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

NFI5
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 2018/26
		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	.	107	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	62	0	.	0	.	70	49	0	.	0	.	182	27
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	278	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	59	0	.	0	.	243	47	0	.	0	.	543	24
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	731	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	128	52	0	.	0	.	430	28	0	.	0	.	1288	16
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1219	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	101	56	0	.	0	.	748	30	0	.	0	.	2068	16
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1514	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	675	30	0	.	0	.	2206	18
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3849	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	270	52	0	.	0	.	2167	22	0	.	0	.	6286	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	.	69	28	0	.	0	.	0	.	12	90	228	27	0	.	0	.	0	.	15	87	20	49	5	78	0	.	0	.	0	.	0	.	40	31	40	58	7	62	220	26	0	.	0	.	656	14
	16-23 cm	0	.	0	.	0	.	245	22	0	.	0	.	0	.	43	96	793	18	0	.	0	.	0	.	61	92	124	57	28	95	0	.	0	.	4	100	0	.	224	28	95	65	17	52	632	19	0	.	0	.	2266	10
	24-35 cm	0	.	0	.	0	.	930	23	0	.	0	.	0	.	39	100	2468	15	0	.	11	100	0	.	127	87	348	56	223	73	0	.	0	.	8	100	0	.	647	27	121	52	56	77	1388	19	0	.	0	.	6365	9
	36-51 cm	0	.	0	.	0	.	2049	21	0	.	0	.	0	.	76	59	5072	12	0	.	9	100	0	.	66	74	357	62	252	57	0	.	0	.	100	88	0	.	1077	22	215	56	194	51	2712	18	0	.	0	.	12177	8
	=52 cm	0	.	0	.	0	.	1492	24	0	.	0	.	0	.	274	60	5992	14	0	.	0	.	0	.	84	100	83	87	269	58	0	.	0	.	237	83	0	.	1299	24	221	55	335	42	4335	17	0	.	0	.	14621	8
	total	0	.	0	.	0	.	4784	19	0	.	0	.	0	.	443	64	14553	11	0	.	20	100	0	.	354	71	931	51	778	54	0	.	0	.	349	72	0	.	3287	20	692	48	608	41	9288	14	0	.	0	.	36086	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	.	137	23	0	.	12	62	0	.	6	75	614	17	0	.	22	60	1	100	1	100	58	36	32	53	0	.	0	.	33	53	0	.	97	25	33	50	130	44	394	16	0	.	0	.	1572	9
	16-23 cm	0	.	0	.	0	.	366	24	0	.	89	48	0	.	28	61	1849	12	0	.	78	52	2	100	8	100	165	37	114	43	0	.	0	.	90	48	0	.	454	25	148	39	277	35	1463	16	0	.	0	.	5130	7
	24-35 cm	0	.	0	.	0	.	1001	20	0	.	322	57	0	.	129	71	3985	12	0	.	140	58	0	.	2	100	483	36	102	41	0	.	0	.	214	42	0	.	937	22	634	34	429	39	3130	15	0	0	0	.	11509	7
	36-51 cm	0	.	0	.	0	.	1813	21	0	.	603	36	0	.	144	65	8255	10	0	.	171	57	0	.	34	100	536	32	308	55	0	.	0	.	501	42	0	.	1490	21	925	41	1229	34	4145	14	0	0	0	.	20154	6
	=52 cm	0	.	0	.	0	.	3701	20	0	.	734	41	0	.	761	55	9905	11	0	.	266	56	0	.	0	.	713	39	236	61	0	.	0	.	467	46	0	.	2428	22	736	35	1760	28	5236	14	0	.	0	.	26942	6
	total	0	.	0	.	0	.	7018	17	0	.	1760	35	0	.	1068	52	24608	8	0	.	677	48	3	100	45	100	1955	31	792	40	0	.	0	.	1305	33	0	.	5407	17	2476	32	3825	26	14368	11	0	0	0	.	65306	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	22	39	6	52	50	36	860	15	56	44	186	21	0	.	39	29	107	22	203	23	325	22	191	23	30	41	99	41	222	25	30	78	187	27	122	26	1	2804	407	22	54	34	377	16	97	33	28	40	150	25	3849	6
	16-23 cm	68	50	95	54	184	42	2744	11	181	39	704	31	0	.	222	27	371	23	712	21	886	17	794	18	262	45	253	28	980	18	52	47	504	20	471	24	113	40	1168	16	323	34	1224	12	344	30	75	44	435	26	13165	4
	24-35 cm	205	43	535	35	1034	30	6436	9	391	32	1973	18	0	.	421	44	1148	22	1220	19	2138	16	1706	17	796	35	741	28	3163	17	325	47	1278	21	1219	21	506	39	2764	21	641	38	3684	13	707	28	308	40	1294	21	34633	4
	36-51 cm	393	37	1380	28	1643	26	11673	9	601	37	3731	17	0	.	1420	24	1884	20	3370	17	3572	15	3187	14	1026	34	1314	26	5995	13	707	37	2285	18	2394	19	654	32	2460	21	782	33	6334	10	1050	26	427	41	2849	17	61131	3
