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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NFI1
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (main diameter classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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 (main diameter 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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.
	24-35 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	.
	36-51 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	.
	=52 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	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.
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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	65	1	.	0	.	43	24	0	.	0	.	136	15
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	260	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	59	4	.	0	.	184	23	0	.	0	.	461	14
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	758	17	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	51	49	25	.	0	.	447	23	0	.	0	.	1286	13
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	989	17	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	89	45	9	.	0	.	486	19	0	.	0	.	1576	12
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	955	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	62	0	.	0	.	467	23	0	.	0	.	1482	16
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3052	13	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	217	36	38	.	0	.	1627	16	0	.	0	.	4941	10
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	.
	12-15 cm	0	.	0	.	0	.	49	22	0	.	0	.	0	.	2	72	257	12	0	.	5	100	0	.	11	77	27	58	30	43	0	.	0	.	0	.	0	.	22	26	3	100	9	57	128	17	0	.	0	.	543	8
	16-23 cm	0	.	0	.	0	.	259	18	0	.	0	.	0	.	28	77	1052	12	0	.	10	90	0	.	34	67	52	47	39	40	0	.	0	.	7	71	0	.	105	27	18	41	36	44	644	15	0	.	0	.	2283	7
	24-35 cm	0	.	0	.	0	.	983	21	0	.	0	.	0	.	79	88	2754	10	0	.	18	100	0	.	23	100	140	40	189	51	0	.	0	.	21	78	0	.	298	24	97	60	118	42	1859	15	0	.	0	.	6579	7
	36-51 cm	0	.	0	.	0	.	1211	20	0	.	0	.	0	.	190	46	5272	9	0	.	3	100	0	.	28	87	163	50	183	53	0	.	0	.	38	63	0	.	593	21	107	57	257	41	2620	11	0	.	0	.	10664	6
	=52 cm	0	.	0	.	0	.	717	24	0	.	0	.	0	.	179	49	3086	11	0	.	0	.	0	.	38	100	125	55	119	45	0	.	0	.	79	83	0	.	653	24	147	55	216	39	2038	14	0	.	0	.	7396	7
	total	0	.	0	.	0	.	3219	17	0	.	0	.	0	.	478	45	12421	8	0	.	36	97	0	.	133	65	507	35	560	38	0	.	0	.	145	64	0	.	1671	17	372	50	634	37	7289	10	0	.	0	.	27466	5
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	.
	12-15 cm	0	.	1	.	0	.	132	22	0	.	49	33	0	.	6	53	424	10	0	.	33	37	4	.	8	74	38	34	22	36	0	.	0	.	50	45	0	.	105	20	42	29	50	32	468	11	1	.	0	.	1433	6
	16-23 cm	0	.	0	.	0	.	498	16	0	.	169	33	0	.	24	59	1507	10	0	.	158	44	0	.	33	65	168	32	81	33	0	.	0	.	129	39	0	.	304	18	182	35	175	31	1249	12	4	.	0	.	4681	6
	24-35 cm	0	.	0	.	0	.	1593	16	0	.	531	31	0	.	62	57	4987	9	0	.	216	37	13	.	85	62	427	28	352	33	0	.	0	.	387	30	0	.	815	19	612	28	667	26	2770	10	9	.	0	.	13526	5
	36-51 cm	0	.	0	.	0	.	2861	15	0	.	650	28	0	.	95	49	8069	8	0	.	241	39	0	.	95	64	571	25	658	33	0	.	0	.	547	26	0	.	1149	15	618	23	1162	19	3624	10	40	.	0	.	20380	4
	=52 cm	0	.	0	.	0	.	1810	18	0	.	167	53	0	.	344	58	6959	10	0	.	100	59	0	.	25	100	446	28	820	31	0	.	0	.	202	34	0	.	1421	20	523	35	697	23	3125	13	0	.	0	.	16638	6
	total	0	.	1	.	0	.	6892	13	0	.	1566	25	0	.	531	49	21945	7	0	.	748	32	17	.	246	56	1651	23	1933	26	0	.	0	.	1316	24	0	.	3794	13	1976	22	2752	17	11236	8	54	.	0	.	56658	4
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	.
	12-15 cm	55	24	54	35	77	26	956	8	57	26	313	15	0	.	140	26	170	17	246	15	312	14	208	16	52	32	109	29	391	14	63	35	182	18	209	18	39	46	418	12	76	30	482	10	137	24	36	36	162	21	4943	3
	16-23 cm	202	24	260	29	549	21	3417	7	165	31	987	14	0	.	525	22	477	16	901	14	940	13	716	13	262	29	443	24	1444	11	205	36	593	16	664	15	155	30	1003	13	178	28	1486	9	320	20	121	36	571	19	16584	3
	24-35 cm	694	23	1025	26	1384	21	8908	6	565	26	2819	14	0	.	1155	21	989	18	2711	12	2924	13	2091	13	501	29	993	21	4177	11	346	35	1678	15	1691	15	391	26	1357	13	429	25	4455	9	639	20	401	31	2086	15	44411	3
	36-51 cm	868	22	897	25	2103	19	15922	6	930	23	3622	13	0	.	1394	19	1502	17	3256	11	5147	12	3479	11	644	30	1554	21	6688	10	214	42	2368	14	2985	14	730	27	1432	13	456	27	6598	8	620	23	897	28	3302	14	67606	2
	=52 cm	340	31	292	48	821	26	10458	7	364	34	2059	17	0	.	741	30	1263	18	1455	17	2423	15	2512	14	187	34	995	25	3729	14	128	45	657	25	2345	17	333	39	1076	19	395	43	4234	11	400	31	413	39	1336	20	38954	4
	total	2159	20	2527	23	4933	17	39661	5	2083	21	9800	11	0	.	3956	17	4401	13	8568	10	11746	10	9007	10	1645	25	4093	18	16429	8	956	29	5477	12	7894	12	1648	24	5286	9	1534	24	17254	7	2116	18	1868	25	7457	12	172498	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	.
