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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NFI4
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 2009/17
		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	.	28	100	0	.	0	.	28	100
	subalpine	0	.	0	.	0	.	176	59	0	.	0	.	0	.	0	.	266	48	0	.	0	.	0	.	0	.	11	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	81	0	.	0	.	533	33
	high-montane	0	.	0	.	0	.	262	44	0	.	3	.	0	.	0	.	435	44	0	.	0	.	0	.	9	.	4	89	3	100	0	.	0	.	102	71	0	.	3	100	0	.	0	.	106	68	0	.	0	.	927	27
	lower and upper montane	0	.	0	.	0	.	84	62	0	.	140	70	0	.	0	.	13	.	0	.	56	53	0	.	0	.	0	.	1	100	0	.	0	.	81	85	0	.	15	100	0	.	27	147	29	.	0	.	0	.	445	32
	submontane	17	101	0	.	0	.	17	84	0	.	13	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	13	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	19	.	88	40
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.
	total	17	101	0	.	0	.	539	31	0	.	156	64	5	.	0	.	714	32	0	.	56	53	0	.	9	.	15	74	17	79	0	.	0	.	183	55	0	.	19	84	0	.	34	118	242	43	0	.	19	.	2025	17
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	.	11	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	90	0	.	0	.	1	100	0	.	0	.	50	70
	subalpine	0	.	0	.	0	.	112	54	0	.	0	.	0	.	-4	.	436	35	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	44	61	9	60	24	.	82	61	0	.	0	.	703	25
	high-montane	0	.	0	.	0	.	356	47	0	.	55	58	0	.	13	100	1022	27	0	.	2	100	0	.	0	.	8	73	24	82	0	.	0	.	76	71	0	.	51	69	104	66	206	42	483	45	0	.	0	.	2400	17
	lower and upper montane	67	100	0	.	160	63	762	29	38	89	252	37	0	.	141	62	67	63	58	70	262	50	117	78	25	86	101	62	428	36	32	.	4	74	93	69	40	74	17	75	18	76	173	46	0	3154	56	60	215	48	3126	13
	submontane	712	26	0	.	0	.	422	31	30	95	16	71	0	.	0	.	7	.	46	72	143	65	0	.	0	.	4	100	63	78	0	.	155	50	0	.	54	54	0	.	0	.	156	49	0	.	0	.	409	37	2219	14
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	53	0	.	0	.	85	76	0	.	11	.	151	48
	total	779	25	0	.	160	63	1651	19	68	65	323	30	0	.	150	59	1541	21	104	50	408	39	117	78	25	86	113	56	515	31	32	100	158	49	169	50	94	44	208	31	132	54	559	25	651	36	56	60	636	29	8647	8
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	.	354	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	33	0	.	0	.	239	46	0	.	0	.	731	21
	subalpine	0	.	0	.	0	.	101	59	0	.	71	.	0	.	11	72	1102	23	0	.	0	.	0	.	152	75	36	.	88	63	0	.	0	.	5	.	0	.	237	35	5	.	63	.	490	29	0	.	0	.	2362	15
	high-montane	0	.	0	.	0	.	462	33	0	.	0	.	0	.	23	64	1984	15	0	.	98	72	0	.	34	79	75	.	6	.	0	.	0	.	70	73	0	.	535	28	77	79	200	47	1597	18	0	.	0	.	5161	9
	lower and upper montane	35	100	97	71	145	68	1420	21	175	63	481	36	0	.	362	38	406	37	675	37	1067	24	350	43	71	85	184	74	565	29	92	91	208	43	911	25	226	67	1598	17	268	45	1234	22	416	44	164	59	642	34	11793	7
	submontane	1739	20	0	.	0	.	1212	20	314	38	569	32	0	.	24	.	100	84	351	45	335	40	82	47	0	.	143	78	286	43	333	42	356	37	31	.	1144	25	0	.	0	.	775	25	422	35	49	.	1277	23	9544	8
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	340	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2462	13	0	.	38	.	209	34	0	.	0	.	3059	12
