Selection from the list of topics

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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 #434
Forest of which less than two-thirds is covered with shrubs that can be accessed 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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NFI5
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
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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	.	1060	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	811	50	0	.	0	.	470	42	0	.	0	.	2341	24
	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	.	1652	39	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	206	79	0	.	0	.	173	96	0	.	0	.	2031	33
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3786	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1414	33	0	.	0	.	2155	23	0	.	0	.	7356	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3398	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	1425	25	0	.	0	.	4872	17
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	300	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	300	40
	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	.	54	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	62	0	.	0	.	87	82	0	.	0	.	211	44
	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	.	10250	13	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2551	29	0	.	0	.	4309	19	0	.	0	.	17110	10
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	.	5745	18	0	.	511	100	0	.	852	59	14959	11	0	.	31	100	0	.	872	82	1630	48	953	47	0	.	0	.	162	75	0	.	2479	23	1241	44	765	39	7240	15	0	.	0	.	37438	7
	fir	0	.	0	.	0	.	6	100	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	0	.	102	57
	pine	0	.	0	.	0	.	191	59	0	.	0	.	0	.	0	.	985	37	0	.	0	.	0	.	66	100	530	90	0	.	0	.	0	.	153	89	0	.	0	.	0	.	0	.	184	61	0	.	0	.	2109	30
	larch	0	.	0	.	0	.	24	100	0	.	0	.	0	.	0	.	2755	20	0	.	0	.	0	.	0	.	0	.	180	90	0	.	0	.	0	.	0	.	2982	23	0	.	14	100	3289	19	0	.	0	.	9245	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	555	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	435	44	0	.	0	.	990	30
	other conifers	0	.	0	.	0	.	215	92	0	.	0	.	0	.	0	.	397	47	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	58	100	0	.	0	.	0	.	0	.	52	74	0	.	0	.	739	38
	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	.	44	100	0	.	0	.	0	.	55	100	47	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	340	91	0	.	38	71	17	100	0	.	0	.	541	60
	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	.	465	54	0	.	0	.	0	.	0	.	299	53	0	.	0	.	0	.	15	100	44	100	57	73	0	.	0	.	0	.	0	.	251	38	62	77	22	100	536	39	0	.	0	.	1750	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	0
	total	0	.	0	.	0	.	6690	17	0	.	511	100	0	.	950	55	19997	10	0	.	31	100	0	.	974	82	2221	44	1191	44	0	.	0	.	372	61	0	.	6124	18	1303	43	839	38	11784	13	0	.	0	.	52987	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	.	6052	17	0	.	2324	36	0	.	616	55	21218	9	0	.	1155	42	38	100	184	73	2362	29	1491	38	0	.	0	.	1493	37	0	.	3458	23	2617	28	3071	25	10599	14	0	0	0	.	56678	5
	fir	0	.	0	.	0	.	777	40	0	.	57	100	0	.	0	.	512	33	0	.	0	.	0	.	15	100	235	57	0	0	0	.	0	.	44	62	0	.	618	44	335	47	693	48	980	32	0	.	0	.	4267	16
