Selection from the list of topics

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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

development stage #20
Stage of stand development, defined by the dominant diameter at breast height (dbhdom = dbh of the 100 largest [thickest] trees per hectare). Young growth/thicket: dbhdom <12 cm, pole timber: dbhdom 12-30 cm, young timber: dbhdom 31-40 cm, medium timber : dbhdom 41-50 cm, old timber: dbhdom >50 cm, mixed: trees of different development stages, no development stage predominant or groups of different development stages covering < 500 m². Reference: Field Survey (MID 261: Entwicklungsstufe)

altitudinal vegetation belts (NaiS; 6 classes) #2633
Altitudinal vegetation belts in the system used in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005), reduced to six classes. The variable represents a simplification of the NaiS altitudinal vegetation belts with ten classes (NAISHSTKOMB) in which the classes «hyperinsubric» and «colline» are merged with «colline with beech» to form the class «hyperinsubric and colline» and the class «lower montane» with «upper montane» and «lower/upper montane» to form the class «lower and upper montane». The information is based on the altitudinal vegetation belts determined by experts (accessible forest sample plots of NFI4 on the 1.4-km network; Arge Frehner et al. 2020), as well as on the altitudinal vegetation belts modelled for the period 1981-2010 (other sample plots; Zischg et al. 2021).

canton #827
Regional demarcation with the cantons as a unit, with the two half-cantons, Basel-Land and Basel-Stadt, combined into one canton for statistical reasons.

accessible forest without shrub forest NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

1.4 km grid, subgrids 1-5 #1746
Sub-grids 1, 2, 3, 4 and 5 of the field surveys on the sampling grid with a mesh size of 1.4 km (base grid).

