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)

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 NFI4/NFI5 #2282
Forest that was less than two-thirds covered with shrubs in both NFI4 (2009-2017) and NFI5 (2018-2026) and is 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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NFI4
growing stock (stemwood)
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: canton
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		canton
		AG		AI		AR		BE		BL/BS		FR		GE		GL		GR		JU		LU		NE		NW		OW		SG		SH		SO		SZ		TG		TI		UR		VD		VS		ZG		ZH		Switzerland
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	±%	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	.
	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	.	322	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	127	47	0	.	0	.	132	43	0	.	0	.	582	25
	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	.	228	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	85	75	0	.	0	.	51	85	0	.	0	.	365	35
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2632	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	242	40	0	.	0	.	1222	23	0	.	0	.	4096	15
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1243	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	755	25	0	.	0	.	2003	15
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	48
	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	.	4	70	0	.	0	.	3	71	0	.	0	.	7	51
	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4449	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	464	30	0	.	0	.	2162	20	0	.	0	.	7075	12
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	.	4923	19	0	.	129	100	0	.	456	54	12290	11	0	.	27	100	0	.	416	62	490	41	642	51	0	.	0	.	348	71	0	.	1633	26	590	49	705	35	5127	17	0	.	0	.	27776	7
	fir	0	.	0	.	0	.	-12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24	100	0	.	0	.	15	176
	pine	0	.	0	.	0	.	61	49	0	.	0	.	0	.	0	.	185	31	0	.	0	.	0	.	0	.	163	91	0	.	0	.	0	.	10	89	0	.	0	.	0	.	0	.	88	48	0	.	0	.	507	33
	larch	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	2808	18	0	.	0	.	0	.	0	.	0	.	93	100	0	.	0	.	0	.	0	.	1502	21	0	.	35	100	3573	22	0	.	0	.	8017	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	288	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	203	42	0	.	0	.	491	30
	other conifers	0	.	0	.	0	.	37	98	0	.	0	.	0	.	0	.	22	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	85	0	.	0	.	67	58
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	20	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	27	95	0	.	29	93	8	100	0	.	0	.	92	47
	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	.	33	69	0	.	0	.	0	.	0	.	24	58	0	.	0	.	0	.	0	.	5	100	8	74	0	.	0	.	0	.	0	.	16	67	6	100	1	100	55	53	0	.	0	.	147	29
	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	.	5047	19	0	.	129	100	0	.	469	53	15633	10	0	.	27	100	0	.	420	62	658	41	742	46	0	.	0	.	358	69	0	.	3198	18	596	48	770	35	9085	14	0	.	0	.	37132	6
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	.	7051	17	0	.	1870	32	0	.	1027	54	18292	9	0	.	750	39	0	.	88	74	1670	31	1192	40	0	.	0	.	1171	35	0	.	3440	22	2331	31	2728	26	7720	13	0	0	0	.	49330	5
	fir	0	.	0	.	0	.	1013	35	0	.	78	100	0	.	0	.	1003	33	0	.	0	.	0	.	5	100	215	53	0	0	0	.	0	.	19	67	0	.	430	48	146	63	571	53	849	34	0	.	0	.	4328	16
