Selection from the list of topics

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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NFI3
total number of stems
giant (yes/no) · main tree species
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2004/06
		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
giant (yes/no)	main tree species	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%	1000 n	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
no	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	657	37	1120	40	941	52	876	30	1840	32	2342	26	16599	10	5400	15	5756	16	4155	21	26	77	379	55	396	51	17	.	858	43	3697	29	2684	24	1718	28	0	.	3730	28	6536	17	4911	20	5891	18	12304	13	8500	14	2667	24	1899	31	2513	23	6124	20	469	65	1595	32	518	37	488	43	1244	28	2073	45	2556	30	2730	33	1367	34	1657	39	2530	27	1074	37	2323	26	2451	37	903	35	679	59	186	60	231	42	1499	26	3434	30	989	32	1444	39	1365	37	2153	41	923	40	2352	26	1797	29	0	.	15	71	28	77	447	56	232	61	436	60	908	40	1207	44	2515	33	675	40	1179	31	1296	44	948	41	113	53	1429	37	593	52	250	65	2653	23	860	60	1179	49	1332	36	552	73	413	56	89	60	201	57	5461	17	7332	20	6902	17	670	41	1218	45	651	40	1916	38	1531	33	646	44	411	53	900	35	0	.	192755	3
	fir	528	60	40	85	106	56	798	31	321	40	973	35	1600	25	5027	14	3446	17	3315	19	59	72	83	61	542	40	0	.	240	70	195	47	1189	40	598	42	0	.	276	44	296	46	406	39	176	60	612	39	69	100	3710	19	1597	29	2209	21	1678	24	384	46	730	32	503	38	443	42	1494	33	295	42	305	50	779	38	454	34	118	70	571	35	425	47	674	30	212	55	837	43	404	50	115	48	816	39	488	55	589	34	451	45	616	43	400	37	528	79	0	.	223	54	202	100	0	.	0	.	8	100	263	72	178	63	212	75	953	36	0	.	541	56	550	41	293	47	889	39	148	55	83	100	860	32	249	71	315	51	261	43	390	39	528	32	203	42	364	80	475	61	128	61	85	64	241	90	432	53	826	31	418	45	46	47	153	49	479	31	279	39	72	80	20	100	56	67	0	.	54143	4
	pine	343	67	169	47	24	77	27	79	0	.	9	100	480	44	161	77	505	53	583	45	23	100	84	55	136	46	0	.	7	100	133	79	0	.	178	59	169	83	0	.	412	42	357	37	22	100	1081	36	3243	26	329	43	7	100	190	65	0	.	66	100	87	100	0	.	0	.	75	100	0	.	437	100	145	100	7	100	0	.	0	.	0	.	17	100	425	44	67	65	106	47	189	77	173	57	142	100	6	100	0	.	44	54	70	56	184	53	0	.	96	72	149	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	288	57	28	100	0	.	0	.	0	.	58	66	7	100	0	.	27	100	0	.	24	77	0	.	52	80	0	.	0	.	77	100	0	.	2976	30	1398	33	1652	32	0	.	55	100	171	41	66	59	158	40	120	49	149	65	33	66	0	.	18512	9
	larch	24	77	34	63	69	100	61	68	0	.	0	.	173	51	7	100	60	65	0	.	0	.	0	.	14	71	0	.	0	.	58	89	0	.	14	71	0	.	7	100	290	49	347	41	413	52	1501	27	3561	15	7	100	33	100	34	71	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	69	100	7	100	124	100	235	57	9	100	0	.	49	58	0	.	0	.	7	100	0	.	0	.	0	.	15	100	84	100	59	100	0	.	154	54	950	43	592	43	262	59	0	.	29	61	1599	30	470	46	334	72	0	.	0	.	7	100	380	42	0	.	0	.	0	.	117	100	0	.	8	100	0	.	0	.	51	100	0	.	47	79	0	.	0	.	0	.	0	.	5121	17	2441	20	1155	31	14	100	7	100	17	100	0	.	0	.	40	71	7	100	74	54	0	.	21249	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	16	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	236	47	2263	22	0	.	0	.	0	.	0	.	0	.	0	.	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	764	31	513	40	232	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4064	15
