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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NFI3
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 2004/07
		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	.	174	56	0	.	0	.	0	.	0	.	107	76	0	.	0	.	0	.	0	.	46	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	45	105	0	.	0	.	372	38
	high-montane	0	.	0	.	0	.	143	88	0	.	6	.	0	.	0	.	780	33	0	.	0	.	0	.	0	.	33	86	0	0	0	.	0	.	87	70	0	.	6	100	0	.	10	.	54	64	0	.	0	.	1119	26
	lower and upper montane	0	.	0	.	0	.	157	53	0	.	117	61	0	.	82	95	49	66	75	100	165	53	0	.	0	.	23	100	101	60	0	.	0	.	65	74	0	.	19	.	77	100	100	100	0	.	14	.	67	82	1113	22
	submontane	63	71	0	.	0	.	64	54	0	.	95	84	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	0	.	0	.	98	83	0	.	0	.	27	64	0	.	0	.	39	100	393	34
	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	63	71	0	.	0	.	539	34	0	0	219	49	0	.	82	95	936	29	75	100	165	53	0	.	0	0	101	58	101	60	6	.	0	.	152	51	98	83	25	79	77	100	136	75	99	59	14	100	107	63	2998	14
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	.	6	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	55	0	.	0	.	67	70	0	.	0	.	95	52
	subalpine	0	.	0	.	0	.	63	77	0	.	0	.	0	.	2	100	313	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	90	16	81	14	.	74	58	0	.	0	.	491	29
	high-montane	0	.	0	.	0	.	250	67	0	.	33	66	0	.	5	100	695	27	0	.	0	.	0	.	2	.	0	.	0	0	0	.	0	.	47	94	0	.	155	38	88	81	55	76	351	51	0	.	0	.	1682	19
	lower and upper montane	81	.	0	.	293	97	601	32	98	91	209	48	0	.	213	56	67	77	32	71	151	73	0	.	14	61	62	71	218	55	35	100	75	81	19	100	0	.	66	62	54	97	165	61	61	76	10	.	136	62	2660	17
	submontane	487	31	0	.	0	.	282	39	160	54	94	63	0	.	9	.	3	.	173	71	121	72	23	.	0	.	0	.	62	.	79	78	142	52	0	.	35	.	0	.	0	.	173	54	0	.	0	.	455	35	2298	15
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	78	51	0	.	0	.	84	49	0	.	12	.	174	33
	total	568	30	0	.	293	97	1197	24	258	48	337	35	0	.	230	53	1085	21	205	61	273	52	23	100	16	55	62	71	279	48	114	62	217	44	66	72	35	.	329	25	158	56	408	35	637	31	10	.	603	30	7400	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	.	230	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	52	0	.	0	.	172	62	0	.	0	.	455	29
	subalpine	0	.	0	.	0	.	131	62	0	.	45	.	0	.	0	.	711	28	0	.	0	.	0	.	23	.	0	.	31	77	0	.	0	.	2	.	0	.	95	45	0	.	54	.	265	38	0	.	0	.	1358	19
	high-montane	0	.	20	.	0	.	266	42	0	.	129	72	0	.	8	.	1738	17	0	.	139	69	0	.	12	.	6	.	15	94	0	.	0	.	30	103	0	.	501	32	20	.	66	64	1382	20	0	.	0	.	4331	11
	lower and upper montane	272	66	47	77	169	59	1271	28	37	.	850	28	0	.	171	43	565	29	521	38	620	33	237	48	79	89	170	59	559	34	63	71	380	39	588	31	482	46	2167	23	276	46	1003	23	330	42	213	67	560	37	11630	8
	submontane	1171	21	0	.	0	.	961	22	77	76	350	41	0	.	0	.	75	85	88	54	366	35	45	68	0	.	20	87	409	38	252	44	284	43	25	.	1029	26	0	.	1	.	569	32	283	41	14	.	1381	22	7401	8
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	286	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1637	15	0	.	42	.	242	36	0	.	0	.	2210	13
	total	1443	21	67	62	169	59	2630	17	114	61	1374	22	3	100	179	41	3604	12	608	34	1125	23	282	42	114	66	196	52	1014	24	316	38	664	29	646	29	1511	23	4452	13	297	43	1736	17	2674	14	227	63	1940	19	27384	5
