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 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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NFI5
number of stems
altitudinal vegetation belts (NaiS; 6 classes) · main tree species
regional demarcation: canton
unit: n/ha
evaluation area: accessible forest without shrub forest NFI4/NFI5
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	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%	n/ha	±%
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	.	38	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	138	44	0	.	0	.	36	39	0	.	0	.	50	23
	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	.	60	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	76	0	.	0	.	13	95	0	.	0	.	44	32
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	128	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	241	23	0	.	0	.	129	16	0	.	0	.	143	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	124	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	98	0	.	0	.	103	21	0	.	0	.	103	15
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	41
	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	.	2	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	12	57	0	.	0	.	7	81	0	.	0	.	5	43
	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	.	363	8	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	434	17	0	.	0	.	288	13	0	.	0	.	350	6
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	.	329	11	0	.	1429	0	0	.	395	53	291	8	0	.	70	0	0	.	801	50	542	35	254	36	0	.	0	.	111	56	0	.	163	18	337	33	207	24	217	12	0	.	0	.	273	6
	fir	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	65
	pine	0	.	0	.	0	.	11	57	0	.	0	.	0	.	0	.	20	36	0	.	0	.	0	.	60	73	176	84	0	.	0	.	0	.	104	74	0	.	0	.	0	.	0	.	6	60	0	.	0	.	16	30
	larch	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	44	17	0	.	0	.	0	.	0	.	0	.	50	85	0	.	0	.	0	.	0	.	184	19	0	.	4	95	97	17	0	.	0	.	63	11
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	43	0	.	0	.	7	30
	other conifers	0	.	0	.	0	.	12	91	0	.	0	.	0	.	0	.	7	50	0	.	0	.	0	.	0	.	6	94	0	.	0	.	0	.	39	87	0	.	0	.	0	.	0	.	2	74	0	.	0	.	5	39
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	99	0	.	0	.	0	.	0	.	0	.	1	100
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	88	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	90	0	.	10	64	1	100	0	.	0	.	4	64
	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	.	16	57	0	.	0	.	0	.	0	.	6	52	0	.	0	.	0	.	14	89	15	91	16	64	0	.	0	.	0	.	0	.	12	45	17	71	6	93	16	39	0	.	0	.	11	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	.
	total	0	.	0	.	0	.	370	10	0	.	1429	0	0	.	426	47	379	7	0	.	70	0	0	.	895	50	739	28	320	31	0	.	0	.	255	36	0	.	386	11	354	31	227	22	352	8	0	.	0	.	379	4
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	.	267	11	0	.	474	26	0	.	287	37	284	6	0	.	421	22	100	0	278	11	452	15	300	27	0	.	0	.	340	22	0	.	165	19	405	16	297	17	215	12	0	.	0	.	269	4
	fir	0	.	0	.	0	.	35	39	0	.	12	96	0	.	0	.	7	32	0	.	0	.	0	.	23	77	45	53	0	.	0	.	0	.	10	55	0	.	30	42	56	41	67	45	20	31	0	.	0	.	20	15
	pine	0	.	0	.	0	.	1	99	0	.	0	.	0	.	0	.	25	27	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	96	0	.	3	58	34	63	0	.	56	26	0	.	0	.	24	18
	larch	0	.	0	.	0	.	2	73	0	.	0	.	0	.	0	.	25	20	0	.	0	.	0	.	0	.	0	.	20	96	0	.	0	.	0	.	0	.	75	22	0	.	31	55	67	15	0	.	0	.	35	11
	Arolla pine	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	74
	other conifers	0	.	0	.	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	96	0	.	0	.	0	.	0	.	0	.	14	97	0	.	0	100	0	.	0	.	1	62
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14	47	0	.	0	.	3	88	0	.	0	.	3	36
	maple	0	.	0	.	0	.	33	29	0	.	5	94	0	.	56	92	5	41	0	.	24	90	0	.	89	8	4	71	0	.	0	.	0	.	10	71	0	.	10	84	6	67	24	62	24	35	0	.	0	.	15	18
	ash	0	.	0	.	0	.	0	99	0	.	0	.	0	.	0	.	9	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	75	0	.	2	98	21	35	0	.	0	.	9	28
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	78	0	.	0	.	0	79
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	78	0	.	0	.	0	.	0	.	0	.	1	79
	other broadleaves	0	.	0	.	0	.	34	38	0	.	0	.	0	.	16	92	57	22	0	.	34	48	0	.	23	77	0	.	14	55	0	.	0	.	7	66	0	.	69	39	26	66	13	60	64	18	0	.	0	.	48	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	.
