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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed on foot.

1.4-km grid #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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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NFI2
growing stock (stemwood)
development stage · altitudinal vegetation belts (NaiS; 6 classes)
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1993/95
		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	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	53	68	0	.	0	.	70	57
	high-montane	0	.	0	.	0	.	104	80	0	.	0	.	0	.	0	.	448	45	0	.	0	.	0	.	0	.	76	100	0	0	0	.	0	.	0	.	0	.	0	.	0	.	34	100	123	97	0	.	0	.	784	33
	lower and upper montane	0	.	0	.	32	100	0	0	0	.	0	.	0	.	8	.	25	94	0	.	37	100	0	.	14	.	37	.	54	100	0	.	0	.	66	73	0	.	24	.	11	74	0	.	0	.	0	.	0	.	308	33
	submontane	0	.	0	.	0	.	19	.	0	.	0	.	0	.	0	.	0	.	0	.	32	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	73
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	70	0	.	0	.	0	.	0	.	0	.	27	70
	total	0	.	0	.	32	100	123	69	0	.	0	.	0	.	8	100	490	42	0	.	69	71	0	.	14	.	113	75	54	100	0	.	0	.	66	73	0	.	51	60	11	74	34	100	176	71	0	.	0	.	1240	23
young growth/ thicket	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	103	0	.	0	.	51	55
	subalpine	0	.	0	.	0	.	69	71	0	.	0	.	0	.	0	.	260	44	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	21	97	11	100	0	.	88	47	0	.	0	.	462	29
	high-montane	0	.	0	.	0	.	150	65	0	.	0	.	0	.	1	.	876	23	0	.	51	.	0	.	0	.	77	77	7	.	0	.	0	.	61	72	0	.	90	51	46	52	37	97	142	40	0	.	0	.	1539	16
	lower and upper montane	0	.	8	.	35	98	367	35	72	98	66	77	0	.	117	57	177	61	27	62	240	55	41	102	0	.	193	65	221	47	0	.	70	65	59	58	3	71	148	55	34	95	152	38	75	66	17	59	62	73	2183	15
	submontane	233	37	0	.	0	.	143	45	1	100	42	60	0	.	30	.	28	.	84	85	14	71	14	.	0	.	0	.	169	50	16	100	36	65	0	.	112	49	0	.	0	.	130	71	5	100	0	.	172	43	1229	17
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	52	0	.	0	.	19	78	0	.	0	.	93	44
	total	233	37	8	.	35	98	729	25	73	97	108	52	0	.	147	49	1391	18	111	66	306	46	55	80	0	.	284	49	398	34	16	100	106	48	120	46	115	47	333	31	91	46	319	36	351	26	17	59	234	37	5578	9
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	.	396	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	195	37	0	.	0	.	592	22
	subalpine	0	.	0	.	0	.	109	56	0	.	3	.	0	.	0	.	567	30	0	.	1	188	0	.	54	.	204	56	62	69	0	.	0	.	0	.	0	.	232	37	0	.	32	.	335	34	0	.	0	.	1600	17
	high-montane	0	.	0	.	0	.	436	39	0	.	111	77	0	.	0	.	1786	17	0	.	279	49	0	.	36	.	249	71	17	84	0	.	0	.	137	81	0	.	624	29	128	78	85	77	1245	18	0	.	0	.	5134	10
	lower and upper montane	271	53	39	72	117	64	2629	16	141	63	1108	24	0	.	643	44	633	31	915	30	881	28	374	45	326	38	395	39	1740	20	220	59	330	35	756	29	373	45	2686	16	628	30	1150	21	409	36	312	46	422	34	17499	6
	submontane	1767	18	0	.	0	.	2019	20	542	36	358	44	0	.	0	.	61	94	395	39	415	39	196	50	0	.	134	80	512	35	380	41	408	35	6	.	902	29	0	.	166	59	1355	22	189	40	226	66	1167	23	11199	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	269	51	0	.	554	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3329	11	0	.	67	71	544	25	0	.	0	.	4763	9
