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growing stock (stemwood) #21
Stemwood volume of living trees and shrubs (standing and lying) with a diameter at breast height (dbh) ≥12 cm with bark.

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 NFI1-NFI5 #2382
Forest that was less than two-thirds covered with shrubs in the five inventories NFI1 (1983-1985), NFI2 (1993-1995), NFI3 (2004-2006), NFI4 (2009-2017) and NFI5 (2018-2026) and was accessible on foot.

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

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI5
growing stock (stemwood)
higher/lower altitude zone · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2018/26
		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 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	351	37	495	37	866	42	879	39	126	.	380	76	1644	28	2343	23	4589	17	981	27	45	81	292	58	414	51	0	.	212	75	661	49	285	65	1005	51	4	75	579	47	146	86	547	45	0	.	340	69	0	.	1247	29	513	38	1768	25	1209	34	57	100	5	100	307	66	215	57	156	60	391	93	0	.	168	84	1201	32	731	59	197	74	286	78	845	48	1388	28	801	48	106	62	271	54	320	55	106	54	0	.	75	92	977	36	769	35	826	38	58	.	1588	73	4	100	0	.	82	71	30	100	51	96	10	100	145	53	161	80	97	.	25	73	66	60	132	67	0	.	613	47	137	62	276	60	138	68	196	60	189	71	170	75	159	58	60	61	135	77	300	47	22	100	425	68	55	93	112	74	132	66	288	67	715	38	694	37	1009	32	1858	29	84	94	439	50	578	38	0	.	43352	5
	fir	249	72	40	96	144	60	965	34	83	.	681	60	558	54	4006	18	4816	17	1546	27	32	64	136	60	373	50	0	.	54	79	263	76	209	60	180	83	4	100	315	68	89	100	109	71	0	.	314	79	0	.	2598	23	1170	36	2788	22	936	33	20	100	18	100	461	39	402	50	362	54	53	64	0	.	332	61	1019	37	0	.	180	59	615	56	788	37	184	59	311	43	211	80	140	46	372	37	16	100	0	.	124	56	536	58	335	39	506	58	0	.	61	100	0	.	0	.	0	.	34	100	277	71	144	70	242	73	182	92	0	.	-2	100	221	57	11	100	47	.	133	65	75	100	285	40	200	84	286	55	0	.	190	71	337	41	204	70	91	69	498	53	140	71	200	68	8	100	2	100	36	54	110	67	140	61	265	53	921	34	422	34	94	85	19	100	86	58	0	.	35606	5
	pine	26	100	21	71	21	100	74	100	0	.	0	.	313	78	109	60	449	35	369	60	45	85	66	69	186	45	0	.	21	100	45	71	0	.	265	67	67	100	0	.	55	88	176	46	0	.	0	.	0	.	348	40	18	100	229	58	0	.	50	100	29	100	0	.	0	.	44	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	53	100	330	39	100	63	97	53	4	71	119	51	0	.	0	.	0	.	85	68	117	77	202	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	0	0	.	0	.	0	.	30	71	9	100	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	69	100	0	.	225	49	65	61	82	67	0	.	25	100	236	46	92	60	192	42	153	49	86	73	56	100	0	.	5613	10
	larch	30	94	55	64	106	71	95	81	0	.	0	.	104	91	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	34	100	0	.	0	.	89	74	45	100	0	.	33	100	9	100	22	100	5	100	22	72	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	37	100	72	100	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	29	100	27	100	0	.	42	100	0	.	100	80	0	.	86	75	305	48	166	82	125	81	0	.	0	.	0	.	41	100	0	.	0	.	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	146	89	8	100	59	71	40	100	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	2870	14
