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
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (10 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	diameter at breast height (10 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
upper subalpine	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	393	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	45	0	.	0	.	170	34	0	.	0	.	637	15
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	632	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	46	0	.	0	.	393	33	0	.	0	.	1127	16
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	949	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	67	0	.	0	.	473	28	0	.	0	.	1470	15
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	849	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	50	0	.	0	.	459	27	0	.	0	.	1380	15
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	731	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	58	0	.	0	.	235	35	0	.	0	.	1006	22
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	280	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	122	48	0	.	0	.	421	27
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	248	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	71	0	.	0	.	177	36	0	.	0	.	473	25
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	220	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	100	0	.	0	.	67	58	0	.	0	.	348	37
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	61	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	100	0	.	0	.	89	75
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	87	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	0	.	125	59
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4449	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	464	30	0	.	0	.	2162	20	0	.	0	.	7075	12
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	.	205	28	0	.	31	.	0	.	24	74	916	17	0	.	0	.	0	.	56	100	110	51	54	55	0	.	0	.	13	72	0	.	211	25	61	62	26	45	513	19	0	.	0	.	2220	10
	21-30 cm	0	.	0	.	0	.	638	22	0	.	47	.	0	.	44	77	1847	14	0	.	7	.	0	.	93	84	171	55	47	56	0	.	0	.	7	100	0	.	493	26	92	45	43	61	1037	18	0	.	0	.	4567	9
	31-40 cm	0	.	0	.	0	.	1166	21	0	.	51	.	0	.	-13	233	2873	13	0	.	20	.	0	.	83	82	158	47	252	61	0	.	0	.	33	76	0	.	550	25	146	60	151	46	1631	18	0	.	0	.	7101	8
	41-50 cm	0	.	0	.	0	.	1421	22	0	.	0	.	0	.	103	58	3723	13	0	.	0	.	0	.	59	60	126	52	183	57	0	.	0	.	74	100	0	.	517	25	93	70	134	42	1720	19	0	.	0	.	8153	8
	51-60 cm	0	.	0	.	0	.	840	24	0	.	0	.	0	.	124	66	3106	13	0	.	0	.	0	.	86	78	42	78	91	53	0	.	0	.	52	75	0	.	797	25	136	52	128	46	2061	18	0	.	0	.	7463	8
	61-70 cm	0	.	0	.	0	.	405	40	0	.	0	.	0	.	52	71	1554	19	0	.	0	.	0	.	19	100	51	71	26	100	0	.	0	.	88	77	0	.	302	36	45	71	55	80	1006	27	0	.	0	.	3605	12
	71-80 cm	0	.	0	.	0	.	337	41	0	.	0	.	0	.	46	100	832	30	0	.	0	.	0	.	25	100	0	.	52	71	0	.	0	.	92	100	0	.	297	38	23	100	113	50	758	30	0	.	0	.	2575	16
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	71	490	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	0	.	119	72	295	42	0	.	0	.	988	22
	91-100 cm	0	.	0	.	0	.	34	100	0	.	0	.	0	.	36	100	92	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	100	0	.	0	.	226	54
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	58	0	.	0	.	0	.	0	.	0	.	36	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	236	51