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI4
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
development stage	altitudinal vegetation belts (NaiS; 6 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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	0	.	51	100
	subalpine	0	.	0	.	0	.	157	.	0	.	0	.	0	.	0	.	132	48	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	83	0	.	0	.	337	51
	high-montane	0	.	0	.	0	.	117	72	0	.	0	.	0	.	0	.	502	59	0	.	0	.	0	.	0	.	1	.	0	0	0	.	0	.	117	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	736	44
	lower and upper montane	0	.	0	.	0	.	54	85	0	.	231	71	0	.	0	.	24	.	0	.	24	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	44	152	0	.	0	.	0	.	399	47
	submontane	0	.	0	.	0	.	6	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	.	0	.	0	.	23	63
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.
	total	0	.	0	.	0	.	333	55	0	.	236	70	9	.	0	.	658	46	0	.	24	.	0	.	0	.	21	96	0	0	0	.	0	.	139	86	0	.	0	.	0	.	57	120	80	70	0	.	0	.	1555	27
young growth/ thicket	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	.
	upper subalpine	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	.
	subalpine	0	.	0	.	0	.	112	80	0	.	0	.	0	.	0	.	114	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	97	82	0	.	0	.	324	51
	high-montane	0	.	0	.	0	.	376	71	0	.	46	75	0	.	0	.	734	44	0	.	0	.	0	.	0	.	6	.	32	100	0	.	0	.	0	.	0	.	63	91	140	82	163	70	344	47	0	.	0	.	1905	25
	lower and upper montane	118	.	0	.	171	82	725	35	0	.	374	43	0	.	222	71	56	100	8	71	429	54	209	79	0	.	30	96	400	47	60	.	4	100	52	67	47	100	0	.	22	100	178	66	-1	3155	61	76	197	66	3365	16
	submontane	727	34	0	.	0	.	425	36	53	97	29	71	0	.	0	.	12	.	32	.	257	64	0	.	0	.	0	.	0	.	0	.	2	100	0	.	40	75	0	.	0	.	189	60	0	.	0	.	394	48	2161	19
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	.	0	.	0	.	43	100	0	.	20	.	91	60
	total	845	32	0	.	171	82	1638	25	53	97	449	37	0	.	222	71	917	38	41	80	686	41	209	79	0	.	36	81	432	44	60	100	6	75	52	67	88	64	93	69	162	72	530	38	482	39	61	76	610	37	7845	11
pole timber	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	259	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	.	0	.	0	.	171	.	0	.	0	.	456	45
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	491	44	0	.	0	.	0	.	211	.	0	.	0	.	0	.	0	.	0	.	0	.	168	63	8	.	0	.	379	48	0	.	0	.	1258	29
	high-montane	0	.	0	.	0	.	489	44	0	.	0	.	0	.	0	.	1381	23	0	.	123	.	0	.	0	.	0	.	0	.	0	.	0	.	13	.	0	.	331	43	147	75	297	54	1501	28	0	.	0	.	4282	14
	lower and upper montane	65	.	0	.	252	69	1291	29	183	82	559	47	0	.	268	65	295	52	505	60	1050	33	281	41	102	.	0	.	485	41	16	.	334	45	633	46	290	84	1071	27	312	61	1262	33	17	.	207	74	617	50	10094	11
	submontane	1677	28	0	.	0	.	1158	25	430	47	496	47	0	.	0	.	185	84	100	84	366	51	145	48	0	.	63	.	65	.	599	42	133	77	57	.	880	31	0	.	0	.	911	32	271	53	0	.	1519	28	9055	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	374	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2837	17	0	.	72	.	172	46	0	.	0	.	3455	16
	total	1741	27	0	.	252	69	2937	18	613	41	1055	33	0	.	268	65	2985	17	605	52	1539	27	426	31	313	75	63	.	549	38	614	41	468	39	703	42	1170	31	4433	13	467	47	2542	21	2512	20	207	74	2136	24	28600	6
young timber	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	413	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	.	0	.	0	.	281	60	0	.	0	.	739	33
	subalpine	0	.	0	.	0	.	232	58	0	.	0	.	0	.	0	.	739	43	0	.	0	.	0	.	0	.	102	.	79	.	0	.	0	.	0	.	0	.	210	65	30	.	0	.	471	55	0	.	0	.	1864	25
	high-montane	0	.	0	.	0	.	355	60	0	.	577	69	0	.	0	.	2160	26	0	.	171	71	0	.	66	.	65	.	27	.	0	.	0	.	64	.	0	.	423	60	0	.	0	.	1053	39	0	.	0	.	4962	18
	lower and upper montane	319	58	161	.	479	72	3505	22	339	50	1639	34	0	.	647	58	523	52	672	46	962	50	503	63	539	62	288	71	2645	26	434	58	1223	36	718	43	211	.	1928	26	562	54	1768	30	338	54	131	.	874	40	21406	9
	submontane	693	39	0	.	0	.	1820	27	342	59	669	58	0	.	0	.	71	95	339	73	461	53	0	.	0	.	109	.	264	59	910	40	577	41	0	.	985	43	0	.	407	75	1252	34	0	.	0	.	1654	29	10552	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	353	58	0	.	214	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1920	26	0	.	186	71	259	58	0	.	0	.	2933	20
	total	1012	32	161	.	479	72	5912	16	682	39	2885	27	353	58	647	58	4119	18	1011	39	1595	35	503	63	605	56	564	46	3016	24	1344	33	1800	28	782	41	1195	40	4526	16	999	43	3206	22	2402	23	131	.	2528	23	42455	6
medium timber	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	845	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	845	36
	subalpine	0	.	0	.	0	.	313	66	0	.	0	.	0	.	100	.	4124	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	518	51	213	.	170	71	650	56	0	.	0	.	6089	17
