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.

higher/lower altitude zone #2635
Areas in higher and lower altitude zones classified according to the system used for altitudinal vegetation belts in the guidelines for monitoring the sustainability and performance of protection forests (NaiS; Frehner et al. 2005). The boundary between the higher and lower altitudes runs between the «upper montane» and «lower montane» levels on the Northern Alps and between the «high montane» and «upper/lower montane» levels to the Southern Alps. 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)

forest district (2024) #2777
Regional demarcation with the forest districts as a unit. This variable was implemented in the year 2024 and is based on a survey with the cantonal forest services in the years 2022/2023.

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 #410
NFI's sampling grid with a mesh size of 1.4 km. The 1.4-km grid is the grid size covering all the previous terrestrial Inventories, which is why it is also called the 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
number of stems
higher/lower altitude zone · main tree species
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
higher/lower altitude zone	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	±%	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	±%	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	±%	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
lower altitude zone	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	543	39	1465	27	1025	30	689	23	257	59	438	35	1573	21	2080	17	4558	13	780	25	83	51	429	52	234	33	0	.	179	45	955	32	540	30	1101	35	4	100	486	41	115	51	380	42	0	.	527	46	0	.	1889	21	793	32	1651	22	891	28	210	63	9	100	250	42	157	49	117	40	219	94	228	84	438	56	1435	24	485	56	601	42	294	46	686	32	1441	26	590	30	327	41	330	42	274	37	519	38	84	.	191	66	1082	26	811	32	1269	39	55	100	453	41	11	100	0	.	68	88	4	100	106	68	69	60	285	55	122	80	109	75	34	68	95	49	157	76	17	79	632	39	185	50	288	68	250	50	123	40	165	79	297	90	156	40	316	35	269	81	432	38	25	75	384	38	103	53	94	77	373	35	614	43	740	40	654	27	760	29	1227	27	501	36	696	38	792	31	0	.	47371	4
	fir	478	43	76	61	266	38	747	39	149	51	281	42	265	41	3238	13	3457	14	1697	22	70	63	155	43	512	29	0	.	129	45	126	60	459	41	360	34	0	.	194	58	32	100	113	54	0	.	130	51	0	.	3056	13	954	26	1886	22	935	28	147	53	4	100	297	35	213	44	244	41	43	61	0	.	319	52	471	28	33	100	100	44	346	39	446	28	102	49	378	44	271	47	79	42	628	29	143	44	0	.	119	50	479	35	395	44	288	79	0	.	83	69	0	.	0	.	0	.	4	100	257	48	297	66	218	57	167	74	30	100	10	100	249	39	4	100	12	74	166	37	66	76	246	34	294	42	460	35	0	.	200	53	269	36	236	40	44	53	162	43	133	62	96	47	4	100	10	100	131	35	188	48	93	46	200	42	405	25	370	32	31	59	11	100	105	57	0	.	30560	4