	total	0	.	0	.	0	.	5047	19	0	.	129	.	0	.	469	53	15633	10	0	.	27	.	0	.	420	62	658	41	742	46	0	.	0	.	358	69	0	.	3198	18	596	48	770	35	9085	14	0	.	0	.	37132	6
high-montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	479	20	0	.	85	39	0	.	27	82	1841	11	0	.	105	46	0	.	6	80	113	46	59	42	0	.	0	.	112	45	0	.	424	23	176	33	201	33	1556	14	0	.	0	.	5184	6
	21-30 cm	0	.	0	.	0	.	815	21	0	.	302	51	0	.	115	72	2972	11	0	.	153	43	0	.	54	84	418	35	102	46	0	.	0	.	89	70	0	.	727	19	410	33	368	61	2690	14	0	.	0	.	9216	7
	31-40 cm	0	.	0	.	0	.	1377	20	0	.	430	36	0	.	7	100	4978	11	0	.	176	46	0	.	43	100	363	37	194	51	0	.	0	.	244	45	0	.	1052	22	611	36	556	33	2756	13	0	.	0	.	12786	6
	41-50 cm	0	.	0	.	0	.	1748	20	0	.	466	36	0	.	164	64	5506	10	0	.	182	50	0	.	9	100	350	36	327	45	0	.	0	.	368	42	0	.	1061	26	754	35	769	35	2797	14	0	.	0	.	14502	6
	51-60 cm	0	.	0	.	0	.	1938	23	0	.	358	40	0	.	103	63	5114	12	0	.	133	57	0	.	0	.	279	46	273	59	0	.	0	.	244	47	0	.	905	24	354	36	739	30	2472	14	0	.	0	.	12910	7
	61-70 cm	0	.	0	.	0	.	927	26	0	.	158	52	0	.	147	84	2400	14	0	.	23	100	0	.	0	.	145	50	124	47	0	.	0	.	149	59	0	.	876	30	193	52	635	40	1183	25	0	.	0	.	6961	9
	71-80 cm	0	.	0	.	0	.	547	30	0	.	78	79	0	.	286	62	1355	20	0	.	0	.	0	.	0	.	228	54	105	78	0	.	0	.	38	100	0	.	562	30	103	58	99	60	806	28	0	.	0	.	4207	12
	81-90 cm	0	.	0	.	0	.	493	44	0	.	39	100	0	.	34	100	860	26	0	.	0	.	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	214	51	0	.	85	73	346	46	0	.	0	.	2115	18
	91-100 cm	0	.	0	.	0	.	109	72	0	.	48	100	0	.	108	71	109	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	112	75	47	100	0	.	144	58	0	.	0	.	676	28
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	211	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	163	100	0	.	38	100	53	100	0	.	0	.	515	43
	total	0	.	0	.	0	.	8433	16	0	.	1964	32	0	.	1041	53	25346	8	0	.	772	39	0	.	111	72	1895	31	1228	39	0	.	0	.	1244	34	0	.	6095	16	2647	30	3492	25	14804	10	0	.	0	.	69071	5
lower and upper montane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	52	54	48	62	207	36	2267	10	164	31	650	18	0	.	197	33	433	23	551	19	804	15	630	17	158	37	210	28	840	16	80	44	408	23	547	23	167	35	1215	14	340	31	1484	13	327	30	73	40	344	32	12197	4
	21-30 cm	199	38	340	38	604	27	5183	10	355	30	1679	20	0	.	727	31	969	21	826	19	1485	16	1138	18	462	34	609	30	2134	15	300	47	745	23	1149	20	416	38	2243	16	488	34	2837	13	566	26	282	36	1097	21	26831	4
	31-40 cm	170	39	764	30	1199	25	7384	9	374	41	2010	17	0	.	888	29	1098	19	1927	18	2441	17	1833	16	732	33	788	27	3600	13	448	37	1278	22	1494	19	591	32	1278	15	419	33	3913	11	684	26	256	47	1733	18	37304	3
	41-50 cm	451	34	899	28	1464	26	8739	9	513	39	2305	18	0	.	1389	23	1354	19	2259	18	3233	15	2378	15	425	36	861	28	3971	14	334	44	1494	18	1785	18	473	32	1140	17	437	39	4199	11	665	30	365	45	2070	17	43201	3
	51-60 cm	482	39	767	32	1309	29	8509	9	533	38	1847	19	0	.	1023	26	1075	23	1456	18	2912	17	1905	18	392	45	821	32	3930	14	201	62	1190	25	1758	19	471	41	879	23	363	40	4073	13	454	37	291	42	1649	22	38290	4
	61-70 cm	160	53	395	40	728	37	4175	13	289	50	1304	26	0	.	365	34	814	24	699	29	2032	21	1309	20	191	45	455	41	2463	19	30	100	450	32	970	27	145	53	545	28	297	36	2161	18	210	39	132	75	1142	27	21461	5