	12-15 cm	449	12	0	.	0	.	497	14	92	30	108	26	0	.	0	.	19	46	92	27	135	28	45	37	0	.	39	62	125	26	132	23	144	19	8	60	213	19	0	.	34	62	291	15	51	45	21	57	279	15	2775	5
	16-23 cm	1357	11	0	.	0	.	1552	11	389	18	466	21	0	.	0	.	54	47	326	20	403	23	98	31	3	100	66	45	343	22	358	22	556	18	51	44	651	17	0	.	89	48	1163	13	79	37	62	58	777	12	8843	4
	24-35 cm	3577	10	0	.	0	.	3606	10	1043	18	1334	17	0	.	20	.	192	45	1182	21	1438	17	290	35	116	71	110	59	811	25	834	25	1562	15	189	54	1493	18	0	.	88	53	2753	12	94	44	62	57	2942	12	23738	4
	36-51 cm	5729	10	0	.	0	.	7124	9	1476	17	2117	17	0	.	51	.	268	39	1304	19	1999	16	399	37	27	71	123	53	1141	23	920	20	2358	16	150	53	2473	16	0	.	32	47	2611	13	77	53	181	56	5840	10	36400	3
	=52 cm	3565	12	0	.	0	.	4772	12	914	25	1151	26	0	.	54	.	158	46	712	29	902	24	82	56	15	100	29	71	495	32	438	28	1106	26	58	100	1370	20	0	.	65	60	1073	18	115	51	25	100	2571	14	19670	5
	total	14677	8	0	.	0	.	17552	8	3915	14	5175	16	0	.	125	.	692	36	3616	16	4877	15	914	30	160	71	369	47	2916	20	2681	18	5726	13	457	45	6200	13	0	.	308	42	7891	10	415	32	352	46	12408	9	91427	3
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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	54	46	0	.	74	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	653	10	0	.	34	55	87	23	0	.	8	67	913	9
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	112	44	0	.	173	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1561	9	0	.	50	53	198	26	0	.	41	67	2136	8
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	74	46	0	.	125	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1435	11	0	.	53	74	258	24	0	.	76	93	2022	9
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	101	64	0	.	151	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	.	955	15	0	.	-2	311	206	32	0	.	59	61	1510	13
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	28	95	0	.	131	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1206	22	0	.	0	.	65	49	0	.	69	85	1498	18
	total	0	.	0	.	0	.	0	.	0	.	0	.	369	39	0	.	654	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	.	5811	8	0	.	136	57	815	20	0	.	253	63	8079	7
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	.
	12-15 cm	504	11	55	35	77	26	1634	6	150	21	471	12	54	46	148	25	1035	6	338	13	484	12	256	14	71	27	213	21	569	11	195	19	326	13	267	16	255	17	1200	7	155	21	865	8	914	8	59	30	450	12	10746	2
	16-23 cm	1559	10	260	29	549	21	5726	5	554	16	1621	11	112	44	578	20	3531	6	1227	12	1511	11	814	12	331	25	729	17	1906	9	563	19	1149	12	852	13	806	15	2987	6	470	20	2909	7	2675	7	187	31	1389	10	34995	2
	24-35 cm	4271	9	1025	26	1384	21	15090	5	1609	15	4684	10	74	46	1316	19	9805	6	3893	10	4597	10	2395	12	725	24	1675	15	5529	9	1180	20	3240	11	2289	13	1884	15	3957	7	1251	17	8046	7	6067	7	472	28	5104	9	91562	2
	36-51 cm	6597	9	897	25	2103	19	27117	5	2406	14	6388	10	101	64	1730	17	16251	5	4559	10	7390	9	3878	10	793	26	2415	15	8672	8	1134	18	4726	10	3720	12	3242	14	4219	7	1222	16	10626	6	7632	6	1118	24	9200	8	138137	1
	=52 cm	3905	11	292	48	821	26	17757	6	1279	20	3377	14	28	95	1317	24	12551	6	2166	14	3425	12	2594	14	264	30	1596	18	5162	12	566	23	1763	19	2684	16	1704	17	4416	10	1131	23	6221	9	6210	8	438	38	3975	11	85639	2
	total	16836	7	2528	23	4933	17	67324	4	5997	12	16541	8	369	39	5089	15	43173	4	12184	8	17407	8	9938	9	2184	21	6628	13	21838	7	3638	15	11203	9	9812	10	7891	11	16779	4	4229	15	28667	5	23499	5	2274	22	20118	7	361078	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. 1234209. Birmensdorf, Swiss Federal Research Institute WSL

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