	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	42	75	0	.	71	100	85	72	0	.	14	74	0	.	0	.	522	44	2	100	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	11	74	75	99	275	86	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	8	100	344	98	5	100	-19	100	7	79	0	.	66	100	11	100	6	100	40	77	0	.	0	.	0	.	4	100	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	81	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	100	27	62	0	.	6	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	1	100	0	.	0	.	18	71	47	71	9	81	0	.	0	.	0	.	0	.	2087	27
	beech	1223	27	818	30	688	34	1839	30	82	.	158	50	1661	30	2314	23	4252	14	2275	20	309	47	885	34	1093	30	0	.	283	65	709	40	782	38	1258	35	0	.	418	43	106	52	292	47	0	.	537	41	0	.	2911	18	1405	27	849	37	937	39	158	59	7	100	212	41	61	54	228	54	365	54	23	.	718	47	793	39	819	49	714	39	595	45	479	50	2159	21	988	38	474	35	907	34	1285	28	195	47	0	.	656	50	589	31	351	39	793	34	0	.	182	58	0	.	738	36	1326	30	743	34	1147	33	989	33	544	43	426	61	0	.	131	75	512	52	54	94	135	.	602	49	71	73	597	39	166	88	67	56	48	98	275	51	854	35	405	55	539	49	234	50	256	59	210	76	0	.	0	.	617	38	272	49	498	38	515	42	779	36	881	28	746	45	352	42	432	32	0	.	57997	4
	maple	366	30	182	36	53	82	34	74	0	.	161	59	230	39	342	36	376	25	451	32	140	49	117	54	123	56	0	.	39	100	28	56	117	57	91	56	0	.	93	46	69	62	61	65	0	.	16	74	0	.	398	29	151	38	44	57	190	46	66	80	24	73	45	82	20	61	15	71	12	288	0	.	18	77	174	52	221	58	139	51	133	58	299	42	101	53	123	51	110	52	99	39	286	40	124	54	0	.	26	71	314	54	54	81	106	52	0	.	59	100	11	100	4	71	22	100	39	61	9	50	56	96	9	62	29	100	3	.	0	.	73	47	95	63	0	.	10	100	9	78	130	50	10	100	31	63	12	100	32	60	69	61	38	86	17	100	10	100	0	.	71	65	0	.	0	.	221	46	100	50	212	43	64	55	302	31	111	48	243	55	70	60	248	51	0	.	8802	6
	ash	258	45	380	44	32	83	74	87	0	.	227	60	52	164	411	50	645	31	32	59	137	82	34	56	127	64	0	.	166	70	409	67	440	45	281	39	9	100	93	62	60	77	10	61	0	.	82	71	0	.	734	30	419	66	97	52	51	60	48	76	1	100	4	100	3	100	0	.	446	67	0	.	0	0	584	45	263	49	45	74	138	80	205	54	231	46	135	49	44	68	176	51	92	55	281	54	0	.	249	67	279	36	262	58	238	45	0	.	211	100	2	100	24	63	35	64	205	56	123	64	25	95	107	47	10	100	11	.	0	.	100	55	58	64	0	.	320	65	97	84	24	74	0	.	9	77	12	100	32	90	89	78	208	58	70	64	47	73	0	.	70	71	16	73	5	100	201	52	115	60	430	50	48	68	182	43	226	38	173	65	71	59	119	66	0	.	12460	8
	oak	349	55	80	70	0	.	260	53	0	.	0	.	17	74	68	73	542	23	0	.	78	100	4	100	107	57	0	.	9	100	60	100	52	79	109	61	384	58	0	.	0	.	64	100	0	.	0	0	85	100	332	45	88	91	177	66	40	100	53	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	28	100	52	89	0	.	17	71	0	.	210	47	13	100	11	100	31	86	158	52	19	100	0	.	0	.	108	53	552	74	303	48	0	.	0	.	0	.	47	57	119	69	474	33	85	65	111	49	102	61	50	100	0	.	36	100	14	72	23	100	0	.	108	91	2	100	44	82	129	71	82	56	0	.	403	50	25	100	10	71	231	49	0	.	41	100	41	94	47	65	102	72	73	54	0	.	17	100	50	58	15	100	98	85	176	41	110	55	92	68	0	.	7456	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	468	48	42	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	.	32	.	344	47	272	72	390	47	397	37	1190	31	468	52	254	57	1453	29	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	66	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5420	13