	total	2038	17	39	72	117	64	5194	12	683	31	1580	21	269	51	643	44	3997	11	1309	24	1577	21	571	34	416	34	982	29	2331	17	599	34	738	25	899	27	1276	24	6871	8	923	25	2689	15	2918	11	538	38	1589	19	40786	4
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	.	585	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	297	45	0	.	0	.	882	24
	subalpine	0	.	0	.	0	.	929	28	0	.	0	.	0	.	140	.	2330	19	0	.	37	.	0	.	0	.	162	67	73	99	0	.	0	.	0	.	0	.	86	71	28	.	47	.	1071	30	0	.	0	.	4904	13
	high-montane	0	.	0	.	0	.	1704	31	0	.	1022	37	0	.	39	.	4421	14	0	.	114	84	0	.	0	.	225	71	109	.	0	.	0	.	234	42	0	.	358	47	874	41	347	43	1846	20	74	.	0	.	11367	9
	lower and upper montane	772	30	779	37	960	39	7193	12	96	71	2896	22	0	.	1133	40	1062	27	3047	19	1365	27	850	28	591	42	1102	30	2193	23	215	58	2490	19	1460	29	228	62	1191	26	193	59	4392	15	796	29	131	75	1540	25	36674	5
	submontane	2928	17	0	.	0	.	2710	21	1364	26	981	30	0	.	0	.	0	.	728	39	844	37	198	60	80	.	185	73	966	38	801	33	1341	24	254	58	1029	32	0	.	35	.	1845	20	22	.	97	.	1318	26	17727	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	150	72	0	.	178	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	911	26	0	.	42	.	165	55	0	.	95	.	1542	20
	total	3700	15	779	37	960	39	12537	9	1460	25	4899	16	150	72	1312	36	8575	10	3775	17	2360	21	1047	26	671	39	1674	24	3341	19	1016	29	3831	15	1948	24	1257	29	2547	17	1129	33	6674	11	4196	13	303	52	2953	18	73095	3
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	.	914	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	.	0	.	0	.	450	35	0	.	0	.	1411	20
	subalpine	0	.	0	.	0	.	1019	32	0	.	0	.	0	.	238	76	4802	13	0	.	0	.	0	.	0	.	64	.	185	58	0	.	0	.	182	62	0	.	313	43	0	.	65	.	2339	22	0	.	0	.	9209	10
	high-montane	0	.	0	.	0	.	2256	26	0	.	374	50	0	.	1	.	6006	14	0	.	335	55	0	.	0	.	807	33	97	.	0	.	0	.	539	43	0	.	1019	28	493	46	1285	32	3424	16	0	.	0	.	16638	8
	lower and upper montane	1215	32	577	46	1676	29	13646	10	1447	29	3297	20	0	.	969	37	1286	27	3669	18	4148	19	3689	17	676	46	1413	38	6623	15	578	43	1764	23	2968	23	621	41	646	30	269	73	6940	13	575	41	817	38	3053	20	62564	4
	submontane	4562	16	0	.	0	.	6084	14	1815	24	2658	24	0	.	0	.	362	53	1402	27	1796	30	374	48	73	.	142	.	1552	31	1098	31	2773	21	66	.	2624	22	0	.	163	.	4414	17	0	.	105	.	5543	15	37606	5
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	51	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	536	40	0	.	75	.	157	61	0	.	0	.	865	30
	total	5777	14	577	46	1676	29	23005	8	3262	18	6329	15	51	.	1208	33	13371	8	5071	15	6279	15	4063	16	749	43	2426	25	8457	13	1677	25	4537	15	3755	19	3291	19	2562	17	926	37	12779	10	6945	11	921	36	8596	12	128293	3
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	.	747	30	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	.	0	.	0	.	153	56	0	.	0	.	925	26
	subalpine	0	.	0	.	0	.	349	51	0	.	0	.	0	.	81	81	2798	18	0	.	0	.	0	.	0	.	91	71	171	71	0	.	0	.	0	.	0	.	391	42	108	.	539	46	1908	23	0	.	0	.	6438	12
	high-montane	0	.	0	.	0	.	2042	30	0	.	132	.	0	.	474	53	7041	14	0	.	0	.	0	.	0	.	207	72	354	46	0	.	0	.	58	.	0	.	1084	28	618	42	608	36	2657	21	0	.	0	.	15274	9
	lower and upper montane	242	65	371	59	1490	34	12909	11	574	49	1749	29	0	.	816	39	1093	31	872	34	4916	18	2991	19	76	.	544	55	5761	18	0	.	867	34	2408	25	623	45	1035	29	488	52	4705	17	222	54	388	77	1940	25	47081	5
