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

NFI3
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 2004/06
		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	.	114	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	40	2	100	0	.	78	33	0	.	0	.	226	16
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	315	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	45	1	100	0	.	206	27	0	.	0	.	560	14
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	847	14	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	84	42	26	100	0	.	525	20	0	.	0	.	1488	11
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1265	15	0	.	0	.	0	.	0	.	-1	100	0	.	0	.	0	.	0	.	0	.	86	46	5	100	0	.	774	17	0	.	0	.	2130	11
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1334	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	170	37	0	.	0	.	768	19	0	.	0	.	2272	13
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3875	12	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	411	27	34	100	0	.	2351	14	0	.	0	.	6676	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	.	63	19	0	.	3	100	0	.	7	63	351	15	0	.	3	76	0	.	13	76	22	64	18	44	0	.	0	.	2	80	0	.	66	24	11	60	10	39	186	17	0	.	0	.	755	9
	16-23 cm	0	.	0	.	0	.	257	23	0	.	21	100	0	.	47	66	1125	10	0	.	17	81	0	.	39	84	66	44	50	44	0	.	0	.	5	84	0	.	169	23	35	53	42	43	711	16	0	.	0	.	2585	7
	24-35 cm	0	.	0	.	0	.	860	18	0	.	21	100	0	.	47	75	3114	10	0	.	9	72	0	.	52	74	215	50	115	49	0	.	0	.	43	59	0	.	480	20	46	58	117	40	1871	14	0	.	0	.	6988	7
	36-51 cm	0	.	0	.	0	.	1464	17	0	.	0	.	0	.	210	52	5878	8	0	.	25	100	0	.	35	62	242	40	153	54	0	.	0	.	44	100	0	.	864	19	109	65	273	33	3232	11	0	.	0	.	12528	5
	=52 cm	0	.	0	.	0	.	1264	20	0	.	0	.	0	.	224	48	5193	10	0	.	0	.	0	.	77	77	124	58	166	40	0	.	0	.	93	88	0	.	1073	19	108	57	455	37	3322	13	0	.	0	.	12100	6
	total	0	.	0	.	0	.	3908	15	0	.	45	100	0	.	535	44	15661	7	0	.	53	84	0	.	216	55	669	35	502	35	0	.	0	.	186	65	0	.	2652	15	309	44	898	33	9321	10	0	.	0	.	34955	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	100	0	.	163	19	0	.	46	44	0	.	7	51	641	13	0	.	28	36	0	.	4	61	34	30	13	43	0	.	0	.	11	78	0	.	195	19	33	36	74	32	433	11	0	.	0	.	1684	7
	16-23 cm	0	.	9	100	0	.	393	16	0	.	118	33	0	.	23	51	1817	9	0	.	105	33	0	.	29	56	159	31	43	38	0	.	0	.	104	34	0	.	512	14	165	27	163	27	1540	10	0	.	0	.	5181	5
	24-35 cm	0	.	4	100	0	.	1422	19	0	.	451	35	0	.	65	80	4514	9	0	.	254	40	0	.	40	71	414	30	183	53	0	.	0	.	136	43	0	.	1016	17	561	28	558	26	2915	10	0	.	0	.	12531	5
	36-51 cm	0	.	5	100	0	.	2665	15	0	.	864	27	0	.	103	57	8144	8	0	.	372	32	0	.	39	58	584	24	373	37	0	.	0	.	590	26	0	.	1479	15	753	27	1179	23	3903	10	0	0	0	.	21054	4
	=52 cm	0	.	0	.	0	.	3283	17	0	.	469	34	0	.	475	50	9570	9	0	.	188	45	0	.	51	100	603	27	456	34	0	.	0	.	358	42	0	.	2249	17	876	32	1647	19	4957	12	0	.	0	.	25183	5
	total	0	.	20	100	0	.	7927	14	0	.	1948	24	0	.	672	47	24686	6	0	.	947	29	0	.	163	55	1795	23	1068	30	0	.	0	.	1200	25	0	.	5450	11	2388	23	3621	17	13748	8	0	0	0	.	65633	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	66	51	43	50	54	35	842	11	47	25	160	17	0	.	90	24	168	17	263	15	303	14	240	15	32	33	123	31	234	19	26	50	109	19	205	19	59	45	513	11	68	39	456	11	159	27	44	30	138	26	4444	4
	16-23 cm	182	25	148	27	291	22	2522	8	152	31	874	16	0	.	476	28	576	15	739	14	840	13	641	14	204	35	346	24	1074	13	142	41	543	17	672	17	222	35	1485	11	224	25	1735	10	471	18	145	39	443	25	15147	3
	24-35 cm	482	31	703	26	1007	22	7713	7	491	24	2332	14	0	.	1203	27	1111	16	2265	12	2356	14	1828	13	535	33	686	21	3706	10	483	32	1424	16	1349	15	582	30	2128	11	475	28	4062	9	877	20	290	30	1507	15	39597	3
	36-51 cm	1146	21	1117	24	2237	18	14871	6	962	23	3871	13	0	.	1949	19	2032	15	4403	11	4553	12	3501	11	392	30	1102	21	6906	10	354	36	2323	14	2972	13	762	24	1912	12	456	26	7466	8	989	20	792	26	2838	14	69906	2