	71-80 cm	0	.	149	75	130	74	2366	16	157	77	471	39	0	.	214	51	496	31	455	35	951	31	507	34	0	.	252	46	1953	22	0	.	133	71	598	42	81	71	213	43	269	54	914	27	47	71	100	77	264	41	10719	8
	81-90 cm	63	100	0	.	0	.	1158	25	0	.	123	71	0	.	139	61	193	46	99	72	42	100	118	71	0	.	0	.	567	56	0	.	52	100	210	51	56	100	128	65	46	100	492	39	64	72	0	.	161	58	3712	15
	91-100 cm	54	100	0	.	0	.	89	100	0	.	0	.	0	.	0	0	69	100	69	100	53	100	156	71	0	.	0	.	136	72	0	.	0	.	106	72	0	.	0	0	0	.	60	100	0	.	0	.	46	100	838	28
	>100 cm	67	100	0	.	0	.	194	74	0	.	0	.	0	.	0	.	0	.	67	100	0	.	0	.	0	.	0	.	62	100	0	.	0	.	43	100	0	.	117	100	0	.	0	.	0	.	0	.	0	.	551	40
	total	1698	30	3362	28	5641	22	40063	7	2384	30	10389	14	0	.	4942	21	6501	15	8409	15	13952	13	9974	12	2358	29	3995	24	19658	11	1394	35	5749	16	8662	15	2400	29	7757	12	2660	26	20133	9	3018	23	1499	35	8505	15	195103	2
submontane	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	800	21	0	.	0	.	963	15	243	27	385	34	0	.	0	.	80	46	201	27	424	29	101	35	0	.	70	83	280	37	246	32	224	29	46	97	684	22	0	.	76	52	586	19	149	48	18	77	590	19	6164	6
	21-30 cm	1417	21	0	.	0	.	2059	14	529	25	750	28	0	.	0	.	87	47	384	26	634	23	145	39	0	.	43	87	425	38	722	29	565	30	25	75	1086	22	0	.	297	55	1361	18	127	61	35	71	1327	18	12018	6
	31-40 cm	1920	15	0	.	0	.	2352	13	833	25	1051	25	0	.	0	.	94	53	793	30	1171	27	181	45	0	.	58	100	374	36	676	28	1044	21	213	64	1313	22	0	.	232	52	1362	17	47	63	44	100	1989	17	15745	5
	41-50 cm	2491	15	0	.	0	.	3656	14	709	27	944	28	0	.	0	.	162	55	1147	24	1363	24	366	36	0	.	0	.	231	45	1005	28	979	26	134	61	1273	22	0	.	246	58	1835	18	70	62	121	71	2013	15	18746	5
	51-60 cm	2306	18	0	.	0	.	3706	14	714	28	930	25	0	.	0	.	120	47	907	27	1209	31	285	59	0	.	0	.	442	40	455	33	948	34	129	60	1280	23	0	.	70	75	1507	23	21	100	18	100	2418	17	17465	6
	61-70 cm	1626	19	0	.	0	.	2261	18	532	36	249	41	0	.	0	.	0	.	331	36	816	29	101	100	0	.	0	.	121	72	538	39	483	34	81	71	520	36	0	.	19	100	509	36	50	76	91	100	926	26	9254	8
	71-80 cm	1001	25	0	.	0	.	853	30	318	43	109	71	0	.	0	.	30	100	66	71	47	100	90	71	0	.	0	.	290	49	132	58	196	50	0	.	379	44	0	.	63	72	247	42	19	100	0	.	228	45	4068	12
	81-90 cm	342	43	0	.	0	.	213	50	58	100	0	.	0	.	0	.	63	100	46	100	0	.	0	.	0	.	0	.	67	100	0	.	66	100	0	.	375	51	0	.	0	.	143	58	0	.	0	.	314	42	1687	20
	91-100 cm	0	.	0	.	0	.	283	62	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	100	0	.	0	.	124	71	0	.	0	.	0	.	575	45
	>100 cm	86	100	0	.	0	.	74	100	0	.	67	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	400	100	0	.	0	.	0	.	0	.	0	.	0	.	627	67
	total	11987	11	0	.	0	.	16420	11	3936	19	4485	20	0	.	0	.	635	40	3875	21	5663	19	1268	34	0	.	172	73	2230	29	3775	21	4505	20	629	56	7477	16	0	.	1005	46	7675	14	482	45	326	72	9804	12	86349	4
hyperinsubric and colline	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	30	54	0	.	180	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	1917	12	0	.	41	83	228	33	0	.	1	100	2410	11
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	132	49	0	.	297	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	.	2512	12	0	.	124	62	265	32	0	.	0	.	3361	10
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	206	53	0	.	216	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	.	2019	14	0	.	50	66	260	34	0	.	74	100	2841	11
