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.

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).

main tree species #90
Type of trees and shrubs ≥12 cm in diameter at breast height (dbh) with the ten most common species or species groups in Switzerland ('main tree species') categorised, and the classes 'other conifers' and 'other broadleaves' for the remaining species. The main tree species are: spruce (Picea spp.), fir (Abies spp.), pine (Pinus sylvestris, P. nigra, P. strobus, P. mugo subsp. uncinata), larch (Larix spp.), Arolla pine (Pinus cembra), beech (Fagus sylvatica), maple (Acer spp.), ash (Fraxinus spp.), oak (Quercus spp.) and chestnut (Castanea sativa). Reference: Field Survey (MID 50: Baumart)

economic region #366
Division of Switzerland into 14 regions (2 in the Jura, 3 on the Plateau, 3 in the Pre-Alps, 5 in the Alps and 1 in the Southern Alps). The economic regions are a subdivision of the production regions according to economic-geographical criteria.

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

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

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI2—NFI5
change: growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: economic region
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 1993/95–2018/26
		economic region
		Western Jura		Eastern Jura		Western Plateau		Central Plateau		Eastern Plateau		Western Pre-Alps		Central Pre-Alps		Eastern Pre-Alps		Northwestern Alps		Central Alps		Northeastern Alps		Southwestern Alps		Southeastern Alps		Southern Alps		Switzerland
altitudinal vegetation belts (NaiS; 6 classes)	main tree species	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	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	fir	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
upper subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	84	45	59	78	62	69	204	36
	fir	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	22	99	-44	133	14	100	-8	833
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	395	31	414	38	115	48	924	22
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	140	65	190	75	7	100	337	50
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	12	53	.	.	12	53
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-9	101	2	100	2	100	-5	190
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	.	.	0	0
	total	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	632	31	633	38	200	41	1464	22
subalpine	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	-46	.	.	.	.	.	260	48	531	49	81	91	684	47	322	42	366	65	921	39	1619	42	934	26	5673	17
	fir	.	.	.	.	.	.	.	.	.	.	.	.	15	98	.	.	.	.	.	.	.	.	.	.	.	.	.	.	15	98
	pine	.	.	.	.	.	.	.	.	.	.	.	.	101	74	.	.	22	66	.	.	.	.	16	90	2	704	.	.	142	56
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-40	95	-18	100	-18	100	633	45	403	37	703	26	1663	22
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-6	100	.	.	.	.	15	306	0	29578	.	.	9	1454
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	1	100	.	.	.	.	.	.	.	.	-10	105	14	57	.	.	5	279
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	12	100	12	100
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	7	74	.	.	.	.	.	.	.	.	17	100	24	74
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other broadleaves	.	.	.	.	.	.	.	.	.	.	2	82	1	121	1	100	16	148	1	100	2	100	37	82	7	51	4	73	70	55
	indeterminable	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	0	0	.	.	0	0
	total	.	.	.	.	-46	.	.	.	.	.	262	48	649	46	81	91	683	47	305	44	350	68	1613	32	2045	36	1670	21	7612	14
high-montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	137	61	.	.	-55	78	.	.	.	.	-359	187	43	907	-241	124	628	96	19	2075	354	55	2057	24	445	219	285	219	3312	51