	pine	111	51	57	55	88	59	70	40	8	100	0	.	93	78	106	80	253	58	320	47	69	54	85	42	146	35	0	.	14	77	42	71	0	.	84	48	64	80	0	.	237	63	133	38	0	.	0	.	0	.	432	44	4	100	66	60	0	.	51	76	28	100	26	100	0	.	28	100	0	.	0	.	0	.	37	60	0	.	112	82	0	.	38	59	245	40	40	56	129	44	56	81	113	38	117	71	0	.	0	.	44	41	36	59	160	46	0	.	0	.	0	.	0	.	0	.	93	100	0	.	0	.	20	100	10	100	18	100	24	100	175	100	0	.	0	.	0	.	14	77	4	100	8	100	16	80	0	.	45	60	0	.	25	58	0	.	0	.	63	100	0	.	792	44	317	40	154	48	0	.	35	80	102	41	31	50	175	33	86	43	103	43	27	62	0	.	6081	10
	larch	74	58	91	65	52	74	32	56	4	100	24	100	26	72	4	100	101	43	0	.	0	.	0	.	8	71	0	.	74	100	38	80	0	.	4	100	0	.	4	100	47	57	55	54	0	.	113	70	4	100	19	65	39	71	14	77	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	52	100	4	100	89	95	56	52	5	100	4	100	50	56	24	85	12	100	12	75	0	.	0	.	0	.	14	72	31	88	54	72	10	100	0	.	15	79	0	.	43	65	30	62	25	71	309	31	191	59	89	50	0	.	0	.	0	.	54	67	0	.	17	100	21	72	92	73	0	.	13	75	0	.	0	.	24	72	0	.	45	58	0	.	4	100	0	.	200	91	79	56	10	100	91	44	8	100	4	100	43	61	27	61	0	.	35	54	8	71	32	56	0	.	2755	12
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	54	53	13	58	12	100	96	47	0	.	21	71	9	100	0	.	233	28	138	93	36	100	0	.	0	.	42	.	8	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	50	53	79	78	33	68	0	.	0	.	0	.	9	100	0	.	0	.	21	100	0	.	10	100	86	50	12	100	28	100	23	71	152	100	24	86	20	100	99	91	79	68	0	.	10	100	0	.	10	100	20	100	0	.	120	84	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	50	100	173	57	4	100	40	79	0	.	0	.	0	.	40	59	11	100	35	76	0	.	0	.	0	.	0	.	30	100	21	100	10	100	99	49	147	76	28	75	0	.	0	.	0	.	0	.	2266	15
	beech	1475	16	1008	23	585	25	1935	22	52	55	414	39	2818	23	2280	15	3930	10	2552	19	254	41	895	24	1743	22	0	.	539	32	746	28	861	31	1021	27	0	.	758	31	138	58	551	31	0	.	890	33	0	.	3818	14	1075	26	797	30	1001	28	253	35	9	100	292	29	232	40	200	44	363	49	123	57	797	37	604	25	783	36	748	31	855	34	702	32	2138	17	870	24	834	28	875	30	1454	20	522	32	0	.	223	37	645	24	217	37	1032	23	0	.	325	41	0	.	1765	27	1540	27	1355	26	2902	21	2521	26	1555	29	222	57	4	100	103	96	421	43	139	53	111	62	443	30	131	75	388	36	283	39	221	40	36	100	218	40	760	28	676	28	343	38	215	42	79	74	274	40	80	100	112	100	1057	29	180	42	507	31	492	26	774	26	813	21	476	31	352	27	425	26	0	.	69206	3
	maple	587	31	250	27	495	30	198	34	71	72	207	71	260	26	282	28	816	24	567	27	152	46	271	28	271	31	0	.	62	47	87	46	183	53	204	44	39	79	126	33	71	62	94	48	110	.	86	71	0	.	1005	21	212	31	134	42	134	39	201	80	65	77	201	58	29	75	24	85	111	45	21	100	44	44	280	29	158	60	139	49	272	38	128	41	191	41	192	37	237	38	100	38	367	29	300	45	0	.	47	65	481	36	236	40	140	39	0	.	156	60	10	100	165	50	97	51	348	34	117	38	98	56	23	61	66	100	14	100	10	100	212	47	255	57	79	64	203	49	19	100	134	40	136	80	79	39	21	100	86	51	120	51	197	44	0	.	32	67	0	.	72	50	0	.	134	62	490	26	94	49	184	47	219	41	295	29	190	31	358	46	155	53	331	62	0	.	16141	5