	pine	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	1936	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	100	0	.	61	59	205	68	0	.	2743	27	0	.	0	.	5081	18
	larch	0	.	0	.	0	.	54	75	0	.	0	.	0	.	0	.	1882	21	0	.	0	.	0	.	0	.	0	.	99	100	0	.	0	.	0	.	0	.	1560	25	0	.	376	52	3944	20	0	.	0	.	7916	13
	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	.	149	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	87	100	0	.	17	100	0	.	0	.	271	55
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	107	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	295	48	84	100	0	.	147	88	0	.	0	.	633	34
	maple	0	.	0	.	0	.	737	32	0	.	29	79	0	.	121	100	407	41	0	.	66	100	0	.	59	71	23	74	0	.	0	.	0	.	45	74	0	.	212	85	52	58	252	64	1160	36	0	.	0	.	3164	18
	ash	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	649	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	75	0	.	17	100	1008	36	0	.	0	.	1775	28
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	79	0	.	0	.	71	79
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	192	79	0	.	0	.	0	.	0	.	0	.	192	79
	other broadleaves	0	.	0	.	0	.	763	40	0	.	0	0	0	.	35	100	4272	23	0	.	94	62	0	.	15	100	0	0	69	61	0	.	0	.	32	71	0	.	1473	40	157	71	133	63	3158	20	0	.	0	.	10201	13
	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	.	8572	15	0	.	2410	35	0	.	772	48	31001	8	0	.	1314	43	38	100	273	71	2620	29	1677	35	0	.	0	.	1730	35	0	.	7964	16	3536	26	4543	26	23828	10	0	0	0	.	90278	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	84	62	1649	30	1493	29	11507	10	377	63	4020	18	0	.	1359	26	3311	23	828	26	4779	18	3408	17	895	50	1632	37	5579	15	287	46	1140	41	2902	19	747	40	1785	60	1010	34	7418	13	782	32	663	39	2348	26	60004	5
	fir	144	68	283	39	929	39	9608	10	364	49	1901	27	0	.	194	46	418	36	2894	19	4240	20	3010	18	194	43	791	36	1719	17	28	79	1219	36	1414	25	235	58	601	39	881	35	4391	14	480	40	459	47	659	22	37055	5
	pine	20	100	0	.	0	.	690	40	52	78	7	100	0	.	0	.	65	56	166	47	7	100	82	72	0	.	0	.	203	71	28	61	143	40	6	100	34	52	64	100	8	100	68	81	469	67	0	.	149	38	2261	21
	larch	33	82	0	.	0	.	62	53	14	71	66	100	0	.	7	100	92	55	0	.	55	67	7	100	0	.	0	.	47	62	7	100	0	.	14	100	137	100	647	31	0	.	15	71	78	54	7	100	66	90	1355	20
	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	.	54	74	179	38	0	.	0	.	0	.	18	100	0	.	51	74	79	59	17	100	0	.	17	100	132	51	0	.	195	54	18	100	0	.	0	.	0	.	250	60	87	100	0	.	367	63	1517	22
	beech	410	35	235	51	400	36	10748	11	1270	32	2245	24	0	.	873	27	2571	22	2611	22	2347	20	1652	19	909	42	1517	28	5596	16	850	41	3196	20	1842	20	390	42	9983	15	792	42	4961	13	1768	31	147	46	2052	23	59367	4
	maple	104	55	28	79	530	42	2913	16	363	37	831	28	0	.	676	32	394	39	660	27	663	22	1069	30	277	36	520	66	1720	18	42	100	789	28	791	34	322	52	651	37	377	39	2387	21	533	37	79	47	734	25	17452	6
	ash	37	71	0	.	235	47	1129	26	54	58	758	33	0	.	311	37	298	48	442	36	281	33	108	52	521	67	166	56	1156	25	38	100	203	33	444	39	134	41	298	45	425	44	708	30	155	60	69	100	397	27	8368	9
	oak	83	60	0	.	0	.	84	63	0	.	79	61	0	.	0	.	113	75	38	100	25	75	7	100	22	100	0	.	35	100	0	.	47	58	6	100	14	71	133	62	0	.	136	52	271	75	7	100	57	48	1158	23
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	99	66	0	.	0	.	0	.	0	.	0	.	99	66
	other broadleaves	253	52	0	.	163	56	2302	22	157	43	843	32	0	.	340	53	982	35	852	32	742	36	267	40	357	46	252	40	953	31	7	100	395	46	906	34	0	0	4748	19	675	65	1498	29	379	35	70	61	505	34	17645	8