	submontane	5094	16	0	.	0	.	6163	15	987	34	1344	32	0	.	0	.	251	64	851	37	2028	26	101	.	0	.	0	.	344	77	747	36	1647	32	160	.	2249	25	0	.	91	.	1341	29	65	.	0	.	3846	18	27309	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	89	.	0	.	144	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	920	30	0	.	0	.	0	.	0	.	165	76	1318	26
	total	5335	15	371	59	1490	34	21463	8	1561	28	3225	21	89	.	1372	30	12074	10	1723	25	6944	15	3092	19	76	.	841	41	6629	16	747	36	2514	24	2626	23	2872	22	3456	15	1305	30	7194	13	5006	14	388	77	5950	14	98345	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	.	671	24	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	181	37	28	.	0	.	718	24	0	.	0	.	1612	15
	subalpine	0	.	0	.	0	.	997	35	0	.	0	.	0	.	43	85	2739	16	0	.	0	.	0	.	110	71	50	.	31	.	0	.	0	.	0	.	0	.	1015	22	132	59	90	59	2367	18	0	.	0	.	7573	10
	high-montane	0	.	9	.	0	.	1100	25	0	.	265	45	0	.	20	139	2655	17	0	.	168	60	0	.	196	58	176	58	460	42	0	.	0	.	173	51	0	.	1398	23	134	82	428	34	2799	17	0	.	0	.	9983	9
	lower and upper montane	77	.	452	49	888	41	7117	12	215	62	2155	24	0	.	483	38	507	29	1441	24	1711	27	1620	21	90	.	298	44	2373	20	0	.	401	41	1018	28	111	77	1160	22	39	.	2303	18	391	47	293	53	483	43	25625	6
	submontane	538	44	0	.	0	.	1330	27	0	.	107	.	0	.	0	.	11	.	226	61	291	63	47	.	0	.	0	.	127	.	0	.	36	.	0	.	223	61	0	.	74	.	260	56	220	53	0	.	292	46	3783	15
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	89	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1716	19	0	.	0	.	65	59	0	.	0	.	1871	18
	total	614	40	461	48	888	41	10544	10	215	62	2527	22	0	.	546	35	6673	10	1667	22	2170	24	1667	21	396	42	539	32	2991	18	0	.	436	39	1191	25	334	48	5469	10	407	40	3082	15	6559	10	293	53	775	32	50446	4
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3342	12	0	.	0	.	0	.	0	.	14	.	0	.	0	.	0	.	0	.	0	.	254	34	28	.	0	.	1835	16	0	.	0	.	5473	9
	subalpine	0	.	0	.	0	.	3473	16	0	.	3	.	0	.	503	48	13512	8	0	.	39	97	0	.	164	58	586	32	521	36	0	.	0	.	182	62	0	.	2058	16	279	49	775	34	8161	10	0	.	0	.	30256	5
	high-montane	0	.	9	.	0	.	7792	13	0	.	1904	24	0	.	535	48	23234	6	0	.	948	29	0	.	232	52	1817	22	1045	28	0	.	0	.	1202	25	0	.	4572	12	2293	22	2825	18	12237	8	74	.	0	.	60719	4
	lower and upper montane	2576	19	2226	22	5197	17	43862	5	2545	21	11271	11	0	.	4169	17	4783	13	9972	10	13298	10	9566	10	1773	24	3983	18	18965	8	1013	30	5922	12	8735	12	1959	23	6891	9	1662	24	19642	7	2468	17	1957	25	7500	12	191934	2
	submontane	15122	8	0	.	0	.	18468	8	4709	14	5490	15	0	.	30	.	713	36	3686	16	5421	16	930	28	153	71	461	48	3670	19	3042	17	6241	14	486	47	7140	13	0	.	530	42	9345	10	501	31	427	48	12338	9	98904	3
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	560	36	0	.	966	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	.	7514	8	0	.	183	53	950	20	0	.	259	61	10479	6
	total	17698	7	2235	22	5197	17	73595	4	7254	11	18668	8	560	36	5236	15	46571	4	13658	8	19706	8	10495	9	2321	20	6860	13	24201	7	4055	15	12163	9	10605	10	9146	11	21290	4	4792	14	32771	5	26153	5	2459	22	20097	7	397785	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. 1343235. Birmensdorf, Swiss Federal Research Institute WSL

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