	pine	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	1905	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	0	.	80	66	146	73	0	.	1345	25	0	.	0	.	3514	16
	larch	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	2666	17	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	1699	25	0	.	103	57	3806	17	0	.	0	.	8322	11
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-5	142
	other conifers	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	2	74	0	.	0	.	8	73
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	47	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	139	66	0	.	0	.	14	100	0	.	0	.	200	50
	maple	0	.	0	.	0	.	274	43	0	.	10	100	0	.	6	100	209	38	0	.	9	100	0	.	19	85	11	77	0	.	0	.	0	.	5	71	0	.	12	100	0	.	63	52	137	36	0	.	0	.	754	21
	ash	0	.	0	.	0	.	6	100	0	.	0	.	0	.	1	100	191	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	80	0	.	0	.	261	42	0	.	0	.	508	28
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	72	0	.	0	.	9	72
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	72	0	.	0	.	0	.	0	.	0	.	32	72
	other broadleaves	0	.	0	.	0	.	73	36	0	.	5	100	0	.	6	100	1033	23	0	.	13	68	0	.	0	.	0	0	9	77	0	.	0	.	11	73	0	.	214	39	19	76	26	82	662	22	0	.	0	.	2070	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	0	0	0	.	0	.	0	0
	total	0	.	0	.	0	.	8433	16	0	.	1964	32	0	.	1041	53	25346	8	0	.	772	39	0	.	111	72	1895	31	1228	39	0	.	0	.	1244	34	0	.	6095	16	2647	30	3492	25	14804	10	0	0	0	.	69071	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	214	52	2526	29	2875	27	15113	9	284	60	5178	18	0	.	2564	26	2843	20	1781	28	6856	17	4170	16	950	42	1330	32	8472	15	478	50	844	32	4240	19	913	37	803	49	1023	39	8511	11	1130	33	811	38	3224	21	77134	4
	fir	268	46	557	38	1541	36	11445	11	294	55	2313	25	0	.	525	49	905	34	2671	23	4047	18	3840	18	323	55	744	44	3595	17	80	76	868	30	1902	23	265	47	665	38	608	38	5040	15	421	51	327	56	1581	22	44825	5
	pine	62	100	0	.	17	100	652	39	22	100	24	100	0	.	0	.	219	58	265	44	16	100	51	71	0	.	0	.	179	52	69	68	213	41	8	100	150	48	0	.	0	.	120	81	232	56	0	.	355	33	2653	15
	larch	101	92	0	.	0	.	110	60	25	72	0	.	0	.	67	87	196	45	0	.	13	77	43	100	0	.	0	.	110	70	9	100	0	.	52	100	0	.	687	31	0	.	49	57	90	56	55	100	24	93	1630	18
	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	15	74	0	.	8	78	205	61	0	.	0	.	0	.	2	100	0	.	-5	145	199	89	7	100	0	.	4	100	287	102	0	.	12	226	3	100	0	.	0	.	0	.	211	88	5	100	0	.	40	62	995	41
	beech	625	41	258	46	557	33	9581	11	1364	35	1885	24	0	.	961	30	1666	25	2548	19	1928	24	1254	18	436	42	1539	30	4253	16	671	40	2999	19	1544	27	452	36	4072	15	554	45	4446	13	747	33	183	52	1712	20	46235	4
	maple	114	75	18	71	245	52	1277	16	269	41	429	29	0	.	346	29	242	66	344	25	483	24	431	29	126	35	273	53	1442	18	29	100	335	25	298	28	283	52	106	54	152	42	619	21	143	47	93	54	371	31	8467	7
	ash	84	76	0	.	335	48	820	32	59	61	454	33	0	.	296	42	71	76	554	31	247	31	70	44	439	57	73	65	1004	23	39	100	229	32	426	40	258	41	56	42	174	53	644	34	136	57	13	87	881	28	7362	9
	oak	58	66	0	.	0	.	49	55	0	.	34	74	0	.	0	.	79	79	24	64	50	77	16	90	5	100	0	.	1	100	8	100	98	58	16	100	78	59	33	48	0	.	116	40	31	56	10	100	93	42	801	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	88	80	0	.	0	.	0	.	0	.	0	.	88	80