	high-montane	0	.	0	.	0	.	831	51	0	.	123	.	0	.	0	.	5515	19	0	.	0	.	0	.	0	.	0	.	352	71	0	.	0	.	174	.	0	.	1084	35	1670	42	632	76	2456	27	0	.	0	.	12837	12
	lower and upper montane	507	61	1539	40	1647	40	6302	17	658	54	3129	26	0	.	1765	38	1166	35	4109	23	1448	36	1126	33	509	58	727	59	3625	24	630	51	1520	31	1950	32	829	47	1003	37	390	72	4196	22	745	53	372	71	2845	26	42735	6
	submontane	2017	25	0	.	0	.	2470	25	1189	39	1619	35	0	.	0	.	87	.	701	46	1842	42	363	57	0	.	0	.	103	.	1108	41	1317	34	398	76	2063	31	0	.	0	.	1683	30	0	.	137	.	2808	25	19905	9
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	934	42	0	.	0	.	127	.	0	.	117	.	1178	36
	total	2524	23	1539	40	1647	40	9916	13	1847	31	4871	20	0	.	1865	37	11737	12	4810	21	3290	28	1489	29	509	58	727	59	4080	22	1738	32	2836	23	2522	28	2892	26	3540	20	2273	34	6681	17	3978	21	508	59	5770	18	83589	4
old timber	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1051	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	555	47	0	.	0	.	1606	33
	subalpine	0	.	0	.	0	.	392	67	0	.	0	.	0	.	0	.	4489	20	0	.	0	.	0	.	0	.	0	.	175	.	0	.	0	.	348	71	0	.	476	52	0	.	204	71	3434	23	0	.	0	.	9518	14
	high-montane	0	.	0	.	0	.	2972	29	0	.	819	50	0	.	867	62	8545	16	0	.	0	.	0	.	0	.	785	53	266	.	0	.	0	.	675	51	0	.	2151	33	217	.	1415	45	3601	25	0	.	0	.	22312	10
	lower and upper montane	542	58	1460	46	3020	34	15104	12	644	65	2881	30	0	.	1316	44	1928	31	1241	39	6935	21	2968	28	698	58	1889	39	7875	19	255	.	1873	30	4078	24	483	71	1065	41	831	52	7740	17	385	60	0	.	2944	28	68154	5
	submontane	5744	19	0	.	0	.	9056	16	888	45	1087	46	0	.	0	.	102	.	1823	35	1850	32	463	72	0	.	0	.	1197	44	995	48	1397	42	174	.	2695	30	0	.	0	.	2367	31	0	.	190	.	2800	26	32828	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	496	48	0	.	0	.	133	.	0	.	0	.	880	42
	total	6286	18	1460	46	3020	34	27523	9	1532	38	4787	22	252	.	2183	36	16115	11	3064	26	8785	18	3431	26	698	58	2675	32	9513	17	1250	43	3271	25	5275	21	3178	27	4187	21	1048	46	11726	14	8107	15	190	.	5744	19	135299	3
mixed	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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1172	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	71	0	.	0	.	917	26	0	.	0	.	2207	19
	subalpine	0	.	0	.	0	.	3132	24	0	.	0	.	0	.	274	79	4187	20	0	.	27	.	0	.	126	.	414	56	385	69	0	.	0	.	0	.	0	.	1462	29	321	59	173	77	3605	24	0	.	0	.	14106	11
	high-montane	0	.	0	.	0	.	2441	33	0	.	157	.	0	.	152	80	4906	19	0	.	353	61	0	.	0	.	1037	41	159	82	0	.	0	.	177	75	0	.	1530	35	407	58	650	44	4744	18	0	.	0	.	16714	10
	lower and upper montane	147	.	-1	.	72	.	11412	13	69	.	1486	37	0	.	547	56	1954	28	1873	29	2756	27	4883	17	505	67	918	39	3120	26	0	.	794	40	479	48	539	62	2026	24	448	59	4297	18	1345	36	200	71	962	42	40833	6
	submontane	981	38	0	.	0	.	1447	34	786	41	580	51	0	.	0	.	178	76	880	41	887	44	296	62	0	.	0	.	601	54	164	.	939	45	0	.	377	51	0	.	385	72	958	41	58	.	0	.	618	43	10134	12
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	445	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	.	3052	20	0	.	0	.	249	58	0	.	0	.	3829	17
	total	1128	35	-1	.	72	.	18432	10	855	39	2224	29	0	.	973	40	12842	11	2753	24	4023	21	5179	16	631	57	2369	25	4264	22	164	.	1733	30	656	40	998	39	8188	12	1561	31	6079	15	10917	12	200	71	1581	30	87821	4
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	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3739	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	189	52	0	.	0	.	1974	21	0	.	0	.	5903	13
	subalpine	0	.	0	.	0	.	4337	20	0	.	0	.	0	.	374	64	14276	11	0	.	27	.	0	.	338	73	536	47	639	51	0	.	0	.	348	71	0	.	2835	20	573	50	547	42	8665	14	0	.	0	.	33496	7
	high-montane	0	.	0	.	0	.	7580	17	0	.	1723	35	0	.	1019	54	23743	8	0	.	647	42	0	.	66	.	1895	31	835	47	0	.	0	.	1219	35	0	.	5582	17	2581	30	3157	27	13699	11	0	.	0	.	63748	5
	lower and upper montane	1698	30	3158	29	5641	22	38392	7	1893	31	10300	14	0	.	4766	21	5947	16	8409	15	13604	13	9969	12	2352	29	3852	24	18149	11	1394	35	5749	16	7933	16	2400	29	7093	12	2565	26	19484	9	2828	24	971	37	8439	15	186986	2
	submontane	11838	11	0	.	0	.	16381	11	3689	20	4485	20	0	.	0	.	635	40	3875	21	5663	19	1268	34	0	.	172	73	2230	29	3775	21	4365	20	629	56	7040	16	0	.	791	52	7373	15	329	47	326	72	9793	12	84658	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	614	53	0	.	1033	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	.	9268	9	0	.	258	58	983	30	0	.	137	87	12375	8
	total	13536	11	3158	29	5641	22	66691	5	5581	16	16508	11	614	53	6159	19	49373	5	12284	12	19942	11	11237	11	2756	27	6455	18	21853	10	5169	18	10114	12	10129	14	9523	14	24967	5	6511	17	30820	7	28478	7	1297	33	18369	9	387165	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 1343020. Birmensdorf, Swiss Federal Research Institute WSL

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