	total	0	.	0	.	0	.	375	9	0	.	490	25	0	.	359	25	414	5	0	.	479	23	100	0	412	3	501	15	337	23	0	.	0	.	394	18	0	.	379	11	541	13	435	19	472	6	0	.	0	.	425	3
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	21	55	304	18	170	22	122	9	64	57	160	15	0	.	112	20	148	19	40	23	165	14	126	14	166	43	154	32	139	12	83	34	67	38	138	14	146	31	49	60	131	27	135	11	80	26	155	25	116	23	122	4
	fir	35	62	52	31	105	34	103	8	61	43	77	24	0	.	16	44	21	34	139	15	147	17	114	15	36	35	75	32	40	15	8	73	72	34	67	22	46	52	17	38	114	28	80	12	49	36	108	36	32	18	76	5
	pine	5	96	0	.	0	.	7	39	9	73	0	99	0	.	0	.	3	54	8	45	0	99	3	71	0	.	0	.	5	71	8	53	8	37	0	99	7	46	2	100	1	97	1	81	48	64	0	.	7	36	5	21
	larch	8	77	0	.	0	.	1	52	2	67	3	99	0	.	1	99	5	53	0	.	2	67	0	99	0	.	0	.	1	61	2	95	0	.	1	99	27	96	16	31	0	.	0	70	8	51	2	96	3	89	3	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	13	69	0	.	6	72	2	38	0	.	0	.	0	.	2	98	0	.	2	73	3	58	1	99	0	.	2	99	3	50	0	.	12	53	1	99	0	.	0	.	0	.	5	60	9	98	0	.	18	62	3	22
	beech	101	20	43	45	45	31	115	9	214	21	91	21	0	.	72	22	119	18	125	18	78	18	62	16	168	33	143	22	137	14	245	27	189	14	82	16	76	34	275	12	103	36	89	10	182	25	34	36	101	19	121	4
	maple	26	46	5	76	60	36	31	15	61	28	34	26	0	.	56	28	20	37	32	24	23	20	40	28	51	26	49	64	43	16	12	95	47	25	37	32	63	45	17	38	49	33	43	19	55	32	18	39	36	21	36	6
	ash	9	65	0	.	27	42	12	25	9	53	31	31	0	.	26	33	15	47	21	34	10	31	4	51	97	62	16	53	26	24	11	95	12	30	21	37	26	33	8	44	55	38	13	29	16	57	16	96	20	24	17	9
	oak	20	53	0	.	0	.	1	62	0	.	3	60	0	.	0	.	6	74	2	99	1	74	0	99	4	95	0	.	1	100	0	.	3	57	0	99	3	66	4	62	0	.	1	51	28	72	2	97	3	47	2	24
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	65	0	.	0	.	0	.	0	.	0	.	0	66
	other broadleaves	62	43	0	.	18	52	20	20	27	35	31	31	0	.	28	50	43	36	41	29	22	36	10	39	66	37	24	36	20	33	2	95	23	44	43	31	0	.	130	17	88	61	27	28	39	29	16	54	25	31	34	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	.
