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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NFI2
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 1993/95
		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	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	.
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	.
	=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	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
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	.	92	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	57	0	.	0	.	28	43	0	.	0	.	129	20
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	348	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	53	6	.	0	.	169	26	0	.	0	.	544	14
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	840	15	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	53	44	22	.	0	.	482	23	0	.	0	.	1403	12
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1023	15	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	62	54	1	.	0	.	574	19	0	.	0	.	1668	11
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1038	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	111	47	0	.	0	.	581	21	0	.	0	.	1730	14
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3342	12	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	254	34	28	.	0	.	1835	16	0	.	0	.	5473	9
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	24	0	.	3	.	0	.	7	66	264	16	0	.	4	88	0	.	6	72	24	43	30	38	0	.	0	.	0	.	0	.	34	24	5	83	10	37	141	20	0	.	0	.	578	10
	16-23 cm	0	.	0	.	0	.	234	19	0	.	0	.	0	.	18	118	986	11	0	.	6	92	0	.	31	60	72	54	21	75	0	.	0	.	4	74	0	.	136	26	19	57	41	45	621	15	0	.	0	.	2187	7
	24-35 cm	0	.	0	.	0	.	893	20	0	.	0	.	0	.	64	109	2886	11	0	.	23	100	0	.	36	90	169	44	128	47	0	.	0	.	42	73	0	.	368	22	76	67	145	41	1921	14	0	.	0	.	6750	7
	36-51 cm	0	.	0	.	0	.	1355	20	0	.	0	.	0	.	175	54	5552	9	0	.	7	100	0	.	32	66	195	42	188	50	0	.	0	.	37	85	0	.	780	19	79	62	245	35	2958	12	0	.	0	.	11604	6
	=52 cm	0	.	0	.	0	.	941	22	0	.	0	.	0	.	239	49	3824	10	0	.	0	.	0	.	58	84	127	53	154	43	0	.	0	.	99	77	0	.	741	22	101	57	334	39	2520	14	0	.	0	.	9138	7
	total	0	.	0	.	0	.	3473	16	0	.	3	.	0	.	503	48	13512	8	0	.	39	97	0	.	164	58	586	32	521	36	0	.	0	.	182	62	0	.	2058	16	279	49	775	34	8161	10	0	.	0	.	30256	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	.	195	35	0	.	69	50	0	.	6	47	504	11	0	.	29	37	0	.	-7	128	33	33	12	39	0	.	0	.	25	39	0	.	131	17	50	30	49	31	433	15	0	.	0	.	1530	8
	16-23 cm	0	.	5	.	0	.	554	17	0	.	204	32	0	.	8	168	1697	10	0	.	136	38	0	.	24	57	186	30	66	36	0	.	0	.	123	37	0	.	392	15	238	33	181	36	1328	10	0	.	0	.	5142	6
	24-35 cm	0	.	0	.	0	.	1525	17	0	.	516	31	0	.	53	77	4574	9	0	.	286	37	0	.	93	61	369	29	236	42	0	.	0	.	284	31	0	.	842	20	547	31	572	27	2843	10	11	.	0	.	12751	5
	36-51 cm	0	.	4	.	0	.	3194	14	0	.	822	25	0	.	105	51	8129	7	0	.	366	32	0	.	94	59	716	25	367	35	0	.	0	.	509	27	0	.	1374	16	744	25	1135	22	3918	10	63	.	0	.	21541	4
	=52 cm	0	.	0	.	0	.	2323	17	0	.	293	46	0	.	362	53	8329	9	0	.	130	53	0	.	28	100	513	27	364	33	0	.	0	.	260	40	0	.	1833	18	713	31	889	21	3715	13	0	.	0	.	19754	5
	total	0	.	9	.	0	.	7792	13	0	.	1904	24	0	.	535	48	23234	6	0	.	948	29	0	.	232	52	1817	22	1045	28	0	.	0	.	1202	25	0	.	4572	12	2293	22	2825	18	12237	8	74	.	0	.	60719	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	52	36	44	35	66	31	923	9	61	30	327	20	0	.	159	38	231	35	289	17	324	16	219	17	58	43	96	26	309	16	51	43	188	23	213	18	55	67	516	17	135	36	546	12	114	20	50	33	176	34	5200	4
	16-23 cm	209	26	163	30	369	25	3129	7	168	35	1226	15	0	.	494	23	533	15	900	16	992	13	749	14	242	28	383	23	1269	12	158	34	566	17	697	17	152	34	1279	12	188	25	1732	10	387	20	151	35	428	19	16566	3
	24-35 cm	689	23	731	25	1190	22	8461	7	530	24	2772	13	0	.	1003	30	1041	18	2634	12	2544	13	2028	13	579	27	754	24	4042	10	409	33	1601	15	1669	16	490	26	1766	12	433	26	4398	9	733	20	364	28	1703	16	42562	3
	36-51 cm	1085	21	862	24	2395	18	16890	6	905	24	4213	13	0	.	1582	19	1616	17	3966	11	5507	11	3589	11	585	30	1584	20	7178	10	244	41	2653	14	3145	14	720	27	1632	13	363	27	7422	8	730	22	758	29	3252	14	72875	2