	other broadleaves	271	45	174	36	140	44	98	67	0	.	63	82	261	50	170	32	563	27	86	35	97	52	54	57	48	57	0	.	20	100	224	57	37	60	152	57	8	86	81	72	61	57	46	69	0	.	452	41	49	100	352	37	121	47	61	63	34	59	0	.	4	100	7	100	-2	100	5	100	63	69	0	.	15	52	3	100	56	58	67	60	136	70	59	56	39	48	93	70	4	100	224	48	16	87	128	58	0	.	0	0	254	55	140	43	76	55	0	.	431	49	101	90	275	40	471	37	1225	26	238	35	269	40	386	42	28	76	0	.	22	100	51	71	299	95	0	.	29	60	194	84	41	70	3	75	4	75	3	100	116	71	10	79	118	86	41	73	122	75	5	100	13	100	97	75	31	59	221	36	7	63	172	57	161	64	64	51	21	59	42	44	127	63	114	59	0	.	10661	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
	total	3166	20	2245	22	2121	26	4404	20	291	.	1685	41	4839	21	9764	14	16843	10	5741	18	884	34	1589	32	2497	27	252	.	805	47	2456	33	1922	30	3375	26	475	55	1581	36	681	40	1350	32	0	.	2242	26	185	72	8954	13	3966	22	6308	18	3397	25	452	37	88	71	1079	39	739	46	811	40	1335	43	23	.	1277	38	4202	25	2184	36	1395	33	1972	38	2730	31	4744	18	2575	27	1062	36	1913	27	2648	24	869	44	0	.	1192	45	3157	24	2610	26	3080	22	90	.	2919	48	391	57	1579	24	2452	22	4026	17	2786	22	2024	24	3113	21	886	54	111	.	258	73	1078	40	678	54	182	.	1813	39	738	45	1433	32	669	53	700	38	264	74	1241	33	1542	31	1131	37	1123	33	1238	33	534	61	1098	46	626	40	324	52	1709	24	932	42	2238	29	2060	28	3432	23	3819	21	1733	29	1299	30	1903	26	0	.	192325	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	.	2439	33	1715	32	12481	12	3220	19	9	.	1704	24	0	.	0	.	0	.	0	.	563	56	1669	35	2010	28	1259	37	0	.	2574	28	7030	16	3113	23	6306	18	9207	13	5973	15	992	39	651	47	158	77	2358	26	260	72	1490	27	209	59	333	54	1452	33	1181	38	1568	31	2196	29	0	.	666	42	2339	28	1656	37	1869	32	0	.	30	77	189	60	0	.	0	.	1600	31	2182	28	1157	35	0	.	0	.	0	.	1041	41	1677	30	1462	31	0	.	0	.	0	.	317	69	84	72	216	74	744	43	1481	44	1582	33	907	55	1458	35	1174	32	305	68	0	.	857	38	0	.	58	100	1703	24	635	47	582	51	671	44	0	.	0	.	10	.	0	.	4289	18	4101	19	5781	17	410	56	38	.	0	.	343	82	0	.	0	.	0	.	0	.	0	.	117734	3
	fir	0	.	0	.	0	.	0	.	525	45	870	44	1661	27	2527	24	28	.	1014	27	0	.	0	.	0	.	0	.	293	73	104	79	736	48	420	52	0	.	186	60	294	42	320	51	291	61	689	42	0	0	1291	38	338	60	415	63	628	43	502	55	1148	35	94	76	195	78	1390	39	315	65	193	63	734	48	0	.	94	64	329	59	153	64	456	39	0	.	202	55	69	73	0	.	0	.	746	53	690	36	454	45	0	.	0	.	0	.	18	100	147	62	167	100	0	.	0	.	0	.	77	100	132	100	0	.	367	43	0	.	314	55	258	48	544	56	1080	42	69	85	0	.	753	45	0	.	39	83	380	52	282	64	282	50	254	63	167	.	0	.	0	.	0	.	154	98	519	48	902	35	159	59	0	.	0	.	106	100	0	.	0	.	0	.	0	.	0	.	27562	7