	other conifers	52	74	0	.	30	100	120	59	0	.	36	71	0	.	0	.	294	32	35	71	0	.	0	.	0	.	108	.	0	.	0	.	0	.	0	.	0	.	18	100	236	100	0	.	0	.	121	100	355	44	51	74	13	100	63	90	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	17	100	56	100	18	100	46	100	21	100	0	.	49	100	34	100	17	100	69	61	0	.	0	.	0	.	18	100	0	.	0	.	51	75	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	69	100	0	.	17	100	0	.	0	.	86	100	180	61	0	.	34	100	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	35	100	65	100	35	71	0	.	0	.	68	80	222	81	33	100	0	.	0	.	0	.	0	.	2831	16
	beech	1499	27	718	30	610	33	2433	26	368	45	516	33	3197	23	3870	16	5292	13	5620	17	288	49	975	32	1688	32	0	.	411	45	1026	32	1686	38	1015	36	0	.	1687	28	829	36	966	33	67	61	835	43	0	.	4013	19	1107	27	1401	31	2106	27	324	41	687	36	338	35	297	41	576	38	731	45	54	66	1754	34	720	35	1517	41	1590	33	1483	33	1863	30	2444	24	1249	32	1416	30	739	34	1434	30	599	33	340	44	832	35	659	30	262	39	955	28	0	.	194	69	0	.	1634	40	1873	34	1540	35	2206	35	2670	33	835	44	937	40	0	.	301	61	574	39	210	51	530	47	415	44	232	83	1417	30	109	86	330	54	545	32	424	37	1136	31	402	48	521	41	223	50	214	56	217	61	0	.	59	73	2138	30	288	50	428	46	455	39	932	28	1013	28	416	40	386	36	442	36	0	.	93331	3
	maple	311	32	194	36	217	55	180	52	34	67	339	46	1166	23	533	28	518	31	1177	23	135	48	157	48	285	36	0	.	105	87	111	43	516	31	168	54	0	.	797	33	211	47	116	51	192	61	247	46	0	.	803	25	218	43	106	41	376	33	102	57	256	38	150	69	247	74	87	51	295	34	57	86	581	80	305	35	286	56	619	38	290	48	760	33	204	54	199	40	336	42	168	41	372	32	271	38	269	47	77	82	470	39	156	51	157	56	97	100	108	100	18	100	35	100	104	70	112	48	36	70	48	100	16	100	318	44	7	100	0	.	454	49	664	44	469	42	160	54	0	.	223	37	0	.	36	71	92	60	139	45	179	37	261	50	34	100	34	100	0	.	22	75	0	.	161	70	880	31	119	72	229	54	116	44	505	34	150	42	655	52	216	62	501	60	0	.	23126	5
	ash	376	40	319	47	301	53	115	64	0	.	442	46	265	56	552	32	557	23	339	46	308	70	108	66	108	51	0	.	109	60	394	50	555	41	261	51	69	100	1018	59	68	84	67	65	710	59	325	46	0	.	1102	30	386	61	226	39	395	52	127	58	116	69	0	.	17	100	19	100	645	63	0	.	61	59	619	36	492	46	120	51	283	54	524	43	312	58	90	52	92	52	253	53	191	73	562	52	17	100	144	63	621	45	299	43	290	42	97	100	322	100	0	.	36	71	145	61	431	53	302	65	85	60	221	62	201	66	166	71	0	.	246	48	165	56	196	67	291	60	114	54	65	90	36	100	0	.	39	100	28	100	223	82	197	59	64	53	130	63	0	.	130	71	445	73	232	73	297	39	136	58	137	40	303	72	327	35	220	38	134	79	96	55	75	52	0	.	21700	7
	oak	432	59	21	75	0	.	95	46	0	.	0	.	47	71	51	88	437	28	0	.	36	81	25	100	264	59	0	.	15	71	14	100	98	66	60	68	854	51	0	.	0	.	28	100	0	.	18	100	55	100	318	41	79	57	100	78	44	100	299	92	0	.	21	100	0	.	0	.	22	100	0	.	0	.	27	100	69	89	0	.	30	76	17	100	247	41	0	.	85	72	24	76	361	71	6	100	0	.	0	.	117	49	276	50	123	44	0	.	0	.	0	.	195	49	253	45	961	35	546	90	204	45	210	56	34	71	0	.	26	100	55	74	123	100	0	.	114	84	0	.	76	91	64	76	252	76	0	.	786	57	7	100	86	57	637	55	103	100	11	100	7	100	193	62	327	62	355	50	0	.	7	100	20	58	23	100	53	54	128	51	84	57	51	67	0	.	10802	11
	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	.	948	59	42	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	184	100	696	50	571	56	2061	42	1435	32	3476	27	577	51	624	59	4244	30	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	100	0	.	0	.	0	.	0	.	92	100	0	.	25	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15050	12
