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 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: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
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	.	0	.	0	.	0	.	0	.
	subalpine	0	.	0	.	0	.	168	94	0	.	0	.	0	.	0	.	223	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	0	.	0	.	414	50
	high-montane	0	.	0	.	0	.	2	71	0	.	0	.	0	.	17	.	422	44	0	.	4	.	0	.	0	.	0	.	68	.	0	.	0	.	0	.	0	.	59	81	0	.	0	.	21	73	0	.	0	.	592	34
	lower and upper montane	0	.	0	.	36	.	34	100	0	.	0	.	0	.	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	97	.	11	100	0	.	0	.	0	.	186	92	10	.	0	.	0	.	375	54
	submontane	0	.	0	.	0	.	73	.	0	.	34	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	0	.	14	.	0	.	140	83	0	.	0	.	104	71	0	.	0	.	3	.	384	42
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	36	.	277	64	0	.	34	.	0	.	19	90	645	35	17	.	4	100	0	.	0	.	0	.	68	.	0	.	111	88	11	100	140	83	59	81	0	.	289	64	54	55	0	.	3	.	1766	22
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	.	44	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	.	0	.	0	.	5	100	0	.	0	.	88	55
	subalpine	0	.	0	.	0	.	264	64	0	.	0	.	0	.	0	.	611	44	0	.	0	.	0	.	0	.	23	.	1	100	0	.	0	.	0	.	0	.	2	100	6	100	0	.	40	68	0	.	0	.	947	34
	high-montane	0	.	0	.	0	.	460	45	0	.	165	62	0	.	0	.	1508	35	0	.	0	.	3	.	0	.	7	.	28	100	0	.	0	.	72	.	0	.	8	54	37	100	91	82	91	59	0	.	0	.	2469	24
	lower and upper montane	0	.	0	.	133	72	844	44	0	.	155	76	0	.	46	100	191	59	35	73	414	57	49	100	0	.	14	77	127	58	66	.	24	94	155	57	117	100	56	92	25	100	254	57	0	.	78	71	241	50	3025	18
	submontane	310	45	0	.	0	.	592	44	74	68	132	80	0	.	0	.	0	.	63	77	130	70	52	.	0	.	0	.	29	74	13	.	33	.	0	.	0	.	0	.	0	.	164	63	0	.	0	.	178	70	1771	21
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	24	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	.	0	.	0	.	2	100	0	.	0	.	53	60
	total	310	45	0	.	133	72	2160	24	74	68	452	42	24	71	46	100	2354	25	98	56	544	46	104	69	0	.	44	60	184	44	80	85	57	70	227	50	117	100	131	53	69	67	509	38	137	44	78	71	420	41	8352	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	.	365	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	49	0	.	0	.	74	65	0	.	0	.	563	23
	subalpine	0	.	0	.	0	.	38	.	0	.	0	.	0	.	0	.	974	33	0	.	0	.	0	.	0	.	94	.	0	.	0	.	0	.	16	.	0	.	327	47	39	85	0	.	506	41	0	.	0	.	1994	21
	high-montane	0	.	0	.	0	.	818	36	0	.	0	.	0	.	0	.	2056	19	0	.	160	.	0	.	0	.	8	.	72	74	0	.	0	.	77	.	0	.	379	40	326	70	418	51	1685	22	0	.	0	.	5998	12
	lower and upper montane	22	.	0	.	266	88	1975	23	192	54	465	42	0	.	448	64	300	52	532	62	1435	31	456	45	187	76	118	67	819	35	46	.	495	42	977	36	147	74	1534	22	480	49	1140	27	106	69	120	71	468	57	12725	9
	submontane	1380	24	0	.	0	.	1007	24	580	38	528	54	0	.	0	.	33	.	151	72	390	46	146	51	0	.	159	.	52	.	273	58	118	69	0	.	784	34	0	.	0	.	1274	31	239	56	0	.	1722	22	8835	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	195	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2052	20	0	.	71	.	185	43	0	.	24	.	2526	18