	total	300	15	405	14	432	13	414	4	447	11	431	8	0	.	312	11	378	8	410	7	449	7	360	6	589	14	462	12	414	7	371	18	433	9	391	8	393	15	521	9	542	15	394	5	514	10	352	11	361	10	419	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	69	20	0	.	0	.	91	15	33	46	81	57	0	.	0	.	87	56	31	35	102	28	50	38	0	.	500	71	81	68	89	37	47	51	103	86	94	23	0	.	60	52	49	30	0	.	0	.	88	22	75	8
	fir	24	32	0	.	0	.	68	16	33	31	25	59	0	.	0	.	36	52	108	25	129	22	138	56	0	.	210	71	20	80	13	70	32	39	5	86	49	33	0	.	142	68	37	24	0	.	40	79	16	32	47	9
	pine	3	69	0	.	0	.	7	49	17	33	15	44	0	.	0	.	52	58	2	54	10	52	7	96	0	.	0	.	0	.	25	40	14	55	0	.	6	46	0	.	33	54	3	45	0	.	0	.	10	38	9	15
	larch	8	54	0	.	0	.	1	86	0	.	5	87	0	.	0	.	13	69	1	98	1	99	0	.	0	.	75	71	19	89	3	75	1	99	0	.	4	74	0	.	0	.	29	65	0	.	0	.	3	36	6	32
	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	4	55	0	.	0	.	3	45	8	99	0	.	0	.	0	.	0	.	0	.	2	70	0	.	0	.	0	.	16	67	2	98	0	.	0	.	2	70	0	.	0	.	4	73	0	.	0	.	2	73	3	25
	beech	99	12	0	.	0	.	103	11	148	20	114	23	0	.	0	.	68	37	91	25	105	21	72	30	0	.	0	.	144	27	159	19	127	17	68	35	57	18	0	.	211	38	112	21	152	56	103	20	82	14	103	5
	maple	26	21	0	.	0	.	23	22	49	31	14	60	0	.	0	.	43	53	22	39	17	43	47	93	0	.	0	.	61	48	21	55	52	48	37	74	30	26	0	.	15	88	46	28	63	71	95	63	44	25	33	9
	ash	21	26	0	.	0	.	13	24	29	35	52	33	0	.	0	.	15	49	19	42	15	54	30	41	0	.	0	.	54	39	20	43	25	40	173	57	27	25	0	.	11	88	46	24	34	41	42	33	15	29	25	9
	oak	8	43	0	.	0	.	10	26	20	44	17	48	0	.	0	.	0	.	14	47	7	50	53	87	0	.	0	.	10	42	21	38	23	74	0	.	16	30	0	.	35	60	74	35	9	64	0	.	7	27	18	16
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	99	64	84	0	.	0	.	1	80
	other broadleaves	35	18	0	.	0	.	40	22	27	32	42	35	0	.	0	.	70	45	26	47	21	41	27	72	0	.	25	71	71	51	13	50	26	37	77	61	54	34	0	.	93	31	54	24	105	32	0	.	39	23	40	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	.