	pine	0	.	0	.	0	.	0	.	0	.	0	.	54	61	0	.	0	.	31	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	147	54	400	45	52	100	639	38	1154	28	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	176	100	42	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	57	100	8	100	0	.	0	.	0	.	0	.	0	.	46	74	66	100	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	178	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	634	38	343	34	542	37	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4619	13
	larch	0	.	0	.	0	.	0	.	0	.	0	.	54	69	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	50	100	259	43	254	45	237	57	1748	20	4941	14	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	99	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	300	60	483	37	469	42	182	71	0	.	0	.	932	34	293	43	431	73	0	.	0	.	0	.	197	51	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4552	19	2568	20	1216	24	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19243	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	225	52	1256	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	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	.	617	31	214	39	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2393	15
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	6	100	22	47	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	1	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	.	7	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	4	100	2	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	76	28
	beech	0	.	0	.	0	.	0	.	241	55	336	48	737	43	980	26	35	.	1137	28	0	.	0	.	30	.	0	.	10	100	40	98	285	40	0	.	0	.	517	43	568	51	299	47	25	61	20	72	0	.	504	45	28	54	3	71	418	46	125	58	163	43	81	61	157	59	112	48	121	53	30	73	595	41	0	.	114	64	362	46	369	38	1012	37	0	.	456	52	320	53	0	.	0	.	235	50	317	67	422	44	0	.	0	.	0	.	0	.	0	.	0	.	78	100	0	.	0	.	54	71	37	85	0	.	318	53	0	.	127	62	109	85	78	69	297	51	12	100	0	.	531	40	0	.	14	100	287	40	80	44	92	59	140	72	53	.	0	.	0	.	0	.	0	.	25	86	244	57	42	74	0	.	0	.	100	92	0	.	0	.	0	.	0	.	0	.	13924	7
	maple	0	.	0	.	0	.	0	.	25	75	105	54	541	24	123	42	23	.	251	32	0	.	0	.	47	.	0	.	20	100	55	71	217	41	0	.	0	.	277	50	92	50	2	100	86	66	103	49	3	100	110	42	47	62	7	100	110	39	34	62	157	43	38	100	138	90	29	59	124	41	35	78	146	75	0	.	20	72	261	47	110	73	388	39	0	.	45	94	64	63	0	.	0	.	48	48	77	59	61	81	0	.	0	.	0	.	34	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	124	51	13	100	1	100	111	73	73	48	175	43	28	81	0	.	28	51	0	.	0	.	20	74	48	47	12	112	71	56	20	.	0	.	0	.	0	.	9	100	50	69	95	47	0	.	0	.	0	.	27	100	0	.	0	.	0	.	0	.	0	.	4959	9
	ash	0	.	0	.	0	.	0	.	0	.	27	100	50	68	40	52	0	.	55	69	0	.	0	.	10	.	0	.	0	.	0	.	44	57	3	100	0	.	167	60	0	.	0	.	127	72	22	73	0	.	35	78	0	.	1	100	7	100	21	100	16	100	0	.	0	.	6	100	66	74	0	.	116	76	0	.	7	100	51	72	0	.	52	67	0	.	3	100	12	100	0	.	0	.	41	68	11	72	-7	100	0	.	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	151	78	39	88	0	.	81	90	28	96	66	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	59	65	57	72	4	100	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	1499	17
	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	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100
