Preselection of the regional demarcation

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number of stems #73
Number of stems of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm.

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)

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

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

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

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3
number of stems
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: canton
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
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
altitudinal vegetation belts (NaiS; 6 classes)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
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	.
	spruce	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	.
	fir	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	.
	pine	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	.
	larch	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	.
	Arolla pine	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	.
	other conifers	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	.
	beech	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	.
	maple	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	.
	ash	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	.
	oak	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	.
	sweet chestnut	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	.
	other broadleaves	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	.
	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	.
	total	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	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	421	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	119	100	0	.	0	.	377	47	0	.	0	.	918	33
	fir	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	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1556	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	133	100	0	.	0	.	155	96	0	.	0	.	1844	35
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1923	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	231	70	0	.	0	.	1825	26	0	.	0	.	3980	16
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1929	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	1038	31	0	.	0	.	3000	18
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	140	68
	beech	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	.
	maple	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	.
	ash	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	.
	oak	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	.
	sweet chestnut	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	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	71	0	.	0	.	35	71
	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	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5970	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	515	51	0	.	0	.	3431	23	0	.	0	.	9917	13
subalpine	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	.
	spruce	0	.	0	.	0	.	5098	19	0	.	0	.	0	.	326	82	11997	12	0	.	88	.	0	.	822	87	1192	58	910	50	0	.	0	.	195	75	0	.	1706	27	683	57	480	51	5877	17	0	.	0	.	29373	8
	fir	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	81
	pine	0	.	0	.	0	.	101	67	0	.	0	.	0	.	0	.	691	43	0	.	0	.	0	.	0	.	473	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	173	66	0	.	0	.	1439	38
	larch	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	1866	21	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	2039	28	7	100	27	100	2750	22	0	.	0	.	6690	14
	Arolla pine	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	433	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	450	43	0	.	0	.	883	29
