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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total 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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NFI1
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 1983/85
		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	.	18	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	.
	subalpine	0	.	0	.	0	.	131	67	0	.	0	.	0	.	0	.	366	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	0	.	2	100	0	.	0	.	508	38
	high-montane	0	.	0	.	0	.	109	80	0	.	48	.	0	.	0	.	72	88	0	.	0	.	31	.	0	.	178	83	0	.	0	.	0	.	0	.	0	.	0	.	33	.	144	73	483	49	0	.	0	.	1098	29
	lower and upper montane	0	.	14	.	0	.	1040	32	120	.	851	47	0	.	265	73	81	84	15	100	62	93	235	74	0	.	11	.	437	69	80	.	0	.	11	100	140	.	26	77	0	.	304	53	0	.	0	.	340	75	4032	19
	submontane	618	45	0	.	0	.	8	100	267	58	0	.	0	.	0	.	0	.	0	.	351	58	263	.	0	.	0	.	0	.	182	.	69	.	0	.	298	84	0	.	0	.	0	.	25	.	0	.	127	100	2206	26
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	.	0	.	26	.	36	77	0	.	0	.	89	52
	total	618	45	14	.	0	.	1287	28	387	51	899	45	0	.	265	73	536	37	15	100	413	51	528	60	0	.	189	79	437	69	262	76	69	.	11	100	438	66	61	56	33	100	474	41	546	43	0	.	467	61	7950	13
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	.	88	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	88	97
	subalpine	0	.	0	.	0	.	62	92	0	.	0	.	0	.	0	.	112	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	4	.	0	.	157	56	0	.	0	.	338	44
	high-montane	0	.	0	.	0	.	656	42	0	.	0	.	0	.	0	.	558	42	0	.	64	.	0	.	0	.	0	.	129	95	0	.	0	.	114	71	0	.	54	76	78	.	45	96	150	51	0	.	0	.	1848	23
	lower and upper montane	9	.	0	.	7	100	427	41	0	.	61	67	0	.	7	83	105	66	0	.	123	48	100	71	3	.	4	75	149	83	0	.	2	100	123	84	18	100	65	48	7	.	152	60	2	71	75	100	11	100	1452	20
	submontane	254	54	0	.	0	.	151	57	74	100	44	100	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	42	77	84	80	76	81	0	.	84	69	0	.	0	.	7	78	0	.	0	.	224	52	1057	23
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	53	0	.	0	.	52	81	0	.	0	.	82	55
	total	263	52	0	.	7	100	1297	27	74	100	105	57	0	.	7	83	863	33	0	.	204	43	100	71	3	.	4	75	320	55	84	80	77	80	237	56	102	59	152	35	90	87	205	49	361	34	75	100	235	50	4866	12
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	.	150	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	240	52	0	.	0	.	390	37
	subalpine	0	.	0	.	0	.	103	58	0	.	0	.	0	.	0	.	524	36	0	.	0	.	0	.	112	.	194	68	10	.	0	.	0	.	0	.	0	.	34	69	0	.	0	.	282	55	0	.	0	.	1259	24
	high-montane	0	.	0	.	0	.	226	64	0	.	105	82	0	.	39	.	1104	29	0	.	142	.	0	.	0	.	172	.	21	.	0	.	0	.	272	86	0	.	93	60	371	72	28	.	1373	27	0	.	0	.	3946	17
	lower and upper montane	67	.	95	.	274	74	2242	22	181	61	703	36	0	.	314	76	249	48	597	45	364	52	189	64	186	54	393	53	1061	32	450	50	256	60	531	36	74	84	970	26	427	43	1880	28	193	74	42	78	1083	34	12821	9
	submontane	1543	25	0	.	0	.	1369	27	651	40	471	55	0	.	0	.	43	.	253	52	474	63	161	71	0	.	0	.	279	56	407	51	398	47	20	.	251	42	0	.	99	.	1215	29	57	74	85	.	1023	27	8801	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	253	51	0	.	437	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2746	15	0	.	59	.	470	37	0	.	0	.	3964	12
