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 NFI1-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	.	2585	23	0	.	0	.	0	.	243	86	9630	13	0	.	0	.	0	.	482	100	823	53	578	54	0	.	0	.	18	100	0	.	1739	26	387	72	150	56	6393	14	0	.	0	.	23028	8
	21-30 cm	0	.	0	.	0	.	1756	21	0	.	0	.	0	.	87	100	5952	12	0	.	22	100	0	.	286	86	518	55	293	62	0	.	0	.	18	100	0	.	1527	24	274	46	85	72	3914	15	0	.	0	.	14732	8
	31-40 cm	0	.	0	.	0	.	1435	21	0	.	0	.	0	.	35	60	4129	11	0	.	22	100	0	.	94	94	259	54	197	58	0	.	0	.	33	81	0	.	756	24	140	58	169	52	2471	15	0	.	0	.	9740	7
	41-50 cm	0	.	0	.	0	.	938	20	0	.	0	.	0	.	35	66	2623	10	0	.	0	.	0	.	45	83	116	49	102	49	0	.	0	.	44	100	0	.	450	24	58	69	120	51	1520	15	0	.	0	.	6051	7
	51-60 cm	0	.	0	.	0	.	310	23	0	.	0	.	0	.	56	64	1443	11	0	.	0	.	0	.	24	100	17	72	37	52	0	.	0	.	22	74	0	.	330	26	64	53	37	63	956	16	0	.	0	.	3295	7
	61-70 cm	0	.	0	.	0	.	94	38	0	.	0	.	0	.	7	100	513	16	0	.	0	.	0	.	6	100	10	100	21	74	0	.	0	.	22	74	0	.	100	31	21	58	21	75	352	22	0	.	0	.	1167	11
	71-80 cm	0	.	0	.	0	.	36	45	0	.	0	.	0	.	7	100	220	24	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	15	100	0	.	49	38	7	100	23	58	189	24	0	.	0	.	560	14
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	123	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	9	100	65	37	0	.	0	.	210	21
	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	.	29	50	0	.	0	.	60	35
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	50	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	43	41
	total	0	.	0	.	0	.	7160	19	0	.	0	.	0	.	483	75	24678	9	0	.	44	100	0	.	937	88	1743	49	1251	50	0	.	0	.	173	76	0	.	4958	19	951	50	614	49	15894	12	0	.	0	.	58886	6
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	558	41	1490	30	20828	8	1032	31	5392	15	0	.	2523	23	17190	9	4243	17	8155	15	4950	15	1092	31	3190	21	6972	15	448	45	3246	21	4151	19	833	31	12095	12	3689	22	12583	11	13400	11	549	42	2966	23	132040	3
	21-30 cm	460	37	517	38	1325	25	12371	8	570	32	3950	16	0	.	1919	23	9635	9	1954	18	3990	15	2379	16	1000	31	2125	22	4567	14	417	45	1793	20	2483	22	765	37	6571	11	1954	22	6579	13	7790	12	484	39	2001	22	77601	3
	31-40 cm	172	39	542	30	1045	24	7970	8	329	35	2330	16	0	.	687	26	6040	9	1601	18	2242	16	1724	16	720	31	996	21	3295	13	334	37	1382	22	1626	17	553	37	2572	13	1068	24	4304	10	3767	12	175	37	1393	17	46867	3
	41-50 cm	194	33	478	28	618	24	5118	7	265	39	1443	16	0	.	713	21	3603	9	1119	17	1588	14	1166	14	244	33	659	21	1939	13	153	47	757	18	1040	17	197	32	1371	13	625	27	2708	10	1917	12	104	53	950	16	28969	3
	51-60 cm	156	42	236	32	367	30	3270	8	118	39	843	17	0	.	391	25	2080	10	457	18	888	16	594	17	104	46	352	26	1175	13	56	61	355	24	664	18	130	42	704	14	265	26	1543	11	1144	13	88	44	476	20	16457	3
	61-70 cm	34	53	90	37	148	36	1094	11	35	53	356	21	0	.	125	34	770	12	151	29	453	19	280	20	40	46	130	32	523	18	7	100	102	35	260	25	34	52	370	20	114	31	625	16	346	22	7	100	217	27	6310	4
	71-80 cm	0	.	22	74	19	73	457	15	7	100	154	34	0	.	92	44	302	16	78	35	159	28	81	34	0	.	91	35	340	20	0	.	22	73	97	37	13	71	179	23	70	43	187	24	170	25	7	100	47	38	2594	6
	81-90 cm	7	100	0	.	0	.	212	22	0	.	20	58	0	.	27	50	154	22	14	71	0	.	15	71	0	.	0	.	77	59	0	.	7	100	28	50	7	100	49	41	7	100	65	33	75	45	0	.	21	58	785	12
	91-100 cm	7	100	0	.	0	.	23	58	0	.	0	.	0	.	22	58	13	71	7	100	7	100	15	71	0	.	0	.	13	71	0	.	0	.	14	71	0	.	25	59	9	100	7	100	22	58	0	.	7	100	190	20
	>100 cm	7	100	0	.	0	.	14	71	0	.	0	.	0	.	7	100	16	71	7	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	0	.	25	71	0	.	7	100	7	100	0	.	0	.	105	29
	total	1498	33	2444	29	5012	22	51358	6	2356	28	14488	13	0	.	6504	19	39805	7	9631	15	17483	12	11205	12	3200	28	7542	18	18909	11	1415	34	7665	16	10369	15	2533	30	23962	9	7801	19	28609	9	28638	10	1413	35	8077	16	311918	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	5466	15	0	.	0	.	6884	13	1881	24	1915	30	175	46	0	.	2009	35	1301	25	2631	24	1206	34	0	.	503	87	2065	35	1860	29	1853	24	315	95	3962	21	16373	11	334	53	5381	19	3200	26	116	77	4225	16	63655	5
