NFI Results

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

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

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

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

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

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

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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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 NFI1-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	.	0	.	422	44	0	.	4	.	0	.	0	.	0	.	68	.	0	.	0	.	0	.	0	.	59	81	0	.	0	.	8	.	0	.	0	.	562	36
	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	.	2	.	645	35	17	.	4	100	0	.	0	.	0	.	68	.	0	.	111	88	11	100	140	83	59	81	0	.	289	64	41	65	0	.	3	.	1736	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	subalpine	0	.	0	.	0	.	251	68	0	.	0	.	0	.	0	.	461	51	0	.	0	.	0	.	0	.	23	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	761	38
	high-montane	0	.	0	.	0	.	451	46	0	.	93	78	0	.	0	.	1501	35	0	.	0	.	3	.	0	.	7	.	28	100	0	.	0	.	72	.	0	.	4	77	37	100	72	.	91	59	0	.	0	.	2359	25
	lower and upper montane	0	.	0	.	133	72	807	46	0	.	155	76	0	.	46	100	123	74	35	73	414	57	49	100	0	.	14	77	82	71	66	.	24	94	155	57	117	100	51	.	25	100	254	57	0	.	78	71	241	50	2871	19
	submontane	310	45	0	.	0	.	592	44	55	85	132	80	0	.	0	.	0	.	63	77	130	70	52	.	0	.	0	.	29	74	13	.	33	.	0	.	0	.	0	.	0	.	164	63	0	.	0	.	174	72	1747	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	.	0	.	0	.	0	.	51	62
	total	310	45	0	.	133	72	2102	25	55	85	381	46	24	71	46	100	2084	28	98	56	544	46	104	69	0	.	44	60	140	49	80	85	57	70	227	50	117	100	82	71	63	72	490	39	116	51	78	71	415	42	7789	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	.	181	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	.	0	.	0	.	55	83	0	.	0	.	270	36
	subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	426	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	181	72	33	.	0	.	351	52	0	.	0	.	991	30
	high-montane	0	.	0	.	0	.	656	40	0	.	0	.	0	.	0	.	1467	24	0	.	160	.	0	.	0	.	8	.	72	74	0	.	0	.	77	.	0	.	351	43	299	76	418	51	1189	26	0	.	0	.	4696	14
	lower and upper montane	22	.	0	.	266	88	1975	23	192	54	465	42	0	.	448	64	242	60	532	62	1346	32	456	45	177	80	118	67	740	38	46	.	495	42	886	38	147	74	1285	26	357	62	911	31	32	77	120	71	468	57	11725	10
	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	137	58	0	.	784	34	0	.	0	.	1274	31	239	56	0	.	1711	23	8842	10
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	195	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1873	21	0	.	71	.	183	43	0	.	24	.	2345	19
	total	1402	23	0	.	266	88	3637	16	771	32	994	35	0	.	448	64	2543	18	682	51	1897	26	602	36	177	80	284	62	864	33	318	52	632	35	963	36	931	31	3725	14	688	46	2674	20	2049	19	120	71	2202	21	28869	6
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	420	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	.	0	.	0	.	513	48	0	.	0	.	1007	31
	subalpine	0	.	0	.	0	.	275	58	0	.	0	.	0	.	0	.	339	69	0	.	20	.	0	.	200	.	519	77	76	.	0	.	0	.	0	.	0	.	294	54	53	.	0	.	424	48	0	.	0	.	2199	27
	high-montane	0	.	0	.	0	.	262	64	0	.	85	.	0	.	0	.	2464	26	0	.	76	.	0	.	0	.	113	.	52	.	0	.	0	.	0	.	0	.	243	63	0	.	259	71	1878	31	0	.	0	.	5432	17
	lower and upper montane	72	.	218	.	284	71	2802	22	295	61	1486	35	0	.	402	53	675	49	1161	39	818	44	280	81	434	78	350	56	1714	35	310	70	516	46	129	.	479	61	2203	26	492	55	1597	29	288	60	222	.	1386	36	18614	9
	submontane	1401	31	0	.	0	.	2104	28	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	10231	11
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	308	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2655	20	0	.	71	.	267	51	0	.	0	.	3301	18
	total	1473	30	218	.	284	71	5444	16	457	53	2103	28	0	.	402	53	4564	18	1177	39	1618	31	280	81	634	62	1135	42	2011	31	889	38	875	34	218	72	1019	37	5469	14	891	43	3204	20	3380	21	222	.	2816	23	40784	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	.	648	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	210	.	0	.	0	.	859	39
	subalpine	0	.	0	.	0	.	588	57	0	.	0	.	0	.	0	.	2635	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	398	66	0	.	96	.	637	65	0	.	0	.	4354	21
	high-montane	0	.	0	.	0	.	565	60	0	.	377	.	0	.	0	.	4922	19	0	.	0	.	0	.	45	.	0	.	319	71	0	.	0	.	210	.	0	.	1049	39	1044	59	249	72	2078	32	0	.	0	.	10858	14