	other broadleaves	378	41	384	37	273	44	314	63	45	74	180	59	1796	23	574	26	1089	35	571	27	375	50	104	45	204	39	0	.	188	61	326	50	113	46	412	60	69	79	943	45	471	37	468	58	1695	41	3277	27	1439	45	1181	30	275	42	153	50	547	39	0	.	292	52	17	100	157	71	147	62	196	56	280	74	141	43	36	71	369	41	779	54	527	58	323	44	143	40	162	57	137	55	563	51	148	50	355	45	122	43	494	81	378	62	197	35	167	59	982	62	684	38	349	60	1246	33	2009	32	4141	25	1712	28	718	39	1015	34	311	43	221	100	181	62	623	53	345	42	241	46	193	50	279	68	142	42	20	100	17	100	154	46	320	65	126	54	255	72	491	54	113	67	39	100	7	100	1945	30	1246	33	1969	26	59	67	273	53	149	69	255	45	17	100	136	39	235	61	164	42	0	.	47358	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	51	58	181	34	0	.	8	100	37	71	0	.	0	.	0	.	0	.	36	100	30	100	0	.	17	100	0	.	17	100	50	53	0	.	51	58	98	50	92	45	0	.	0	.	0	.	26	72	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	35	71	7	100	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	49	100	0	.	0	.	0	.	79	61	37	71	0	.	19	100	0	.	0	.	18	100	59	100	34	71	0	.	15	100	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	51	74	14	71	100	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1292	13
	total	4600	23	2999	25	2571	28	5019	21	2609	29	4888	22	25522	9	16174	11	17962	10	15831	13	1250	41	1914	30	3636	25	125	.	1969	34	5985	22	6840	20	4440	21	1161	46	8495	22	9405	15	7667	17	9217	16	21605	11	19621	11	14183	12	5616	19	6997	18	11297	16	1769	31	3763	23	1563	32	1656	34	3643	22	4257	30	3704	28	6276	23	3597	24	4685	27	6496	21	4156	26	6500	21	6535	20	3539	24	3273	26	2312	28	3726	25	3923	23	4778	25	3019	27	4433	24	3101	26	4608	25	2437	33	5675	20	3710	22	5469	24	5833	21	10805	17	7741	19	5230	24	7561	20	3663	29	1601	41	3946	27	3643	26	3039	26	3621	33	2283	33	1082	48	4400	23	1096	43	1261	36	3743	22	3022	29	3377	27	2924	27	2681	35	1491	40	557	55	668	46	17324	12	14220	14	16567	13	1704	31	2401	30	2103	28	4724	24	3453	22	2347	28	1603	33	2298	26	0	.	506212	1
yes	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	.	7	100	6	100	0	.	39	45	35	45	7	100	22	76	0	.	7	100	0	.	0	.	0	.	7	100	22	75	8	100	0	.	50	43	23	58	14	71	54	43	43	46	7	100	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	15	70	0	.	0	.	0	.	8	100	0	.	14	71	17	73	58	75	0	.	0	.	0	.	0	.	0	.	7	100	0	.	13	100	0	.	0	.	0	.	7	100	37	66	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	14	71	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	42	70	14	71	21	58	7	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	685	13
	fir	0	.	7	100	7	100	8	100	0	.	0	.	0	.	35	45	30	50	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	58	0	.	14	70	0	.	0	.	0	.	8	100	15	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	6	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	8	100	0	.	0	.	0	.	0	.	8	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	250	18
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	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	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	14	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	22	58	49	47	0	.	0	.	0	.	0	.	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	65	40	29	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	186	23
