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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NFI1
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 1983/85
		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	.	90	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	.
	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	.	90	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	90	.
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	738	28	1142	28	1634	32	1568	22	321	51	989	41	1832	21	2845	14	6928	12	1092	22	93	53	224	55	410	39	0	.	712	31	1225	29	866	28	1011	29	15	78	953	41	93	54	367	32	36	100	568	50	0	.	2240	20	1231	24	1957	19	1269	29	77	50	4	100	404	40	106	50	250	40	239	57	161	71	333	43	1945	23	396	59	705	36	580	46	784	30	1788	26	622	26	494	43	691	39	437	48	369	36	4	.	229	43	1540	26	1379	30	917	34	13	100	340	46	0	.	9	100	98	65	8	71	32	72	43	74	169	42	50	82	65	71	167	65	107	45	148	50	44	91	914	30	392	47	138	48	715	41	247	52	150	78	81	71	209	42	653	36	109	65	506	48	84	60	1192	36	86	81	94	72	277	30	294	40	722	34	1260	26	840	24	1536	21	913	31	671	31	1180	29	0	.	61368	3
	fir	514	31	163	34	287	45	1148	24	282	54	436	41	234	37	3789	13	3890	14	1713	20	57	69	135	47	366	34	0	.	75	51	101	61	516	35	554	44	0	.	254	53	48	100	85	54	0	.	89	71	0	.	2729	14	925	35	2094	20	976	30	244	42	0	.	371	36	367	39	422	47	72	58	32	100	181	53	609	24	67	75	173	51	270	44	496	32	53	48	386	33	309	38	205	46	541	26	218	48	0	.	55	44	461	31	173	41	71	62	0	.	89	71	0	.	0	.	0	.	8	71	143	63	162	43	204	69	151	93	0	.	0	.	196	49	9	100	32	80	152	40	10	100	285	38	457	44	513	35	0	.	141	49	314	34	328	39	158	66	398	55	88	72	197	49	0	.	0	.	128	46	173	38	176	52	312	41	826	25	465	31	64	59	73	68	104	51	0	.	33591	4
	pine	274	40	331	33	43	41	99	37	8	100	0	.	105	73	23	54	315	25	360	43	91	58	178	52	276	35	0	.	11	73	100	51	0	.	182	50	143	88	0	.	421	54	326	39	0	.	0	.	0	.	467	33	0	.	172	73	0	.	69	71	0	.	64	100	4	100	44	100	0	.	0	.	23	100	46	58	0	.	135	83	4	100	62	55	330	42	106	45	214	44	152	62	199	36	177	60	0	.	0	.	106	55	56	54	183	38	0	.	0	.	0	.	0	.	0	.	122	100	0	.	0	.	33	100	128	100	0	.	75	79	5	100	0	.	0	.	0	.	4	100	9	100	90	77	12	75	0	.	23	61	0	.	176	66	20	71	0	.	53	100	0	.	924	34	641	44	223	45	10	100	49	85	191	37	110	39	276	33	272	39	178	42	81	46	0	.	9605	8
	larch	64	56	259	69	52	76	84	47	4	100	22	100	33	63	13	74	149	49	0	.	0	.	0	.	13	76	12	.	103	100	21	74	0	.	37	90	0	.	0	.	111	74	84	52	0	.	40	77	0	.	8	71	25	86	10	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	7	100	10	100	67	59	6	100	0	.	85	54	4	100	10	100	10	100	0	.	0	.	0	.	15	72	8	100	48	100	0	.	0	.	17	71	0	.	68	55	43	62	41	70	295	31	239	73	87	52	0	.	0	.	0	.	4	100	0	.	27	100	12	100	0	.	0	.	0	.	4	100	0	.	20	100	0	.	51	58	0	.	0	.	0	.	0	.	90	57	10	100	175	63	8	100	4	100	16	100	24	72	0	.	44	72	24	74	91	47	0	.	2812	13
	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	19	71	0	.	30	100	24	61	0	.	10	100	9	100	0	.	193	32	109	100	0	.	0	.	0	.	119	.	14	100	0	.	0	.	30	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	10	100	47	74	53	83	0	.	0	.	0	.	9	100	0	.	0	.	21	100	0	.	0	.	45	100	0	.	28	100	12	100	30	100	22	85	20	100	50	82	30	75	0	.	0	.	0	.	10	100	10	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	61	0	.	12	100	0	.	0	.	0	.	38	90	0	.	90	100	0	.	0	.	0	.	0	.	0	.	0	.	10	100	34	89	127	71	24	61	0	.	0	.	0	.	0	.	1351	18