	ash	602	28	292	33	274	39	81	53	44	59	254	42	420	38	337	29	709	21	197	70	192	40	116	43	156	41	0	.	134	55	258	52	316	40	532	33	48	100	190	50	105	54	70	55	40	.	356	33	0	.	919	20	266	44	326	35	194	45	50	72	47	100	14	77	20	71	14	100	257	60	35	60	42	61	544	30	392	44	153	44	286	42	257	38	273	52	76	43	82	48	190	42	180	42	343	48	20	.	76	67	359	31	228	33	223	35	0	.	195	100	0	.	180	41	281	38	543	29	708	30	56	52	233	45	10	100	40	80	21	100	88	44	148	54	126	79	269	44	137	43	53	50	153	39	11	100	21	100	48	67	137	62	235	39	46	47	125	53	0	.	209	44	50	60	47	65	297	29	177	38	150	33	268	46	446	36	365	35	129	49	159	58	82	43	0	.	17834	5
	oak	220	39	146	55	27	54	284	32	0	.	4	100	51	51	18	82	390	22	35	74	37	59	174	77	123	48	0	.	14	79	9	100	61	50	69	42	336	42	0	.	4	100	82	80	4	.	138	53	34	100	149	34	46	54	66	62	19	70	276	64	0	.	11	100	20	100	0	.	11	100	0	.	5	100	81	78	19	63	15	76	28	61	0	.	224	28	57	93	92	47	85	61	215	61	14	78	0	.	12	100	70	47	101	45	86	39	0	.	21	100	0	.	146	42	291	32	560	31	327	67	398	39	157	48	19	71	4	100	22	100	112	81	77	88	0	.	69	83	0	.	110	77	96	47	535	48	0	.	457	51	27	87	126	79	335	52	66	89	7	100	59	52	238	44	342	53	521	38	5	100	8	71	16	50	18	81	102	71	74	45	70	45	66	43	0	.	9445	8
	sweet chestnut	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	673	42	25	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	100	736	35	309	60	1578	34	1052	28	2153	22	707	29	797	31	2320	27	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	89	0	.	0	.	0	.	0	.	30	100	0	.	199	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10752	10
	other broadleaves	834	49	566	28	269	31	280	32	10	100	89	63	664	28	394	26	1061	19	298	28	149	38	154	42	299	31	0	.	177	54	140	58	123	46	280	77	189	47	110	46	214	47	134	49	0	.	1245	34	69	100	952	22	188	37	94	46	64	51	62	62	9	100	48	73	71	62	34	72	179	41	65	65	89	43	123	46	138	47	269	41	367	47	146	51	175	40	312	56	129	48	297	47	107	53	169	41	0	.	27	86	334	50	393	34	265	39	24	100	942	26	235	54	1331	25	2068	22	3497	18	1659	26	1105	21	1378	21	71	59	34	71	139	73	353	47	178	57	336	70	215	61	173	53	139	55	204	41	49	52	11	100	154	63	82	50	122	61	93	63	69	49	11	100	58	49	253	40	341	38	1202	25	31	58	159	46	248	39	146	49	109	43	145	38	160	41	156	37	0	.	30529	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	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	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	4978	16	3964	17	3096	17	4412	16	595	47	1731	27	6179	16	8740	11	15517	8	6585	14	1041	26	2280	21	3491	17	42	.	1331	25	2400	23	2542	22	3655	19	681	36	1878	26	963	35	1611	23	154	.	4158	18	131	72	12290	9	3655	19	5065	15	3237	20	1251	28	172	73	1149	28	746	31	660	31	1205	35	472	62	1797	29	3665	17	2110	29	2221	24	2476	26	2560	29	4863	15	2560	20	2213	23	2103	22	3339	17	2136	26	103	.	720	37	3547	18	2471	21	3594	20	149	85	2926	20	565	42	5208	17	5427	15	8582	13	7102	14	5531	17	6278	15	687	53	253	61	363	52	1759	28	961	43	711	47	2018	25	868	35	1534	30	1440	26	1532	30	255	71	1529	30	1550	24	2104	22	1141	34	1140	29	318	59	1352	28	1630	29	1407	31	4545	16	1317	29	1889	23	2343	19	3050	19	3379	16	1835	22	1715	23	2016	22	0	.	242941	2
