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 NFI1-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	.	2614	22	0	.	0	.	0	.	295	89	10136	14	0	.	0	.	0	.	578	95	971	53	499	52	0	.	0	.	18	100	0	.	1871	25	546	61	200	56	7040	14	0	.	0	.	24767	8
	21-30 cm	0	.	0	.	0	.	1679	21	0	.	0	.	0	.	121	100	5931	12	0	.	22	100	0	.	265	85	588	57	334	60	0	.	0	.	18	100	0	.	1521	25	365	47	124	63	3810	15	0	.	0	.	14778	8
	31-40 cm	0	.	0	.	0	.	1408	21	0	.	0	.	0	.	38	68	4186	10	0	.	9	100	0	.	102	94	470	64	204	57	0	.	0	.	22	74	0	.	833	23	178	50	122	59	2532	15	0	.	0	.	10105	7
	41-50 cm	0	.	0	.	0	.	928	20	0	.	0	.	0	.	42	63	2765	11	0	.	0	.	0	.	39	81	180	64	80	54	0	.	0	.	44	100	0	.	535	23	100	56	133	46	1655	15	0	.	0	.	6503	7
	51-60 cm	0	.	0	.	0	.	334	23	0	.	0	.	0	.	42	62	1566	11	0	.	0	.	0	.	12	100	17	72	64	53	0	.	0	.	22	74	0	.	379	24	51	66	43	68	918	15	0	.	0	.	3448	7
	61-70 cm	0	.	0	.	0	.	134	29	0	.	0	.	0	.	21	74	568	16	0	.	0	.	0	.	18	100	20	100	14	71	0	.	0	.	29	79	0	.	113	29	42	62	17	72	489	19	0	.	0	.	1466	10
	71-80 cm	0	.	0	.	0	.	75	35	0	.	0	.	0	.	0	.	255	19	0	.	0	.	0	.	6	100	0	.	22	75	0	.	0	.	7	100	0	.	57	35	0	.	37	52	195	28	0	.	0	.	655	13
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	71	130	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	7	100	0	.	86	37	0	.	0	.	251	22
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	35	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	36	45	0	.	0	.	88	31
	>100 cm	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	29	50	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	50	38
	total	0	.	0	.	0	.	7179	18	0	.	0	.	0	.	579	80	25603	9	0	.	31	100	0	.	1020	87	2245	51	1224	49	0	.	0	.	169	76	0	.	5317	19	1290	46	685	47	16769	12	0	.	0	.	62110	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	464	42	506	39	1335	30	20302	8	1334	33	5256	15	0	.	2179	23	16502	9	4528	16	7856	16	4051	15	1135	30	3541	23	6867	15	514	65	3631	22	3772	19	679	35	12625	13	3201	23	11940	11	13064	11	546	39	3338	22	129164	3
	21-30 cm	411	40	382	37	1214	26	12017	8	572	31	3494	16	0	.	1338	23	9894	9	2045	18	3708	14	2544	15	1165	35	1981	22	4349	14	295	44	1882	20	2532	20	562	35	6638	12	2029	22	6252	12	7570	12	431	38	1758	21	75061	3
	31-40 cm	143	42	605	29	981	25	7298	8	423	32	2348	16	0	.	767	24	5694	9	1695	18	2122	14	1489	18	730	33	1169	19	3160	13	285	37	1372	22	1500	19	418	48	3294	15	1100	24	4172	11	4019	12	273	37	1214	19	46272	3
	41-50 cm	167	37	419	29	475	26	4761	8	202	39	1452	15	0	.	624	22	3558	8	1131	18	1204	14	1075	14	329	33	703	20	1966	13	174	39	727	19	1032	18	205	33	1654	13	626	28	2667	10	2094	11	151	41	848	17	28244	3
	51-60 cm	169	46	255	32	400	29	3087	8	118	38	922	16	0	.	382	26	2198	10	505	19	933	15	667	16	98	39	418	24	1218	13	56	47	372	23	742	17	120	41	968	24	312	25	1451	11	1279	13	80	38	520	20	17270	3
	61-70 cm	27	71	106	36	98	40	1244	11	28	61	279	20	0	.	150	34	939	12	211	25	504	20	260	23	73	46	125	34	683	17	28	61	81	35	218	28	64	42	385	18	138	33	816	14	423	19	14	100	223	27	7117	4
	71-80 cm	27	50	22	58	36	45	524	14	21	74	167	26	0	.	106	41	350	17	101	36	161	25	134	24	13	71	111	38	310	21	0	.	21	74	122	33	14	71	202	26	64	46	233	20	224	23	14	71	130	31	3106	6
	81-90 cm	0	.	7	100	0	.	190	22	0	.	55	42	0	.	14	71	163	25	15	71	28	50	53	42	0	.	21	77	113	29	0	.	7	100	28	59	0	.	70	33	7	100	52	38	50	38	0	.	38	45	909	10
	91-100 cm	7	100	0	.	0	.	67	34	0	.	0	.	0	.	35	45	42	41	7	100	0	.	8	100	0	.	0	.	26	60	0	.	0	.	14	71	8	100	29	58	0	.	14	71	37	45	0	.	7	100	300	16
	>100 cm	0	.	0	.	0	.	14	71	0	.	0	.	0	.	7	100	16	71	14	71	7	100	15	71	0	.	0	.	7	100	0	.	0	.	7	100	0	.	25	71	0	.	7	100	7	100	0	.	0	.	127	26
	total	1416	33	2302	29	4540	22	49505	6	2698	28	13974	13	0	.	5602	19	39356	7	10251	14	16522	12	10296	12	3544	28	8069	18	18698	11	1351	37	8093	17	9967	15	2070	30	25889	11	7477	20	27603	9	28766	10	1509	34	8074	16	307571	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	5690	15	0	.	0	.	7834	14	2458	25	1823	31	261	52	0	.	1902	36	1256	27	2536	22	1427	40	0	.	347	95	1380	40	1506	27	2244	28	421	92	3188	19	15568	11	210	51	6131	19	3379	25	42	100	4140	18	63744	5