	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	.	14	100	22	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	.	0	.	0	.	36	72
	other broadleaves	0	.	0	.	0	.	0	.	0	.	12	75	202	33	43	51	1	.	24	61	0	.	0	.	0	.	0	.	3	100	7	71	23	71	26	67	0	.	74	57	21	59	8	100	371	39	146	36	408	59	41	52	4	100	2	100	69	62	0	.	26	55	0	.	0	.	18	71	18	66	6	76	23	72	0	.	15	89	64	65	0	.	66	56	0	.	1	100	2	100	0	.	0	.	23	54	19	68	59	88	0	.	0	.	0	.	92	67	10	88	5	58	0	.	0	.	0	.	1	71	0	.	37	100	38	63	11	100	9	74	1	100	13	64	38	61	11	91	0	.	17	86	0	.	0	.	21	59	0	.	12	79	0	.	6	.	0	.	0	.	0	.	239	34	193	32	124	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2708	13
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	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	.	3229	31	3066	30	15794	11	6933	16	96	.	4217	19	0	.	0	.	88	.	0	.	889	50	1874	33	3315	23	1714	34	0	.	3845	23	8437	15	4397	20	7494	16	12804	11	13757	10	3009	29	1067	41	586	65	3593	23	942	46	3000	24	422	58	822	49	3007	27	1828	35	2007	30	3852	26	0	.	923	38	3460	24	2287	33	3843	25	0	.	737	45	656	45	0	.	0	.	2751	28	3304	24	2146	30	0	.	0	.	0	.	1504	32	2377	26	2200	26	262	59	0	.	0	.	1382	32	545	47	697	61	1741	32	1544	42	2218	30	1665	37	2205	31	2849	30	425	62	0	.	2187	28	0	.	110	83	2411	23	1045	44	980	41	1137	37	247	.	0	.	10	.	0	.	10559	13	8084	14	8955	14	612	52	38	.	0	.	587	70	0	.	0	.	0	.	0	.	0	.	194764	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	351	37	495	37	866	42	879	39	2564	31	2096	30	14125	11	5563	14	4598	17	2685	18	45	81	292	58	414	51	0	.	775	46	2330	29	2295	26	2264	30	4	75	3152	24	7176	16	3660	21	6306	18	9547	13	5973	15	2239	23	1164	31	1925	24	3567	20	317	62	1495	27	516	46	547	40	1608	31	1573	37	1568	31	2364	28	1201	32	1398	36	2536	26	1942	34	2714	27	1388	28	831	46	294	44	271	54	320	55	1706	29	2182	28	1232	34	977	36	769	35	826	38	1098	39	3265	38	1466	31	0	.	82	71	30	100	368	60	94	65	361	49	905	38	1578	41	1607	32	974	52	1590	32	1174	32	918	39	137	62	1134	32	138	68	253	51	1892	23	805	40	741	42	732	40	135	77	300	47	32	75	425	68	4345	18	4213	18	5913	16	699	43	752	37	694	37	1352	32	1858	29	84	94	439	50	578	38	0	.	161087	3
	fir	249	72	40	96	144	60	965	34	609	41	1551	36	2219	24	6533	14	4844	17	2559	19	32	64	136	60	373	50	0	.	347	63	366	59	946	40	600	44	4	100	501	48	383	40	429	42	291	61	1003	38	0	0	3889	20	1507	31	3202	20	1564	26	522	53	1167	35	555	35	596	42	1751	33	368	56	193	63	1066	38	1019	37	94	64	509	43	768	47	1244	27	184	59	514	34	280	62	140	46	372	37	762	52	690	36	578	38	536	58	335	39	506	58	18	100	208	52	167	100	0	.	0	.	34	100	354	59	276	60	242	73	549	42	0	.	313	55	479	37	556	55	1126	41	202	52	75	100	1037	35	200	84	325	50	380	52	472	47	619	32	458	47	259	69	498	53	140	71	200	68	162	93	521	48	938	34	269	44	140	61	265	53	1027	32	422	34	94	85	19	100	86	58	0	.	63168	4