	fir	.	.	.	.	.	.	.	.	.	.	515	41	54	117	25	93	456	38	78	93	-14	100	415	40	116	71	234	72	1879	20
	pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	2	100	39	90	.	.	55	256	210	64	-72	59	234	86
	larch	.	.	.	.	.	.	.	.	.	.	20	100	.	.	.	.	116	60	.	.	25	100	788	34	467	38	589	27	2005	18
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	5	100	.	.	.	.	.	.	-7	108	.	.	-2	421
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	4	100	1	100	-1	76	.	.	.	.	3	123
	beech	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	21	97	25	62	66	50	113	37
	maple	4	100	.	.	12	97	.	.	.	.	-19	206	53	53	5	73	37	126	1	100	.	.	63	52	87	54	41	82	283	34
	ash	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	8	85	.	.	.	.	69	62	1	3442	4	100	82	65
	oak	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	7	100	.	.	.	.	7	100
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	13	167	13	167
	other broadleaves	2	100	.	.	.	.	.	.	.	.	4	177	8	65	-2	159	18	79	19	88	17	60	220	46	274	62	185	107	745	38
	indeterminable	0	0	.	.	.	.	.	.	.	.	0	0	0	0	.	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0
	total	143	60	.	.	-43	102	.	.	.	.	160	452	159	263	-214	135	1269	51	160	257	382	52	3695	19	1618	63	1346	53	8674	21
lower and upper montane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-1740	51	-153	74	-1486	56	-1808	43	-632	95	-1342	75	-4389	27	-463	225	-590	104	-50	778	-634	105	154	154	1015	34	1339	72	-10780	26
	fir	2365	30	101	64	236	124	9	5485	-257	132	-187	259	447	162	1275	42	360	55	528	53	116	108	325	54	188	47	412	42	5919	25
	pine	-36	327	-121	54	4	71	-193	55	-110	67	.	.	-3	2288	-48	86	-64	108	9	100	.	.	-3	1147	-8	224	.	.	-574	38
	larch	-16	93	-22	79	7	335	1	643	-11	681	6	100	-75	72	-3	649	-92	88	.	.	-83	87	-13	223	-60	123	328	37	-32	645
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	43	38	35	66	-91	107	192	91	225	80	-1	100	29	79	20	48	6	100	-1	100	1	100	4	100	.	.	.	.	463	59
	beech	3	21636	295	70	143	127	-242	144	-115	261	322	69	91	585	743	51	459	50	405	36	330	92	208	51	136	90	2263	14	5039	24
	maple	709	20	41	187	151	42	69	79	156	74	267	26	309	46	289	47	173	51	99	40	89	102	117	62	74	53	99	52	2644	13
	ash	-9	1523	-48	125	-53	171	235	36	65	324	239	41	235	86	238	72	136	66	118	69	-87	163	54	98	9	216	46	90	1177	38
	oak	120	66	-13	64	-2	1807	-17	310	-129	56	31	98	-41	106	.	.	11	69	6	100	.	.	17	94	21	100	18	83	24	594
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-19	297	-19	297
	other broadleaves	150	48	7	397	77	66	56	92	73	83	-16	266	107	67	14	512	-7	886	-53	88	-40	113	16	217	-51	65	348	56	679	40
	indeterminable	0	0	0	0	.	.	.	.	0	0	0	0	0	0	0	0	0	0	.	.	.	.	0	0	0	0	0	0	0	0
	total	1590	90	120	227	-1013	95	-1699	58	-735	130	-683	185	-3289	49	2065	70	392	202	1061	52	-309	262	879	40	1325	31	4834	25	4540	83
submontane	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-1505	31	-1311	49	-2651	33	-4964	22	-6497	21	.	.	-662	68	-397	83	61	138	47	128	-59	100	-106	100	26	124	.	.	-18016	12
	fir	409	62	-105	242	316	71	-632	92	197	203	.	.	-109	190	41	74	35	109	92	102	.	.	.	.	70	73	.	.	313	273
	pine	-120	61	-109	82	-386	53	-17	417	-818	31	.	.	-1	3337	.	.	.	.	2	100	.	.	.	.	-240	66	.	.	-1689	23
	larch	3	100	6	699	98	146	-63	136	-84	136	.	.	18	100	12	237	-39	258	.	.	.	.	.	.	-120	104	.	.	-170	155
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	5	100	246	71	43	100	187	57	111	68	.	.	.	.	5	100	.	.	.	.	-1	100	.	.	.	.	.	.	596	37
	beech	-328	104	-1290	44	273	128	-713	79	379	119	-29	.	280	76	-274	118	-40	286	247	57	31	220	34	145	-105	91	.	.	-1535	73
	maple	175	51	7	1487	122	50	303	35	536	37	.	.	28	344	138	84	-36	162	18	100	21	100	46	92	57	61	.	.	1414	23
