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).

economic region #366
Division of Switzerland into 14 regions (2 in the Jura, 3 on the Plateau, 3 in the Pre-Alps, 5 in the Alps and 1 in the Southern Alps). The economic regions are a subdivision of the production regions according to economic-geographical criteria.

accessible forest without shrub forest NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is 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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NFI5
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		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	±%
n/a	n/a	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	.
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	158	100	0	.	0	.	23	100	178	70	45	100	414	50
	high-montane	0	.	0	.	0	.	0	.	0	.	0	.	4	100	68	100	2	71	0	.	17	100	21	73	413	45	68	71	592	34
	lower and upper montane	97	100	0	.	16	100	0	.	0	.	170	100	0	.	36	100	34	100	11	100	2	100	10	100	0	.	0	.	375	54
	submontane	17	100	0	.	85	72	87	85	143	81	0	.	0	.	0	.	52	100	0	.	0	.	0	.	0	.	0	.	384	42
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0
	total	114	86	0	.	101	63	87	85	143	81	170	100	13	77	104	74	246	69	11	100	19	90	54	55	590	38	113	58	1766	22
young growth/ thicket	n/a	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	.	5	100	44	65	39	100	88	55
	subalpine	0	.	0	.	0	.	0	.	0	.	145	100	140	66	0	.	3	100	6	100	1	100	40	68	584	46	30	94	947	34
	high-montane	3	100	0	.	0	.	0	.	0	.	249	53	98	93	72	100	376	48	37	100	28	100	91	59	988	43	527	57	2469	24
	lower and upper montane	347	44	66	100	14	207	221	87	314	51	155	76	875	43	496	33	177	58	39	74	73	73	0	.	191	59	56	92	3025	18
	submontane	286	44	246	50	231	57	466	48	330	46	0	.	47	100	29	74	136	100	0	.	0	.	0	.	0	.	0	.	1771	21
	hyperinsubric and colline	0	.	0	.	24	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	27	100	53	60
	total	636	31	313	45	269	51	687	43	644	34	549	41	1160	35	597	30	691	36	82	58	101	59	137	44	1807	28	678	46	8352	11
pole timber	n/a	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	.	74	65	311	31	178	40	563	23
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	149	68	0	.	506	41	964	33	336	46	1994	21
	high-montane	56	76	0	.	0	.	0	.	0	.	212	77	168	96	124	72	968	33	326	70	24	100	1685	22	1878	20	557	35	5998	12
	lower and upper montane	1871	25	186	54	525	39	1074	33	862	38	682	36	2103	24	1707	28	600	38	580	42	597	52	106	69	59	100	1776	21	12725	9
	submontane	799	32	1029	26	1474	31	1416	21	3505	15	0	.	159	100	0	.	181	64	0	.	0	.	239	56	33	100	0	.	8835	10
	hyperinsubric and colline	0	.	0	.	71	100	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	185	43	0	.	2247	19	2526	18
	total	2725	19	1214	23	2070	24	2491	18	4391	14	894	33	2430	22	1831	26	1787	23	1055	33	621	50	2794	17	3246	16	5093	12	32642	5
young timber	n/a	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	.	513	48	511	40	211	74	1235	29
	subalpine	0	.	0	.	0	.	0	.	0	.	166	100	830	55	0	.	302	58	53	100	193	72	439	46	445	60	320	50	2746	24
	high-montane	0	.	0	.	118	100	0	.	0	.	177	71	253	59	0	.	311	64	131	100	52	100	2171	29	2757	24	292	55	6262	16
	lower and upper montane	4199	19	767	40	256	61	1533	34	1623	30	1710	33	1576	31	2073	33	710	37	573	49	1146	38	385	51	489	61	2918	22	19957	9
	submontane	836	36	884	38	957	35	2875	23	3227	20	0	.	308	71	170	74	212	77	435	65	0	.	11	100	358	60	0	.	10273	11
	hyperinsubric and colline	71	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	267	51	0	.	3115	18	3453	17
	total	5106	17	1651	27	1331	29	4408	19	4850	16	2053	29	2967	24	2243	31	1534	26	1192	35	1391	33	3784	19	4559	18	6857	13	43925	6