	total	1611	24	95	.	274	74	3939	16	832	34	1279	29	253	51	353	69	2507	17	851	35	980	39	350	48	299	50	759	40	1370	27	858	36	653	37	823	37	325	38	3843	12	897	38	3182	20	2615	18	127	72	2106	22	31181	5
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	.	150	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	.	0	.	0	.	221	61
	subalpine	0	.	0	.	0	.	1127	36	0	.	0	.	0	.	0	.	918	48	0	.	0	.	0	.	0	.	0	.	248	73	0	.	0	.	0	.	0	.	277	58	236	.	80	.	1296	42	0	.	0	.	4182	21
	high-montane	0	.	0	.	0	.	581	47	0	.	653	60	0	.	66	.	1781	34	0	.	90	.	0	.	0	.	0	.	0	.	0	.	0	.	131	75	0	.	456	65	953	47	466	68	798	35	0	.	0	.	5974	17
	lower and upper montane	658	48	470	76	1655	39	4444	19	167	71	2709	29	0	.	1246	41	827	46	1908	31	2270	28	971	37	403	64	483	54	2201	29	493	60	1631	33	1033	45	249	82	837	32	0	.	3510	20	509	46	0	.	2038	30	30713	7
	submontane	2718	24	0	.	0	.	2285	29	663	49	997	45	0	.	0	.	198	.	945	43	712	42	175	72	0	.	158	.	173	.	1100	40	1099	40	177	.	348	59	0	.	103	.	2201	26	0	.	0	.	1022	40	15073	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1175	27	0	.	0	.	0	.	0	.	208	.	1383	27
	total	3376	22	470	76	1655	39	8436	14	831	41	4358	23	0	.	1312	39	3874	22	2853	25	3072	23	1146	33	403	64	642	47	2621	26	1592	33	2730	25	1341	38	597	48	2745	19	1292	40	6256	15	2674	24	0	.	3269	23	57546	5
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	.	498	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	.	0	.	0	.	605	40
	subalpine	0	.	0	.	0	.	1429	42	0	.	0	.	0	.	45	.	3502	22	0	.	0	.	0	.	0	.	183	.	0	.	0	.	0	.	219	74	0	.	503	46	177	.	0	.	1263	32	0	.	0	.	7322	15
	high-montane	0	.	0	.	0	.	2601	29	0	.	267	63	0	.	0	.	6401	18	0	.	247	71	0	.	0	.	238	.	149	.	0	.	0	.	381	71	0	.	1362	30	330	71	1107	43	1566	31	110	.	0	.	14759	11
	lower and upper montane	692	51	1650	37	1583	41	14723	13	414	63	2092	38	0	.	1357	41	630	42	2848	24	5139	22	1481	36	849	51	2402	38	5221	21	213	.	2292	25	2418	30	324	71	561	49	1041	45	4080	22	338	59	495	57	3103	25	55945	6
	submontane	5646	19	0	.	0	.	8933	16	1079	43	2058	35	0	.	0	.	695	47	1422	34	3416	24	389	66	0	.	0	.	507	58	473	51	1616	35	218	71	2428	27	0	.	0	.	2240	31	106	.	125	.	4970	20	36320	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	183	.	0	.	48	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	353	54	0	.	0	.	107	.	0	.	0	.	766	39
	total	6338	18	1650	37	1583	41	27685	9	1493	35	4417	24	183	.	1402	40	11773	12	4270	20	8801	16	1870	32	849	51	2823	34	5877	19	686	47	3908	21	3237	25	2826	25	2780	20	1548	35	7428	16	3488	20	731	45	8073	15	115717	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	.	873	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	267	62	0	.	0	.	1140	35
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	240	71	1874	29	0	.	0	.	0	.	112	.	0	.	0	.	0	.	0	.	0	.	0	.	298	76	0	.	181	70	1718	30	0	.	0	.	4423	18
	high-montane	0	.	0	.	0	.	2064	34	0	.	0	.	0	.	669	65	6928	17	0	.	0	.	0	.	0	.	0	.	1137	47	0	.	0	.	0	.	0	.	1274	34	118	.	246	75	1056	40	0	.	0	.	13492	12
	lower and upper montane	0	.	467	75	1064	58	8892	16	561	60	998	61	0	.	779	52	1648	32	1723	29	1691	40	1190	39	0	.	802	60	3683	27	138	.	1144	43	2597	29	272	.	706	51	218	.	2942	28	119	.	205	.	988	42	32827	8