	beech	2226	16	1433	20	1053	26	2005	23	79	58	527	40	1986	20	2543	17	5138	12	3135	19	335	31	1213	25	1589	22	0	.	607	40	1037	28	945	30	1419	25	10	100	1140	33	228	51	582	35	0	.	743	35	0	.	4250	13	1361	25	839	23	1128	29	150	43	19	100	382	35	195	42	203	54	440	38	200	61	1398	33	475	38	969	40	694	34	1119	32	629	28	2875	18	1171	26	1148	25	940	28	1981	21	769	32	0	.	309	39	298	29	408	30	1057	24	0	.	193	49	0	.	1288	29	1607	27	1218	27	1957	25	1679	28	1418	29	249	57	0	.	168	97	491	41	235	52	163	53	550	34	75	71	437	39	423	44	255	37	26	100	199	43	1097	29	418	36	573	35	338	37	59	72	477	33	0	0	70	100	1114	31	184	41	821	42	512	28	927	24	1098	21	608	33	557	29	525	30	0	.	77385	3
	maple	554	22	192	37	113	47	158	35	51	71	188	48	233	32	355	28	320	28	540	28	68	46	281	32	183	39	4	.	45	80	61	85	61	54	50	72	10	100	121	36	55	83	16	78	40	100	48	60	0	.	788	18	234	46	105	78	155	35	39	62	0	.	114	68	19	100	0	.	83	47	0	.	51	53	248	32	78	60	212	42	207	42	265	34	220	39	139	81	142	41	160	37	412	30	143	41	10	.	25	85	351	51	96	42	72	59	0	.	10	100	0	.	79	51	55	71	122	43	90	81	22	100	0	.	51	100	0	.	0	.	172	48	225	58	19	71	328	72	0	.	66	51	94	75	20	71	11	100	69	47	181	44	148	64	19	100	21	71	0	.	35	61	0	.	73	56	279	33	148	50	101	53	197	37	348	37	208	34	118	55	191	53	94	56	0	.	11703	5
	ash	424	26	337	35	198	36	147	56	89	69	394	40	195	50	524	26	649	26	203	53	171	51	79	95	89	49	0	.	53	62	212	58	500	35	302	37	29	74	295	61	36	71	0	.	10	100	132	43	0	.	1119	21	183	33	163	41	199	53	81	71	0	.	4	100	0	.	30	100	375	63	116	67	46	82	706	33	260	42	155	46	225	48	239	42	243	40	67	56	58	75	177	51	304	47	307	43	21	.	100	52	303	41	212	32	199	41	0	.	41	100	0	.	146	64	130	51	539	31	116	52	90	49	176	58	10	100	10	100	33	100	199	40	59	72	10	100	318	39	106	71	41	81	156	52	0	.	11	100	77	60	348	50	311	48	39	50	158	44	11	100	222	43	42	61	10	100	345	38	425	47	274	42	139	33	330	41	217	34	105	61	198	42	116	36	0	.	16514	5
	oak	228	30	381	41	74	44	290	40	0	.	4	100	58	58	45	59	647	22	25	100	80	37	192	98	158	53	0	.	26	73	43	51	108	54	131	46	933	43	0	.	4	100	97	86	4	100	80	58	35	100	311	32	90	41	111	63	32	64	260	55	0	.	23	71	0	.	0	.	11	100	0	.	5	100	137	89	57	66	0	0	29	62	21	71	312	26	30	57	81	58	65	70	232	54	14	78	0	.	19	100	74	53	149	42	186	34	0	.	0	.	0	.	72	54	255	37	738	30	333	75	378	48	148	46	14	78	0	.	16	100	218	63	67	86	0	.	98	81	0	0	133	77	151	42	482	54	0	.	633	46	39	79	96	42	345	44	22	61	0	.	156	41	69	53	148	66	201	31	5	100	4	100	14	77	30	100	80	50	91	39	125	43	122	49	0	.	11174	8
	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	.	933	40	33	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	100	365	43	395	61	903	39	1430	27	3276	20	414	38	934	33	2335	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	110	100	0	.	20	100	0	.	0	.	51	100	0	.	63	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11360	10
	other broadleaves	609	31	854	28	246	36	181	59	110	62	99	61	496	34	380	30	774	28	263	29	296	36	179	53	281	34	0	.	133	71	124	50	85	42	112	47	69	59	122	49	166	48	78	58	0	.	1168	26	0	.	1128	20	222	31	73	44	27	71	111	52	28	100	197	91	19	100	10	100	183	52	130	72	124	45	39	79	611	54	413	45	364	48	162	43	100	43	100	39	129	38	302	39	301	32	178	45	31	.	94	60	485	39	296	40	322	45	11	100	705	27	125	60	1243	27	1495	23	2430	20	1490	24	645	31	1066	24	80	77	29	100	96	67	267	48	252	52	93	82	129	55	201	88	37	70	358	70	52	45	0	.	172	58	60	48	224	69	171	55	104	45	11	100	141	77	412	53	46	59	962	26	40	48	156	44	202	49	177	38	89	40	359	39	192	49	422	48	0	.	27752	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	.