higher altitude zone	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	1407	26	1556	26	14342	8	2734	15	10	100	1968	20	0	.	0	.	0	.	0	.	314	50	1866	29	2389	22	661	34	0	.	2765	18	6686	12	4147	15	9605	13	12486	9	8118	10	885	28	784	33	88	58	2697	18	279	60	1285	25	173	55	359	43	1510	35	1336	36	2401	21	2879	23	0	.	1282	36	2476	21	650	32	1439	21	0	.	88	53	292	61	0	.	34	100	1350	25	2312	18	875	24	0	.	0	.	0	.	1008	25	2138	22	2054	22	57	100	0	.	0	.	549	34	89	72	425	52	797	32	1110	31	1664	25	827	30	1158	24	2344	23	131	62	0	.	1346	29	0	.	96	64	2340	17	810	36	789	42	525	37	204	64	0	.	27	54	0	.	5886	12	5942	14	6622	11	510	40	272	.	0	.	295	78	0	.	0	.	0	.	0	.	0	.	136542	2
	fir	0	.	0	.	0	.	0	.	201	37	441	41	1155	20	1942	16	20	71	1208	21	0	.	0	.	0	.	0	.	111	76	50	47	760	37	286	46	0	.	174	55	173	46	293	29	124	47	321	31	93	100	807	30	370	45	229	49	685	27	161	48	866	24	98	68	152	52	1012	29	181	38	233	47	301	37	11	.	197	44	340	36	111	55	466	36	0	.	308	65	125	56	0	.	4	100	584	38	575	27	392	41	0	.	0	.	0	.	16	80	178	47	108	96	0	.	0	.	0	.	51	52	132	72	10	100	452	35	12	100	328	42	259	48	133	52	1016	28	49	61	0	.	303	41	0	.	118	62	446	32	244	41	232	32	65	59	179	86	0	.	117	75	0	.	381	47	302	38	1063	26	454	39	0	.	0	.	25	74	0	.	0	.	0	.	0	.	0	.	22234	5
	pine	0	.	0	.	0	.	0	.	10	100	5	100	449	39	0	.	0	.	22	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	376	39	336	42	146	92	1089	27	2988	19	12	100	0	.	0	.	155	83	0	.	5	100	0	.	0	.	0	.	0	.	318	91	15	100	0	.	0	.	213	73	0	.	0	0	0	.	0	.	9	100	0	.	97	100	144	66	4	100	21	100	0	.	0	.	0	.	0	.	152	75	96	92	0	.	0	.	0	.	0	.	0	.	14	100	58	100	0	.	183	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1820	26	739	32	808	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10285	10
	larch	0	.	0	.	0	.	0	.	11	100	0	.	93	43	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	228	36	655	41	313	40	2369	17	4948	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	310	41	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	492	33	1088	26	779	32	493	39	0	.	0	.	1717	21	356	38	256	55	0	.	0	.	0	.	160	63	18	81	43	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5860	11	2680	15	1422	18	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	24345	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	26	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	83	4	100	20	100	360	30	2824	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	100	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	38	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1019	21	674	25	489	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5720	10
	other conifers	0	.	0	.	0	.	0	.	30	100	0	.	168	73	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	91	0	.	0	.	122	54	639	31	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	10	100	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	155	59	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	75	41	100	21	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1414	20
