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 (main diameter classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
altitudinal vegetation belts (NaiS; 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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	16-23 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	.
	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	.	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	69	0	.	0	.	53	38	0	.	0	.	134	21
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	323	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	54	0	.	0	.	217	49	0	.	0	.	575	23
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	746	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	55	0	.	0	.	482	27	0	.	0	.	1308	15
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1115	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	53	0	.	0	.	642	25	0	.	0	.	1808	16
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1483	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	580	29	0	.	0	.	2077	22
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3739	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	189	52	0	.	0	.	1974	21	0	.	0	.	5903	13
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	.	83	34	0	.	0	.	0	.	8	75	321	27	0	.	0	.	0	.	13	100	21	52	16	61	0	.	0	.	0	.	0	.	56	45	14	74	3	100	226	31	0	.	0	.	762	16
	16-23 cm	0	.	0	.	0	.	190	27	0	.	0	.	0	.	28	100	778	17	0	.	0	.	0	.	78	94	93	59	57	59	0	.	0	.	3	100	0	.	148	36	53	76	12	52	515	18	0	.	0	.	1954	10
	24-35 cm	0	.	0	.	0	.	909	23	0	.	0	.	0	.	15	100	2388	15	0	.	27	100	0	.	126	91	198	64	155	70	0	.	0	.	22	100	0	.	541	27	131	55	87	62	1366	18	0	.	0	.	5965	9
	36-51 cm	0	.	0	.	0	.	1996	21	0	.	0	.	0	.	64	87	5397	13	0	.	0	.	0	.	40	77	157	48	254	61	0	.	0	.	108	92	0	.	918	23	186	57	215	46	2709	18	0	.	0	.	12044	8
	=52 cm	0	.	0	.	0	.	1160	25	0	.	0	.	0	.	259	61	5392	15	0	.	0	.	0	.	81	100	67	85	157	58	0	.	0	.	215	89	0	.	1172	24	188	58	231	48	3849	18	0	.	0	.	12771	9
	total	0	.	0	.	0	.	4337	20	0	.	0	.	0	.	374	64	14276	11	0	.	27	100	0	.	338	73	536	47	639	51	0	.	0	.	348	71	0	.	2835	20	573	50	547	42	8665	14	0	.	0	.	33496	7
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	.	0	.	0	.	152	27	0	.	18	60	0	.	1	100	566	14	0	.	26	54	0	.	2	100	67	43	15	50	0	.	0	.	60	67	0	.	129	29	37	34	57	35	411	17	0	.	0	.	1543	8
	16-23 cm	0	.	0	.	0	.	338	24	0	.	103	49	0	.	16	113	1736	12	0	.	93	48	0	.	12	100	156	39	49	48	0	.	0	.	79	48	0	.	464	25	192	34	178	38	1526	16	0	.	0	.	4941	7
	24-35 cm	0	.	0	.	0	.	1092	22	0	.	406	54	0	.	103	80	4400	11	0	.	176	50	0	.	35	100	493	32	129	53	0	.	0	.	139	48	0	.	940	23	664	33	479	47	3088	14	0	0	0	.	12145	7
	36-51 cm	0	.	0	.	0	.	2401	21	0	.	634	37	0	.	171	62	8210	10	0	.	241	51	0	.	17	100	539	34	312	55	0	.	0	.	511	43	0	.	1569	21	1025	36	1089	33	4119	13	0	0	0	.	20838	6
	=52 cm	0	.	0	.	0	.	3597	20	0	.	561	44	0	.	728	58	8831	11	0	.	112	65	0	.	0	.	641	39	329	63	0	.	0	.	431	46	0	.	2480	23	663	38	1354	29	4555	15	0	.	0	.	24281	7
	total	0	.	0	.	0	.	7580	17	0	.	1723	35	0	.	1019	54	23743	8	0	.	647	42	0	.	66	100	1895	31	835	47	0	.	0	.	1219	35	0	.	5582	17	2581	30	3157	27	13699	11	0	0	0	.	63748	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	.