	=52 cm	542	29	426	41	1177	22	14460	7	881	29	2734	16	0	.	931	24	1362	18	2183	16	3931	13	2980	13	309	36	1166	24	6167	12	151	39	914	22	3011	16	542	34	1698	20	544	41	5543	11	505	27	634	38	1941	16	54731	3
	total	2576	19	2226	22	5197	17	43862	5	2545	21	11271	11	0	.	4169	17	4783	13	9972	10	13298	10	9566	10	1773	24	3983	18	18965	8	1013	30	5922	12	8735	12	1959	23	6891	9	1662	24	19642	7	2468	17	1957	25	7500	12	191934	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	483	16	0	.	0	.	493	17	132	24	102	24	0	.	0	.	19	42	96	29	144	23	24	40	1	100	31	57	138	23	94	26	156	24	3	63	223	24	0	.	8	74	268	16	46	43	9	56	390	27	2861	6
	16-23 cm	1391	12	0	.	0	.	1644	12	406	23	386	23	0	.	0	.	83	44	283	25	459	26	101	33	4	100	84	46	305	23	288	22	493	18	25	52	716	20	0	.	128	47	1028	14	138	35	111	65	903	13	8977	4
	24-35 cm	3055	10	0	.	0	.	3516	10	1134	18	1105	18	0	.	11	.	154	43	909	20	1231	21	203	30	93	75	76	63	1017	22	606	23	1387	16	180	53	1604	16	0	.	160	42	2539	13	109	48	83	51	2185	14	21358	4
	36-51 cm	5655	10	0	.	0	.	6574	9	1774	17	2401	18	0	.	18	.	292	40	1456	19	2335	18	433	31	45	73	192	51	1410	22	1335	20	2562	15	140	52	2595	16	0	.	125	57	3677	12	84	52	129	59	5244	10	38476	3
	=52 cm	4538	12	0	.	0	.	6240	11	1262	24	1496	22	0	.	0	.	164	51	943	22	1252	21	168	44	10	100	78	80	801	29	720	23	1643	24	138	81	2002	17	0	.	109	54	1833	17	124	52	96	63	3615	13	27233	4
	total	15122	8	0	.	0	.	18468	8	4709	14	5490	15	0	.	30	.	713	36	3686	16	5421	16	930	28	153	71	461	48	3670	19	3042	17	6241	14	486	47	7140	13	0	.	530	42	9345	10	501	31	427	48	12338	9	98904	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	.	47	54	0	.	99	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	.	657	13	0	.	13	63	86	26	0	.	4	71	909	10
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	177	48	0	.	222	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	.	1802	9	0	.	56	57	226	23	0	.	22	68	2524	8
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	151	41	0	.	284	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2028	10	0	.	62	57	292	25	0	.	71	68	2888	8
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	107	52	0	.	160	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	.	1180	13	0	.	2	100	263	29	0	.	89	64	1826	11
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	77	72	0	.	201	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1847	18	0	.	49	100	84	43	0	.	74	83	2332	15
	total	0	.	0	.	0	.	0	.	0	.	0	.	560	36	0	.	966	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	7514	8	0	.	183	53	950	20	0	.	259	61	10479	6
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	535	15	44	34	66	31	1661	8	193	19	501	15	47	54	172	35	1210	9	384	14	501	12	243	16	59	46	184	18	489	12	145	22	344	16	241	16	280	23	1346	9	197	26	886	9	848	9	59	29	570	21	11207	3
	16-23 cm	1600	11	168	29	369	25	5561	6	574	19	1816	12	177	48	520	22	3875	6	1183	13	1593	11	850	13	300	24	725	16	1660	10	446	19	1059	12	850	15	887	17	3629	6	580	19	3038	8	2868	7	262	34	1353	10	35944	2
	24-35 cm	3743	9	731	25	1190	22	14395	5	1664	14	4393	10	151	41	1131	28	9784	6	3543	10	4084	10	2231	12	801	22	1373	16	5423	9	1014	19	2988	11	2176	13	2094	13	5056	6	1237	18	7716	7	6379	7	458	24	3959	10	87715	2
	36-51 cm	6740	9	866	24	2395	18	28013	4	2679	14	7436	10	107	52	1881	17	16782	5	5422	9	8214	9	4022	10	757	25	2694	14	9143	8	1579	18	5215	10	3830	12	3340	14	5029	7	1311	17	12482	6	8527	6	950	26	8586	8	148000	1
	=52 cm	5080	11	426	41	1177	22	23965	5	2143	18	4523	12	77	72	1532	21	14919	6	3125	13	5313	11	3148	13	405	31	1884	17	7486	10	871	20	2557	17	3508	14	2544	15	6230	9	1467	22	8649	8	7529	8	730	34	5630	10	114918	2
	total	17698	7	2235	22	5197	17	73595	4	7254	11	18668	8	560	36	5236	15	46571	4	13658	8	19706	8	10495	9	2321	20	6860	13	24201	7	4055	15	12163	9	10605	10	9146	11	21290	4	4792	14	32771	5	26153	5	2459	22	20097	7	397785	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. 1234229. Birmensdorf, Swiss Federal Research Institute WSL

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