	other broadleaves	157	70	3	100	63	75	811	31	68	43	71	36	0	.	181	49	280	29	227	48	113	44	91	46	79	55	32	52	314	27	10	100	151	54	173	52	0	0	1247	24	149	55	377	28	84	48	7	100	224	50	4915	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	0	0	0	0	0	0	0	0	.	0	.	0	0
	total	1698	30	3362	28	5641	22	40063	7	2384	30	10389	14	0	.	4942	21	6501	15	8409	15	13952	13	9974	12	2358	29	3995	24	19658	11	1394	35	5749	16	8662	15	2400	29	7757	12	2660	26	20133	9	3018	23	1499	35	8505	15	195103	2
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	3015	22	0	.	0	.	5298	17	460	51	1011	36	0	.	0	.	150	73	991	36	994	30	452	47	0	.	48	100	283	65	1410	29	1249	39	4	100	2238	23	0	.	154	64	1002	30	0	.	5	100	3673	19	22438	7
	fir	1619	28	0	.	0	.	3315	21	162	51	51	56	0	.	0	.	84	65	897	42	1806	25	283	43	0	.	109	100	24	100	60	88	448	40	31	100	1023	36	0	.	223	83	805	29	0	.	44	100	555	38	11539	10
	pine	150	46	0	.	0	.	707	46	196	45	471	48	0	.	0	.	95	67	104	69	170	56	107	100	0	.	0	.	0	.	291	45	117	46	0	.	291	46	0	.	92	71	88	49	1	100	0	.	573	31	3453	15
	larch	199	46	0	.	0	.	175	60	0	.	71	74	0	.	0	.	84	72	20	100	5	100	0	.	0	.	14	100	75	72	30	81	17	100	0	.	55	85	0	.	0	.	158	44	0	.	0	.	263	40	1167	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	106	55	0	.	0	.	159	70	177	100	0	.	0	.	0	.	0	.	0	.	60	100	0	.	0	.	0	.	-14	112	57	100	0	.	0	.	4	74	0	.	0	.	108	96	0	.	0	.	12	90	669	38
	beech	4403	17	0	.	0	.	4161	16	1931	23	1418	30	0	.	0	.	109	63	1046	35	1513	26	284	63	0	.	0	0	1010	38	1437	26	1936	24	201	68	1239	24	0	.	321	51	2199	25	196	63	90	74	2487	19	25979	6
	maple	469	22	0	.	0	.	473	29	218	39	45	64	0	.	0	.	23	121	102	47	35	70	-1	154	0	.	0	.	-16	265	56	59	221	37	47	88	156	41	0	.	33	100	275	35	29	80	29	100	787	27	2980	11
	ash	933	26	0	.	0	.	694	27	453	39	834	33	0	.	0	.	44	92	368	52	682	67	58	76	0	.	0	.	512	39	176	46	273	47	286	67	801	29	0	.	8	100	1026	28	20	66	158	76	572	25	7899	10
	oak	638	38	0	.	0	.	645	22	197	52	126	57	0	.	0	.	0	.	275	51	259	57	70	69	0	.	0	.	82	55	233	53	103	49	0	.	805	45	0	.	95	54	849	29	28	79	0	.	538	30	4942	12
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	84	129	83	0	.	0	.	144	75
	other broadleaves	450	25	0	.	0	.	791	29	141	39	458	60	0	.	0	.	47	60	72	45	139	50	14	63	0	.	2	100	274	53	26	52	141	42	59	73	864	54	0	.	79	56	1157	33	79	55	0	.	347	32	5139	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	0	0	0	0	.	0	0	0	0
	total	11987	11	0	.	0	.	16420	11	3936	19	4485	20	0	.	0	.	635	40	3875	21	5663	19	1268	34	0	.	172	73	2230	29	3775	21	4505	20	629	56	7477	16	0	.	1005	46	7675	14	482	45	326	72	9804	12	86349	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	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	23	100	182	46	0	.	0	.	138	56	0	.	14	100	367	32
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	27	100	0	.	0	.	17	77	0	.	0	.	55	58
	pine	0	.	0	.	0	.	0	.	0	.	0	.	110	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	304	43	0	.	69	100	483	35
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	227	58	0	.	0	.	243	55
	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	.	50	100	0	.	0	.	0	.	0	.	0	.	50	100
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	773	29	0	.	0	.	0	.	0	.	0	.	853	27
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	37	0	.	0	.	5	73	0	.	0	.	123	36
	ash	0	.	0	.	0	.	0	.	0	.	0	.	28	100	0	.	8	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	572	31	0	.	2	100	21	52	0	.	0	.	638	28
