Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
total number of stems
altitudinal vegetation belts (NaiS, 3 classes) · diameter at breast height (10 classes)
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
altitudinal vegetation belts (NaiS, 3 classes)	diameter at breast height (10 classes)	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
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	.	2777	22	0	.	271	100	0	.	307	70	13698	10	0	.	0	.	0	.	482	100	1040	45	649	49	0	.	0	.	200	83	0	.	3518	19	491	59	347	47	7102	13	0	.	0	.	30881	7
	21-30 cm	0	.	0	.	0	.	1957	20	0	.	135	100	0	.	123	76	7589	11	0	.	22	100	0	.	286	86	627	49	311	58	0	.	0	.	18	100	0	.	2008	20	292	43	174	62	4295	14	0	.	0	.	17837	7
	31-40 cm	0	.	0	.	0	.	1583	20	0	.	60	100	0	.	64	49	4738	10	0	.	22	100	0	.	108	83	302	47	238	51	0	.	0	.	40	69	0	.	844	22	140	58	211	46	2716	14	0	.	0	.	11066	6
	41-50 cm	0	.	0	.	0	.	1035	19	0	.	0	.	0	.	66	59	2879	10	0	.	0	.	0	.	59	68	116	49	125	44	0	.	0	.	44	100	0	.	484	23	65	62	142	44	1636	14	0	.	0	.	6651	6
	51-60 cm	0	.	0	.	0	.	361	22	0	.	0	.	0	.	62	58	1576	10	0	.	0	.	0	.	31	81	17	72	45	46	0	.	0	.	22	74	0	.	402	23	64	53	60	49	1015	15	0	.	0	.	3656	7
	61-70 cm	0	.	0	.	0	.	131	39	0	.	0	.	0	.	13	71	538	15	0	.	0	.	0	.	6	100	17	72	21	74	0	.	0	.	22	74	0	.	114	29	21	58	21	75	365	22	0	.	0	.	1270	10
	71-80 cm	0	.	0	.	0	.	68	41	0	.	0	.	0	.	7	100	234	23	0	.	0	.	0	.	7	100	0	.	15	71	0	.	0	.	15	100	0	.	80	33	7	100	30	50	212	23	0	.	0	.	673	13
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	153	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	51	0	.	23	73	79	33	0	.	0	.	299	18
	91-100 cm	0	.	0	.	0	.	7	100	0	.	0	.	0	.	7	100	17	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45	0	.	0	.	67	33
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	50	38
	total	0	.	0	.	0	.	7919	17	0	.	466	100	0	.	662	58	31458	8	0	.	44	100	0	.	979	85	2120	43	1410	46	0	.	0	.	362	58	0	.	7478	15	1081	45	1009	41	17463	11	0	.	0	.	72451	5
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	463	48	629	38	1490	30	22534	8	1235	29	5664	14	0	.	2977	22	20129	9	4243	17	8386	14	4950	15	1193	29	3340	21	7926	14	448	45	3246	21	5326	17	833	31	14119	10	4571	22	13838	11	15317	10	662	39	3075	22	146594	3
	21-30 cm	460	37	571	36	1325	25	13376	8	795	33	4207	16	0	.	1953	23	11024	9	1954	18	4043	14	2379	16	1148	30	2188	21	4997	13	417	45	1793	20	3167	20	765	37	7998	11	2114	21	6888	13	8819	11	592	35	2037	22	85009	3
	31-40 cm	172	39	575	28	1045	24	8466	8	377	32	2415	16	0	.	728	25	6468	9	1601	18	2424	15	1724	16	720	31	1013	21	3628	12	334	37	1382	22	1737	16	553	37	2911	12	1127	23	4439	10	4117	12	310	39	1412	17	49678	3
	41-50 cm	194	33	501	27	618	24	5396	7	299	35	1457	16	0	.	767	21	3777	8	1119	17	1641	14	1166	14	244	33	673	20	2240	12	153	47	757	18	1109	16	197	32	1456	13	639	27	2794	10	2031	12	193	43	950	16	30371	3
