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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NFI5
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 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
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	.	2877	21	0	.	271	100	0	.	362	73	14642	11	0	.	0	.	0	.	578	95	1206	45	569	47	0	.	0	.	267	87	0	.	4288	19	627	54	361	44	7920	13	0	.	0	.	33968	7
	21-30 cm	0	.	0	.	0	.	1903	19	0	.	180	100	0	.	158	80	8166	10	0	.	22	100	0	.	265	85	678	51	351	57	0	.	0	.	18	100	0	.	2346	20	402	43	231	48	4302	14	0	.	0	.	19023	7
	31-40 cm	0	.	0	.	0	.	1477	21	0	.	45	100	0	.	44	61	5195	9	0	.	9	100	0	.	116	84	568	55	257	50	0	.	0	.	29	61	0	.	1132	20	178	50	183	49	2813	14	0	.	0	.	12047	7
	41-50 cm	0	.	0	.	0	.	1033	19	0	.	15	100	0	.	86	53	3202	10	0	.	0	.	0	.	60	63	180	64	116	49	0	.	0	.	44	100	0	.	579	21	108	52	155	41	1821	14	0	.	0	.	7399	6
	51-60 cm	0	.	0	.	0	.	406	22	0	.	0	.	0	.	48	56	1759	10	0	.	0	.	0	.	12	100	17	72	64	53	0	.	0	.	22	74	0	.	442	22	51	66	57	57	978	15	0	.	0	.	3856	7
	61-70 cm	0	.	0	.	0	.	158	29	0	.	0	.	0	.	21	74	611	15	0	.	0	.	0	.	25	77	27	78	14	71	0	.	0	.	29	79	0	.	143	25	42	62	17	72	510	18	0	.	0	.	1597	9
	71-80 cm	0	.	0	.	0	.	99	31	0	.	0	.	0	.	6	100	255	19	0	.	0	.	0	.	13	71	0	.	22	75	0	.	0	.	7	100	0	.	57	35	0	.	45	47	220	26	0	.	0	.	724	12
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	177	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	36	55	7	100	0	.	100	35	0	.	0	.	349	18
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	35	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	9	100	44	41	0	.	0	.	102	28
	>100 cm	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	36	45	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	57	35
	total	0	.	0	.	0	.	7959	17	0	.	511	100	0	.	753	64	34079	8	0	.	31	100	0	.	1069	83	2676	45	1400	44	0	.	0	.	425	62	0	.	9029	15	1414	42	1059	38	18714	11	0	.	0	.	79120	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	464	42	577	36	1335	30	22223	8	1384	32	5860	15	0	.	2439	22	19198	9	4528	16	8028	15	4051	15	1258	28	3698	23	7689	14	514	65	3631	22	4767	17	679	35	14639	11	3959	22	12963	11	15581	10	592	36	3521	21	143578	3
	21-30 cm	411	40	436	34	1214	26	12947	8	673	30	3734	15	0	.	1425	22	11588	8	2045	18	3796	14	2544	15	1279	33	2016	21	4825	13	295	44	1882	20	3144	18	562	35	7800	11	2330	20	6862	12	8839	11	476	36	1813	21	82936	3
	31-40 cm	143	42	638	28	981	25	7720	8	448	30	2600	15	0	.	788	23	6210	9	1695	18	2197	14	1489	18	730	33	1247	19	3424	12	285	37	1372	22	1593	18	418	48	3727	14	1195	23	4316	10	4391	11	330	34	1255	18	49193	3
	41-50 cm	167	37	427	28	475	26	5091	8	230	36	1521	14	0	.	638	22	3899	8	1131	18	1286	14	1075	14	329	33	710	19	2195	12	174	39	727	19	1139	16	205	33	1819	12	640	28	2781	10	2246	11	224	36	855	17	29985	3