	21-30 cm	2785	15	0	.	0	.	3334	14	1107	26	1140	25	138	61	0	.	1163	29	775	27	1452	23	416	35	0	.	174	72	997	39	859	29	1238	24	33	71	1873	20	7978	11	362	57	2544	18	1314	26	42	100	2567	17	32289	5
	31-40 cm	1326	17	0	.	0	.	2317	13	707	24	686	27	110	60	0	.	580	36	435	34	865	23	112	47	0	.	76	100	445	35	606	30	655	25	95	54	948	21	2783	12	229	60	1319	17	432	28	58	71	1244	16	16027	5
	41-50 cm	1045	15	0	.	0	.	1375	14	362	27	353	25	64	57	0	.	217	31	379	27	615	22	125	38	0	.	0	.	151	35	423	25	459	24	61	60	588	20	1220	13	120	54	866	18	150	32	40	85	883	17	9497	5
	51-60 cm	600	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	205	27	40	61	323	22	468	19	20	100	401	20	75	56	15	71	523	16	5298	5
	61-70 cm	268	20	0	.	0	.	439	17	70	39	67	37	7	100	0	.	43	41	143	32	112	33	21	100	0	.	0	.	9	100	100	33	88	41	14	71	163	27	154	23	20	100	146	30	7	100	13	100	249	21	2134	7
	71-80 cm	165	24	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	54	47	6	100	47	55	127	29	0	.	35	45	0	.	0	.	60	33	880	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	.	0	.	7	100	0	.	7	100	106	29
	>100 cm	0	.	0	.	0	.	14	71	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	44	57	0	.	0	.	0	.	0	.	0	.	82	40
	total	11944	12	0	.	0	.	16506	10	4975	20	4346	23	610	52	0	.	4026	29	3246	22	6100	18	2190	33	0	.	597	77	3135	34	3699	23	4957	21	671	64	7215	16	28433	9	979	54	11457	15	5364	23	209	77	9701	13	130359	4
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	6154	14	506	39	1335	30	30750	6	3792	20	7079	14	261	52	2473	23	28540	7	5784	14	10392	13	5478	15	1713	38	4859	21	8746	14	2019	26	5875	17	4211	20	3867	17	30063	7	3957	21	18270	9	23483	8	588	36	7478	14	217675	2
	21-30 cm	3196	14	382	37	1214	26	17030	7	1679	20	4634	13	138	61	1460	23	16988	7	2820	15	5182	12	2959	14	1430	32	2742	20	5680	13	1154	24	3120	15	2584	20	2436	17	16137	7	2756	19	8920	10	12693	9	472	36	4325	13	122129	2
	31-40 cm	1469	16	605	29	981	25	11022	6	1130	19	3035	14	110	60	805	23	10460	6	2130	16	2996	12	1601	17	832	31	1715	22	3809	12	891	23	2027	17	1617	18	1366	21	6910	9	1506	20	5613	9	6983	8	331	33	2458	12	72404	2
	41-50 cm	1212	14	419	29	475	26	7064	6	564	22	1804	13	64	57	666	21	6540	6	1510	15	1819	12	1200	13	368	31	883	20	2198	12	597	21	1185	15	1138	17	794	17	3409	8	847	23	3666	9	3899	9	191	37	1731	11	44244	2
	51-60 cm	769	15	255	32	400	29	4412	6	338	22	1157	14	22	100	424	24	3872	7	749	16	1390	13	744	15	110	36	435	23	1401	12	210	24	578	18	804	16	443	19	1816	14	383	22	1895	9	2272	9	95	34	1042	12	26016	2
	61-70 cm	295	19	106	36	98	40	1817	9	98	33	346	17	7	100	171	31	1551	9	354	19	616	17	281	23	91	42	145	32	706	17	127	29	169	27	262	25	227	23	652	12	200	28	978	13	919	13	28	71	472	17	10717	3
	71-80 cm	191	21	22	58	36	45	761	11	64	39	195	23	7	100	106	41	612	12	115	33	223	21	141	23	19	58	111	38	339	20	51	42	75	39	135	31	60	45	385	17	64	46	305	17	419	18	14	71	190	23	4641	5
	81-90 cm	53	39	7	100	0	.	216	20	7	100	62	39	0	.	27	50	293	20	15	71	28	50	60	39	0	.	21	77	140	26	0	.	14	71	35	51	55	43	130	27	32	51	67	34	136	27	0	.	65	34	1462	8
	91-100 cm	20	58	0	.	0	.	81	30	0	.	0	.	0	.	42	41	85	31	7	100	0	.	8	100	0	.	0	.	26	60	0	.	7	100	14	71	21	58	67	42	0	.	23	58	80	30	0	.	14	71	494	13
	>100 cm	0	.	0	.	0	.	35	45	0	.	7	100	0	.	7	100	44	41	14	71	7	100	15	71	0	.	0	.	14	71	0	.	0	.	7	100	16	100	69	45	0	.	7	100	14	71	0	.	0	.	258	19
	total	13360	11	2302	29	4540	22	73189	5	7672	16	18320	11	610	52	6182	19	68984	6	13497	12	22652	10	12487	11	4564	29	10910	17	23058	10	5050	19	13050	13	10807	14	9285	14	59639	6	9746	17	39745	8	50898	7	1719	31	17775	10	500040	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. 1318902. Birmensdorf, Swiss Federal Research Institute WSL

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