	total	1402	23	0	.	266	88	3837	15	771	32	994	35	0	.	448	64	3923	14	682	51	1986	25	602	36	187	76	379	53	942	32	318	52	613	36	1069	34	931	31	4416	12	845	39	2903	19	2794	17	120	71	2213	21	32642	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	.	511	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	211	74	0	.	0	.	513	48	0	.	0	.	1235	29
	subalpine	0	.	0	.	0	.	275	58	0	.	166	.	0	.	0	.	471	57	0	.	20	.	0	.	200	.	519	77	193	72	0	.	0	.	0	.	0	.	294	54	53	.	118	.	439	46	0	.	0	.	2746	24
	high-montane	0	.	0	.	0	.	262	64	0	.	85	.	0	.	0	.	2757	24	0	.	76	.	0	.	64	.	113	.	52	.	0	.	0	.	0	.	0	.	292	55	131	.	259	71	2171	29	0	.	0	.	6262	16
	lower and upper montane	72	.	218	.	284	71	2872	22	439	52	1707	33	0	.	408	52	831	44	1161	39	818	44	280	81	434	78	431	49	1714	35	310	70	516	46	129	.	479	61	2576	24	492	55	1791	28	385	51	222	.	1386	36	19957	9
	submontane	1401	31	0	.	0	.	2146	27	162	.	533	49	0	.	0	.	358	60	16	405	704	49	0	.	0	.	153	.	170	74	578	45	360	51	89	.	540	46	0	.	346	77	1277	29	11	.	0	.	1430	31	10273	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	308	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2807	19	0	.	71	.	267	51	0	.	0	.	3453	17
	total	1473	30	218	.	284	71	5555	16	602	47	2491	26	0	.	408	52	5236	17	1177	39	1618	31	280	81	698	57	1216	40	2129	30	889	38	875	34	218	72	1019	37	6180	13	1022	40	3515	19	3784	19	222	.	2816	23	43925	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	.	755	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	.	0	.	0	.	966	36
	subalpine	0	.	0	.	0	.	588	57	0	.	0	.	0	.	97	.	2718	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	398	66	0	.	96	.	736	58	0	.	0	.	4634	20
	high-montane	0	.	0	.	0	.	565	60	0	.	583	74	0	.	0	.	5222	19	0	.	0	.	0	.	45	.	0	.	319	71	0	.	0	.	210	.	0	.	1157	37	1044	59	373	58	2078	32	0	.	0	.	11595	13
	lower and upper montane	254	75	812	59	1000	60	6443	18	710	48	2319	29	0	.	1079	38	1207	41	1549	32	983	39	1212	38	984	53	657	59	4219	23	555	51	2366	28	1451	35	97	.	1046	36	704	60	4639	19	787	46	333	71	2652	27	38057	7
	submontane	1954	27	0	.	0	.	2253	28	982	43	1247	46	0	.	0	.	108	.	439	62	1543	40	120	.	0	.	0	.	68	.	963	41	1243	34	401	72	794	38	0	.	0	.	1611	33	0	.	300	71	1357	30	15382	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	301	71	0	.	88	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1418	33	0	.	94	.	215	66	0	.	60	.	2175	25
	total	2208	25	812	59	1000	60	9849	14	1692	32	4150	24	301	71	1176	36	10098	13	1988	28	2526	29	1331	36	1029	51	657	59	4606	22	1518	32	3608	22	2062	30	891	36	4019	19	1748	43	6813	16	4027	22	632	50	4069	20	72809	5
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	.	1102	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	559	46	0	.	0	.	1660	27
	subalpine	0	.	0	.	0	.	1048	42	0	.	0	.	0	.	0	.	6408	18	0	.	0	.	0	.	255	72	0	.	278	76	0	.	0	.	349	72	0	.	1030	37	228	.	301	61	4303	21	0	.	0	.	14199	11
	high-montane	0	.	0	.	0	.	3194	29	0	.	1112	42	0	.	859	63	10327	15	0	.	207	.	0	.	0	.	995	48	495	72	0	.	0	.	611	52	0	.	2492	28	669	54	2554	36	5283	21	0	.	0	.	28799	9
	lower and upper montane	969	44	2430	36	2831	33	16983	12	706	65	4718	24	0	.	1011	48	2666	26	3478	25	5796	21	3534	26	943	51	2034	40	10206	17	284	.	1669	33	4930	23	931	50	1806	35	786	52	6959	17	711	57	734	60	3332	28	80445	5