	pine	26	100	21	71	21	100	74	100	0	.	0	.	367	67	109	60	449	35	400	55	45	85	66	69	186	45	0	.	21	100	45	71	0	.	265	67	67	100	0	.	201	46	576	34	52	100	639	38	1154	28	383	37	18	100	229	58	0	.	50	100	29	100	0	.	0	.	44	100	4	100	176	100	42	100	41	100	0	.	0	.	0	.	53	100	330	39	100	63	97	53	4	71	119	51	57	100	8	100	0	.	85	68	117	77	202	66	0	.	46	74	66	100	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	224	60	0	0	0	.	0	.	0	.	30	71	9	100	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	69	100	0	.	858	31	408	30	624	33	0	.	25	100	236	46	92	60	192	42	153	49	86	73	56	100	0	.	10232	8
	larch	30	94	55	64	106	71	95	81	0	.	0	.	158	64	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	40	87	0	.	50	100	348	37	300	41	237	57	1781	20	4950	14	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	37	100	123	71	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	29	100	27	100	300	60	524	35	469	42	282	53	0	.	86	75	1237	28	459	40	556	59	0	.	0	.	0	.	237	46	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	4698	19	2576	20	1275	23	40	100	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	22114	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	225	52	1256	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	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	.	617	31	214	39	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2393	15
	other conifers	42	75	0	.	71	100	85	72	0	.	14	74	1	100	0	.	522	44	3	71	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	7	100	0	.	0	.	6	100	22	47	11	74	75	99	275	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	8	100	344	98	12	72	-19	104	7	79	0	.	66	100	11	100	6	100	40	77	0	.	0	.	0	.	4	100	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	81	100	1	100	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	56	100	27	62	0	.	6	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	6	100	4	100	3	64	0	.	0	.	18	71	47	71	9	81	0	.	0	.	0	.	0	.	2163	26
	beech	1223	27	818	30	688	34	1839	30	323	48	494	36	2398	25	3294	18	4287	14	3412	16	309	47	885	34	1123	30	0	.	292	63	749	39	1067	30	1258	35	0	.	935	31	675	44	591	33	25	61	557	39	0	.	3416	17	1433	27	852	37	1355	30	282	42	170	41	294	34	218	45	341	40	486	42	53	60	1313	31	793	39	932	44	1077	30	964	31	1491	30	2159	21	1444	31	794	30	907	34	1285	28	430	35	317	67	1078	35	589	31	351	39	793	34	0	.	182	58	0	.	816	34	1326	30	743	34	1202	32	1025	32	544	43	743	42	0	.	258	49	621	45	132	56	432	47	614	48	71	73	1128	28	166	88	81	49	335	37	355	40	946	32	546	45	592	45	234	50	256	59	210	76	0	.	25	86	861	32	314	43	498	38	515	42	879	33	881	28	746	45	352	42	432	32	0	.	71921	3
	maple	366	30	182	36	53	82	34	74	25	75	266	42	771	21	465	29	399	24	702	23	140	49	117	54	170	49	0	.	60	74	82	51	334	33	91	56	0	.	370	39	162	39	63	63	86	66	119	43	3	100	508	24	198	32	51	51	301	32	100	57	181	39	83	64	158	79	45	46	136	46	35	78	164	67	174	52	241	54	400	35	244	45	687	29	101	53	168	45	174	40	99	39	286	40	172	41	77	59	87	60	314	54	54	81	106	52	34	100	59	100	11	100	5	63	22	100	39	61	9	50	56	96	9	62	153	46	16	85	1	100	184	48	168	41	175	43	37	66	9	78	158	42	10	100	31	63	32	59	79	37	80	55	109	47	38	71	10	100	0	.	71	65	9	100	50	69	317	35	100	50	212	43	64	55	329	29	111	48	243	55	70	60	248	51	0	.	13760	5