young timber	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	upper subalpine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	266	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	336	43	0	.	0	.	602	31
	subalpine	0	.	0	.	0	.	140	51	0	.	0	.	0	.	0	.	1158	27	0	.	0	.	0	.	83	.	168	.	51	.	0	.	0	.	0	.	0	.	198	52	35	.	0	.	1260	29	0	.	0	.	3093	17
	high-montane	0	.	0	.	0	.	305	48	0	.	321	67	0	.	0	.	1406	21	0	.	277	56	0	.	84	71	0	.	137	86	0	.	0	.	0	.	0	.	333	44	0	.	95	71	1837	20	0	.	0	.	4795	12
	lower and upper montane	193	52	128	80	326	72	3672	15	300	61	1419	28	0	.	1757	41	811	28	2232	22	579	43	493	39	333	52	103	.	2783	20	628	42	1071	29	683	35	212	59	1705	19	379	46	2105	18	738	34	201	71	464	39	23312	7
	submontane	1442	24	0	.	0	.	1975	22	751	33	537	45	0	.	0	.	188	59	181	55	624	42	112	72	0	.	69	.	554	43	621	38	663	31	115	.	556	38	0	.	65	.	1392	25	69	72	58	.	1041	28	11014	9
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	245	52	0	.	298	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2639	15	0	.	72	73	327	37	0	.	57	.	3637	12
	total	1635	22	128	80	326	72	6093	12	1051	29	2277	23	245	52	1757	41	4125	13	2413	21	1480	26	605	34	501	40	339	61	3524	18	1249	28	1734	21	798	34	768	32	4875	11	479	39	3664	14	4567	13	259	60	1563	22	46453	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	.	608	33	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	302	44	0	.	0	.	914	26
	subalpine	0	.	0	.	0	.	461	45	0	.	0	.	0	.	206	73	4104	15	0	.	44	.	0	.	0	.	72	.	15	.	0	.	0	.	0	.	0	.	413	43	0	.	84	68	1865	22	0	.	0	.	7264	11
	high-montane	0	.	0	.	0	.	1622	32	0	.	724	43	0	.	0	.	5607	13	0	.	64	.	0	.	65	.	278	71	181	71	0	.	0	.	390	39	0	.	1111	28	1213	34	994	40	3557	16	0	.	0	.	15806	8
	lower and upper montane	866	34	794	39	1017	37	11174	11	1497	30	2710	22	0	.	1261	31	1308	26	4678	16	2585	23	1329	22	762	38	242	55	5603	16	387	51	2770	18	1700	27	501	53	1186	25	196	58	7427	12	732	37	244	50	2743	21	53712	4
	submontane	2493	19	0	.	0	.	3645	18	1758	25	1561	29	0	.	0	.	99	.	1216	26	977	46	266	54	126	75	115	74	826	40	1533	25	2706	19	202	75	1724	28	0	.	196	82	3453	19	32	.	106	64	2358	22	25391	6
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	173	60	0	.	148	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	742	28	0	.	0	.	176	53	0	.	0	.	1239	21
	total	3360	16	794	39	1017	37	16903	9	3255	19	4995	16	173	60	1467	29	11873	9	5894	14	3670	20	1595	21	953	33	711	37	6625	14	1920	22	5476	13	2292	22	2225	25	3452	14	1604	28	11958	10	6665	11	350	40	5101	15	104326	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	.	1177	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	202	46	0	.	0	.	609	30	0	.	0	.	1988	18
	subalpine	0	.	0	.	0	.	477	46	0	.	0	.	0	.	212	72	5991	13	0	.	0	.	0	.	110	75	109	71	175	64	0	.	0	.	183	66	0	.	830	29	143	76	616	45	3440	18	0	.	0	.	12285	9
	high-montane	0	.	0	.	0	.	3079	27	0	.	510	45	0	.	559	55	9813	12	0	.	209	71	0	.	0	.	620	42	406	49	0	.	0	.	588	41	0	.	1823	23	765	42	1135	28	4185	17	0	.	0	.	23693	7
	lower and upper montane	1001	32	1842	28	3627	22	17139	9	603	53	3433	22	0	.	972	39	1804	25	2704	22	6462	16	4045	18	271	60	2242	26	8303	14	0	.	1474	31	5215	16	947	40	1034	30	524	47	6982	14	453	51	1353	32	2764	21	75191	4