	total	5649	13	5090	15	3731	20	5705	14	945	48	2668	27	5181	16	10516	11	19003	8	7440	14	1192	26	2482	22	3365	18	135	.	1779	26	2924	21	3081	21	3828	19	1209	38	2894	26	1162	35	1633	24	90	72	3802	19	68	80	13050	9	4317	18	5576	15	3788	20	1031	28	50	93	1567	28	713	35	958	32	1424	31	639	55	2161	28	4257	18	2449	29	2602	24	2815	25	2688	23	6029	15	2646	19	2634	21	2731	21	4406	17	2175	25	67	.	856	34	3636	18	2819	21	3017	20	100	90	1759	22	520	50	3808	18	5113	17	8503	13	4871	17	4192	19	5637	16	733	53	103	74	555	52	1658	30	997	38	389	51	2501	23	788	39	1186	30	2443	26	1597	28	196	71	1414	30	2248	23	2553	22	1435	29	1657	30	305	52	2420	24	1674	29	1091	34	3766	19	1287	30	2317	25	2877	19	3739	18	3992	16	2574	21	2208	22	2735	21	0	.	264615	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	.	1545	28	1878	25	15656	8	3746	14	55	71	2227	19	0	.	0	.	0	.	0	.	328	58	1923	29	3360	21	1128	30	0	.	2337	23	5245	14	4115	16	5925	13	10907	10	7321	11	1434	33	740	38	286	49	3666	19	216	59	1620	29	269	54	313	39	1239	25	1400	31	2376	23	2329	23	64	.	977	33	2817	20	742	39	1867	25	0	.	114	47	261	72	0	.	44	81	1266	24	3450	20	884	25	0	.	0	.	0	.	580	31	1332	23	1415	24	0	.	0	.	0	.	303	49	31	73	167	52	840	30	809	38	1948	26	589	36	735	27	2120	23	136	63	0	.	853	24	0	.	207	48	2326	18	498	30	804	34	629	34	83	62	0	.	36	54	0	.	5416	13	5568	14	6361	13	752	32	8	.	0	.	150	63	0	.	0	.	0	.	0	.	0	.	130767	2
	fir	0	.	0	.	0	.	0	.	334	39	296	34	1103	21	2245	17	10	100	1197	23	0	.	0	.	0	.	0	.	168	78	58	48	680	36	398	51	0	.	221	44	166	39	421	32	182	44	294	36	0	.	753	38	226	39	372	44	610	29	258	63	854	25	144	69	115	56	1092	29	304	40	272	45	352	43	0	.	322	38	302	36	101	57	627	36	0	.	105	59	254	62	0	.	18	100	298	43	693	24	459	35	0	.	0	.	0	.	0	0	55	47	105	96	0	.	0	.	0	.	62	53	58	100	10	100	450	34	0	.	283	53	213	46	169	47	1012	29	19	71	0	.	377	51	0	.	223	61	242	42	154	41	262	39	75	63	54	75	0	.	81	59	0	.	494	42	254	44	913	32	410	54	0	.	0	.	55	85	0	.	0	.	0	.	0	.	0	.	22334	5
	pine	0	.	0	.	0	.	0	.	0	.	5	100	410	36	0	.	0	.	17	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	215	49	352	43	16	100	1003	26	3271	19	12	100	0	.	0	.	141	78	0	.	7	100	0	.	11	100	0	.	0	.	272	85	25	100	0	.	0	.	87	100	20	100	9	100	0	.	0	.	33	77	0	.	117	100	62	100	4	100	74	100	0	.	0	.	0	.	13	77	191	61	74	100	0	.	0	.	0	.	0	.	0	.	10	100	54	100	0	.	258	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2597	25	862	33	882	33	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	11107	10
	larch	0	.	0	.	0	.	0	.	0	.	0	.	51	51	0	.	0	.	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	373	38	339	32	93	45	1394	18	3392	11	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	211	48	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	173	36	501	29	250	36	144	52	0	.	0	.	740	26	215	49	260	54	0	.	0	.	16	100	81	56	14	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4022	12	2406	16	1099	19	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15831	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	180	40	2083	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	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	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	861	26	546	31	213	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4047	12