	ash	258	45	380	44	32	83	74	87	0	.	254	55	102	90	451	46	645	31	87	49	137	82	34	56	138	59	0	.	166	70	409	67	484	42	284	38	9	100	260	44	60	77	10	61	127	72	104	59	0	.	770	29	419	66	99	51	57	54	70	61	17	94	4	100	3	100	6	100	511	59	0	.	116	76	584	45	270	48	97	52	138	80	258	45	231	46	138	48	56	58	176	51	92	55	323	48	11	72	242	69	279	36	262	58	238	45	18	100	211	100	2	100	24	63	35	64	205	56	123	64	25	95	107	47	161	73	50	72	0	.	181	51	86	53	66	61	320	65	97	84	24	74	0	.	9	77	12	100	32	90	89	78	208	58	70	64	47	73	0	.	70	71	75	54	63	67	205	50	115	60	430	50	48	68	194	40	226	38	173	65	71	59	119	66	0	.	13959	7
	oak	349	55	80	70	0	.	260	53	0	.	0	.	17	74	68	73	542	23	0	.	78	100	4	100	107	57	0	.	9	100	60	100	52	79	109	61	384	58	0	.	0	.	64	100	0	.	0	0	85	100	332	45	88	91	177	66	40	100	53	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	28	100	52	89	0	.	17	71	0	.	210	47	13	100	11	100	31	86	158	52	19	100	0	.	0	.	108	53	552	74	303	48	0	.	0	.	0	.	47	57	119	69	474	33	85	65	111	49	102	61	50	100	0	.	36	100	14	72	23	100	0	.	108	91	2	100	44	82	129	71	82	56	0	.	403	50	25	100	10	71	231	49	0	.	41	100	41	94	47	65	111	66	73	54	0	.	17	100	50	58	15	100	98	85	176	41	110	55	92	68	0	.	7465	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	468	48	42	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	.	32	100	358	45	294	67	390	47	397	37	1190	31	468	52	254	57	1453	29	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	66	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5456	12
	other broadleaves	271	45	174	36	140	44	98	67	0	.	75	69	463	32	213	27	564	27	110	31	97	52	54	57	48	57	0	.	23	87	231	55	61	46	177	49	8	86	155	46	83	45	54	60	371	39	598	33	457	54	393	34	126	45	63	60	103	46	0	.	30	50	7	100	-2	100	24	60	81	55	6	76	38	48	3	100	71	49	131	44	136	70	125	40	39	48	94	69	5	75	224	48	16	87	151	50	19	68	59	88	254	55	140	43	76	55	92	67	441	48	106	85	275	40	471	37	1225	26	239	35	269	40	423	40	66	48	11	100	31	75	53	69	312	91	38	61	40	50	194	84	58	56	3	75	4	75	24	53	116	71	22	57	118	86	46	66	122	75	5	100	13	100	337	33	224	29	345	28	7	63	172	57	161	64	64	51	21	59	42	44	127	63	114	59	0	.	13369	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	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	3166	20	2245	22	2121	26	4404	20	3520	29	4750	24	20632	9	16697	10	16939	10	9958	12	884	34	1589	32	2585	26	252	.	1694	35	4330	23	5236	18	5089	21	475	55	5426	19	9118	14	5748	17	7494	16	15046	10	13943	10	11963	12	5033	19	6895	17	6990	17	1393	33	3088	24	1501	32	1562	34	3819	23	3164	27	2030	30	5129	22	4202	25	3108	28	4855	20	4259	25	6573	19	4744	18	3312	23	1717	28	1913	27	2648	24	3620	24	3304	24	3337	25	3157	24	2610	26	3080	22	1593	31	5295	29	2591	24	1840	22	2452	22	4026	17	4168	18	2569	22	3810	20	2627	28	1655	40	2476	28	2743	27	2883	27	3031	29	2238	34	738	45	3620	21	669	53	809	34	2675	22	2286	27	2523	25	2267	26	1370	33	1238	33	544	60	1098	46	11185	13	8408	14	10664	13	1544	33	2276	28	2060	28	4019	22	3819	21	1733	29	1299	30	1903	26	0	.	387089	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. 2281797. Birmensdorf, Swiss Federal Research Institute WSL

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