	51-60 cm	156	42	259	30	367	30	3487	8	149	37	850	17	0	.	391	25	2208	10	457	18	904	16	602	17	104	46	359	25	1336	13	56	61	355	24	704	17	130	42	732	14	265	26	1575	11	1199	13	108	38	491	20	17243	3
	61-70 cm	34	53	97	35	148	36	1129	11	55	50	356	21	0	.	125	34	825	12	151	29	469	19	280	20	40	46	137	31	619	19	7	100	102	35	273	24	34	52	377	20	114	31	681	16	363	21	28	79	224	26	6668	4
	71-80 cm	0	.	22	74	19	73	481	14	28	79	154	34	0	.	92	44	343	16	78	35	159	28	81	34	0	.	91	35	350	20	0	.	22	73	97	37	13	71	179	23	70	43	194	23	177	25	18	72	47	38	2713	6
	81-90 cm	7	100	0	.	0	.	212	22	0	.	20	58	0	.	27	50	165	22	14	71	9	100	15	71	0	.	0	.	90	53	0	.	7	100	28	50	7	100	64	36	7	100	79	33	85	42	0	.	21	58	858	11
	91-100 cm	7	100	0	.	0	.	29	50	0	.	9	100	0	.	22	58	20	58	7	100	7	100	15	71	0	.	0	.	21	58	0	.	0	.	14	71	0	.	25	59	9	100	7	100	22	58	0	.	7	100	220	18
	>100 cm	7	100	0	.	0	.	14	71	0	.	0	.	0	.	7	100	24	58	7	100	0	.	0	.	0	.	0	.	14	71	0	.	0	.	7	100	0	.	25	71	0	.	7	100	7	100	0	.	0	.	120	26
	total	1498	33	2654	28	5012	22	55123	6	2938	27	15132	13	0	.	7089	18	44983	7	9631	15	18042	12	11212	12	3447	26	7801	17	21221	11	1415	34	7665	16	12462	14	2533	30	27885	9	8914	19	30503	9	32137	9	1910	32	8264	16	339473	2
hyperinsubric, colline, 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	5560	15	0	.	0	.	6917	13	1939	23	1915	30	284	48	0	.	2009	35	1301	25	2631	24	1206	34	0	.	503	87	2065	35	1860	29	1853	24	315	95	3962	21	17526	11	334	53	5397	18	3540	25	116	77	4263	16	65498	5
	21-30 cm	2718	18	0	.	0	.	3910	13	1218	25	1167	23	311	49	0	.	1080	33	809	25	1207	21	262	40	0	.	69	79	1202	31	1290	30	1182	27	82	71	2083	21	7654	10	513	51	3177	16	1320	25	75	71	2785	16	34116	4
	31-40 cm	1637	15	0	.	0	.	2055	13	808	24	874	24	186	56	0	.	476	35	656	30	943	25	164	44	0	.	52	100	425	33	637	25	901	21	146	57	1115	20	2669	13	200	51	1360	16	431	28	42	100	1766	16	17542	4
	41-50 cm	1132	14	0	.	0	.	1697	14	372	28	431	26	91	57	0	.	220	31	510	23	659	23	188	35	0	.	0	.	191	34	452	26	510	24	61	60	538	21	983	15	115	56	910	17	176	31	68	72	1022	15	10327	4
	51-60 cm	705	16	0	.	0	.	1013	13	217	26	265	24	21	100	0	.	88	35	261	27	376	31	98	47	0	.	0	.	166	38	139	32	265	30	33	64	387	21	360	21	31	79	461	23	60	65	7	100	727	16	5681	5
	61-70 cm	334	18	0	.	0	.	453	17	112	35	61	37	7	100	0	.	23	58	77	35	174	27	21	100	0	.	0	.	27	75	108	38	95	33	13	71	104	35	123	24	8	100	111	35	36	82	20	100	171	26	2078	7
	71-80 cm	143	25	0	.	0	.	131	28	47	41	21	58	0	.	0	.	6	100	14	71	7	100	14	71	0	.	0	.	53	47	23	58	27	50	0	.	64	44	107	33	15	71	45	41	10	100	0	.	41	41	766	11
	81-90 cm	47	43	0	.	0	.	27	50	14	71	0	.	0	.	0	.	7	100	7	100	0	.	0	.	0	.	0	.	9	100	0	.	7	100	0	.	47	51	57	42	0	.	22	58	8	100	0	.	43	41	295	17