	12-15 cm	14	47	-6	333	49	44	750	15	57	31	167	21	0	.	69	25	156	26	183	20	314	18	226	19	29	35	61	31	228	20	40	59	158	25	101	33	42	55	365	17	81	35	458	14	138	47	28	48	80	59	3787	5
	16-23 cm	96	53	81	37	298	29	2687	10	130	37	900	19	0	.	280	31	374	27	651	19	769	15	694	18	211	40	272	27	1003	18	103	50	435	24	540	24	215	36	1273	17	282	31	1473	14	256	26	100	46	463	28	13586	4
	24-35 cm	205	38	542	34	1028	27	7038	10	368	36	2132	19	0	.	858	35	1037	21	1374	18	2381	17	1784	18	772	35	836	28	3029	14	411	38	1129	23	1363	20	680	35	1897	16	548	37	4071	13	704	28	206	41	1527	18	35921	4
	36-51 cm	597	33	1347	30	2308	24	12986	8	733	36	3640	17	0	.	1906	24	1988	19	3582	17	4424	14	3476	14	776	33	1317	28	5785	12	631	38	2373	17	2558	18	731	30	1755	16	753	32	6473	11	1030	29	341	45	3396	16	64909	3
	=52 cm	785	34	1193	36	1959	28	14931	9	605	41	3460	20	0	.	1652	25	2391	20	2619	20	5716	18	3790	16	564	42	1366	31	8104	15	209	53	1653	23	3370	21	731	39	1803	23	901	40	7008	13	700	34	295	47	2974	22	68783	4
	total	1698	30	3158	29	5641	22	38392	7	1893	31	10300	14	0	.	4766	21	5947	16	8409	15	13604	13	9969	12	2352	29	3852	24	18149	11	1394	35	5749	16	7933	16	2400	29	7093	12	2565	26	19484	9	2828	24	971	37	8439	15	186986	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	325	30	0	.	0	.	365	15	94	30	191	46	0	.	0	.	28	49	57	32	112	23	44	60	0	.	29	94	102	36	90	33	104	51	31	96	263	29	0	.	26	65	219	24	27	63	4	76	232	22	2343	8
	16-23 cm	861	20	0	.	0	.	1046	15	264	29	456	38	0	.	0	.	71	47	227	31	455	27	87	41	0	.	47	79	317	35	362	30	226	27	23	74	646	24	0	.	104	55	641	19	114	58	28	88	874	19	6848	6
	24-35 cm	1902	18	0	.	0	.	2669	14	746	26	965	26	0	.	0	.	112	54	634	29	1009	24	174	38	0	.	95	100	360	40	871	28	911	25	137	59	1621	21	0	.	306	61	1648	18	106	73	63	76	1976	16	16305	5
	36-51 cm	3621	14	0	.	0	.	5312	13	1330	24	1708	25	0	.	0	.	212	51	1749	23	2288	27	534	34	0	.	0	.	591	36	1392	25	1579	22	257	60	1958	20	0	.	217	51	2792	17	46	81	121	71	3093	15	28801	5
	=52 cm	5129	15	0	.	0	.	6989	14	1254	29	1165	28	0	.	0	.	213	55	1208	26	1799	24	429	58	0	.	0	.	860	38	1060	30	1546	33	181	61	2553	25	0	.	139	78	2073	23	36	71	109	100	3619	18	30361	6
	total	11838	11	0	.	0	.	16381	11	3689	20	4485	20	0	.	0	.	635	40	3875	21	5663	19	1268	34	0	.	172	73	2230	29	3775	21	4365	20	629	56	7040	16	0	.	791	52	7373	15	329	47	326	72	9793	12	84658	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	.
	12-15 cm	0	.	0	.	0	.	0	.	0	.	0	.	6	61	0	.	108	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	605	15	0	.	8	75	88	34	0	.	1	100	822	15
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	43	52	0	.	127	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	100	2035	12	0	.	46	76	256	28	0	.	0	.	2534	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	193	57	0	.	366	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	2675	12	0	.	145	59	247	40	0	.	33	100	3670	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	285	56	0	.	250	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	2140	13	0	.	59	71	261	42	0	.	103	83	3113	12
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	87	100	0	.	181	49	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	1814	19	0	.	0	.	131	68	0	.	0	.	2236	17
	total	0	.	0	.	0	.	0	.	0	.	0	.	614	53	0	.	1033	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	100	9268	9	0	.	258	58	983	30	0	.	137	87	12375	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	.
	12-15 cm	340	29	-6	333	49	44	1351	10	151	22	376	25	6	61	78	23	1251	12	240	17	452	14	270	19	44	38	178	25	360	16	130	29	262	25	192	31	310	25	1164	10	158	23	745	11	944	13	33	43	313	22	9391	4
	16-23 cm	957	19	81	37	298	29	4261	8	394	23	1460	17	43	52	324	28	3409	8	879	16	1317	13	780	16	300	37	568	20	1427	15	464	26	661	18	645	21	888	20	3954	8	631	21	2350	11	2884	10	128	41	1336	16	30439	3
	24-35 cm	2107	16	542	34	1028	27	11707	7	1114	21	3503	15	193	57	976	32	9050	7	2008	15	3593	14	1958	16	933	32	1623	20	3674	13	1282	22	2040	17	1660	18	2312	18	6132	8	1650	22	6431	10	5992	9	270	36	3535	12	75312	2
	36-51 cm	4218	13	1347	30	2308	24	22695	6	2063	20	5983	13	285	56	2142	22	17173	6	5331	13	6953	13	4010	13	834	31	2013	21	6941	11	2023	21	3952	13	3435	16	2704	16	6433	8	2181	21	10629	9	8807	9	462	38	6592	11	131513	2
	=52 cm	5915	14	1193	36	1959	28	26677	7	1860	24	5186	15	87	100	2639	23	18491	7	3827	15	7627	14	4219	16	645	39	2074	24	9450	13	1269	26	3199	20	4197	18	3308	21	7284	10	1890	24	10666	10	9851	10	404	44	6593	14	140509	2
	total	13536	11	3158	29	5641	22	66691	5	5581	16	16508	11	614	53	6159	19	49373	5	12284	12	19942	11	11237	11	2756	27	6455	18	21853	10	5169	18	10114	12	10129	14	9523	14	24967	5	6511	17	30820	7	28478	7	1297	33	18369	9	387165	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. 1234259. Birmensdorf, Swiss Federal Research Institute WSL

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