	lower and upper montane	254	75	812	59	1000	60	6443	18	710	48	2319	29	0	.	1035	40	1207	41	1549	32	983	39	1212	38	984	53	657	59	4132	24	555	51	2366	28	1451	35	97	.	1046	36	704	60	4639	19	787	46	154	.	2652	27	37748	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	.	1372	34	0	.	94	.	215	66	0	.	60	.	2129	26
	total	2208	25	812	59	1000	60	9849	14	1692	32	3943	24	301	71	1035	40	9608	13	1988	28	2526	29	1331	36	1029	51	657	59	4519	22	1518	32	3608	22	2062	30	891	36	3865	20	1748	43	6689	16	3928	23	454	58	4069	20	71329	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	.	1021	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	559	46	0	.	0	.	1580	28
	subalpine	0	.	0	.	0	.	1048	42	0	.	0	.	0	.	0	.	6298	19	0	.	0	.	0	.	154	.	0	.	278	76	0	.	0	.	349	72	0	.	903	40	228	.	249	71	4163	21	0	.	0	.	13668	12
	high-montane	0	.	0	.	0	.	2899	30	0	.	1112	42	0	.	859	63	9895	15	0	.	207	.	0	.	0	.	995	48	192	.	0	.	0	.	611	52	0	.	2467	29	669	54	2513	36	4992	22	0	.	0	.	27411	9
	lower and upper montane	969	44	2199	39	2831	33	16362	12	577	76	4718	24	0	.	1011	48	2544	27	3478	25	5625	22	3500	26	943	51	2034	40	9500	17	284	.	1669	33	4777	23	931	50	1806	35	786	52	6710	18	645	62	420	73	3332	28	77648	5
	submontane	4553	20	0	.	0	.	7711	16	1128	40	1003	46	0	.	0	.	118	.	1651	39	1207	38	414	76	0	.	0	.	536	60	1199	42	1535	35	189	.	3937	23	0	.	0	.	1743	35	83	.	0	.	2591	24	29598	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	151	.	0	.	73	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1576	32	0	.	0	.	142	.	0	.	0	.	1941	28
	total	5522	18	2199	39	2831	33	28020	9	1705	36	6833	19	151	.	1870	39	19948	10	5129	21	7038	19	3914	24	1097	46	3029	31	10505	16	1482	39	3204	24	5926	20	4868	21	6751	16	1683	35	11215	14	10584	14	420	73	5923	19	151846	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	.	1580	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	162	71	0	.	0	.	830	29	0	.	0	.	2572	19
	subalpine	0	.	0	.	0	.	2454	28	0	.	0	.	0	.	443	64	4172	19	0	.	0	.	0	.	0	.	389	66	424	83	0	.	0	.	0	.	0	.	1511	30	379	61	263	57	3665	24	0	.	0	.	13699	11
	high-montane	0	.	0	.	0	.	2183	28	0	.	93	.	0	.	210	75	3937	18	0	.	230	75	0	.	0	.	832	45	62	.	0	.	0	.	335	52	0	.	1234	39	426	54	314	65	4132	19	0	.	0	.	13989	10
	lower and upper montane	189	71	291	.	200	71	8777	13	88	.	1454	32	0	.	970	44	1658	31	1944	33	3356	23	4812	17	227	87	788	46	3499	24	121	.	771	46	519	50	327	63	3006	45	367	74	4654	19	1235	37	251	73	241	51	39745	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	333	51	0	.	469	51	0	.	514	75	939	46	0	.	0	.	1440	32	11470	12
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	398	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	2481	21	0	.	0	.	297	56	0	.	0	.	3262	18
	total	1020	30	291	.	200	71	14899	10	629	41	2061	28	0	.	1622	33	11956	10	2872	26	5292	19	5130	17	227	87	2009	29	4889	21	457	58	1104	36	854	36	882	37	8394	19	1685	34	6170	17	10160	12	251	73	1681	29	84736	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	.	3849	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	270	52	0	.	0	.	2167	22	0	.	0	.	6286	13
	subalpine	0	.	0	.	0	.	4784	19	0	.	0	.	0	.	443	64	14553	11	0	.	20	.	0	.	354	71	931	51	778	54	0	.	0	.	349	72	0	.	3287	20	692	48	608	41	9288	14	0	.	0	.	36086	7
	high-montane	0	.	0	.	0	.	7018	17	0	.	1760	35	0	.	1068	52	24608	8	0	.	677	48	3	.	45	.	1955	31	792	40	0	.	0	.	1305	33	0	.	5407	17	2476	32	3825	26	14368	11	0	.	0	.	65306	5
	lower and upper montane	1505	33	3520	29	4750	24	37200	7	1861	32	10598	14	0	.	3914	21	6447	16	8698	15	12542	13	10309	12	2765	30	3961	25	19667	11	1381	34	5937	16	7928	16	2098	29	9396	17	2731	27	18952	9	2998	24	1244	37	8321	15	188726	3
	submontane	10430	12	0	.	0	.	15224	10	3448	20	3991	22	0	.	0	.	829	37	3265	24	5679	18	1050	38	0	.	312	70	1759	32	3362	22	3653	20	679	53	6663	16	0	.	860	55	7112	15	332	47	300	71	8706	12	77654	4
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	475	55	0	.	1061	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	9984	9	0	.	236	58	1104	28	0	.	83	77	13029	8
	total	11935	11	3520	29	4750	24	64227	5	5310	17	16349	11	475	55	5426	19	51347	5	11963	12	18918	10	11363	11	3164	27	7159	18	22996	10	4744	18	9590	12	10261	14	8847	14	28344	7	6758	18	30732	7	30257	7	1544	33	17110	9	387089	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. 1343029. Birmensdorf, Swiss Federal Research Institute WSL

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