	total	1774	20	97	71	145	68	3196	13	489	33	1122	23	10	100	420	34	4287	11	1026	28	1499	20	432	36	257	51	438	44	945	22	425	38	565	28	1018	24	1370	24	4970	9	350	39	2310	15	3373	12	214	51	1920	19	32649	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	.	402	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	.	0	.	0	.	401	44	0	.	0	.	827	26
	subalpine	0	.	0	.	0	.	196	51	0	.	0	.	0	.	0	.	1172	27	0	.	0	.	0	.	0	.	62	.	43	.	0	.	0	.	0	.	0	.	253	45	56	76	0	.	754	36	0	.	0	.	2537	18
	high-montane	0	.	0	.	0	.	392	48	0	.	378	63	0	.	0	.	2372	18	0	.	215	62	0	.	108	73	142	77	146	91	0	.	0	.	38	.	0	.	685	31	32	.	253	60	1469	23	0	.	0	.	6229	12
	lower and upper montane	471	44	134	76	366	60	3411	16	191	51	1549	28	0	.	683	43	767	32	882	31	844	37	601	40	399	53	677	47	2254	24	319	47	969	30	936	33	223	71	1947	19	456	44	2100	20	576	38	377	61	770	32	21903	7
	submontane	1391	25	0	.	0	.	1707	22	434	42	526	47	0	.	0	.	39	95	243	62	368	43	0	.	0	.	66	.	268	55	787	30	595	34	0	.	787	35	0	.	230	74	1493	25	22	.	36	.	1590	23	10580	9
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	275	50	0	.	233	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2130	17	0	.	104	71	267	39	0	.	0	.	3010	14
	total	1862	22	134	76	366	60	5706	12	625	33	2453	23	275	50	683	43	4985	12	1124	28	1427	26	601	40	508	44	946	37	2711	21	1106	25	1564	22	974	32	1010	31	5040	11	773	35	3950	15	3490	15	413	56	2360	19	45086	4
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	.	912	28	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	.	0	.	0	.	84	76	0	.	0	.	1032	26
	subalpine	0	.	0	.	0	.	406	62	0	.	0	.	0	.	235	70	3972	16	0	.	80	.	0	.	50	.	118	77	56	.	0	.	0	.	0	.	0	.	490	41	116	.	92	71	1158	29	0	.	0	.	6772	12
	high-montane	0	.	0	.	0	.	1099	35	0	.	606	52	0	.	0	.	5231	14	0	.	0	.	0	.	0	.	91	.	206	71	0	.	0	.	240	60	0	.	1078	28	981	40	452	61	2198	21	0	.	0	.	12182	9
	lower and upper montane	797	34	1273	32	1402	35	6987	13	629	38	3219	20	0	.	1436	33	1850	24	3720	17	1008	32	1155	27	347	52	824	39	4337	17	492	42	1968	21	2039	27	554	44	1457	24	317	58	4203	16	668	41	347	58	1938	24	42968	5
	submontane	2312	19	0	.	0	.	2712	20	1424	26	1230	31	0	.	0	.	88	72	792	32	1173	39	298	45	114	73	113	.	535	46	1472	26	1789	22	327	61	2024	25	0	.	116	.	1732	22	0	.	140	71	2561	20	20953	7
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	72	.	0	.	69	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	825	32	0	.	0	.	141	71	0	.	66	.	1173	26
	total	3109	16	1273	32	1402	35	11203	10	2052	21	5055	16	72	.	1671	30	12123	9	4513	15	2261	25	1452	24	511	40	1146	32	5134	16	1964	22	3757	15	2607	23	2578	21	3885	14	1530	30	6479	13	4249	15	487	46	4565	15	85080	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	.	1082	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	65	0	.	0	.	521	38	0	.	0	.	1721	23
	subalpine	0	.	0	.	0	.	609	41	0	.	0	.	0	.	275	72	5833	17	0	.	0	.	0	.	0	.	79	.	95	.	0	.	0	.	203	67	0	.	837	30	0	.	591	50	3519	18	0	.	0	.	12042	11
	high-montane	0	.	0	.	0	.	2590	23	0	.	741	41	0	.	548	55	9329	12	0	.	11	.	0	.	87	.	755	42	358	58	0	.	0	.	684	39	0	.	1851	24	677	45	2045	26	4509	17	0	.	0	.	24185	7
	lower and upper montane	1113	34	1206	37	3700	24	18696	9	1525	33	3604	22	0	.	1176	35	1627	28	2269	23	6391	17	4676	18	457	53	1384	34	8545	14	145	.	1802	24	4046	19	576	50	1310	29	480	52	7771	13	481	48	579	45	3085	22	76648	4