	51-60 cm	169	46	285	31	400	29	3229	8	128	36	936	16	0	.	396	25	2325	9	505	19	967	15	667	16	98	39	425	24	1352	12	56	47	372	23	799	16	120	41	1018	23	319	24	1511	11	1322	13	134	37	535	20	18069	3
	61-70 cm	27	71	121	34	98	40	1313	10	38	52	279	20	0	.	150	34	1019	11	211	25	513	20	268	23	73	46	125	34	781	16	28	61	81	35	224	27	64	42	385	18	138	33	856	14	468	18	14	100	230	26	7505	4
	71-80 cm	27	50	22	58	36	45	546	13	31	60	167	26	0	.	106	41	390	16	101	36	168	24	134	24	13	71	118	36	338	20	0	.	21	74	128	32	14	71	215	25	64	46	248	20	231	23	14	71	130	31	3262	6
	81-90 cm	0	.	7	100	0	.	190	22	0	.	55	42	0	.	14	71	163	25	15	71	37	45	53	42	0	.	21	77	125	28	0	.	7	100	28	59	0	.	84	30	7	100	60	36	60	36	11	100	38	45	973	9
	91-100 cm	7	100	0	.	0	.	76	32	0	.	9	100	0	.	35	45	59	36	7	100	0	.	8	100	0	.	0	.	26	60	0	.	0	.	14	71	8	100	29	58	0	.	21	58	37	45	0	.	7	100	343	15
	>100 cm	0	.	0	.	0	.	14	71	0	.	0	.	0	.	7	100	23	58	14	71	7	100	15	71	0	.	0	.	14	71	0	.	0	.	7	100	0	.	25	71	0	.	7	100	7	100	0	.	0	.	142	24
	total	1416	33	2513	28	4540	22	53350	6	2934	26	15162	13	0	.	5998	18	44874	7	10251	14	16999	12	10304	12	3781	27	8361	17	20770	11	1351	37	8093	17	11844	14	2070	30	29742	10	8652	19	29623	9	33181	9	1794	31	8383	16	335987	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	5783	15	0	.	0	.	7884	13	2502	24	1823	31	433	43	0	.	1902	36	1256	27	2536	22	1427	40	0	.	347	95	1380	40	1506	27	2279	28	421	92	3188	19	16647	10	210	51	6215	18	3595	24	42	100	4212	18	65590	5
	21-30 cm	2808	15	0	.	0	.	3384	14	1125	26	1140	25	138	61	0	.	1163	29	775	27	1452	23	416	35	0	.	174	72	997	39	859	29	1255	24	33	71	1873	20	8342	10	436	50	2544	18	1350	25	42	100	2600	17	32904	4
	31-40 cm	1336	17	0	.	0	.	2334	13	733	24	686	27	110	60	0	.	580	36	435	34	865	23	112	47	0	.	76	100	445	35	606	30	662	25	95	54	955	21	2904	12	236	58	1319	17	517	26	58	71	1244	16	16309	5
	41-50 cm	1055	15	0	.	0	.	1382	14	362	27	353	25	64	57	0	.	217	31	379	27	615	22	125	38	0	.	0	.	151	35	423	25	479	23	61	60	595	20	1266	13	144	48	873	18	159	31	40	85	883	17	9626	5
	51-60 cm	621	15	0	.	0	.	990	13	220	27	235	28	22	100	0	.	108	34	244	31	457	24	76	43	0	.	0	.	119	46	155	29	212	26	40	61	323	22	487	18	36	71	424	20	92	49	15	71	523	16	5400	5
	61-70 cm	268	20	0	.	0	.	439	17	77	37	67	37	7	100	0	.	43	41	143	32	112	33	21	100	0	.	0	.	9	100	100	33	95	39	14	71	163	27	154	23	36	71	152	29	16	71	13	100	249	21	2179	7
	71-80 cm	175	23	0	.	0	.	162	24	43	47	27	50	7	100	0	.	6	100	14	71	62	40	7	100	0	.	0	.	8	100	51	42	61	43	6	100	47	55	127	29	0	.	35	45	8	100	0	.	60	33	906	10
	81-90 cm	53	39	0	.	0	.	27	50	7	100	8	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	27	58	0	.	7	100	0	.	55	43	53	47	17	72	15	71	0	.	0	.	27	50	302	17
