	53	32	100	21	100	26	100	0	.	0	.	580	29
	total	0	.	20	100	0	.	7927	14	0	.	1948	24	0	.	672	47	24686	6	0	.	947	29	0	.	163	55	1795	23	1068	30	0	.	0	.	1200	25	0	.	5450	11	2388	23	3621	17	13748	8	0	0	0	.	65633	4
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	184	30	124	34	180	28	2203	8	129	26	607	16	0	.	285	22	497	16	717	15	770	12	595	14	146	35	297	30	822	14	136	41	426	16	569	16	208	33	1422	10	209	31	1357	10	449	19	140	30	316	27	12789	3
	21-30 cm	350	27	370	27	746	21	5157	7	342	25	1518	14	0	.	1062	27	859	16	1370	12	1745	14	1177	13	360	31	574	21	2593	11	285	38	929	16	1094	16	385	31	1902	11	359	29	3046	9	656	19	228	33	933	16	28041	3
	31-40 cm	593	24	786	24	1119	20	7451	7	507	23	2414	14	0	.	1034	22	1243	15	2682	12	2163	13	1952	12	424	32	554	22	3587	10	418	31	1527	16	1382	15	508	26	1441	12	437	27	4103	8	795	20	288	28	1697	16	39106	3
	41-50 cm	658	23	698	25	1401	20	10072	6	632	25	2389	14	0	.	1219	22	1185	16	2710	12	3070	12	2236	12	231	33	741	23	4354	11	166	45	1410	15	1996	14	481	26	1207	13	194	35	4708	9	535	23	581	27	1833	14	44709	2
	51-60 cm	434	28	586	27	1563	27	9062	8	614	31	1926	14	0	.	914	21	991	19	1623	15	2725	13	2246	12	192	45	656	23	3839	11	111	48	1177	19	1658	17	483	38	1037	16	222	35	4379	10	392	25	415	32	1603	17	38849	3
	61-70 cm	275	43	145	39	850	25	4822	9	338	43	783	22	0	.	173	32	640	21	716	21	1624	16	1152	16	76	51	545	30	2152	15	16	100	322	33	1057	20	321	40	459	19	166	47	2142	14	166	45	351	37	679	24	19972	4
	71-80 cm	24	100	72	77	0	.	2315	13	51	71	347	33	0	.	272	39	321	31	473	28	508	26	624	25	29	100	131	63	1086	21	31	100	122	45	560	27	118	61	221	29	95	71	976	21	51	60	103	59	256	32	8787	6
	81-90 cm	36	100	29	100	35	100	849	22	0	.	192	47	0	.	40	76	53	73	245	77	28	100	303	45	0	.	97	73	332	53	0	.	151	79	241	38	0	.	159	47	25	100	371	31	58	61	31	100	73	58	3346	13
	91-100 cm	69	71	0	.	0	.	239	41	0	.	36	100	0	.	0	0	49	73	78	71	26	100	0	.	0	.	0	.	165	51	0	.	0	.	49	100	0	.	138	51	0	.	31	100	81	72	0	.	0	.	963	20
	>100 cm	0	.	0	0	0	.	115	71	0	.	0	.	0	.	68	75	0	.	36	100	0	.	0	.	0	.	0	.	106	71	0	.	0	.	24	100	0	.	492	88	0	.	0	.	0	.	0	.	0	.	841	54
	total	2623	19	2810	22	5895	17	42285	5	2614	22	10213	11	0	.	5068	19	5838	12	10651	10	12660	10	10285	9	1459	26	3595	19	19036	8	1164	29	6064	12	8630	12	2504	22	8476	9	1708	22	21115	7	3183	16	2136	24	7390	12	197403	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	1004	12	0	.	0	.	1005	12	253	22	342	21	0	.	2	100	48	41	222	22	371	22	52	46	3	100	73	52	290	24	274	27	249	24	23	71	764	18	0	.	28	70	695	15	121	33	30	50	786	15	6635	4
	21-30 cm	1841	12	0	.	0	.	2440	12	611	19	620	21	0	.	0	.	68	60	518	22	737	19	76	35	9	100	112	54	552	22	581	23	796	19	56	51	987	17	0	.	190	49	1467	13	152	42	64	47	1474	14	13352	4
	31-40 cm	2502	12	0	.	0	.	2744	11	1052	17	932	19	0	.	0	.	126	42	715	22	989	23	220	36	80	84	55	74	793	24	580	23	1203	16	192	56	1246	17	0	.	127	46	2226	14	117	40	91	53	1838	14	17827	4
	41-50 cm	2966	11	0	.	0	.	3449	11	903	19	1026	21	0	.	8	100	148	47	841	21	1171	23	222	37	23	97	47	78	925	23	999	22	1435	16	91	52	1426	18	0	.	86	86	2504	13	56	53	69	69	2453	12	20847	4
	51-60 cm	3050	11	0	.	0	.	3786	11	803	22	921	21	0	.	0	.	122	39	623	25	1054	23	167	43	11	100	88	86	602	24	499	25	1257	20	68	76	1073	20	0	.	40	79	1503	17	28	72	41	100	2718	13	18453	4
	61-70 cm	1329	16	0	.	0	.	1820	16	568	26	324	40	0	.	0	.	14	100	194	38	574	28	34	100	0	.	0	.	260	33	331	37	474	32	94	86	469	28	0	.	13	100	786	22	52	56	21	100	1053	18	8409	7
	71-80 cm	563	26	0	.	0	.	825	23	82	61	165	62	0	.	0	.	0	.	18	100	108	59	46	71	0	.	0	.	181	36	88	50	338	34	44	100	361	38	0	.	50	61	410	32	0	.	0	.	624	25	3905	10
	81-90 cm	167	48	0	.	0	.	198	41	36	100	31	100	0	.	0	.	31	100	0	.	32	100	0	.	0	.	0	.	48	100	0	.	80	100	0	.	26	100	0	.	0	.	57	73	0	.	0	.	145	48	851	21
	91-100 cm	63	73	0	.	0	.	210	51	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	70	71	0	.	0	.	66	72	530	30
	>100 cm	31	100	0	.	0	.	37	100	47	100	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	155	51