	other conifers	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	347	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	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	.	55	64	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	62	85	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	525	35
	beech	0	.	0	.	0	.	0	.	331	40	375	32	920	25	999	22	31	100	1933	19	0	.	0	.	88	80	0	.	35	100	43	100	380	37	68	95	0	.	522	31	544	36	332	34	55	55	28	61	0	.	454	41	151	47	76	59	438	37	159	46	660	33	88	55	133	43	383	33	356	42	22	83	483	40	0	.	215	43	459	40	370	35	1083	32	0	.	281	49	608	41	0	.	95	85	352	37	242	43	381	37	0	.	0	.	0	.	0	.	42	84	0	.	24	100	0	.	0	.	44	75	61	100	10	100	353	44	0	.	20	62	79	47	45	66	372	42	27	100	0	.	797	33	0	.	101	61	532	28	440	34	278	36	62	64	139	68	0	.	59	54	0	.	0	.	0	.	596	36	174	61	0	.	0	.	52	67	0	.	0	.	0	.	0	.	0	.	18485	5
	maple	0	.	0	.	0	.	0	.	57	70	87	73	988	22	149	44	23	100	324	26	0	.	0	.	4	100	0	.	0	.	34	62	210	38	44	66	0	.	489	36	87	45	40	61	122	49	101	47	0	.	97	35	122	47	28	58	76	42	34	76	90	39	32	100	133	85	63	40	138	39	98	57	181	37	0	.	144	62	256	52	51	84	376	33	0	.	22	73	102	47	0	.	30	100	147	48	196	40	118	70	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	130	47	51	72	0	.	103	73	188	58	433	30	41	60	0	.	116	52	0	.	0	.	50	53	36	59	92	39	91	61	4	100	0	.	11	100	0	.	60	71	10	100	168	59	0	.	0	.	0	.	13	75	0	.	0	.	0	.	0	.	0	.	6937	7
	ash	0	.	0	.	0	.	0	.	35	100	113	62	157	49	61	59	0	.	99	55	0	.	0	.	0	.	0	.	0	.	0	.	27	74	0	.	0	.	101	47	4	100	0	.	367	59	10	100	66	71	74	64	0	.	0	.	31	87	0	.	106	90	0	.	11	100	0	.	40	62	0	.	31	100	0	.	60	76	25	71	4	100	119	58	0	.	0	.	51	83	0	.	0	.	70	55	18	100	12	100	0	.	0	.	0	.	67	74	0	.	97	91	0	.	0	.	0	.	0	.	0	.	0	.	75	88	82	79	0	.	20	100	22	100	128	56	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	73	63	58	74	40	61	0	.	0	.	0	.	14	78	0	.	0	.	0	.	0	.	0	.	2367	15
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	206	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	100	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	71	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	393	55
	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	.	4	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	.	153	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	157	83