	beech	0	.	0	.	0	.	0	.	238	41	315	34	728	27	1060	20	12	100	1977	19	0	.	0	.	89	71	0	.	27	100	26	85	457	50	56	100	0	.	635	33	657	30	393	39	91	66	54	58	0	.	416	35	177	41	140	69	470	41	180	46	577	33	65	53	136	44	336	33	302	45	43	72	415	33	0	.	139	46	644	38	580	39	908	28	0	.	337	45	451	42	0	.	83	80	511	30	318	44	472	38	0	.	0	.	0	.	0	.	42	72	0	.	34	100	0	.	0	.	79	71	76	77	0	.	365	46	0	.	83	65	132	48	45	66	450	38	35	90	0	.	1064	36	0	.	36	64	600	30	199	42	298	45	52	76	17	100	0	.	89	59	0	.	0	.	99	60	777	36	186	51	0	.	0	.	46	76	0	.	0	.	0	.	0	.	0	.	19317	6
	maple	0	.	0	.	0	.	0	.	325	82	94	53	1353	19	306	27	12	74	532	26	0	.	0	.	32	100	0	.	53	100	34	82	376	30	21	72	0	.	688	27	89	37	167	77	191	44	256	44	15	74	120	48	120	50	32	52	216	32	81	52	150	31	32	100	120	78	44	44	147	35	65	45	547	47	0	.	86	60	353	39	65	70	362	30	0	.	30	58	104	51	0	.	14	100	248	41	219	33	215	50	0	.	0	.	0	.	178	71	134	100	0	.	0	.	0	.	0	.	118	68	0	.	0	.	195	42	9	71	31	100	140	67	402	39	603	28	70	67	0	.	72	46	0	.	0	.	81	46	69	42	121	35	102	59	9	100	0	.	4	100	0	.	151	49	140	53	460	28	16	78	0	.	0	.	10	71	0	.	0	.	0	.	0	.	0	.	11029	7
	ash	0	.	0	.	0	.	0	.	32	75	18	100	101	35	93	41	20	100	127	51	0	.	0	.	9	100	0	.	0	.	0	.	79	54	10	100	0	.	229	43	33	76	0	.	482	43	59	58	211	90	101	57	10	100	0	.	122	52	26	100	15	77	0	.	0	.	11	100	17	71	32	74	93	43	0	.	18	72	25	71	4	100	63	63	0	.	10	100	19	71	0	.	0	.	171	49	212	87	39	72	0	.	0	.	0	.	113	50	0	.	189	54	0	.	0	.	0	.	0	.	0	.	0	.	137	59	90	71	0	.	32	100	80	62	192	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	455	45	248	51	76	54	0	.	0	.	0	.	26	86	0	.	0	.	0	.	0	.	0	.	4124	12
	oak	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	24	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	0	.	0	.	0	.	143	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	57	82	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	370	47
	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	152	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	185	61
	other broadleaves	0	.	0	.	0	.	0	.	38	71	98	68	1151	24	258	42	20	100	324	30	0	.	0	.	0	0	0	.	18	100	45	67	68	45	72	66	0	.	351	37	308	37	210	52	2259	24	1349	25	1192	35	111	45	79	77	0	.	202	45	25	86	105	49	0	.	34	75	73	66	24	61	110	51	243	57	0	.	71	60	341	46	10	100	166	39	0	.	10	100	19	71	0	.	10	100	263	53	269	35	379	60	0	.	0	.	0	.	1053	40	118	44	544	38	30	58	0	.	0	.	417	40	0	.	211	47	84	65	461	66	126	45	100	60	183	36	333	47	52	100	0	.	140	62	0	.	0	.	95	60	39	71	62	63	10	100	78	100	0	.	0	.	0	.	1399	22	1422	24	1184	24	0	.	0	.	0	.	26	100	0	.	0	.	0	.	0	.	0	.	18471	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	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	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	.	2291	24	2528	23	19590	8	6394	12	93	72	6202	14	0	.	0	.	130	73	0	.	523	46	2021	28	4129	18	1107	32	0	.	