	total	295	7	0	.	0	.	357	6	365	11	365	15	0	.	0	.	390	13	314	10	410	9	423	22	0	.	810	34	476	24	366	13	346	13	462	43	338	9	0	.	599	19	453	9	428	21	280	22	307	7	359	3
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	.	20	65	0	.	2	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	57	0	.	0	.	33	59	0	.	9	75	11	38
	fir	0	.	0	.	0	.	0	.	0	.	0	.	14	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	150	0	0	100	0	.	0	.	5	58	0	.	0	.	2	49
	pine	0	.	0	.	0	.	0	.	0	.	0	.	23	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	125	46	0	.	28	75	19	46
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	14	47	0	.	0	.	2	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	.	1	70	0	.	0	.	2	98	0	.	0	.	1	58
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	94	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	240	0	33	26	0	.	0	.	1	98	0	.	0	.	25	25
	maple	0	.	0	.	0	.	0	.	0	.	0	.	8	91	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	44	0	.	0	.	10	69	0	.	0	.	8	37
	ash	0	.	0	.	0	.	0	.	0	.	0	.	14	93	0	.	17	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	0	40	28	0	.	16	82	18	44	0	.	0	.	34	24
	oak	0	.	0	.	0	.	0	.	0	.	0	.	105	45	0	.	16	96	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	50	0	45	25	0	.	263	15	88	33	0	.	100	53	56	17
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	215	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	215	13	0	.	0	.	9	73	0	.	0	.	169	13
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	66	55	0	.	381	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	187	13	0	.	115	65	88	24	0	.	42	75	175	12
	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	.	250	31	0	.	636	22	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	540	0	539	6	0	.	395	32	392	16	0	.	179	4	504	5
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	64	20	304	18	170	22	153	6	43	37	188	15	20	65	166	21	224	5	37	19	160	12	114	13	273	37	306	16	156	11	87	28	59	31	167	13	104	19	73	16	250	15	134	9	166	8	122	29	98	16	147	3
	fir	25	29	52	31	105	34	76	8	43	27	55	23	14	93	12	44	6	22	130	13	131	14	116	16	32	31	60	27	32	15	11	58	55	27	52	22	50	28	10	29	77	24	64	11	13	24	90	36	23	17	45	4
	pine	3	59	0	.	0	.	7	27	15	30	4	45	23	93	0	.	27	19	6	41	3	51	4	58	9	96	27	89	4	71	20	37	11	35	10	64	6	37	3	54	14	52	1	46	40	21	0	.	9	27	12	10
	larch	8	49	0	.	0	.	1	37	1	70	3	67	0	.	0	99	43	10	0	100	2	56	0	99	0	.	3	99	8	49	3	62	0	99	1	99	9	69	52	14	0	.	11	47	72	10	1	97	3	42	21	6
	Arolla pine	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	22	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	15	22	0	.	0	.	5	15
	other conifers	4	45	0	.	6	72	3	40	5	99	0	.	0	.	1	99	4	34	2	74	2	46	1	99	0	.	2	70	4	39	2	99	7	54	3	79	1	70	0	70	5	99	4	49	1	56	0	.	8	56	3	15
	beech	99	11	43	45	45	31	86	7	170	15	85	16	0	.	55	23	15	20	115	15	80	14	63	14	127	35	78	25	116	13	183	16	162	11	65	17	64	16	96	13	61	31	80	10	20	27	41	30	89	12	73	3
	maple	26	19	5	76	60	36	26	12	53	22	25	24	8	91	53	26	5	25	29	21	21	18	41	28	47	24	28	62	38	16	19	48	49	26	31	29	36	24	13	29	23	32	40	15	17	24	27	47	40	18	25	5
	ash	19	25	0	.	27	42	10	19	23	32	32	23	14	93	19	34	6	34	20	27	11	29	8	34	73	63	9	55	24	21	17	39	17	27	24	35	28	20	18	24	23	40	19	19	12	27	19	69	17	19	15	6
	oak	9	35	0	.	0	.	3	25	14	45	6	39	105	45	0	.	1	60	5	44	3	43	8	88	3	96	0	.	2	49	15	39	11	65	0	100	14	28	17	24	3	74	21	31	10	32	1	98	7	26	8	11
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	80	14	0	.	0	100	2	67	0	.	0	.	9	13
	other broadleaves	38	17	0	.	18	52	26	14	27	24	30	24	66	55	23	47	39	18	36	25	22	26	12	35	54	36	16	33	24	26	10	48	25	29	37	28	42	34	120	10	55	42	32	18	44	13	13	56	33	19	41	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	.
	total	296	7	405	14	432	13	390	3	393	8	429	7	250	31	331	11	396	3	381	6	435	6	366	6	619	16	528	10	407	6	367	10	396	8	388	8	352	8	482	4	511	9	406	5	413	4	314	14	327	6	405	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. 1326002. Birmensdorf, Swiss Federal Research Institute WSL

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