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	378	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	74	0	.	0	.	430	47
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	72	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	72	0	.	0	.	0	.	113	68
	ash	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	.
	oak	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	.
	sweet chestnut	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	.
	other broadleaves	0	.	0	.	0	.	232	66	0	.	0	.	0	.	0	.	38	71	0	.	0	.	0	.	0	.	66	100	75	77	0	.	0	.	0	.	0	.	56	77	0	.	0	.	336	58	0	.	0	.	803	33
	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	0
	total	0	.	0	.	0	.	5437	19	0	.	0	.	0	.	326	82	15475	11	0	.	88	.	0	.	844	88	1731	50	985	51	0	.	0	.	195	75	0	.	3873	22	690	56	550	49	9637	14	0	.	0	.	39831	7
high-montane	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	.
	spruce	0	.	0	.	0	.	4859	17	0	.	1801	41	0	.	556	58	18927	10	0	.	1450	43	0	.	79	.	2278	30	530	49	0	.	0	.	1285	36	0	.	3218	22	2512	31	1988	31	9612	15	0	.	0	.	49095	6
	fir	0	.	0	.	0	.	696	40	0	.	57	100	0	.	0	.	669	30	0	.	7	100	0	.	15	.	167	65	7	100	0	.	0	.	22	100	0	.	429	54	154	55	525	51	924	35	0	.	0	.	3672	17
	pine	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	2064	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	118	100	0	.	107	61	225	68	0	.	3101	26	0	.	0	.	5637	17
	larch	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	1592	22	0	.	0	.	0	.	0	.	0	.	95	100	0	.	0	.	0	.	0	.	1301	30	0	.	178	64	2734	18	0	.	0	.	5923	12
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	74
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	69	100	0	.	17	100	0	.	0	.	87	82
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	92	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	242	59	0	.	0	.	37	100	0	.	0	.	371	41
	maple	0	.	0	.	0	.	583	37	0	.	22	100	0	.	0	.	297	40	0	.	22	100	0	.	15	.	22	76	0	.	0	.	0	.	30	71	0	.	97	100	0	.	257	54	386	47	0	.	0	.	1732	20
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	706	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	603	60	0	.	0	.	1358	38
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	0	.	35	100
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	168	74	0	.	0	.	0	.	0	.	0	.	168	74
	other broadleaves	0	.	0	.	0	.	320	31	0	.	0	0	0	.	18	100	3781	26	0	.	37	72	0	.	0	.	17	100	35	100	0	.	0	.	57	58	0	.	1236	51	117	64	99	74	2856	25	0	.	0	.	8572	16
	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	0	0	0	.	0	.	0	0
	total	0	.	0	.	0	.	6514	16	0	.	1880	40	0	.	574	57	28145	9	0	.	1516	42	0	.	109	.	2484	30	666	43	0	.	0	.	1511	35	0	.	6846	17	3078	29	3047	31	20306	11	0	.	0	.	76676	5
lower and upper montane	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	.
	spruce	158	60	1705	32	2097	28	12230	10	128	52	4254	18	0	.	2420	34	2762	24	1242	31	6011	16	3864	17	979	51	1050	32	5901	16	521	57	1047	40	3569	20	1395	40	536	47	804	39	8589	14	688	35	562	45	3323	26	65833	4
	fir	229	70	321	40	850	36	9177	10	408	46	1920	28	0	.	242	44	382	34	2725	22	3264	18	3212	18	201	46	647	38	1897	18	38	84	1378	30	1350	26	367	50	619	42	970	38	4038	15	440	44	361	48	676	23	35715	5
	pine	20	100	0	.	9	100	903	33	14	100	7	100	0	.	0	.	72	55	229	47	7	100	155	71	0	.	0	.	24	76	35	72	144	42	6	100	90	53	0	.	0	.	90	85	553	60	0	.	256	36	2613	19
	larch	57	76	0	.	0	.	104	70	14	71	0	.	0	.	7	100	158	49	0	.	51	74	7	100	0	.	0	.	67	61	17	100	0	.	15	100	0	.	560	34	0	.	120	65	96	49	14	100	7	100	1293	19
	Arolla pine	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	.
	other conifers	52	75	0	.	36	71	150	41	0	.	0	.	0	.	18	100	0	.	51	74	63	90	17	100	0	.	17	100	95	66	0	.	120	47	18	100	0	.	0	.	0	.	201	52	65	100	0	.	272	67	1177	22
	beech	614	37	320	45	480	34	12274	11	1094	37	2596	28	0	.	1476	29	2237	24	2998	22	2599	22	1980	18	713	45	1710	34	5382	17	821	44	2967	20	1539	22	604	36	9050	16	804	42	5115	13	1621	33	182	63	1925	20	61102	5