	other broadleaves	0	.	0	.	0	.	0	.	18	71	27	100	728	19	221	41	12	100	286	33	0	.	0	.	0	.	0	.	97	91	55	61	102	43	118	70	0	.	252	39	138	54	190	71	906	29	769	26	500	36	191	69	9	100	0	.	511	47	36	89	32	57	0	.	78	100	86	50	35	61	148	74	119	56	0	.	87	53	129	41	25	85	113	48	0	.	10	100	10	100	0	.	0	.	102	38	219	38	285	82	0	.	0	.	0	.	406	47	53	81	189	46	19	100	0	.	0	.	693	54	0	.	69	71	60	58	218	77	61	57	58	74	228	49	199	34	30	76	0	.	76	58	0	.	11	100	73	43	10	100	32	74	0	.	21	100	0	.	0	.	0	.	1277	30	621	33	613	39	9	100	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	11692	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	2339	25	2781	23	20018	8	7421	11	131	76	6134	14	0	.	0	.	92	78	0	.	629	53	2112	28	4759	19	1757	30	0	.	3926	19	6775	13	5801	15	7665	12	14893	9	16985	8	3016	23	1248	30	762	38	5473	17	704	39	3369	20	532	54	793	39	2864	20	2273	26	3189	21	3617	20	64	.	1848	26	4285	18	1312	29	4194	19	0	.	533	41	1318	33	0	.	303	80	2297	21	4823	18	2212	23	0	.	0	.	0	.	1290	23	2174	21	2282	20	188	45	0	.	0	.	1843	26	365	48	525	43	1961	25	1160	34	2675	23	1144	27	1507	25	4264	21	253	52	0	.	2230	23	0	.	541	45	3224	17	1138	26	1469	27	856	30	301	61	0	.	186	54	0	.	14863	9	10345	11	10946	11	1345	30	8	.	0	.	308	59	0	.	0	.	0	.	0	.	0	.	224643	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	738	28	1142	28	1634	32	1568	22	1865	25	2867	22	17488	8	6591	10	6983	12	3319	14	93	53	224	55	410	39	0	.	1040	28	3148	21	4226	18	2139	21	15	78	3290	20	5337	14	4482	15	5960	13	11565	10	7321	11	3674	18	1972	21	2244	18	4935	16	293	45	1624	29	673	32	419	32	1489	22	1640	28	2537	22	2662	21	2010	23	1373	29	3522	18	1322	30	2652	19	1788	26	736	23	755	37	691	39	480	44	1635	20	3455	20	1113	22	1540	26	1379	30	917	34	594	30	1672	21	1415	24	9	100	98	65	8	71	336	45	74	53	336	33	890	29	874	36	2115	25	695	31	883	24	2164	23	1050	28	392	47	991	22	715	41	455	35	2475	17	579	28	1014	28	1282	25	192	45	506	48	120	45	1192	36	5502	13	5661	14	6638	12	1047	26	730	34	1260	26	991	23	1536	21	913	31	671	31	1180	29	0	.	192225	2
	fir	514	31	163	34	287	45	1148	24	616	32	732	28	1338	19	6033	10	3900	14	2910	15	57	69	135	47	366	34	0	.	243	56	159	43	1195	25	953	33	0	.	475	35	214	38	505	28	182	44	384	32	0	.	3482	14	1151	29	2466	18	1586	21	502	38	854	25	514	32	482	33	1514	25	376	34	304	42	533	34	609	24	389	34	475	29	372	35	1123	24	53	48	492	29	563	35	205	46	559	25	516	32	693	24	514	32	461	31	173	41	71	62	0	0	144	47	105	96	0	.	0	.	8	71	205	47	220	41	214	66	601	34	0	.	283	53	409	34	178	45	1044	29	171	37	10	100	662	33	457	44	735	30	242	42	295	32	577	26	402	34	212	53	398	55	168	47	197	49	494	42	254	44	1041	29	583	40	176	52	312	41	881	24	465	31	64	59	73	68	104	51	0	.	55925	3
	pine	274	40	331	33	43	41	99	37	8	100	5	100	515	32	23	54	315	25	377	41	91	58	178	52	276	35	0	.	11	73	100	51	0	.	182	50	143	88	0	.	636	39	678	29	16	100	1003	26	3271	19	479	33	0	.	172	73	141	78	69	71	7	100	64	100	14	78	44	100	0	.	272	85	48	71	46	58	0	.	222	64	24	84	71	50	330	42	106	45	247	40	152	62	316	43	239	51	4	100	74	100	106	55	56	54	183	38	13	77	191	61	74	100	0	.	0	.	122	100	0	.	0	.	43	80	181	76	0	.	332	41	5	100	0	.	0	.	0	.	4	100	9	100	90	77	12	75	0	.	23	61	0	.	176	66	20	71	0	.	53	100	0	.	3522	21	1503	27	1105	27	10	100	49	85	191	37	113	38	276	33	272	39	178	42	81	46	0	.	20712	6