	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	.	14	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	45
	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	.	14	71	0	.	14	71	0	.	0	.	7	100	21	74	8	100	0	.	15	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	.	10	100	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	10	100	0	.	0	.	0	.	0	.	144	24
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	72
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	7	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	.	7	100	7	100	0	.	0	.	54	43
	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	.	36	100	8	100	0	.	8	100	24	74	29	79	0	.	38	65	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	.	150	36
	other broadleaves	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	79
	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	7	100	20	58	7	100	29	50	6	100	0	.	46	41	101	29	73	39	29	62	15	71	7	100	0	.	0	.	0	.	7	100	22	75	8	100	0	.	56	40	23	58	21	58	61	40	102	28	57	43	21	58	7	100	11	100	0	.	8	100	15	71	7	100	0	.	15	70	0	.	0	.	0	.	19	71	0	.	14	71	32	51	64	69	0	.	0	.	0	.	0	.	0	.	14	71	0	.	28	60	0	.	16	100	7	100	21	74	74	59	16	71	7	100	8	100	24	74	49	62	8	100	38	65	0	.	7	100	7	100	15	71	21	58	9	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	14	71	8	100	0	.	128	32	43	48	36	45	7	100	7	100	7	100	7	100	10	100	14	71	7	100	0	.	0	.	1570	8
total	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	657	37	1120	40	941	52	883	30	1846	31	2342	26	16639	10	5434	15	5763	16	4177	21	26	77	386	54	396	51	17	.	858	43	3703	29	2706	24	1725	28	0	.	3780	28	6558	17	4926	20	5945	18	12347	13	8507	14	2673	24	1906	31	2513	23	6124	20	469	65	1595	32	518	37	488	43	1259	28	2073	45	2556	30	2730	33	1375	34	1657	39	2544	27	1090	37	2381	25	2451	37	903	35	679	59	186	60	231	42	1506	26	3434	30	1002	32	1444	39	1365	37	2153	41	930	40	2390	26	1805	29	0	.	15	71	28	77	447	56	232	61	436	60	908	40	1215	44	2515	33	682	40	1194	31	1305	43	948	41	113	53	1429	37	593	52	250	65	2653	23	860	60	1179	49	1332	36	552	73	420	56	89	60	201	57	5503	17	7345	20	6923	17	677	41	1218	45	651	40	1916	38	1531	33	653	44	411	53	900	35	0	.	193440	3
	fir	528	60	46	74	113	58	805	31	321	40	973	35	1600	25	5062	14	3476	17	3322	19	59	72	83	61	542	40	0	.	240	70	195	47	1189	40	598	42	0	.	276	44	296	46	406	39	176	60	635	38	69	100	3724	19	1597	29	2209	21	1678	24	392	45	745	32	510	38	443	42	1494	33	295	42	305	50	779	38	454	34	118	70	571	35	431	46	680	29	212	55	837	43	404	50	115	48	816	39	496	54	589	34	458	44	616	43	400	37	528	79	0	.	223	54	202	100	0	.	0	.	8	100	283	68	186	62	212	75	953	36	0	.	541	56	558	40	300	47	889	39	148	55	83	100	860	32	249	71	315	51	268	42	390	39	528	32	203	42	364	80	475	61	135	60	85	64	241	90	432	53	826	31	418	45	46	47	159	47	479	31	279	39	72	80	20	100	56	67	0	.	54394	4
	pine	343	67	169	47	24	77	27	79	0	.	9	100	480	44	161	77	505	53	583	45	23	100	84	55	136	46	0	.	7	100	133	79	0	.	178	59	169	83	0	.	412	42	364	36	22	100	1081	36	3243	26	329	43	7	100	190	65	0	.	66	100	87	100	0	.	0	.	75	100	0	.	437	100	145	100	7	100	0	.	0	.	0	.	17	100	425	44	67	65	106	47	189	77	173	57	142	100	6	100	0	.	44	54	70	56	184	53	0	.	96	72	149	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	288	57	28	100	0	.	0	.	0	.	58	66	7	100	0	.	27	100	0	.	24	77	0	.	52	80	0	.	0	.	77	100	0	.	2976	30	1398	33	1652	32	0	.	55	100	171	41	73	54	158	40	120	49	149	65	33	66	0	.	18526	9