	91-100 cm	13	71	0	.	0	.	14	71	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	13	71	38	60	0	.	7	100	7	100	0	.	7	100	113	28
	>100 cm	0	.	0	.	0	.	14	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	71	50	51	0	.	7	100	0	.	0	.	0	.	108	33
	total	12114	11	0	.	0	.	16630	10	5069	20	4346	23	782	44	0	.	4026	29	3246	22	6100	18	2190	33	0	.	597	77	3135	34	3699	23	5058	21	671	64	7241	16	30068	9	1116	48	11590	15	5744	21	209	77	9805	13	133437	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	6247	14	577	36	1335	30	32985	6	3886	19	7954	13	433	43	2801	21	35742	6	5784	14	10564	13	5478	15	1836	35	5251	20	9639	13	2019	26	5910	17	5456	17	3867	17	35574	6	4796	20	19539	9	27095	7	633	35	7732	13	243136	2
	21-30 cm	3219	14	436	34	1214	26	18234	6	1798	19	5054	13	138	61	1582	22	20917	6	2820	15	5270	12	2959	14	1544	31	2868	20	6173	12	1154	24	3137	15	3196	18	2436	17	18487	6	3168	17	9637	10	14491	8	518	34	4413	13	134863	2
	31-40 cm	1480	16	638	28	981	25	11530	6	1182	18	3332	13	110	60	833	22	11985	6	2130	16	3070	12	1601	17	846	31	1891	21	4126	11	891	23	2034	17	1717	17	1373	20	7764	8	1609	20	5817	9	7722	8	388	30	2499	12	77548	2
	41-50 cm	1223	14	427	28	475	26	7506	6	592	21	1889	12	64	57	724	20	7318	6	1510	15	1902	12	1200	13	389	30	890	20	2462	11	597	21	1206	14	1245	16	800	17	3664	8	892	22	3809	8	4225	8	264	33	1739	11	47010	2
	51-60 cm	790	15	285	31	400	29	4625	6	349	21	1171	14	22	100	444	23	4192	7	749	16	1425	13	744	15	110	36	442	23	1535	12	210	24	585	17	861	16	443	19	1947	13	406	22	1991	9	2392	9	149	34	1057	12	27325	2
	61-70 cm	295	19	121	34	98	40	1911	8	115	30	346	17	7	100	171	31	1673	9	354	19	625	17	289	22	98	39	152	31	804	16	127	29	176	26	268	25	227	23	681	12	216	27	1025	13	993	12	28	71	480	16	11281	3
	71-80 cm	202	21	22	58	36	45	807	11	75	37	195	23	7	100	112	39	651	12	115	33	230	21	141	23	26	50	118	36	367	19	51	42	82	37	142	30	60	45	399	17	64	46	327	17	460	17	14	71	190	23	4892	4
	81-90 cm	53	39	7	100	0	.	216	20	7	100	62	39	0	.	35	45	339	18	15	71	37	45	60	39	0	.	21	77	152	25	0	.	14	71	35	51	55	43	174	23	32	51	74	32	160	26	11	100	65	34	1624	7
	91-100 cm	20	58	0	.	0	.	90	29	0	.	9	100	0	.	42	41	102	29	7	100	0	.	8	100	0	.	0	.	26	60	0	.	7	100	14	71	21	58	74	39	0	.	37	45	87	29	0	.	14	71	558	12
	>100 cm	0	.	0	.	0	.	35	45	0	.	7	100	0	.	7	100	59	35	14	71	7	100	15	71	0	.	0	.	21	58	0	.	0	.	7	100	30	71	76	42	0	.	14	71	14	71	0	.	0	.	306	17
	total	13529	11	2513	28	4540	22	77939	5	8003	16	20020	11	782	44	6751	18	82978	5	13497	12	23130	10	12494	11	4850	28	11633	16	25305	10	5050	19	13150	13	12941	13	9311	14	68840	5	11183	16	42272	7	57640	6	2003	29	18188	10	548544	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. 1318919. Birmensdorf, Swiss Federal Research Institute WSL

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