	oak	0	.	0	.	0	.	0	.	0	.	0	.	473	55	0	.	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	903	21	0	.	231	60	138	52	0	.	37	71	1864	20
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4792	14	0	.	0	.	65	63	0	.	0	.	5305	12
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	21	87	0	.	439	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2142	16	0	.	25	96	106	79	0	.	18	100	2751	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	0
	total	0	.	0	.	0	.	0	.	0	.	0	.	632	51	0	.	1033	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	100	9568	9	0	.	258	58	1021	29	0	.	137	87	12732	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	3229	21	2526	29	2875	27	32385	7	744	39	8189	14	0	.	4047	22	33907	6	2773	22	8628	14	4622	15	1454	33	3537	20	10588	13	1888	25	2092	26	5764	16	3174	19	6186	15	4098	21	12945	10	14247	9	816	38	6910	14	177626	2
	fir	1887	25	557	38	1541	36	15761	9	457	40	2442	24	0	.	525	49	1991	23	3568	20	5854	15	4123	17	331	54	1068	34	3619	17	139	57	1316	24	1953	23	1299	30	1121	29	977	31	6417	13	1311	28	371	51	2135	19	60763	4
	pine	212	44	0	.	17	100	1422	29	217	41	495	46	110	79	0	.	2632	18	369	37	186	52	159	71	0	.	163	91	179	52	360	39	330	31	55	71	441	35	165	50	238	53	207	51	2020	19	0	.	997	22	10974	8
	larch	300	43	0	.	0	.	311	40	25	72	71	74	0	.	67	87	8395	10	20	100	18	62	43	100	0	.	14	100	306	44	39	66	17	100	52	100	55	85	4139	13	0	.	345	29	8917	11	55	100	287	38	23475	6
	Arolla pine	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	1532	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	958	22	0	.	0	.	2489	13
	other conifers	121	49	0	.	8	78	402	43	177	100	0	.	0	.	2	100	45	35	-5	145	259	72	7	100	0	.	4	100	273	107	57	100	12	226	3	100	4	74	50	100	6	100	320	66	15	58	0	.	52	52	1813	27
	beech	5027	16	258	46	557	33	13742	9	3295	20	3303	19	0	.	961	30	1869	22	3594	16	3441	18	1538	19	436	42	1539	30	5263	15	2108	21	4936	15	1745	25	1723	19	5004	13	875	34	6645	12	958	29	273	43	4199	14	73286	3
	maple	583	23	18	71	245	52	2024	13	487	28	484	26	0	.	365	28	490	37	445	22	526	22	430	29	145	33	284	51	1426	18	86	52	556	21	349	26	439	37	263	29	185	39	986	17	322	27	122	47	1157	20	12416	5
	ash	1017	24	0	.	335	48	1520	21	512	35	1288	25	28	100	297	42	314	34	922	27	929	50	128	42	439	57	73	65	1516	20	215	42	503	29	712	36	1065	24	678	27	182	51	1672	21	437	31	171	70	1453	19	16407	6
	oak	696	35	0	.	0	.	694	21	197	52	160	48	473	55	0	.	151	63	299	48	308	49	86	59	5	100	0	.	82	54	241	52	201	38	16	100	895	41	936	20	95	54	1197	24	205	37	10	100	668	25	7615	9
	sweet chestnut	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	449	45	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4911	13	0	.	9	84	194	59	0	.	0	.	5569	12
	other broadleaves	606	26	3	100	63	75	1708	20	209	30	534	52	21	87	187	47	1822	17	299	38	265	32	105	40	79	55	39	45	605	28	37	47	292	35	244	41	864	54	3624	12	253	37	1586	25	987	18	7	100	589	27	15029	7
	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	0	0	0	.	0	0	0	0
	total	13685	11	3362	28	5641	22	69962	5	6320	16	16966	11	632	51	6452	18	53598	5	12284	12	20415	10	11241	11	2890	26	6720	17	23858	9	5169	18	10254	12	10892	13	9960	14	27082	5	6908	17	32328	7	30572	7	1826	31	18446	9	407462	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. 1372314. Birmensdorf, Swiss Federal Research Institute WSL

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