4846	15	8690	11	6217	13	13232	11	18489	8	21027	7	2452	21	1540	29	489	46	4557	16	752	36	3003	19	367	55	800	35	2987	22	2007	28	3202	21	4596	19	11	.	1852	29	4711	17	1469	26	3405	18	0	.	784	38	1020	34	0	.	242	86	3270	20	3919	16	2394	22	0	.	0	.	0	.	2870	22	3881	18	3960	16	614	38	0	.	0	.	2949	18	652	36	925	40	2088	26	1682	30	2643	22	1650	24	2173	23	4980	19	336	51	0	.	2924	25	0	.	250	60	3562	17	1362	30	1502	31	754	31	487	65	0	.	237	57	0	.	17000	8	12346	10	13004	10	1172	31	272	.	0	.	428	61	0	.	0	.	0	.	0	.	0	.	254038	2
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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	543	39	1465	27	1025	30	689	23	1664	24	1995	22	15915	8	4813	11	4567	13	2749	16	83	51	429	52	234	33	0	.	493	36	2821	22	2929	19	1762	25	4	100	3251	17	6800	12	4527	14	9605	13	13013	9	8118	10	2774	17	1577	23	1738	21	3588	15	489	44	1295	25	423	34	516	34	1627	32	1555	34	2630	21	3317	21	1435	24	1767	30	3077	18	944	26	2125	17	1441	26	678	27	619	36	330	42	308	35	1869	21	2395	18	1066	23	1082	26	811	32	1269	39	1063	25	2591	19	2065	22	57	100	68	88	4	100	655	30	158	48	710	38	919	30	1219	29	1698	25	922	27	1315	23	2360	23	763	34	185	50	1634	27	250	50	219	36	2505	17	1107	35	944	35	841	26	473	53	432	38	52	45	384	38	5989	11	6036	14	6995	11	1124	29	1012	40	654	27	1055	30	1227	27	501	36	696	38	792	31	0	.	183914	2
	fir	478	43	76	61	266	38	747	39	350	30	722	30	1420	18	5180	10	3477	14	2904	15	70	63	155	43	512	29	0	.	240	43	176	45	1218	27	645	27	0	.	368	40	205	42	406	26	124	47	451	26	93	100	3863	12	1324	23	2115	20	1620	20	309	35	870	24	395	31	365	33	1256	24	224	32	233	47	621	32	482	27	231	40	440	29	457	32	912	23	102	49	686	38	396	37	79	42	632	29	727	32	575	27	512	33	479	35	395	44	288	79	16	80	261	39	108	96	0	.	0	.	4	100	309	41	429	51	228	55	620	33	42	77	338	41	507	31	137	51	1027	28	215	32	66	76	549	27	294	42	578	31	446	32	445	33	501	24	302	33	223	70	162	43	250	48	96	47	385	46	312	37	1193	23	642	31	93	46	200	42	430	24	370	32	31	59	11	100	105	57	0	.	52794	3
	pine	111	51	57	55	88	59	70	40	18	71	5	100	542	35	106	80	253	58	342	44	69	54	85	42	146	35	0	.	14	77	42	71	0	.	84	48	64	80	0	.	613	34	469	32	146	92	1089	27	2988	19	444	43	4	100	66	60	155	83	51	76	33	87	26	100	0	.	28	100	0	.	318	91	15	100	37	60	0	.	325	56	0	.	38	59	245	40	40	56	138	42	56	81	210	51	261	48	4	100	21	100	44	41	36	59	160	46	0	.	152	75	96	92	0	.	0	.	93	100	0	.	0	.	33	72	68	86	18	100	207	50	175	100	0	.	0	.	0	.	14	77	4	100	8	100	16	80	0	.	45	60	0	.	25	58	0	.	0	.	63	100	0	.	2612	23	1056	25	962	28	0	.	35	80	102	41	31	50	175	33	86	43	103	43	27	62	0	.	16367	7