	total	13514	9	0	.	0	.	16512	8	4404	15	4402	16	0	.	9	100	556	35	3132	17	5036	17	817	30	126	75	375	54	3652	18	3352	16	5832	13	568	45	6423	13	0	.	534	44	9719	11	527	29	316	52	11156	10	90964	3
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	.	63	42	0	.	243	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	1755	10	0	.	34	79	202	21	0	.	4	100	2353	8
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	169	46	0	.	279	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	2150	9	0	.	50	61	270	25	0	.	38	102	2967	8
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	155	43	0	.	241	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	1536	12	0	.	30	69	290	24	0	.	49	100	2301	9
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	117	60	0	.	51	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	1047	15	0	.	0	.	151	40	0	.	47	64	1421	13
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	30	85	0	.	103	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	654	18	0	.	0	.	112	47	0	.	0	.	911	15
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	31	71	0	.	117	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	325	24	0	.	0	.	27	75	0	.	0	.	500	23
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	206	28	0	.	0	.	0	.	0	.	0	.	219	27
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	169	32	0	.	0	.	11	100	0	.	35	100	216	31
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	82	61	0	.	0	.	0	.	0	.	33	100	132	47
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	606	41	0	.	0	.	0	.	0	.	0	.	606	41
	total	0	.	0	.	0	.	0	.	0	.	0	.	565	39	0	.	1065	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	100	8530	7	0	.	114	59	1064	20	0	.	206	72	11627	6
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	1188	11	129	33	180	28	3789	6	383	17	1082	12	63	42	353	19	3719	6	939	12	1252	10	647	13	200	29	546	19	1199	11	411	22	674	14	656	14	1023	15	3935	6	387	20	2280	8	2837	7	170	26	1106	13	29147	2
	21-30 cm	2191	11	379	27	746	21	9084	5	953	15	2458	11	169	46	1172	25	6819	5	1888	10	2693	11	1253	13	466	27	1136	16	3362	9	865	20	1725	12	1274	14	1384	15	5188	6	982	17	5055	7	5006	7	292	28	2445	10	58986	2
	31-40 cm	3095	11	786	24	1119	20	12564	5	1559	14	3790	11	155	43	1132	21	10272	5	3397	11	3351	11	2172	11	560	27	1200	16	4629	9	998	18	2731	11	1944	13	1754	14	4437	7	1152	18	7011	7	6133	6	379	25	3584	10	79903	2
	41-50 cm	3624	10	703	25	1401	20	16144	5	1536	15	3986	10	117	60	1441	20	11386	5	3551	10	4500	10	2458	11	295	28	1259	16	5657	9	1166	20	2845	11	2455	13	1915	15	3766	7	816	20	8160	7	5722	7	649	25	4332	9	89885	2
	51-60 cm	3484	10	586	27	1563	27	15572	6	1417	18	3166	11	30	85	1076	19	9712	5	2246	13	3927	11	2414	12	261	36	1155	17	4760	10	610	22	2435	13	1958	15	1568	18	3124	8	684	20	6925	7	4937	8	456	30	4321	10	78387	2
	61-70 cm	1604	15	145	39	850	25	7772	7	905	23	1213	18	31	71	372	30	5046	8	910	18	2252	14	1186	15	124	39	710	24	2511	14	347	36	796	23	1308	18	790	23	1690	11	389	32	3546	11	2849	11	372	35	1732	14	39452	3
	71-80 cm	587	25	72	77	0	.	3677	10	133	46	592	28	0	.	428	33	2815	11	492	27	636	24	670	24	58	71	260	38	1413	17	119	45	460	27	673	24	479	32	1112	14	439	31	1702	15	1611	14	103	59	880	20	19412	4
	81-90 cm	202	43	29	100	35	100	1243	18	36	100	242	40	0	.	126	42	1151	17	245	77	59	71	303	45	0	.	142	55	411	45	0	.	231	62	241	38	26	100	610	21	68	73	632	24	791	19	31	100	254	35	7108	8
	91-100 cm	132	51	0	.	0	.	564	29	50	100	36	100	0	.	82	59	414	26	78	71	26	100	0	.	0	.	31	100	183	47	0	.	0	.	49	100	72	100	343	30	26	100	133	50	219	37	0	.	99	58	2538	12
	>100 cm	31	100	0	0	0	.	222	47	47	100	41	100	0	.	101	60	347	31	36	100	0	.	0	.	0	.	0	.	133	60	0	.	0	.	24	100	0	.	1316	39	32	100	21	100	88	51	0	.	0	.	2439	23
	total	16137	8	2830	22	5895	17	70632	4	7019	12	16608	8	565	39	6284	16	51680	4	13783	8	18697	8	11103	9	1964	21	6439	13	24258	7	4516	14	11896	9	10584	10	9010	11	25519	4	4974	14	35467	5	30193	5	2452	22	18752	7	407258	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. 1321219. Birmensdorf, Swiss Federal Research Institute WSL

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