	larch	64	56	259	69	52	76	84	47	4	100	22	100	84	40	13	74	149	49	50	100	0	.	0	.	13	76	12	.	103	100	21	74	0	.	37	90	0	.	4	100	484	34	422	28	93	45	1434	18	3392	11	8	71	25	86	10	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	7	100	10	100	278	39	6	100	0	.	85	54	4	100	10	100	10	100	0	.	0	.	0	.	15	72	8	100	48	100	0	.	173	36	518	29	250	36	212	40	43	62	41	70	1036	21	454	45	347	42	0	.	0	.	16	100	85	54	14	77	27	100	12	100	0	.	0	.	0	.	4	100	0	.	20	100	0	.	51	58	0	.	0	.	0	.	0	.	4112	12	2416	16	1273	18	8	100	4	100	16	100	24	72	0	.	44	72	24	74	91	47	0	.	18643	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	180	40	2083	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	94	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	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	861	26	546	31	213	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4047	12
	other conifers	19	71	0	.	30	100	24	61	20	100	10	100	9	100	0	.	193	32	109	100	0	.	0	.	0	.	119	.	14	100	0	.	0	.	30	100	0	.	10	100	0	.	10	100	0	.	0	.	347	50	10	100	47	74	53	83	0	.	0	.	0	.	9	100	0	.	0	.	21	100	0	.	0	.	45	100	21	71	28	100	12	100	30	100	22	85	20	100	50	82	30	75	0	.	0	.	0	.	10	100	10	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	64	0	.	10	100	0	.	0	.	0	.	40	61	0	.	12	100	0	.	0	.	0	.	38	90	0	.	90	100	0	.	0	.	62	85	0	.	0	.	0	.	10	100	34	89	127	71	24	61	0	.	0	.	0	.	0	.	1876	17
	beech	2226	16	1433	20	1053	26	2005	23	411	34	901	27	2906	16	3542	14	5170	11	5069	14	335	31	1213	25	1677	21	0	.	643	38	1080	27	1325	24	1488	24	10	100	1662	24	772	30	914	25	55	55	771	34	0	.	4704	12	1512	23	915	21	1566	23	309	31	679	32	470	30	328	31	586	28	796	28	222	56	1881	27	475	38	1184	34	1153	26	1488	25	1712	22	2875	18	1452	23	1755	21	940	28	2076	20	1121	24	242	43	690	27	298	29	408	30	1057	24	0	.	234	43	0	.	1313	29	1607	27	1218	27	2001	25	1739	28	1427	29	602	35	0	.	188	87	570	36	280	45	536	33	577	33	75	71	1234	25	423	44	356	31	558	27	639	27	1374	24	479	32	712	31	338	37	118	45	477	33	0	0	70	100	1711	24	359	36	821	42	512	28	980	23	1098	21	608	33	557	29	525	30	0	.	95870	2
	maple	554	22	192	37	113	47	158	35	107	50	275	40	1221	19	504	24	343	27	864	20	68	46	281	32	187	38	4	.	45	80	94	59	272	31	94	49	10	100	610	29	141	42	55	49	162	45	149	37	0	.	885	16	355	34	133	63	231	27	73	48	90	39	146	58	152	75	63	40	221	30	98	57	232	31	248	32	221	45	468	34	257	38	641	24	220	39	161	70	243	31	160	37	441	29	291	32	207	39	143	60	351	51	96	42	72	59	50	100	10	100	0	.	79	51	55	71	122	43	90	81	22	100	0	.	181	44	51	72	0	.	275	41	414	41	451	28	370	65	0	.	182	38	94	75	20	71	61	47	104	37	273	32	239	46	23	86	21	71	11	100	35	61	60	71	83	51	446	30	148	50	101	53	197	37	361	36	208	34	118	55	191	53	94	56	0	.	18641	4
