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 (“±”).

top

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).

top

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).

top

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.

top

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.

top

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

top

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.

top

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.

top

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.

top

NFI2
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · diameter at breast height (10 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
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 (10 classes)	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.
	21-30 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	.
	31-40 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	.
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	297	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	45	1	.	0	.	118	28	0	.	0	.	435	15
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	574	16	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	43	43	27	.	0	.	346	22	0	.	0	.	996	12
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	759	17	0	.	0	.	0	.	0	.	8	.	0	.	0	.	0	.	0	.	0	.	46	49	-7	.	0	.	349	23	0	.	0	.	1155	13
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	629	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	66	7	.	0	.	410	19	0	.	0	.	1081	12
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	547	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	50	0	.	0	.	216	24	0	.	0	.	793	15
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	213	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	50	0	.	0	.	188	32	0	.	0	.	446	20
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	188	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	71	0	.	0	.	82	49	0	.	0	.	305	25
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	71	0	.	0	.	91	46
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	58	0	.	0	.	71	51
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	71	0	.	0	.	100	46
	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	.
	12-20 cm	0	.	0	.	0	.	169	20	0	.	3	.	0	.	12	114	723	12	0	.	5	137	0	.	29	78	64	45	33	53	0	.	0	.	2	100	0	.	122	25	19	59	32	39	457	15	0	.	0	.	1669	8
	21-30 cm	0	.	0	.	0	.	606	18	0	.	0	.	0	.	31	115	2080	11	0	.	11	84	0	.	36	64	152	45	91	43	0	.	0	.	11	74	0	.	244	23	41	64	84	53	1351	15	0	.	0	.	4739	7
	31-40 cm	0	.	0	.	0	.	903	20	0	.	0	.	0	.	117	67	2793	10	0	.	18	100	0	.	37	58	140	37	126	46	0	.	0	.	53	86	0	.	378	21	48	61	120	45	1813	13	0	.	0	.	6545	6
	41-50 cm	0	.	0	.	0	.	791	23	0	.	0	.	0	.	104	56	3781	10	0	.	4	100	0	.	3	199	94	46	109	76	0	.	0	.	18	74	0	.	448	22	71	68	196	34	1906	13	0	.	0	.	7525	6
	51-60 cm	0	.	0	.	0	.	529	20	0	.	0	.	0	.	75	66	2416	11	0	.	0	.	0	.	39	100	51	57	42	55	0	.	0	.	35	78	0	.	417	23	54	63	135	56	1417	16	0	.	0	.	5210	7