	larch	74	58	91	65	52	74	32	56	15	77	24	100	119	37	4	100	101	43	34	100	0	.	0	.	8	71	0	.	74	100	38	80	0	.	4	100	0	.	8	71	276	31	709	38	313	40	2481	16	4952	11	19	65	39	71	14	77	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	59	89	4	100	89	95	366	36	5	100	4	100	50	56	24	85	12	100	12	75	0	.	0	.	10	100	14	72	31	88	54	72	10	100	492	33	1103	26	779	32	536	36	30	62	25	71	2026	18	546	32	345	43	0	.	0	.	0	.	214	50	18	81	59	77	21	72	92	73	0	.	13	75	0	.	0	.	24	72	0	.	45	58	0	.	4	100	0	.	200	91	5939	11	2690	15	1513	17	8	100	4	100	43	61	27	61	0	.	35	54	8	71	32	56	0	.	27100	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	26	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	83	4	100	20	100	360	30	2824	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	100	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	38	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1019	21	674	25	489	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5720	10
	other conifers	54	53	13	58	12	100	96	47	30	100	21	71	177	69	0	.	233	28	148	87	36	100	0	.	0	.	42	.	8	100	0	.	0	.	0	.	0	.	10	100	100	91	0	.	0	.	122	54	639	31	50	53	79	78	33	68	9	100	0	.	0	.	9	100	0	.	0	.	21	100	0	.	10	100	86	50	22	71	38	78	72	72	152	100	24	86	20	100	99	91	79	68	0	.	10	100	0	.	10	100	20	100	0	.	120	84	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	10	100	0	.	0	.	155	59	0	.	10	100	13	100	0	.	50	100	173	57	4	100	40	79	0	.	0	.	0	.	40	59	11	100	35	76	0	.	0	.	30	75	41	100	51	66	21	100	10	100	99	49	147	76	28	75	0	.	0	.	0	.	0	.	3680	12
	beech	1475	16	1008	23	585	25	1935	22	290	35	729	27	3546	19	3340	12	3941	10	4529	13	254	41	895	24	1832	21	0	.	566	31	771	27	1318	26	1077	26	0	.	1394	22	795	27	943	24	91	66	945	32	0	.	4234	13	1252	23	937	27	1471	23	433	28	586	32	357	26	368	30	535	27	666	34	166	46	1212	26	604	25	922	31	1391	24	1435	25	1611	21	2138	17	1207	21	1285	23	875	30	1537	20	1033	22	318	44	695	29	645	24	217	37	1032	23	0	.	367	37	0	.	1799	26	1540	27	1355	26	2980	20	2597	26	1555	29	587	36	4	100	186	61	553	34	184	43	562	33	478	29	131	75	1451	28	283	39	256	36	636	29	417	29	1058	24	728	26	360	36	215	42	167	46	274	40	80	100	211	60	1834	23	366	33	507	31	492	26	821	25	813	21	476	31	352	27	425	26	0	.	88523	3
	maple	587	31	250	27	495	30	198	34	396	68	301	51	1612	17	589	19	828	24	1100	19	152	46	271	28	303	30	0	.	115	53	121	40	559	27	226	41	39	79	814	24	159	34	261	52	301	46	342	37	15	74	1126	19	332	27	166	35	350	25	282	59	216	32	233	52	149	65	68	41	259	28	86	42	591	43	280	29	244	44	492	31	337	34	491	24	191	41	223	33	341	30	100	38	381	28	548	31	219	33	261	42	481	36	236	40	140	39	178	71	289	56	10	100	165	50	97	51	348	34	235	39	98	56	23	61	261	40	23	67	40	80	353	39	657	32	683	26	273	40	19	100	206	31	136	80	79	39	102	42	155	34	241	31	298	35	9	100	32	67	4	100	72	50	151	49	275	40	950	19	110	44	184	47	219	41	305	29	190	31	358	46	155	53	331	62	0	.	27171	4
	ash	602	28	292	33	274	39	81	53	76	46	271	40	521	31	429	24	729	20	324	47	192	40	116	43	166	39	0	.	134	55	258	52	395	33	542	32	48	100	419	32	137	45	70	55	522	40	415	29	211	90	1020	19	276	42	326	35	316	34	75	59	62	78	14	77	20	71	25	71	274	57	67	47	135	35	544	30	409	42	178	39	290	41	320	33	273	52	86	40	101	41	190	42	180	42	513	36	232	80	116	50	359	31	228	33	223	35	113	50	195	100	189	54	180	41	281	38	543	29	708	30	56	52	233	45	147	55	129	55	21	100	120	42	228	41	318	44	269	44	137	43	53	50	153	39	11	100	21	100	48	67	137	62	235	39	46	47	125	53	0	.	209	44	505	41	295	44	373	25	177	38	150	33	268	46	471	34	365	35	129	49	159	58	82	43	0	.	21958	4