	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
	total	1220	33	2195	28	3803	24	39223	7	2650	26	10749	14	0	.	3779	20	8244	15	8541	14	13217	13	9629	12	3175	29	4894	22	17141	11	1286	36	7327	17	8344	15	2013	30	19010	13	4169	26	21833	9	5002	21	1501	31	7333	16	206279	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	2595	22	0	.	0	.	4010	17	393	49	863	60	0	.	0	.	276	65	278	40	1433	32	228	47	0	.	347	100	454	73	804	41	614	52	141	100	1819	26	0	.	103	67	1100	32	0	.	0	.	2569	24	18027	8
	fir	895	33	0	.	0	.	3062	18	395	35	268	61	0	.	0	.	114	62	982	31	1804	27	623	62	0	.	146	100	110	85	115	73	416	41	7	100	942	36	0	.	245	82	813	27	0	.	30	100	478	33	11443	9
	pine	95	69	0	.	0	.	305	50	207	37	158	48	0	.	0	.	164	67	21	58	144	54	31	100	0	.	0	.	0	.	223	45	181	57	0	.	115	48	0	.	57	71	68	46	0	.	0	.	291	40	2060	15
	larch	317	55	0	.	0	.	44	86	0	.	55	89	0	.	0	.	40	76	7	100	17	100	0	.	0	.	52	100	106	93	24	77	7	100	0	.	74	75	0	.	0	.	653	66	0	.	0	.	94	37	1490	33
	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	136	56	0	.	0	.	115	46	98	100	0	.	0	.	0	.	0	.	0	.	33	72	0	.	0	.	0	.	88	71	21	100	0	.	0	.	34	71	0	.	0	.	78	74	0	.	0	.	53	74	656	26
	beech	3749	15	0	.	0	.	4542	14	1764	26	1213	29	0	.	0	.	216	50	828	31	1465	26	327	40	0	.	0	0	800	36	1437	27	1706	22	94	61	1108	22	0	.	363	57	2479	25	385	67	77	76	2396	17	24950	6
	maple	979	23	0	.	0	.	992	23	580	35	147	63	0	.	0	.	135	63	198	43	239	45	211	97	0	.	0	.	342	54	190	58	666	51	50	87	601	28	0	.	26	100	1017	31	160	80	71	100	1296	27	7902	10
	ash	777	28	0	.	0	.	566	25	348	39	555	37	0	.	0	.	48	59	170	46	213	56	134	49	0	.	0	.	303	46	176	47	336	41	238	77	517	28	0	.	18	100	1025	27	87	56	32	82	452	31	5992	10
	oak	303	43	0	.	0	.	457	27	243	47	178	51	0	.	0	.	0	.	125	51	94	52	238	92	0	.	0	.	53	49	192	42	294	76	0	.	305	33	0	.	61	75	1634	37	24	74	0	.	214	28	4416	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	163	91	0	.	0	.	189	80
	other broadleaves	1338	21	0	.	0	.	1742	23	328	36	442	40	0	.	0	.	222	55	236	50	299	44	121	77	0	.	17	100	398	57	116	54	358	39	105	78	1122	35	0	.	160	55	1206	27	266	49	0	.	1126	26	9602	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
	total	11183	12	0	.	0	.	15835	10	4355	20	3880	23	0	.	0	.	1233	36	2846	22	5743	18	1913	35	0	.	562	78	2655	35	3298	23	4576	21	635	66	6637	16	0	.	1034	48	10080	15	1086	43	209	77	8969	13	86728	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	.	52	74	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	335	57	0	.	0	.	292	62	0	.	7	100	693	38
	fir	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	100	8	100	0	.	0	.	42	61	0	.	0	.	136	50
	pine	0	.	0	.	0	.	0	.	0	.	0	.	59	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1102	50	0	.	21	100	1181	47
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	121	51	0	.	0	.	135	46
	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	.	44	70	0	.	0	.	17	100	0	.	0	.	61	58
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	83	100	1428	27	0	.	0	.	12	100	0	.	0	.	1531	26
	maple	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	408	45	0	.	0	.	125	54	0	.	0	.	553	35
	ash	0	.	0	.	0	.	0	.	0	.	0	.	34	100	0	.	60	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	100	1763	29	0	.	17	100	257	49	0	.	0	.	2166	24
	oak	0	.	0	.	0	.	0	.	0	.	0	.	266	58	0	.	55	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	1970	26	0	.	268	60	775	38	0	.	73	91	3425	19