	ash	-365	58	58	199	426	70	-106	201	-311	88	.	.	-51	76	34	239	-49	176	71	71	22	100	9	134	-3	276	.	.	-265	202
	oak	-43	231	68	238	-47	310	-39	334	77	329	-28	.	35	100	-39	100	6	100	35	83	.	.	15	117	.	.	.	.	40	943
	sweet chestnut	.	.	.	.	-1	100	.	.	.	.	.	.	.	.	.	.	6	100	.	.	.	.	19	100	5	100	.	.	28	73
	other broadleaves	-141	49	-6	1345	262	76	227	62	97	193	5	.	-31	145	-55	121	-16	1342	62	59	-28	82	45	63	42	77	.	.	464	87
	indeterminable	0	0	.	.	.	.	0	0	0	0	.	.	.	.	.	.	0	0	.	.	.	.	.	.	.	.	.	.	0	0
	total	-1912	36	-2436	41	-1545	72	-5818	24	-6313	25	-52	.	-493	129	-535	109	-72	426	573	49	-14	247	63	232	-268	94	.	.	-18821	15
hyperinsubric and colline	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	.	.	.	.	1	478	.	.	-69	78	.	.	.	.	.	.	.	.	.	.	.	.	-2	1402	.	.	117	51	47	176
	fir	.	.	.	.	4	100	.	.	15	100	.	.	.	.	.	.	.	.	.	.	.	.	14	66	.	.	12	100	45	48
	pine	.	.	.	.	-51	70	.	.	-93	77	.	.	.	.	.	.	.	.	.	.	.	.	-66	145	.	.	.	.	-210	59
	larch	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	15	187	9	.	-24	76	0	20979
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	other conifers	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	1	100	.	.	70	98	71	97
	beech	.	.	.	.	.	.	.	.	45	100	.	.	.	.	.	.	.	.	.	.	.	.	-11	100	.	.	239	73	273	66
	maple	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	11	204	.	.	45	92	56	84
	ash	.	.	.	.	2	71	.	.	-16	100	.	.	.	.	.	.	.	.	.	.	.	.	16	50	.	.	516	43	519	43
	oak	38	72	.	.	-193	95	.	.	-25	140	.	.	.	.	.	.	.	.	.	.	.	.	89	53	23	.	189	60	120	188
	sweet chestnut	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-22	64	1	.	757	46	736	48
	other broadleaves	-7	118	.	.	12	72	.	.	-28	111	.	.	.	.	.	.	.	.	.	.	.	.	10	319	.	.	992	29	978	30
	indeterminable	0	0	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	0	0	0	0
	total	31	71	.	.	-225	80	.	.	-171	75	.	.	.	.	.	.	.	.	.	.	.	.	55	205	34	.	2912	19	2635	23
total	n/a	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.
	spruce	-3108	32	-1464	44	-4236	28	-6772	20	-7198	21	-1441	84	-4477	30	-1020	111	783	118	339	171	27	2702	3109	22	3165	39	2736	43	-19559	20
	fir	2774	27	-4	5988	557	66	-623	121	-45	1154	328	160	408	186	1340	40	851	31	698	43	101	124	754	32	374	35	658	37	8170	21
	pine	-156	89	-230	48	-433	48	-210	61	-1021	27	.	.	97	113	-48	86	-40	178	51	72	.	.	25	719	-80	273	-58	77	-2104	24
	larch	-13	113	-16	293	106	138	-62	139	-95	144	25	81	-58	99	8	426	-54	282	-18	100	-76	102	1818	22	1113	28	1711	16	4389	15
	Arolla pine	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	.	-1	546	.	.	.	.	155	66	183	101	7	100	344	62
	other conifers	48	35	281	63	-47	224	379	54	336	58	-1	100	30	77	25	43	6	100	2	174	1	123	-6	188	25	39	70	98	1150	31
	beech	-325	214	-996	60	416	94	-956	69	309	176	293	77	372	155	469	107	419	61	652	31	361	87	253	47	57	273	2580	14	3903	42
	maple	888	19	48	265	285	31	372	32	693	33	247	32	390	45	432	41	181	64	118	36	110	85	236	39	218	32	203	38	4419	11
	ash	-375	68	10	1296	376	83	129	179	-261	133	239	41	184	111	272	69	95	131	188	51	-65	221	148	47	7	571	567	40	1512	49
	oak	115	113	55	294	-242	98	-56	251	-77	345	3	1581	-6	990	-39	100	18	57	41	72	.	.	128	42	45	71	207	55	192	240
	sweet chestnut	.	.	.	.	-1	100	.	.	.	.	.	.	.	.	.	.	6	100	.	.	.	.	-3	775	6	80	751	47	758	47
	other broadleaves	5	2085	1	11190	350	59	282	53	141	141	-6	783	86	99	-41	241	10	2238	29	214	-50	103	319	37	274	64	1530	26	2930	22
	indeterminable	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	0	0
	total	-148	1069	-2316	45	-2871	50	-7517	22	-7219	26	-313	466	-2974	61	1398	114	2273	49	2098	36	409	212	6936	14	5387	25	10962	13	6104	82

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. 1408383. Birmensdorf, Swiss Federal Research Institute WSL

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