	61-70 cm	0	.	0	.	0	.	308	29	0	.	0	.	0	.	34	71	1075	16	0	.	0	.	0	.	19	71	38	60	62	58	0	.	0	.	30	100	0	.	175	29	28	71	133	48	663	22	0	.	0	.	2565	10
	71-80 cm	0	.	0	.	0	.	92	55	0	.	0	.	0	.	39	71	389	25	0	.	0	.	0	.	0	.	47	74	18	100	0	.	0	.	34	71	0	.	174	37	19	100	57	71	350	27	0	.	0	.	1218	14
	81-90 cm	0	.	0	.	0	.	57	59	0	.	0	.	0	.	17	100	154	36	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	0	.	74	63	0	.	18	100	145	42	0	.	0	.	479	22
	91-100 cm	0	.	0	.	0	.	18	100	0	.	0	.	0	.	20	100	48	76	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	26	100	0	.	0	.	26	100	0	.	0	.	165	39
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	54	100	53	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	140	53
	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	.
	12-20 cm	0	.	2	.	0	.	482	21	0	.	212	33	0	.	14	53	1386	10	0	.	100	32	0	.	9	137	114	32	47	39	0	.	0	.	99	37	0	.	371	15	213	33	157	29	1209	11	0	.	0	.	4415	6
	21-30 cm	0	.	3	.	0	.	1143	17	0	.	401	29	0	.	35	107	3021	9	0	.	275	42	0	.	55	55	295	32	132	43	0	.	0	.	218	38	0	.	595	17	390	31	331	30	1962	10	0	.	0	.	8855	5
	31-40 cm	0	.	4	.	0	.	1606	16	0	.	515	29	0	.	59	73	4627	9	0	.	185	31	0	.	77	60	397	26	240	40	0	.	0	.	366	28	0	.	881	18	546	28	618	28	2628	11	21	.	0	.	12771	5
	41-50 cm	0	.	0	.	0	.	1972	15	0	.	463	28	0	.	56	50	5444	8	0	.	240	34	0	.	63	60	422	27	215	43	0	.	0	.	278	27	0	.	824	17	438	26	759	22	2489	11	48	.	0	.	13710	5
	51-60 cm	0	.	0	.	0	.	1752	18	0	.	209	48	0	.	80	65	4566	9	0	.	92	43	0	.	8	100	277	26	201	33	0	.	0	.	138	55	0	.	762	18	342	29	532	24	2065	13	5	.	0	.	11029	5
	61-70 cm	0	.	0	.	0	.	460	24	0	.	88	55	0	.	137	69	2330	12	0	.	57	71	0	.	10	100	218	32	72	56	0	.	0	.	47	58	0	.	548	25	157	60	201	34	1057	18	0	.	0	.	5383	8
	71-80 cm	0	.	0	.	0	.	212	35	0	.	15	100	0	.	65	63	1321	17	0	.	0	.	0	.	10	100	46	78	55	59	0	.	0	.	55	75	0	.	212	36	123	54	141	42	577	22	0	.	0	.	2833	11
	81-90 cm	0	.	0	.	0	.	165	48	0	.	0	.	0	.	42	71	274	36	0	.	0	.	0	.	0	.	24	100	45	71	0	.	0	.	0	.	0	.	100	53	33	100	62	79	202	34	0	.	0	.	947	18
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	71	93	61	0	.	0	.	0	.	0	.	24	100	38	100	0	.	0	.	0	.	0	.	80	72	51	72	0	.	48	71	0	.	0	.	381	29
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	171	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	199	52	0	.	24	100	0	.	0	.	0	.	394	33
	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	.
	12-20 cm	179	25	119	32	271	23	2555	8	166	28	1055	15	0	.	388	27	524	20	813	14	847	13	596	13	189	33	315	23	974	13	135	34	510	17	650	17	128	50	1329	12	264	25	1482	10	367	18	154	33	419	23	14427	3
	21-30 cm	358	29	459	27	837	21	5734	7	331	24	1887	14	0	.	848	27	817	17	1647	13	1680	13	1363	12	391	29	500	24	2848	10	273	35	1022	16	1009	15	337	30	1540	12	274	33	3013	9	510	21	261	30	1037	17	28976	3
	31-40 cm	786	21	703	24	1253	20	9255	7	536	23	2710	13	0	.	1017	24	978	16	2770	12	2960	12	2087	13	506	29	791	21	3885	10	341	34	1690	15	1657	16	409	27	1264	14	356	25	4362	9	654	20	389	26	1918	15	43276	3
	41-50 cm	675	23	484	27	1567	19	10888	6	585	27	2686	13	0	.	908	20	979	18	2366	12	3509	12	2375	11	378	33	1122	21	4627	10	111	45	1669	15	2227	15	469	28	991	13	208	32	4833	9	401	25	479	31	2031	15	46570	3