medium timber	n/a	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	.	210	100	733	38	22	100	966	36
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	134	100	0	.	551	58	0	.	97	100	736	58	2587	27	529	55	4634	20
	high-montane	0	.	0	.	0	.	0	.	0	.	728	62	213	82	529	58	624	54	1044	59	0	.	2078	32	3568	22	2811	25	11595	13
	lower and upper montane	11354	12	1518	31	679	56	1118	50	3402	24	2389	28	3485	25	6554	19	1862	32	1055	48	1600	31	787	46	1135	43	1118	34	38057	7
	submontane	1726	30	2994	24	2395	30	3799	22	3106	20	0	.	532	72	627	54	96	100	0	.	0	.	0	.	108	100	0	.	15382	10
	hyperinsubric and colline	94	100	0	.	301	71	0	.	60	100	0	.	0	.	0	.	0	.	0	.	0	.	215	66	0	.	1506	32	2175	25
	total	13173	11	4512	18	3375	25	4917	20	6567	15	3118	26	4364	22	7711	17	3133	24	2099	38	1698	29	4027	22	8131	14	5986	16	72809	5
old timber	n/a	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	.	559	46	1021	35	81	100	1660	27
	subalpine	0	.	0	.	0	.	0	.	0	.	595	56	668	49	82	100	341	62	576	59	196	100	4303	21	5379	20	2059	28	14199	11
	high-montane	396	65	0	.	55	100	0	.	0	.	2417	33	1543	37	914	48	3651	29	669	54	1050	54	5283	21	8196	17	4623	21	28799	9
	lower and upper montane	14552	11	1666	39	4118	26	4568	24	6696	19	3957	23	13982	13	16260	12	3534	26	3280	31	2648	29	711	57	2187	27	2285	31	80445	5
	submontane	2610	29	3061	24	2170	30	8151	15	11190	12	122	100	1390	39	151	100	744	59	407	71	142	100	83	100	118	100	0	.	30339	7
	hyperinsubric and colline	0	.	0	.	160	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	100	0	.	1648	30	1951	28
	total	17558	10	4727	20	6503	19	12718	12	17887	10	7091	18	17584	11	17407	12	8270	17	4933	24	4036	24	11081	14	16901	11	10697	12	157392	3
mixed	n/a	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	.	1050	26	1580	26	763	28	3393	16
	subalpine	0	.	0	.	77	100	0	.	0	.	198	87	1126	37	75	100	2395	29	396	58	846	53	3854	23	4898	18	2098	24	15965	10
	high-montane	0	.	0	.	69	100	0	.	0	.	1003	42	1399	33	218	51	2014	28	636	43	271	62	4464	18	3630	19	2541	24	16246	9
	lower and upper montane	12643	10	422	50	334	60	1004	39	1177	35	2827	28	9201	13	4066	22	1773	32	870	46	1857	30	1235	37	1515	31	3468	40	42391	7
	submontane	2083	24	917	38	1261	41	2226	30	2234	22	0	.	965	39	1086	47	61	100	514	75	92	100	120	100	212	74	0	.	11771	12
	hyperinsubric and colline	0	.	0	.	0	0	0	.	86	100	0	.	0	.	0	.	0	.	0	.	0	.	321	53	113	100	2877	19	3397	18
	total	14726	9	1338	30	1742	32	3230	24	3497	18	4028	23	12691	11	5445	19	6243	17	2416	27	3067	24	11046	12	11948	10	11747	14	93163	4
total	n/a	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	.	2411	20	4200	15	1294	22	7906	11
	subalpine	0	.	0	.	77	100	0	.	0	.	1103	39	2908	24	157	71	3787	22	1180	36	1333	39	9901	13	15034	11	5418	15	40899	6
	high-montane	454	57	0	.	242	61	0	.	0	.	4787	22	3679	21	1925	29	7946	16	2843	28	1442	41	15793	10	21431	9	11420	11	71961	5
	lower and upper montane	45061	4	4625	19	5942	20	9518	15	14075	12	11891	12	31222	7	31192	8	8690	14	6409	19	7922	15	3234	23	5575	17	11620	14	196975	2
	submontane	8356	13	9130	12	8573	14	19020	9	23735	7	122	100	3401	23	2063	31	1482	35	1356	41	235	72	452	43	829	37	0	.	78755	4
	hyperinsubric and colline	165	71	0	.	556	49	0	.	169	63	0	.	0	.	0	.	0	.	0	.	0	.	1132	27	113	100	11420	8	13555	7
	total	54037	4	13755	10	15390	11	28538	7	37979	5	17902	10	41210	6	35338	7	21905	9	11788	14	10932	13	32923	7	47182	5	41172	4	410050	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. 1362731. Birmensdorf, Swiss Federal Research Institute WSL

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