	submontane	5639	15	0	.	0	.	7125	14	1633	26	1951	28	0	.	0	.	62	.	1142	33	1882	26	404	54	0	.	192	.	1329	31	1111	35	2307	24	276	73	2359	27	0	.	0	.	3669	19	43	.	115	100	4814	16	36056	5
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	177	82	0	.	181	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1067	30	0	.	0	.	72	.	0	.	0	.	1498	27
	total	6753	13	1206	37	3700	24	29020	7	3159	20	6296	16	177	82	1999	27	18115	9	3411	19	8284	14	5080	17	545	47	2410	25	10327	13	1256	33	4109	17	5209	16	2935	24	5183	13	1157	34	14077	10	9146	11	694	41	7899	13	152150	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	.	1666	17	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	266	31	39	.	0	.	1513	16	0	.	0	.	3501	11
	subalpine	0	.	0	.	0	.	3028	19	0	.	0	.	0	.	250	56	5425	13	0	.	13	.	0	.	74	.	390	40	400	45	0	.	0	.	1	.	0	.	1323	22	186	56	168	50	3948	16	0	.	0	.	15206	8
	high-montane	0	.	38	.	0	.	2474	22	0	.	427	43	0	.	114	63	5785	13	0	.	487	38	0	.	0	.	1032	30	577	40	0	.	0	.	183	49	0	.	1764	21	690	36	868	31	4502	14	0	.	0	.	18940	7
	lower and upper montane	144	73	83	74	267	46	11093	10	37	.	1608	27	0	.	999	32	1809	22	2865	18	2727	20	4074	14	295	67	913	31	3017	21	0	.	851	31	972	31	530	45	2862	17	488	41	5702	14	1266	27	453	42	778	33	43834	5
	submontane	1477	27	0	.	0	.	1492	25	605	36	383	47	0	.	0	.	97	76	758	33	801	36	160	62	0	.	0	.	491	43	93	.	566	42	0	.	468	38	0	.	279	62	1642	23	114	77	0	.	526	34	9951	9
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	320	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	3233	14	0	.	0	.	290	37	0	.	94	.	3983	13
	total	1621	25	121	60	267	46	18087	8	642	34	2419	21	0	.	1364	26	15102	7	3623	16	4028	16	4234	14	369	57	2351	19	4485	16	93	.	1417	25	1156	27	1045	29	9447	8	1681	23	8380	11	11633	8	453	42	1398	24	95415	3
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	.	4427	12	0	.	0	.	0	.	0	.	17	.	0	.	0	.	0	.	0	.	0	.	620	22	39	.	0	.	2786	14	0	.	0	.	7889	8
	subalpine	0	.	0	.	0	.	4628	15	0	.	71	.	0	.	768	38	18207	8	0	.	94	87	0	.	275	52	696	30	683	33	0	.	0	.	208	65	0	.	3185	14	372	44	938	34	10030	10	0	.	0	.	40155	5
	high-montane	0	.	38	.	0	.	7636	12	0	.	2210	24	0	.	697	45	26158	6	0	.	813	29	0	.	238	51	2106	22	1320	28	0	.	0	.	1393	24	0	.	5967	11	2561	22	4024	17	14863	8	0	.	0	.	70025	3
	lower and upper montane	2627	20	2794	22	6041	17	42452	5	2595	22	10853	11	0	.	4798	16	6540	12	10469	10	12354	11	10973	9	1595	26	4082	18	19146	8	1080	28	5802	12	9080	12	2148	23	9206	8	2027	21	21210	7	3437	17	1977	23	7430	12	200716	2
	submontane	13288	8	0	.	0	.	14686	8	4441	14	4689	16	0	.	24	.	393	37	3332	17	4703	16	944	29	114	73	519	50	2986	19	3796	16	5768	13	634	45	6836	13	0	.	625	43	9473	10	602	30	341	48	11197	9	89390	3
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	539	39	0	.	1141	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	.	9774	7	0	.	143	58	1065	20	0	.	170	67	12878	6
	total	15915	8	2832	22	6041	17	69403	4	7035	12	17824	8	539	39	6288	14	56866	4	13802	8	17964	8	11917	9	2223	21	7420	12	24134	7	4876	14	11570	9	11316	10	9031	11	28752	4	5622	14	35787	5	32783	5	2317	21	18797	7	421054	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. 1362717. Birmensdorf, Swiss Federal Research Institute WSL

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