	51-60 cm	404	29	282	39	1007	21	8532	7	611	28	1820	15	0	.	549	24	902	19	1410	15	2986	13	1912	14	277	34	810	26	3786	11	107	53	712	20	1585	17	428	33	705	17	303	40	3627	10	323	29	429	38	1360	17	34869	3
	61-70 cm	52	75	105	60	220	35	4331	10	179	49	744	22	0	.	254	34	376	25	685	23	1176	19	791	17	0	.	334	33	1691	17	46	72	225	40	1086	21	169	49	353	27	193	51	1625	15	101	49	220	39	488	28	15442	5
	71-80 cm	82	73	74	76	0	.	1570	15	106	50	315	38	0	.	119	47	125	49	157	36	118	50	379	35	31	100	111	66	794	24	0	.	66	58	316	36	20	100	211	33	40	73	539	24	63	58	24	100	181	40	5440	8
	81-90 cm	0	.	0	.	43	100	722	23	32	100	31	100	0	.	25	100	83	53	92	58	23	100	62	71	0	.	0	0	278	37	0	.	28	100	119	51	0	.	95	49	24	100	118	60	50	72	0	.	31	100	1854	14
	91-100 cm	41	100	0	.	0	.	112	58	0	.	22	100	0	.	43	100	0	.	33	100	0	.	0	.	0	.	0	.	81	71	0	.	0	.	67	71	0	.	91	61	0	.	43	100	0	.	0	.	34	100	567	26
	>100 cm	0	.	0	.	0	.	165	51	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	19	100	0	.	311	77	0	.	0	.	0	.	0	.	0	.	513	50
	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	.
	12-20 cm	1313	14	0	.	0	.	1333	14	356	22	377	22	0	.	0	.	61	39	238	26	437	25	83	33	1	100	76	49	297	22	276	21	434	20	9	52	658	21	0	.	82	50	802	14	118	35	52	57	935	17	7939	5
	21-30 cm	2047	11	0	.	0	.	2747	11	754	20	682	20	0	.	0	.	123	46	658	20	788	21	166	29	32	79	77	56	606	22	447	23	922	17	81	49	1069	16	0	.	181	41	1849	13	151	34	122	70	1402	14	14905	4
	31-40 cm	3244	10	0	.	0	.	3253	10	1158	17	1353	17	0	.	11	.	169	43	932	22	1312	20	259	31	91	71	112	59	976	22	825	22	1509	16	193	53	1558	16	0	.	114	49	2488	13	38	96	60	49	2602	12	22258	4
	41-50 cm	3594	11	0	.	0	.	4422	10	1083	18	1425	19	0	.	18	.	165	43	839	20	1566	20	231	34	19	71	117	53	907	22	740	23	1572	17	65	57	1723	18	0	.	43	83	2229	13	62	60	98	66	3411	11	24331	4
	51-60 cm	2960	12	0	.	0	.	3680	11	745	20	972	20	0	.	0	.	147	43	669	20	875	22	152	44	10	100	78	80	478	26	499	21	989	24	52	60	1176	18	0	.	79	58	1147	16	58	57	65	65	2663	13	17494	4
	61-70 cm	1320	16	0	.	0	.	1610	16	353	34	407	28	0	.	0	.	26	100	276	37	318	33	22	100	0	.	0	.	250	36	256	32	492	27	58	100	584	26	0	.	15	100	526	26	74	61	30	100	833	18	7450	7
	71-80 cm	363	31	0	.	0	.	781	25	97	77	102	61	0	.	0	.	23	100	73	58	65	71	17	100	0	.	0	.	157	61	0	.	235	51	29	100	239	36	0	.	15	100	251	36	0	.	0	.	297	36	2743	12
	81-90 cm	183	42	0	.	0	.	473	33	74	71	132	62	0	.	0	.	0	.	0	.	59	100	0	.	0	.	0	.	0	.	0	.	88	74	0	.	87	73	0	.	0	.	54	71	0	.	0	.	196	42	1345	18
	91-100 cm	62	71	0	.	0	.	124	58	49	100	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	0	.	0	.	322	36
	>100 cm	35	100	0	.	0	.	42	100	40	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	58
	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	.
	12-20 cm	0	.	0	.	0	.	0	.	0	.	0	.	149	42	0	.	236	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	.	1806	9	0	.	55	55	252	22	0	.	12	71	2528	8
	21-30 cm	0	.	0	.	0	.	0	.	0	.	0	.	182	41	0	.	233	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	.	1945	9	0	.	61	52	230	25	0	.	48	76	2703	8