	=52 cm	816	40	1504	34	1840	29	15488	9	631	47	4005	18	0	.	1812	25	2938	20	3192	20	5622	16	4431	16	651	44	1553	32	9307	14	268	53	1684	23	3722	20	823	37	2598	24	931	40	7333	13	801	37	406	45	3593	20	75948	4
	total	1505	33	3520	29	4750	24	37200	7	1861	32	10598	14	0	.	3914	21	6447	16	8698	15	12542	13	10309	12	2765	30	3961	25	19667	11	1381	34	5937	16	7928	16	2098	29	9396	17	2731	27	18952	9	2998	24	1244	37	8321	15	188726	3
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	299	18	0	.	0	.	462	18	138	28	120	35	0	.	0	.	26	48	10	484	184	36	63	47	0	.	36	100	54	53	78	30	118	37	54	97	142	23	0	.	8	53	229	23	26	76	0	.	261	25	2306	8
	16-23 cm	887	18	0	.	0	.	1179	15	349	27	476	44	0	.	0	.	75	43	178	28	423	26	112	39	0	.	112	93	221	44	212	31	298	32	32	100	557	23	0	.	73	51	717	21	88	55	9	100	687	20	6686	6
	24-35 cm	1764	18	0	.	0	.	2318	15	567	27	891	27	0	.	0	.	216	45	390	35	1129	24	185	47	0	.	152	88	433	42	722	29	634	30	45	100	1378	21	0	.	284	65	1540	18	85	89	68	71	1726	17	14525	6
	36-51 cm	3112	15	0	.	0	.	4778	12	1178	26	1230	26	0	.	0	.	205	43	1260	26	1765	22	274	41	0	.	13	100	468	32	1144	25	1190	21	302	61	1769	20	0	.	285	52	2464	18	75	58	118	87	2591	16	24220	5
	=52 cm	4368	15	0	.	0	.	6487	13	1217	30	1275	25	0	.	0	.	308	44	1427	28	2178	23	417	61	0	.	0	.	583	44	1207	30	1413	33	247	63	2817	24	0	.	210	82	2161	22	58	77	104	85	3442	16	29918	5
	total	10430	12	0	.	0	.	15224	10	3448	20	3991	22	0	.	0	.	829	37	3265	24	5679	18	1050	38	0	.	312	70	1759	32	3362	22	3653	20	679	53	6663	16	0	.	860	55	7112	15	332	47	300	71	8706	12	77654	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	.	14	67	0	.	65	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	539	17	0	.	5	71	88	30	0	.	7	78	725	14
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	61	85	0	.	132	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	1823	12	0	.	33	100	254	29	0	.	0	.	2321	10
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	71	54	0	.	387	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	3000	12	0	.	117	60	273	38	0	.	0	.	3879	10
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	217	61	0	.	275	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	2543	12	0	.	81	71	305	33	0	.	57	75	3508	10
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	113	100	0	.	201	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2079	19	0	.	0	.	185	62	0	.	19	100	2596	17
	total	0	.	0	.	0	.	0	.	0	.	0	.	475	55	0	.	1061	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	100	9984	9	0	.	236	58	1104	28	0	.	83	77	13029	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	322	17	6	52	50	36	1528	10	194	24	318	18	14	67	57	28	1148	12	214	32	531	18	254	21	47	39	212	27	313	21	107	31	306	22	209	30	152	30	1088	12	134	25	749	13	894	11	28	40	417	18	9290	4
	16-23 cm	955	17	95	54	184	42	4534	8	530	22	1269	24	61	85	294	25	3498	8	890	18	1388	14	908	16	331	40	655	24	1343	15	264	27	802	17	597	21	688	20	3691	8	639	22	2267	10	3024	10	83	40	1121	16	30110	3
	24-35 cm	1969	17	535	35	1034	30	10685	7	958	21	3185	15	71	54	589	35	8934	8	1610	17	3417	13	1891	16	926	32	1723	20	3922	15	1047	25	1911	17	1486	19	1914	18	7475	9	1681	22	5826	10	6013	10	377	35	3020	13	72200	2
	36-51 cm	3505	13	1380	28	1643	26	20314	6	1779	21	5563	13	217	61	1639	22	16911	6	4629	14	5516	12	3462	14	1126	31	2220	20	7024	11	1850	21	3474	14	3297	16	2454	17	7670	8	2206	22	10302	9	9034	9	545	37	5497	11	123258	2
	=52 cm	5184	14	1504	34	1840	29	27167	7	1848	25	6014	14	113	100	2847	23	20857	7	4619	16	8066	13	4848	15	735	41	2349	24	10394	13	1475	26	3097	20	4672	18	3640	20	8420	11	2099	24	11589	10	11291	9	510	40	7054	13	152231	2
	total	11935	11	3520	29	4750	24	64227	5	5310	17	16349	11	475	55	5426	19	51347	5	11963	12	18918	10	11363	11	3164	27	7159	18	22996	10	4744	18	9590	12	10261	14	8847	14	28344	7	6758	18	30732	7	30257	7	1544	33	17110	9	387089	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. 1234288. Birmensdorf, Swiss Federal Research Institute WSL

back