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	178	65	0	.	158	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1216	16	0	.	44	71	104	64	0	.	62	75	1762	14
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	52	100	0	.	135	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	.	722	23	0	.	0	.	134	77	0	.	0	.	1065	20
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	47	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	320	28	0	.	0	.	0	.	0	.	0	.	401	25
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	209	32	0	.	0	.	0	.	0	.	0	.	209	32
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	46	0	.	0	.	30	100	0	.	0	.	164	42
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	102	62	0	.	0	.	0	.	0	.	0	.	102	62
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	417	49	0	.	0	.	0	.	0	.	0	.	417	49
	total	0	.	0	.	0	.	0	.	0	.	0	.	632	51	0	.	1033	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	.	9568	9	0	.	258	58	1021	29	0	.	137	87	12732	8
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	851	20	48	62	207	36	3914	8	407	20	1151	16	30	54	248	29	3844	8	752	15	1333	13	730	15	219	37	503	22	1233	14	326	26	633	18	718	20	865	18	3841	7	653	20	2338	10	2943	9	91	35	935	17	28812	2
	21-30 cm	1616	19	340	38	604	27	8695	7	883	19	2778	15	132	49	885	27	6804	7	1210	15	2280	12	1283	16	608	30	1241	20	2708	13	1022	25	1310	18	1270	19	1532	19	6078	7	1287	21	4734	10	5078	9	316	33	2424	13	57120	2
	31-40 cm	2089	14	764	30	1199	25	12279	6	1206	21	3541	13	206	53	881	30	10208	7	2721	15	3808	14	2014	15	858	29	1367	20	4421	11	1124	22	2323	15	1984	16	1919	18	4948	8	1409	21	6032	9	5850	8	300	43	3795	12	77247	2
	41-50 cm	2941	14	899	28	1464	26	15564	6	1222	23	3716	14	178	65	1656	20	11752	6	3407	14	4778	12	2743	14	493	32	1337	21	4712	12	1339	24	2473	15	2362	16	1746	18	4005	9	1530	23	6981	9	5816	9	486	38	4144	11	87743	2
	51-60 cm	2788	16	767	32	1309	29	14992	7	1248	22	3135	14	52	100	1250	23	10281	8	2363	15	4254	15	2191	18	477	39	1142	26	4736	13	656	29	2138	20	2182	17	1773	20	3342	11	924	23	6448	11	5377	10	309	40	4066	13	78198	2
	61-70 cm	1786	18	395	40	728	37	7769	9	821	29	1711	21	35	100	565	32	5094	9	1030	23	2871	17	1410	20	209	42	651	31	2734	18	568	37	932	24	1289	22	665	30	2061	15	555	27	3361	15	2571	16	223	60	2068	19	42102	3
	71-80 cm	1001	25	149	75	130	74	4103	12	474	38	658	32	0	.	545	39	2962	14	520	32	997	30	597	31	25	100	480	35	2400	19	132	58	329	41	728	37	460	38	1329	17	458	36	1373	20	1808	18	100	77	493	30	22251	5
	81-90 cm	406	40	0	.	0	.	1865	20	58	100	162	59	0	.	227	44	1825	17	145	58	42	100	118	71	0	.	0	.	678	48	0	.	117	71	210	51	430	46	567	29	46	100	840	28	803	26	0	.	475	34	9013	9
	91-100 cm	54	100	0	.	0	.	514	42	0	.	48	100	0	.	144	59	331	46	69	100	53	100	156	71	0	.	0	.	136	72	0	.	0	.	106	72	168	100	214	49	47	100	184	58	236	46	0	.	46	100	2506	17
	>100 cm	153	71	0	.	0	.	268	60	0	.	67	100	0	.	51	100	497	36	67	100	0	.	0	.	0	.	0	.	99	73	0	.	0	.	43	100	400	100	696	41	0	.	38	100	91	72	0	.	0	.	2470	23
	total	13685	11	3362	28	5641	22	69962	5	6320	16	16966	11	632	51	6452	18	53598	5	12284	12	20415	10	11241	11	2890	26	6720	17	23858	9	5169	18	10254	12	10892	13	9960	14	27082	5	6908	17	32328	7	30572	7	1826	31	18446	9	407462	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. 1321259. Birmensdorf, Swiss Federal Research Institute WSL

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