	larch	24	77	34	63	69	100	61	68	0	.	0	.	173	51	7	100	60	65	0	.	0	.	0	.	14	71	0	.	0	.	58	89	0	.	14	71	0	.	7	100	290	49	347	41	420	53	1523	26	3610	15	7	100	33	100	34	71	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	69	100	7	100	124	100	235	57	9	100	0	.	49	58	0	.	0	.	7	100	0	.	0	.	0	.	15	100	84	100	59	100	0	.	169	51	950	43	592	43	262	59	0	.	29	61	1599	30	470	46	334	72	0	.	0	.	7	100	380	42	0	.	0	.	0	.	117	100	0	.	8	100	0	.	0	.	51	100	0	.	47	79	0	.	0	.	0	.	0	.	5186	17	2470	20	1155	31	14	100	7	100	17	100	0	.	0	.	40	71	7	100	74	54	0	.	21435	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	16	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	251	48	2263	22	0	.	0	.	0	.	0	.	0	.	0	.	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	785	31	513	40	232	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4100	15
	other conifers	52	74	0	.	30	100	120	59	0	.	36	71	0	.	0	.	294	32	35	71	0	.	0	.	0	.	108	.	0	.	0	.	0	.	0	.	0	.	18	100	236	100	0	.	0	.	121	100	355	44	51	74	13	100	63	90	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	17	100	56	100	18	100	46	100	21	100	0	.	49	100	34	100	17	100	69	61	0	.	0	.	0	.	18	100	0	.	0	.	51	75	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	69	100	0	.	17	100	0	.	0	.	86	100	180	61	0	.	34	100	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	35	100	65	100	35	71	0	.	0	.	68	80	222	81	33	100	0	.	0	.	0	.	0	.	2831	16
	beech	1499	27	731	30	610	33	2447	26	368	45	516	33	3204	23	3890	16	5300	13	5620	17	303	47	975	32	1688	32	0	.	411	45	1026	32	1686	38	1015	36	0	.	1687	28	829	36	966	33	67	61	835	43	0	.	4013	19	1107	27	1401	31	2106	27	324	41	687	36	338	35	297	41	576	38	731	45	54	66	1754	34	730	36	1517	41	1590	33	1492	33	1863	30	2444	24	1249	32	1416	30	739	34	1434	30	599	33	340	44	839	35	659	30	262	39	955	28	0	.	194	69	0	.	1641	40	1873	34	1540	35	2206	35	2670	33	835	44	937	40	0	.	309	60	574	39	210	51	530	47	415	44	232	83	1417	30	109	86	330	54	545	32	424	37	1136	31	402	48	521	41	223	50	214	56	217	61	0	.	59	73	2145	30	288	50	435	46	455	39	932	28	1023	28	416	40	386	36	442	36	0	.	93475	3
	maple	311	32	194	36	217	55	180	52	34	67	339	46	1166	23	533	28	518	31	1177	23	135	48	157	48	285	36	0	.	105	87	111	43	516	31	168	54	0	.	803	33	211	47	116	51	192	61	247	46	0	.	803	25	218	43	106	41	376	33	102	57	256	38	150	69	247	74	87	51	295	34	57	86	581	80	305	35	286	56	619	38	290	48	760	33	204	54	199	40	336	42	168	41	372	32	271	38	269	47	77	82	470	39	156	51	157	56	97	100	108	100	18	100	35	100	104	70	112	48	36	70	48	100	16	100	318	44	7	100	0	.	454	49	664	44	469	42	160	54	0	.	223	37	0	.	36	71	92	60	139	45	179	37	261	50	34	100	34	100	0	.	22	75	0	.	161	70	880	31	119	72	229	54	116	44	505	34	150	42	655	52	216	62	501	60	0	.	23132	5
	ash	376	40	319	47	301	53	115	64	0	.	442	46	265	56	562	32	557	23	339	46	308	70	108	66	108	51	0	.	109	60	394	50	555	41	261	51	69	100	1018	59	68	84	67	65	710	59	325	46	0	.	1102	30	386	61	226	39	395	52	127	58	116	69	0	.	17	100	19	100	645	63	0	.	61	59	619	36	492	46	120	51	283	54	524	43	312	58	90	52	92	52	253	53	191	73	562	52	17	100	144	63	621	45	299	43	290	42	97	100	322	100	0	.	36	71	145	61	431	53	302	65	85	60	221	62	201	66	166	71	0	.	246	48	165	56	196	67	291	60	114	54	65	90	36	100	0	.	39	100	28	100	223	82	197	59	64	53	137	63	0	.	130	71	445	73	232	73	297	39	136	58	137	40	303	72	327	35	220	38	134	79	96	55	75	52	0	.	21717	7