	submontane	4746	20	0	.	0	.	7711	16	1169	38	1003	46	0	.	0	.	118	.	1651	39	1207	38	414	76	0	.	0	.	536	60	1199	42	1535	35	189	.	4050	23	0	.	218	.	1919	33	83	.	0	.	2591	24	30339	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	160	94	0	.	73	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1576	32	0	.	0	.	142	.	0	.	0	.	1951	28
	total	5715	18	2430	36	2831	33	28936	9	1875	34	6833	19	160	94	1870	39	20694	10	5129	21	7209	18	3948	24	1198	43	3029	31	11514	15	1482	39	3204	24	6079	20	4982	20	6904	16	1901	33	11734	14	11081	14	734	60	5923	19	157392	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	.	2077	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	266	48	0	.	0	.	1050	26	0	.	0	.	3393	16
	subalpine	0	.	0	.	0	.	3042	26	0	.	0	.	0	.	498	58	5229	17	0	.	0	.	0	.	0	.	444	60	424	83	0	.	0	.	2	.	0	.	1768	26	395	58	309	51	3854	23	0	.	0	.	15965	10
	high-montane	0	.	0	.	0	.	2569	26	0	.	247	73	0	.	210	75	4504	17	0	.	371	60	0	.	0	.	832	45	113	71	0	.	0	.	376	48	0	.	1667	31	426	54	467	54	4464	18	0	.	0	.	16246	9
	lower and upper montane	189	71	291	.	200	71	9639	13	88	.	1454	32	0	.	1014	43	1909	28	1944	33	3356	23	4812	17	227	87	877	43	4058	23	121	.	771	46	1032	36	327	63	3073	44	367	74	4812	19	1235	37	251	73	344	46	42391	7
	submontane	831	34	0	.	0	.	1485	32	541	45	514	62	0	.	0	.	212	74	928	40	1706	36	318	59	0	.	0	.	905	49	336	71	489	47	0	.	469	51	0	.	514	75	964	44	120	.	0	.	1440	32	11771	12
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	398	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	2592	20	0	.	0	.	321	53	0	.	0	.	3397	18
	total	1020	30	291	.	200	71	16735	10	629	41	2215	27	0	.	1721	31	14329	9	2872	26	5433	18	5130	17	227	87	2153	27	5499	20	457	58	1260	33	1409	30	882	37	9366	17	1701	34	6553	16	11046	12	251	73	1784	28	93163	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	.	4854	14	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	640	33	0	.	0	.	2411	20	0	.	0	.	7906	11
	subalpine	0	.	0	.	0	.	5424	18	0	.	166	.	0	.	595	51	16634	10	0	.	20	.	0	.	454	60	1081	45	895	48	0	.	0	.	366	68	0	.	3818	18	720	46	825	35	9901	13	0	.	0	.	40899	6
	high-montane	0	.	0	.	0	.	7870	16	0	.	2192	31	0	.	1085	52	26797	8	0	.	818	43	3	.	109	72	1955	31	1146	39	0	.	0	.	1345	32	0	.	6054	16	2633	30	4161	24	15793	10	0	.	0	.	71961	5
	lower and upper montane	1505	33	3751	28	4750	24	38790	7	2134	29	10820	14	0	.	4009	21	7105	15	8698	15	12802	12	10343	12	2775	29	4130	24	21142	10	1381	34	5937	16	8685	15	2098	29	10090	16	2854	26	19781	9	3234	23	1737	33	8423	15	196975	2
	submontane	10622	12	0	.	0	.	15266	10	3509	20	3991	22	0	.	0	.	829	37	3265	24	5679	18	1050	38	0	.	312	70	1759	32	3362	22	3790	20	679	53	6777	16	0	.	1078	48	7313	15	452	43	300	71	8722	12	78755	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	485	54	0	.	1061	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	10472	9	0	.	236	58	1132	27	0	.	83	77	13555	7
	total	12128	11	3751	28	4750	24	67349	5	5643	16	17168	11	485	54	5689	18	57280	5	11963	12	19319	10	11396	11	3339	26	7478	17	24943	9	4744	18	9727	12	11076	13	8960	13	31074	6	7284	17	32316	7	32923	7	2036	30	17228	9	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. 1343034. Birmensdorf, Swiss Federal Research Institute WSL

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