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	741	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9444	14	0	.	0	.	75	76	0	.	0	.	10259	14
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	167	67	0	.	1313	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8206	14	0	.	118	87	888	28	0	.	31	100	10722	13
	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	.	632	48	0	.	2190	40	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	186	100	23613	9	0	.	403	66	3706	24	0	.	132	71	30862	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	2679	21	1649	30	1493	29	27313	7	770	39	7719	17	52	74	2826	25	40832	6	1106	22	7398	14	3674	16	1950	44	5970	21	8477	14	1090	33	1754	32	4699	17	2567	22	8869	17	4970	19	12355	11	19382	9	663	39	4925	18	175182	3
	fir	1039	30	283	39	929	39	13453	9	759	30	2226	24	34	100	238	42	1044	23	3876	16	6044	16	3634	19	231	38	1172	29	1829	16	142	60	1634	29	1466	25	1228	30	1227	29	1461	28	5897	13	1532	24	488	44	1137	19	53003	4
	pine	115	60	0	.	0	.	1210	28	259	33	164	46	59	100	0	.	4802	19	187	42	151	52	113	59	66	100	530	90	203	71	251	40	324	36	273	65	149	39	331	54	271	54	136	47	4670	21	0	.	461	28	14724	10
	larch	350	50	0	.	0	.	185	37	14	71	121	68	0	.	7	100	8562	11	7	100	73	57	7	100	0	.	52	100	433	50	31	64	7	100	14	100	211	70	6612	14	0	.	1058	45	9587	12	7	100	159	43	27496	6
	Arolla pine	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	3971	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	100	0	.	0	.	1860	22	0	.	0	.	5889	15
	other conifers	188	45	0	.	54	74	658	38	98	100	0	.	0	.	18	100	696	32	51	74	112	47	17	100	0	.	35	71	237	39	21	100	195	54	76	80	34	71	44	70	87	100	328	49	173	56	0	.	420	56	3543	14
	beech	4159	14	235	51	400	36	15290	8	3034	20	3458	19	0	.	873	27	2902	20	3439	18	3812	16	1980	17	909	42	1517	28	6396	15	2288	22	4902	15	1936	19	1581	19	11778	13	1239	32	7440	12	2313	27	224	40	4448	14	86553	3
	maple	1083	21	28	79	530	42	4686	12	943	26	1007	25	20	100	852	30	983	25	857	23	968	20	1280	30	336	32	542	64	2063	17	232	51	1454	28	887	31	923	26	1611	29	455	34	3694	17	1997	24	149	53	2030	19	29611	5
	ash	814	26	0	.	235	47	1701	19	401	34	1312	25	34	100	311	37	1055	34	612	29	493	30	242	36	521	67	166	56	1459	22	214	43	539	28	682	37	685	23	2156	24	444	42	1767	20	1508	27	101	73	849	21	18301	6
	oak	385	36	0	.	0	.	541	25	243	47	258	40	266	58	0	.	168	60	163	45	119	44	245	89	22	100	0	.	88	49	192	42	341	66	6	100	337	30	2103	25	61	75	2038	31	1142	32	7	100	345	27	9071	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	758	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9735	14	0	.	7	100	238	67	0	.	0	.	10739	13
	other broadleaves	1591	19	0	.	163	56	5271	14	485	28	1285	25	167	67	375	49	7141	18	1088	27	1135	27	388	37	388	42	313	36	1478	26	123	51	752	30	1044	31	1122	35	14748	10	1054	44	2976	19	5314	14	70	61	1662	20	50132	5
	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
	total	12403	11	2195	28	3803	24	70319	5	7005	16	17550	11	632	48	5501	18	72916	5	11387	12	20305	10	11580	11	4422	28	10298	16	22663	10	4584	19	11903	13	11081	13	8836	14	59262	5	10041	16	37697	7	49715	6	1710	29	16434	10	484244	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. 1325901. Birmensdorf, Swiss Federal Research Institute WSL

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