	21-30 cm	2718	18	0	.	0	.	3826	13	1195	25	1167	23	293	51	0	.	1080	33	809	25	1207	21	262	40	0	.	69	79	1202	31	1290	30	1164	28	82	71	2083	21	7394	10	421	59	3177	16	1209	27	75	71	2765	16	33490	4
	31-40 cm	1637	15	0	.	0	.	2055	13	774	25	874	24	186	56	0	.	476	35	656	30	943	25	164	44	0	.	52	100	425	33	637	25	894	22	146	57	1115	20	2575	13	174	57	1353	16	375	30	42	100	1766	16	17317	4
	41-50 cm	1110	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	490	25	61	60	520	22	967	15	59	57	903	17	166	32	68	72	1022	15	10178	4
	51-60 cm	694	16	0	.	0	.	1013	13	206	27	265	24	21	100	0	.	88	35	261	27	376	31	98	47	0	.	0	.	166	38	139	32	251	31	33	64	387	21	344	22	23	100	405	23	60	65	7	100	727	16	5564	5
	61-70 cm	324	18	0	.	0	.	453	17	94	40	61	37	7	100	0	.	23	58	77	35	174	27	21	100	0	.	0	.	27	75	108	38	88	35	13	71	104	35	123	24	8	100	111	35	7	100	20	100	171	26	2014	7
	71-80 cm	143	25	0	.	0	.	131	28	37	45	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	0	.	0	.	33	45	739	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	.	15	71	8	100	0	.	43	41	288	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	.	7	100	0	.	0	.	0	.	90	40
	>100 cm	8	100	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	49	0	.	0	.	0	.	0	.	0	.	72	39
	total	12147	12	0	.	0	.	16129	10	4571	20	4742	21	772	48	0	.	3909	29	3636	21	5997	18	1953	30	0	.	625	77	4138	29	4510	22	4774	20	651	58	8299	16	28019	9	1034	54	11396	14	5026	23	327	73	10754	12	133407	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	5929	14	558	41	1490	30	30297	6	2913	19	7307	14	175	46	2766	22	28830	7	5544	14	10786	12	6155	14	1574	37	4516	20	9615	13	2309	25	5099	16	4485	19	4796	18	30207	7	4411	20	18115	9	22992	8	665	37	7191	13	218723	2
	21-30 cm	3178	16	517	38	1325	25	17953	6	1765	20	5117	14	293	51	2005	23	16666	7	2764	14	5219	12	2642	15	1287	31	2713	20	6063	12	1707	25	2958	16	2584	21	2848	18	15492	7	2648	19	9841	10	12913	9	558	35	4766	13	125822	2
	31-40 cm	1808	14	542	30	1045	24	11460	6	1103	20	3204	14	186	56	721	25	10645	6	2257	15	3207	13	1888	15	814	29	1307	20	3917	11	971	21	2276	15	1805	16	1668	18	5903	8	1382	20	5826	9	6613	9	217	36	3160	11	73924	2
	41-50 cm	1304	13	478	28	618	24	7753	6	636	23	1875	13	91	57	747	20	6446	6	1629	13	2247	12	1354	13	289	31	775	19	2232	12	606	23	1247	14	1145	16	717	18	2788	9	742	24	3732	9	3603	9	172	43	1973	11	45198	2
	51-60 cm	850	15	236	32	367	30	4592	7	324	22	1109	14	21	100	447	23	3611	7	717	14	1264	14	692	16	128	41	369	25	1378	12	195	28	606	19	718	17	518	19	1378	10	352	22	1985	10	2159	10	95	41	1203	12	25316	2
	61-70 cm	357	17	90	37	148	36	1641	9	129	32	417	19	7	100	132	33	1306	9	229	22	627	16	302	19	46	42	140	31	571	17	115	36	190	25	295	23	137	29	593	14	143	27	757	15	705	15	27	78	388	18	9491	3
	71-80 cm	143	25	22	74	19	73	625	12	44	41	175	31	0	.	99	41	528	14	92	32	166	27	95	31	0	.	91	35	408	18	23	58	49	43	111	35	77	39	336	17	92	36	254	20	358	17	7	100	80	29	3893	5
	81-90 cm	54	39	0	.	0	.	239	20	14	71	20	58	0	.	34	45	284	17	21	58	0	.	15	71	0	.	0	.	86	54	0	.	14	71	28	50	54	46	114	28	7	100	89	29	148	28	0	.	64	33	1284	9
	91-100 cm	7	100	0	.	0	.	67	44	0	.	0	.	0	.	29	50	31	51	7	100	7	100	15	71	0	.	0	.	13	71	0	.	0	.	14	71	16	100	55	42	9	100	14	71	51	38	0	.	7	100	340	16
	>100 cm	14	71	0	.	0	.	21	58	0	.	7	100	0	.	7	100	45	41	7	100	0	.	0	.	0	.	0	.	14	71	0	.	0	.	7	100	0	.	76	40	0	.	7	100	14	71	0	.	0	.	220	20
	total	13645	11	2444	29	5012	22	74647	5	6927	16	19230	11	772	48	6987	18	68391	5	13267	12	23523	10	13157	11	4137	29	9910	17	24298	10	5925	19	12439	13	11192	14	10832	14	56940	5	9785	17	40620	8	49558	7	1740	32	18831	9	504211	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. 1319516. Birmensdorf, Swiss Federal Research Institute WSL

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