	oak	432	59	21	75	0	.	95	46	0	.	0	.	47	71	51	88	444	27	0	.	36	81	25	100	264	59	0	.	15	71	14	100	98	66	60	68	854	51	0	.	0	.	28	100	0	.	18	100	55	100	318	41	79	57	111	80	44	100	299	92	0	.	21	100	0	.	0	.	22	100	0	.	0	.	27	100	69	89	0	.	30	76	17	100	247	41	0	.	85	72	24	76	361	71	6	100	0	.	0	.	117	49	291	51	130	42	0	.	0	.	0	.	195	49	253	45	961	35	546	90	204	45	210	56	34	71	0	.	26	100	55	74	123	100	0	.	114	84	0	.	76	91	64	76	252	76	0	.	786	57	7	100	86	57	637	55	103	100	11	100	7	100	193	62	327	62	355	50	0	.	7	100	20	58	23	100	53	54	135	48	91	55	51	67	0	.	10856	11
	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	.	948	59	42	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	184	100	733	48	579	57	2061	42	1443	32	3500	27	605	49	624	59	4281	29	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	100	0	.	0	.	0	.	0	.	92	100	0	.	33	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15200	12
	other broadleaves	385	40	384	37	273	44	314	63	45	74	180	59	1796	23	574	26	1110	35	571	27	375	50	104	45	204	39	0	.	188	61	326	50	113	46	412	60	69	79	943	45	471	37	468	58	1695	41	3277	27	1439	45	1181	30	275	42	153	50	547	39	0	.	292	52	17	100	157	71	147	62	196	56	280	74	141	43	36	71	369	41	779	54	527	58	323	44	143	40	162	57	137	55	563	51	148	50	355	45	122	43	494	81	378	62	197	35	167	59	982	62	684	38	349	60	1246	33	2009	32	4141	25	1712	28	718	39	1015	34	311	43	221	100	181	62	623	53	345	42	241	46	193	50	279	68	142	42	20	100	17	100	154	46	320	65	126	54	255	72	491	54	113	67	39	100	7	100	1945	30	1246	33	1969	26	59	67	273	53	149	69	255	45	17	100	136	39	235	61	164	42	0	.	47386	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	51	58	181	34	0	.	8	100	37	71	0	.	0	.	0	.	0	.	36	100	30	100	0	.	17	100	0	.	17	100	50	53	0	.	51	58	98	50	92	45	0	.	0	.	0	.	26	72	0	.	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	35	71	7	100	15	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	49	100	0	.	0	.	0	.	79	61	37	71	0	.	19	100	0	.	0	.	18	100	59	100	34	71	0	.	15	100	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	51	74	14	71	100	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1292	13
	total	4607	23	3019	24	2578	28	5048	21	2615	29	4888	22	25568	9	16275	11	18035	10	15860	13	1265	41	1921	30	3636	25	125	.	1969	34	5992	22	6863	20	4448	21	1161	46	8551	22	9427	15	7688	17	9278	16	21707	11	19677	11	14203	12	5623	19	7008	18	11297	16	1777	31	3778	23	1570	32	1656	34	3658	22	4257	30	3704	28	6276	23	3616	24	4685	27	6510	21	4188	25	6564	21	6535	20	3539	24	3273	26	2312	28	3726	25	3937	23	4778	25	3047	27	4433	24	3117	26	4615	25	2459	33	5748	20	3725	22	5477	24	5841	21	10829	17	7790	19	5237	24	7599	20	3663	29	1608	41	3954	27	3658	25	3060	26	3630	33	2283	33	1082	48	4400	23	1096	43	1261	36	3750	22	3022	29	3377	27	2924	27	2681	35	1505	40	564	55	668	46	17452	12	14263	14	16603	13	1711	31	2408	30	2109	28	4731	24	3463	22	2361	28	1610	33	2298	26	0	.	507783	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. 2296864. Birmensdorf, Swiss Federal Research Institute WSL

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