	submontane	5902	14	0	.	0	.	8028	13	1264	29	1380	32	0	.	0	.	122	71	1282	30	2869	22	236	66	0	.	171	.	1659	28	862	37	1997	28	226	76	2598	22	0	.	105	.	3741	19	75	72	138	100	5552	15	38207	5
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	145	.	0	.	105	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	.	1317	26	0	.	0	.	66	.	0	.	137	.	1853	23
	total	6902	13	1842	28	3627	22	28723	7	1867	26	5323	17	145	.	1743	29	19013	8	3986	17	9541	13	4281	17	381	48	3142	21	10543	12	862	37	3471	21	6211	15	3627	19	5206	13	1537	28	12474	10	8827	11	1491	31	8453	12	153217	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	.	1588	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	134	39	34	.	0	.	865	19	0	.	0	.	2622	12
	subalpine	0	.	0	.	0	.	2461	20	0	.	0	.	0	.	115	84	3277	14	0	.	10	100	0	.	0	.	275	43	230	54	0	.	0	.	0	.	0	.	1108	22	115	65	129	45	2373	20	0	.	0	.	10092	9
	high-montane	0	.	0	.	0	.	2261	21	0	.	225	54	0	.	100	63	4647	13	0	.	258	53	0	.	0	.	859	31	330	54	0	.	0	.	58	88	0	.	1520	21	303	49	1265	29	2381	17	0	.	0	.	14207	7
	lower and upper montane	210	56	0	.	464	50	8270	11	78	79	1475	26	0	.	612	41	1233	27	408	42	2098	23	4182	15	0	.	753	35	1469	30	51	.	294	47	360	49	363	54	2300	17	203	55	3333	17	869	29	102	.	655	36	29785	6
	submontane	1956	28	0	.	0	.	1556	26	395	40	384	49	0	.	0	.	70	.	192	71	78	.	135	58	0	.	0	.	142	67	0	.	41	.	0	.	384	47	0	.	167	74	364	39	66	.	0	.	330	46	6260	13
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	228	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2117	16	0	.	0	.	169	57	0	.	0	.	2514	15
	total	2166	26	0	.	464	50	14547	8	474	36	2084	21	0	.	828	33	11044	8	601	37	2445	21	4318	14	0	.	1887	21	2171	23	51	.	335	43	418	44	747	36	7179	9	822	29	5090	14	6724	10	102	.	985	28	65481	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	.	3875	12	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	0	.	0	.	411	27	34	.	0	.	2351	14	0	.	0	.	6676	9
	subalpine	0	.	0	.	0	.	3908	15	0	.	45	.	0	.	535	44	15661	7	0	.	53	84	0	.	216	55	669	35	502	35	0	.	0	.	186	65	0	.	2652	15	309	44	898	33	9321	10	0	.	0	.	34955	5
	high-montane	0	.	20	.	0	.	7927	14	0	.	1948	24	0	.	672	47	24686	6	0	.	947	29	0	.	163	55	1795	23	1068	30	0	.	0	.	1200	25	0	.	5450	11	2388	23	3621	17	13748	8	0	.	0	.	65633	4
	lower and upper montane	2623	19	2810	22	5895	17	42285	5	2614	22	10213	11	0	.	5068	19	5838	12	10651	10	12660	10	10285	9	1459	26	3595	19	19036	8	1164	29	6064	12	8630	12	2504	22	8476	9	1708	22	21115	7	3183	16	2136	24	7390	12	197403	2
	submontane	13514	9	0	.	0	.	16512	8	4404	15	4402	16	0	.	9	.	556	35	3132	17	5036	17	817	30	126	75	375	54	3652	18	3352	16	5832	13	568	45	6423	13	0	.	534	44	9719	11	527	29	316	52	11156	10	90964	3
	hyperinsubric and colline	0	.	0	.	0	.	0	.	0	.	0	.	565	39	0	.	1065	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	.	8530	7	0	.	114	59	1064	20	0	.	206	72	11627	6
	total	16137	8	2830	22	5895	17	70632	4	7019	12	16608	8	565	39	6284	16	51680	4	13783	8	18697	8	11103	9	1964	21	6439	13	24258	7	4516	14	11896	9	10584	10	9010	11	25519	4	4974	14	35467	5	30193	5	2452	22	18752	7	407258	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. 1362686. Birmensdorf, Swiss Federal Research Institute WSL

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