	ash	424	26	337	35	198	36	147	56	125	57	506	34	352	35	585	24	649	26	302	40	171	51	79	95	89	49	0	.	53	62	212	58	527	34	302	37	29	74	396	47	40	65	0	.	377	57	143	40	66	71	1193	20	183	33	163	41	230	47	81	71	106	90	4	100	11	100	30	100	415	57	116	67	77	63	706	33	319	37	180	41	228	47	358	34	243	40	67	56	109	56	177	51	304	47	377	36	39	71	111	48	303	41	212	32	199	41	67	74	41	100	97	91	146	64	130	51	539	31	116	52	90	49	176	58	85	79	92	72	33	100	219	37	82	59	138	52	318	39	106	71	41	81	156	52	0	.	11	100	77	60	348	50	311	48	39	50	158	44	11	100	222	43	115	45	68	65	385	35	425	47	274	42	139	33	343	40	217	34	105	61	198	42	116	36	0	.	18881	5
	oak	228	30	381	41	74	44	290	40	0	.	4	100	58	58	45	59	647	22	25	100	80	37	192	98	158	53	0	.	26	73	43	51	108	54	131	46	933	43	0	.	4	100	97	86	4	100	286	66	35	100	311	32	90	41	111	63	32	64	260	55	0	.	23	71	0	.	0	.	11	100	0	.	5	100	137	89	57	66	0	0	29	62	21	71	312	26	30	57	81	58	65	70	232	54	14	78	0	.	19	100	74	53	149	42	186	34	0	.	0	.	0	.	72	54	255	37	738	30	333	75	378	48	148	46	14	78	0	.	16	100	218	63	163	69	0	.	98	81	0	0	144	72	151	42	482	54	0	.	633	46	39	79	96	42	345	44	22	61	0	.	156	41	69	53	167	59	263	33	5	100	4	100	14	77	30	100	80	50	91	39	125	43	122	49	0	.	11567	8
	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	.	933	40	37	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	75	100	365	43	548	49	903	39	1430	27	3276	20	414	38	934	33	2335	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	110	100	0	.	20	100	0	.	0	.	51	100	0	.	63	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11517	9
	other broadleaves	609	31	854	28	246	36	181	59	128	55	126	52	1225	18	600	24	786	28	549	22	296	36	179	53	281	34	0	.	231	56	179	39	188	30	229	43	69	59	373	31	304	36	267	53	906	29	1937	19	500	36	1319	20	231	30	73	44	539	45	148	45	60	56	197	91	97	83	96	46	219	44	279	52	243	36	39	79	698	48	542	36	389	45	275	32	100	43	110	37	139	36	302	39	301	32	280	32	251	36	379	64	485	39	296	40	322	45	417	46	758	26	314	37	1262	27	1495	23	2430	20	2183	24	645	31	1135	23	140	51	246	69	156	46	325	42	480	36	292	35	158	47	201	88	113	45	358	70	62	41	73	43	182	55	92	41	224	69	193	50	104	45	11	100	141	77	1689	26	667	31	1575	22	49	43	156	44	202	49	197	35	89	40	359	39	192	49	422	48	0	.	39444	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	.
	total	5649	13	5090	15	3731	20	5705	14	3284	22	5449	18	25199	7	17937	8	19135	8	13574	10	1192	26	2482	22	3457	18	135	.	2408	24	5036	17	7840	14	5584	16	1209	38	6820	15	7936	12	7434	13	7754	12	18785	8	17053	8	16066	8	5565	16	6338	14	9260	13	1735	23	3420	19	2100	25	1507	27	3822	17	3697	20	3828	20	5778	16	4322	17	4297	20	6887	14	4127	19	6883	14	6029	15	3179	17	3952	18	2731	21	4710	17	4472	16	4890	18	3068	19	3636	18	2819	21	3017	20	1390	23	3933	15	2802	19	3996	18	5113	17	8503	13	6713	14	4557	18	6162	15	2695	23	1263	32	3230	21	2802	21	2504	21	4653	19	2754	22	788	39	3417	18	2443	26	2138	24	3420	16	2553	20	3717	18	3409	18	1735	26	1657	30	492	38	2420	24	16538	9	11436	10	14712	9	2633	21	2325	25	2877	19	4047	17	3992	16	2574	21	2208	22	2735	21	0	.	489347	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. 2297814. Birmensdorf, Swiss Federal Research Institute WSL

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