	submontane	2853	29	0	.	0	.	4470	22	687	55	1078	45	0	.	0	.	162	.	722	50	1317	40	0	.	0	.	0	.	1031	61	472	61	941	55	0	.	2034	31	0	.	94	.	335	74	123	.	0	.	2589	30	18907	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	649	50	0	.	0	.	0	.	0	.	64	.	760	44
	total	2853	29	467	75	1064	58	15426	12	1248	41	2076	38	0	.	1687	37	11532	12	2445	25	3008	28	1190	39	112	.	802	60	5850	22	611	53	2084	34	2597	29	2306	29	2927	23	430	62	3704	24	3283	21	205	.	3641	24	71549	5
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	.	1173	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	57	0	.	0	.	744	28	0	.	0	.	2037	18
	subalpine	0	.	0	.	0	.	767	39	0	.	0	.	0	.	0	.	3997	17	0	.	2	.	0	.	0	.	203	73	371	63	0	.	0	.	0	.	0	.	814	31	205	64	149	56	2410	23	0	.	0	.	8917	11
	high-montane	0	.	0	.	0	.	792	38	0	.	195	71	0	.	0	.	5052	17	0	.	41	.	0	.	87	.	956	39	213	.	0	.	0	.	222	62	0	.	623	40	162	.	459	47	4663	17	0	.	0	.	13464	10
	lower and upper montane	68	.	65	.	514	69	6715	15	299	74	1944	29	0	.	358	71	732	39	367	54	1882	28	4165	17	224	93	520	44	2979	23	0	.	671	43	600	44	601	49	1216	26	108	76	3546	18	738	34	186	.	675	45	29174	7
	submontane	879	41	0	.	0	.	2001	29	279	67	530	51	0	.	0	.	0	.	296	71	201	71	184	50	0	.	0	.	243	77	83	.	399	53	0	.	734	39	0	.	98	.	468	51	64	.	0	.	799	40	7258	13
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1126	28	0	.	46	.	246	51	0	.	0	.	1428	24
	total	947	39	65	.	514	69	10275	12	577	50	2669	24	0	.	358	71	10963	10	663	43	2126	26	4349	17	311	72	1679	27	3806	21	83	.	1070	34	821	36	1335	31	3899	14	572	43	4668	15	8866	11	186	.	1475	30	62279	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	.	2951	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	120	57	0	.	0	.	1429	22	0	.	0	.	4500	14
	subalpine	0	.	0	.	0	.	3618	22	0	.	0	.	0	.	285	62	11292	11	0	.	2	.	0	.	225	71	580	46	628	47	0	.	0	.	219	74	0	.	1937	22	622	52	410	42	7128	14	0	.	0	.	26948	7
	high-montane	0	.	0	.	0	.	7029	16	0	.	1268	36	0	.	773	57	21895	9	0	.	584	43	31	.	87	.	1544	32	1649	37	0	.	0	.	1120	36	0	.	3863	18	2045	30	2496	26	10088	11	110	.	0	.	54581	5
	lower and upper montane	1494	32	2760	29	5097	23	38483	7	1743	30	9358	15	0	.	4326	21	4272	18	7458	14	11531	13	8330	12	1665	33	4616	24	15732	11	1374	33	5995	16	7313	16	1678	31	4382	14	1801	30	16413	10	1900	23	1004	40	8239	14	166964	3
	submontane	14513	11	0	.	0	.	19215	10	3699	20	5178	20	0	.	0	.	1097	38	3640	21	6487	17	1172	35	0	.	158	100	2273	33	2801	23	4596	20	415	57	6177	16	0	.	393	50	6467	16	376	48	211	72	10754	13	89623	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	435	51	0	.	542	36	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	.	6106	10	0	.	131	60	912	27	0	.	272	80	8473	9
	total	16007	10	2760	29	5097	23	68345	5	5442	16	15804	11	435	51	5385	19	42049	6	11098	11	18604	10	9533	12	1977	29	6898	18	20282	10	4175	19	10592	12	9067	14	7930	14	16408	6	4862	18	25917	8	21833	7	1324	33	19265	9	351089	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. 1362674. Birmensdorf, Swiss Federal Research Institute WSL

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