	31-40 cm	0	.	0	.	0	.	0	.	0	.	0	.	66	56	0	.	182	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1208	12	0	.	16	71	236	26	0	.	73	78	1781	10
	41-50 cm	0	.	0	.	0	.	0	.	0	.	0	.	86	55	0	.	109	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	.	673	17	0	.	2	100	138	34	0	.	52	61	1085	13
	51-60 cm	0	.	0	.	0	.	0	.	0	.	0	.	65	84	0	.	118	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	506	20	0	.	49	100	54	62	0	.	14	100	806	19
	61-70 cm	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	20	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	196	24	0	.	0	.	24	73	0	.	0	.	253	21
	71-80 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	226	27	0	.	0	.	15	100	0	.	0	.	256	25
	81-90 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	194	35	0	.	0	.	0	.	0	.	60	100	274	34
	91-100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	103	63	0	.	0	.	0	.	0	.	0	.	136	54
	>100 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	657	38	0	.	0	.	0	.	0	.	0	.	657	38
	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	.
	12-20 cm	1492	12	121	31	271	23	4539	6	521	18	1648	11	149	42	414	26	3232	7	1051	12	1389	12	679	12	228	30	570	16	1351	10	410	18	944	13	759	15	804	19	3647	6	578	18	2528	7	2521	7	206	29	1366	14	31417	2
	21-30 cm	2405	10	462	26	837	21	10230	5	1085	16	2969	10	182	41	914	25	6854	6	2305	11	2754	11	1530	12	515	24	1029	17	3677	9	720	19	1944	11	1319	13	1409	14	4367	6	913	19	5339	7	4550	7	383	30	2487	10	61179	2
	31-40 cm	4030	9	707	24	1253	20	15017	5	1694	14	4578	10	66	56	1204	21	9518	5	3702	11	4475	10	2346	12	711	23	1447	15	5227	9	1167	18	3199	11	2269	13	1967	14	3776	7	1057	18	7604	7	5718	7	470	23	4593	9	87796	2
	41-50 cm	4269	10	484	27	1567	19	18074	5	1669	15	4574	10	86	55	1086	18	11106	5	3205	10	5319	10	2606	11	463	28	1756	15	5858	9	851	21	3241	11	2588	13	2217	15	2972	8	767	19	8019	7	5405	7	626	27	5494	8	94303	2
	51-60 cm	3363	11	282	39	1007	21	14493	6	1356	17	3001	12	65	84	704	21	8696	6	2079	12	3952	11	2064	13	335	31	1216	19	4507	10	606	20	1701	16	1811	15	1604	16	2421	9	778	21	5490	8	4133	9	500	33	4037	10	70201	2
	61-70 cm	1373	16	105	60	220	35	6708	7	532	28	1239	16	12	100	425	31	4039	9	961	20	1551	16	813	17	29	58	590	23	2075	15	301	29	718	23	1221	19	753	23	1318	14	393	35	2484	12	2108	12	250	36	1321	15	31540	3
	71-80 cm	445	28	74	76	0	.	2655	12	202	45	432	31	0	.	223	33	2062	13	230	31	184	41	396	34	41	80	203	43	1024	21	0	.	301	42	433	29	258	34	857	16	197	39	988	18	1087	15	24	100	478	27	12795	5
	81-90 cm	183	42	0	.	43	100	1418	17	105	58	163	53	0	.	84	51	590	22	92	58	82	77	62	71	0	.	24	100	336	32	0	.	115	61	119	51	87	73	464	23	57	71	251	38	427	23	0	.	287	37	4990	8
	91-100 cm	103	58	0	.	0	.	254	39	49	100	63	74	0	.	110	52	186	41	33	100	0	.	0	.	0	.	24	100	147	51	0	.	0	.	67	71	46	100	300	35	51	72	43	100	132	41	0	.	34	100	1643	15
	>100 cm	35	100	0	.	0	.	207	45	40	100	0	.	0	.	72	79	287	33	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	19	100	0	.	1168	31	0	.	24	100	70	60	0	.	0	.	1922	21
	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. 1321174. Birmensdorf, Swiss Federal Research Institute WSL

back