	91-100 cm	0	.	0	.	0	.	37	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	30	61	0	.	13	71	0	.	0	.	0	.	97	38
	>100 cm	8	100	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	57	45	0	.	0	.	0	.	0	.	0	.	115	40
	total	12283	12	0	.	0	.	16246	10	4726	20	4742	21	900	44	0	.	3909	29	3636	21	5997	18	1953	30	0	.	625	77	4138	29	4510	22	4841	20	651	58	8354	16	29566	9	1217	48	11496	14	5581	22	327	73	10819	12	136515	3
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	6023	14	629	38	1490	30	32228	6	3174	18	7849	13	284	48	3284	21	35837	6	5544	14	11017	12	6155	14	1674	35	4883	19	10640	13	2309	25	5099	16	5841	17	4796	18	35162	6	5397	20	19583	9	25958	7	778	35	7339	13	242973	2
	21-30 cm	3178	16	571	36	1325	25	19243	6	2013	20	5509	13	311	49	2076	22	19693	6	2764	14	5271	12	2642	15	1434	29	2885	19	6510	12	1707	25	2975	16	3267	19	2848	18	17660	6	2918	18	10238	10	14434	8	667	32	4822	13	136962	2
	31-40 cm	1808	14	575	28	1045	24	12104	6	1185	19	3349	13	186	56	791	23	11682	6	2257	15	3389	13	1888	15	828	29	1368	19	4291	11	971	21	2283	15	1923	15	1668	18	6425	7	1467	20	6010	8	7264	8	351	36	3178	11	78286	2
	41-50 cm	1325	13	501	27	618	24	8127	6	671	22	1889	13	91	57	833	20	6876	6	1629	13	2300	12	1354	13	303	30	789	19	2556	11	606	23	1268	14	1215	15	736	18	2922	8	819	23	3847	8	3844	8	261	37	1973	11	47350	2
	51-60 cm	861	15	259	30	367	30	4861	6	366	22	1116	14	21	100	453	23	3872	7	717	14	1280	14	699	16	135	40	376	24	1547	12	195	28	620	19	759	16	518	19	1494	10	360	22	2096	10	2275	9	115	36	1218	12	26579	2
	61-70 cm	368	17	97	35	148	36	1713	9	167	29	417	19	7	100	138	31	1386	9	229	22	644	15	302	19	46	42	154	29	667	18	115	36	197	24	308	22	137	29	614	14	143	27	813	14	764	15	48	62	396	18	10016	3
	71-80 cm	143	25	22	74	19	73	680	12	75	39	175	31	0	.	99	41	583	13	92	32	166	27	95	31	7	100	91	35	418	18	23	58	49	43	111	35	77	39	366	16	92	36	269	19	398	16	18	72	87	27	4153	5
	81-90 cm	54	39	0	.	0	.	239	20	14	71	20	58	0	.	41	41	325	16	21	58	9	100	15	71	0	.	0	.	99	49	0	.	14	71	28	50	54	46	151	24	7	100	124	27	173	26	0	.	64	33	1451	8
	91-100 cm	7	100	0	.	0	.	73	41	0	.	9	100	0	.	29	50	37	45	7	100	7	100	15	71	0	.	0	.	21	58	0	.	0	.	14	71	16	100	55	42	9	100	20	58	58	35	0	.	7	100	383	15
	>100 cm	14	71	0	.	0	.	21	58	0	.	7	100	0	.	7	100	59	35	7	100	0	.	0	.	0	.	0	.	21	58	0	.	0	.	7	100	37	100	82	38	0	.	7	100	14	71	0	.	0	.	285	21
	total	13782	11	2654	28	5012	22	79288	5	7664	16	20340	11	900	44	7751	17	80350	5	13267	12	24083	10	13165	11	4426	28	10545	16	26768	10	5925	19	12506	12	13475	13	10887	14	64930	5	11212	16	43009	7	55180	6	2237	29	19083	9	548438	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. 1319532. Birmensdorf, Swiss Federal Research Institute WSL

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