	oak	220	39	146	55	27	54	284	32	0	.	4	100	76	47	18	82	390	22	35	74	37	59	174	77	123	48	0	.	14	79	9	100	61	50	69	42	336	42	0	.	4	100	95	71	4	100	162	47	34	100	149	34	46	54	66	62	19	70	276	64	0	.	11	100	20	100	0	.	11	100	0	.	5	100	81	78	19	63	15	76	28	61	0	.	224	28	57	93	92	47	85	61	215	61	14	78	0	.	12	100	70	47	101	45	86	39	0	.	21	100	0	.	146	42	291	32	560	31	346	64	398	39	157	48	19	71	4	100	22	100	112	81	220	72	0	.	69	83	0	.	110	77	96	47	535	48	0	.	457	51	27	87	126	79	335	52	66	89	7	100	59	52	238	44	401	46	604	36	10	71	8	71	16	50	18	81	102	71	74	45	70	45	66	43	0	.	9815	8
	sweet chestnut	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	673	42	25	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	100	769	33	461	46	1578	34	1052	28	2153	22	707	29	797	31	2320	27	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	89	0	.	0	.	0	.	0	.	30	100	0	.	199	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10937	10
	other broadleaves	834	49	566	28	269	31	280	32	47	60	187	46	1814	18	653	23	1081	19	622	21	149	38	154	42	299	31	0	.	194	50	185	47	192	34	352	63	189	47	460	30	522	29	343	37	2259	24	2595	21	1261	33	1063	20	266	35	94	46	266	37	87	51	115	46	48	73	104	48	107	51	203	36	175	40	333	44	123	46	209	37	611	31	377	46	312	32	175	40	322	54	149	43	297	47	117	49	431	36	269	35	406	57	334	50	393	34	265	39	1077	39	1060	23	779	31	1360	25	2068	22	3497	18	2077	22	1105	21	1589	19	155	45	495	62	265	44	453	39	361	34	669	42	267	53	173	53	279	41	204	41	49	52	106	55	193	53	144	39	132	57	171	57	69	49	11	100	58	49	1652	19	1762	21	2386	17	31	58	159	46	248	39	172	44	109	43	145	38	160	41	156	37	0	.	49001	4
	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	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	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	4978	16	3964	17	3096	17	4412	16	2886	21	4259	17	25770	7	15134	8	15610	8	12787	10	1041	26	2280	21	3621	17	42	.	1853	22	4420	18	6671	14	4761	17	681	36	6724	13	9652	10	7828	12	13386	11	22647	7	21158	7	14742	8	5195	16	5554	15	7793	12	2002	22	3175	18	1516	25	1546	23	3646	19	3212	22	3675	20	6393	16	3675	17	3962	21	6932	14	3945	19	5965	16	4863	15	3343	18	3233	19	2103	22	3581	17	5406	16	4022	16	3114	19	3547	18	2471	21	3594	20	3019	21	6808	13	4524	15	5822	15	5427	15	8582	13	10052	11	6184	16	7204	14	2775	23	1935	27	3006	20	3409	18	3134	20	5691	17	2354	22	868	35	4458	19	1440	26	1782	27	3817	16	2890	21	3052	20	2858	18	1628	31	1140	29	556	42	1352	28	18630	8	13753	9	17549	8	2489	21	2161	24	2343	19	3478	19	3379	16	1835	22	1715	23	2016	22	0	.	496978	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. 2297818. Birmensdorf, Swiss Federal Research Institute WSL

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