	maple	132	62	34	67	339	46	1904	16	205	46	741	26	0	.	741	35	277	43	596	30	605	25	749	30	279	35	616	76	1820	20	42	100	608	28	494	32	356	47	244	52	326	43	1572	21	376	42	36	60	606	26	13697	7
	ash	51	58	0	.	399	49	898	24	59	67	638	36	0	.	904	57	231	60	724	35	589	34	179	50	627	64	61	59	1312	23	42	100	296	52	501	51	312	55	115	47	348	51	653	30	137	49	52	100	569	22	9700	10
	oak	59	67	0	.	0	.	55	63	0	.	98	66	0	.	0	.	46	72	65	69	51	88	7	100	22	100	0	.	17	100	17	100	66	53	6	100	63	63	126	55	0	.	190	59	242	67	0	.	59	51	1187	21
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	73	0	.	0	.	0	.	0	.	0	.	44	73
	other broadleaves	133	86	30	71	150	64	1822	21	96	49	475	42	0	.	875	48	776	32	816	37	517	36	464	36	196	56	179	57	936	34	7	100	533	49	535	53	17	100	3135	22	304	66	861	26	379	35	35	70	365	38	13637	9
	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	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	1504	33	2411	29	4360	23	39518	7	2018	29	10730	14	0	.	6683	24	6941	16	9446	15	13757	13	10634	12	3017	31	4280	24	17451	12	1541	37	7160	17	8032	16	3204	29	14429	13	3557	28	21432	10	4598	23	1242	35	8057	16	206001	3
submontane	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	.
	spruce	3146	23	0	.	0	.	5573	18	561	45	1223	32	0	.	0	.	329	54	1282	37	1652	28	295	44	0	.	264	92	515	54	1814	46	714	31	53	100	3311	28	0	.	94	72	1527	28	0	.	50	100	3494	21	25896	8
	fir	1097	32	0	.	0	.	2548	20	195	51	75	73	0	.	0	.	190	66	567	34	1869	24	319	39	0	.	174	100	104	100	174	65	447	46	7	100	1143	42	0	.	211	91	906	33	0	.	36	100	359	34	10422	10
	pine	404	40	0	.	0	.	487	57	204	34	236	44	0	.	0	.	209	50	28	61	169	62	66	100	0	.	0	.	0	.	373	49	323	51	0	.	159	44	0	.	44	100	120	46	0	.	0	.	403	28	3225	15
	larch	132	56	0	.	0	.	69	65	0	.	65	81	0	.	0	.	65	63	7	100	17	100	0	.	0	.	69	100	133	94	31	71	7	100	0	.	142	72	0	.	0	.	224	64	0	.	0	.	138	38	1099	23
	Arolla pine	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	.
	other conifers	150	51	0	.	0	.	179	42	108	100	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	46	100	49	100	0	.	0	.	51	75	0	.	0	.	103	75	0	.	0	.	52	100	750	26
	beech	4465	17	0	.	0	.	5211	14	1709	27	1399	25	0	.	0	.	258	59	862	35	1770	26	222	41	0	.	17	100	1362	36	1570	29	1727	25	115	63	1168	22	0	.	426	55	2112	22	481	70	106	81	1998	17	26979	6
	maple	712	23	0	.	0	.	824	26	372	29	137	64	0	.	0	.	85	61	160	42	56	63	92	93	0	.	0	.	323	55	163	63	450	27	43	100	410	34	0	.	0	.	795	36	90	72	83	100	1643	30	6435	11
	ash	996	26	0	.	0	.	743	25	466	49	630	37	0	.	0	.	73	62	331	58	380	62	80	72	0	.	0	.	650	41	270	65	330	41	189	65	826	33	0	.	18	100	1146	25	35	100	84	71	626	40	7874	10
	oak	489	53	0	.	0	.	455	27	265	48	82	53	0	.	0	.	0	.	177	44	165	59	312	88	0	.	0	.	107	52	178	48	349	68	0	.	434	35	0	.	60	59	1620	35	8	100	0	.	299	30	5000	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	74	91	15	100	0	.	0	.	88	77
	other broadleaves	1146	24	0	.	0	.	1332	28	510	37	265	59	0	.	0	.	149	60	265	53	146	50	52	75	0	.	17	100	636	48	119	46	435	34	151	84	702	37	0	.	163	60	1599	28	198	52	7	100	704	30	8596	10
	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	.	0	.	0	0
	total	12736	13	0	.	0	.	17420	11	4389	21	4113	20	0	.	0	.	1358	40	3679	22	6238	18	1438	32	0	.	541	72	3876	28	4740	24	4781	20	558	57	8348	17	0	.	1016	53	10224	15	827	50	366	71	9716	13	96365	4
hyperinsubric and colline	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	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	278	56	0	.	0	.	389	67	0	.	34	100	716	42
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	8	100	0	.	0	.	24	77	0	.	0	.	49	54
	pine	0	.	0	.	0	.	0	.	0	.	0	.	162	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	.	0	.	0	.	0	.	1247	39	0	.	45	100	1461	35
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	150	52	0	.	0	.	158	50
	Arolla pine	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	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	0	.	16	100