	=52 cm	747	28	800	28	2305	23	16336	7	962	30	2976	16	0	.	1350	22	1951	17	2980	17	4609	13	4075	12	297	39	1338	24	7116	11	158	46	1665	22	3432	16	879	31	2438	21	485	38	7395	10	687	25	865	34	2463	16	68310	3
	total	2623	19	2810	22	5895	17	42285	5	2614	22	10213	11	0	.	5068	19	5838	12	10651	10	12660	10	10285	9	1459	26	3595	19	19036	8	1164	29	6064	12	8630	12	2504	22	8476	9	1708	22	21115	7	3183	16	2136	24	7390	12	197403	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	329	15	0	.	0	.	312	14	101	20	118	26	0	.	2	100	5	227	94	25	129	32	20	52	2	100	26	55	97	29	100	26	113	24	15	71	254	21	0	.	9	52	278	17	52	39	14	56	255	19	2324	5
	16-23 cm	1188	12	0	.	0	.	1284	13	303	22	356	20	0	.	0	.	54	44	243	22	446	21	65	39	6	75	71	59	318	24	333	25	367	23	17	51	757	17	0	.	71	60	732	15	111	36	43	46	943	14	7708	4
	24-35 cm	2531	12	0	.	0	.	3213	11	967	18	929	21	0	.	0	.	130	45	722	22	959	20	128	35	28	100	119	52	814	22	694	23	1110	17	154	60	1383	17	0	.	195	45	2211	13	199	35	80	56	1961	14	18526	4
	36-51 cm	4644	10	0	.	0	.	5256	10	1617	17	1624	19	0	.	8	100	223	47	1292	20	1792	23	358	32	79	71	84	66	1406	22	1380	20	2259	15	177	47	2098	16	0	.	170	52	3828	12	96	40	127	62	3641	11	32158	4
	=52 cm	4822	11	0	.	0	.	6447	11	1416	20	1375	23	0	.	0	.	143	46	780	22	1711	23	247	42	11	100	75	100	1019	23	844	24	1983	21	206	65	1931	20	0	.	89	65	2671	16	69	55	52	100	4356	13	30248	4
	total	13514	9	0	.	0	.	16512	8	4404	15	4402	16	0	.	9	100	556	35	3132	17	5036	17	817	30	126	75	375	54	3652	18	3352	16	5832	13	568	45	6423	13	0	.	534	44	9719	11	527	29	316	52	11156	10	90964	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	.	29	53	0	.	95	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	574	13	0	.	3	100	78	22	0	.	2	100	831	12
	16-23 cm	0	.	0	.	0	.	0	.	0	.	0	.	62	45	0	.	226	25	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	1817	9	0	.	36	80	239	22	0	.	11	100	2398	7
	24-35 cm	0	.	0	.	0	.	0	.	0	.	0	.	239	43	0	.	332	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	2457	9	0	.	65	63	277	26	0	.	56	71	3435	8
	36-51 cm	0	.	0	.	0	.	0	.	0	.	0	.	173	51	0	.	160	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	1679	12	0	.	10	100	329	28	0	.	69	61	2427	10
	=52 cm	0	.	0	.	0	.	0	.	0	.	0	.	61	73	0	.	251	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	100	2002	17	0	.	0	.	141	37	0	.	68	100	2535	14
	total	0	.	0	.	0	.	0	.	0	.	0	.	565	39	0	.	1065	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	100	8530	7	0	.	114	59	1064	20	0	.	206	72	11627	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	395	15	44	49	54	35	1380	8	148	15	328	14	29	53	105	21	1375	8	357	13	463	13	260	14	50	29	206	21	362	14	127	23	222	15	233	18	361	21	1381	7	123	24	821	9	986	8	58	27	395	15	10264	2
	16-23 cm	1369	11	157	26	291	22	4457	6	456	18	1368	12	62	45	547	25	4112	5	983	12	1408	11	706	13	278	29	643	17	1484	11	475	21	910	14	798	15	985	15	4021	6	497	17	2709	8	3278	7	189	32	1397	12	33579	2
	24-35 cm	3013	11	707	26	1007	22	13209	5	1458	14	3733	11	239	43	1315	25	10049	5	2987	10	3577	11	1956	12	654	28	1440	16	4817	9	1178	19	2534	12	1681	14	1974	15	6165	6	1303	17	7014	7	6662	7	369	27	3524	10	82564	2
	36-51 cm	5790	9	1122	24	2237	18	24256	5	2579	13	6358	10	173	51	2269	17	17701	5	5695	10	6742	10	3859	10	545	24	2011	14	8839	8	1735	18	4583	10	3783	11	2868	14	6020	6	1493	17	12755	6	9323	6	919	24	6548	8	140203	1
	=52 cm	5570	10	800	28	2305	23	27331	5	2378	17	4820	12	61	73	2049	19	18443	6	3760	14	6508	11	4322	12	436	32	2140	17	8757	10	1003	22	3648	15	4089	15	2822	16	7932	9	1558	22	12168	8	9943	7	918	32	6888	10	140648	2
	total	16137	8	2830	22	5895	17	70632	4	7019	12	16608	8	565	39	6284	16	51680	4	13783	8	18697	8	11103	9	1964	21	6439	13	24258	7	4516	14	11896	9	10584	10	9010	11	25519	4	4974	14	35467	5	30193	5	2452	22	18752	7	407258	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. 1234249. Birmensdorf, Swiss Federal Research Institute WSL

back