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	.	1265	31	0	.	0	.	11	100	0	.	0	.	1370	30
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	215	39	0	.	0	.	90	58	0	.	0	.	305	32
	ash	0	.	0	.	0	.	0	.	0	.	0	.	69	100	0	.	80	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	.	1342	33	0	.	17	100	106	61	0	.	0	.	1648	28
	oak	0	.	0	.	0	.	0	.	0	.	0	.	650	49	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	.	2183	28	0	.	404	59	535	43	0	.	15	71	3859	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	813	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11050	14	0	.	0	.	74	77	0	.	0	.	11937	13
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	52	100	0	.	1370	51	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6709	16	0	.	100	100	749	36	0	.	17	100	8996	14
	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	0
	total	0	.	0	.	0	.	0	.	0	.	0	.	933	46	0	.	2334	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	169	.	23073	9	0	.	521	63	3376	25	0	.	111	93	30516	8
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	.
	spruce	3304	22	1705	32	2097	28	27760	7	689	38	7278	15	0	.	3302	28	34445	7	2524	24	9201	14	4158	17	1880	47	4783	22	7855	14	2336	38	1761	27	5101	17	4713	23	5856	15	4094	22	12584	12	16943	11	612	42	6851	16	171831	3
	fir	1326	29	321	40	850	36	12427	9	603	35	2052	26	0	.	242	44	1241	22	3292	19	5140	14	3530	17	238	40	988	32	2008	17	212	55	1825	25	1379	26	1527	34	1056	33	1335	32	5469	13	1389	27	398	45	1035	19	49886	4
	pine	424	38	0	.	9	100	1515	28	219	33	243	43	162	86	0	.	4593	19	257	42	176	60	220	58	0	.	473	93	24	76	408	46	467	37	124	95	256	33	239	62	269	59	210	45	5229	19	0	.	703	21	16220	9
	larch	189	45	0	.	0	.	196	45	14	71	65	81	0	.	7	100	5605	12	7	100	68	61	7	100	0	.	69	100	296	55	49	58	7	100	15	100	142	72	4139	17	7	100	549	36	7555	12	14	100	145	36	19143	7
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	2380	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	1488	25	0	.	0	.	3910	16
	other conifers	202	43	0	.	36	71	329	30	108	100	0	.	0	.	18	100	518	43	51	74	76	76	17	100	0	.	17	100	141	55	49	100	120	47	18	100	51	75	16	100	69	100	304	43	134	58	0	.	324	59	2600	15
	beech	5078	15	320	45	480	34	17486	9	2802	21	3996	20	0	.	1476	29	2595	21	3860	18	4368	17	2202	17	713	45	1728	33	6744	16	2391	24	4694	15	1654	21	1858	18	10630	14	1230	33	7227	11	2149	30	288	50	3923	13	89894	3
	maple	843	22	34	67	339	46	3311	13	576	24	900	23	0	.	741	35	729	26	756	25	683	23	841	28	295	34	638	74	2142	19	204	54	1058	20	567	29	767	28	555	32	326	43	2667	17	942	27	119	72	2249	23	22282	5
	ash	1047	25	0	.	399	49	1642	17	524	44	1268	26	69	100	904	57	1089	37	1055	30	970	32	259	41	627	64	61	59	1962	20	312	58	626	33	690	41	1173	28	1506	30	367	48	1816	19	882	43	136	58	1195	23	20580	7
	oak	548	48	0	.	0	.	509	25	265	48	180	43	650	49	0	.	102	64	242	37	216	50	319	86	22	100	0	.	124	47	195	45	415	58	6	100	514	30	2308	26	60	59	2214	28	821	34	0	.	373	26	10082	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	813	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11262	14	0	.	74	91	89	66	0	.	0	.	12238	13
	other broadleaves	1279	24	30	71	150	64	3707	15	606	32	740	34	52	100	893	47	6113	20	1081	31	700	28	516	33	196	56	280	44	1682	27	126	44	968	31	743	42	719	36	11136	12	584	40	2659	19	4554	18	41	61	1086	23	40639	6
	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	0	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	14240	12	2411	29	4360	23	68889	5	6407	17	16722	11	933	46	7583	22	60223	5	13126	12	21598	10	12072	11	3970	30	9037	17	22977	10	6281	20	11941	12	10297	14	11720	14	48736	6	8341	18	35774	8	42174	7	1608	31	17884	10	459304	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. 1325841. Birmensdorf, Swiss Federal Research Institute WSL

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