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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NFI2
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 1993/95
		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	1046	33	1573	44	851	47	1326	27	1848	31	2836	29	17605	11	6681	14	7036	17	4088	19	110	75	448	78	507	52	17	.	1314	43	4062	27	3547	25	2704	27	20	100	3793	30	5891	17	4463	20	5441	19	11429	14	7857	15	3964	25	2063	29	2929	24	6180	20	537	78	1509	37	537	38	419	43	1477	29	2016	45	2318	31	2527	31	1443	37	1566	40	2526	28	1164	36	2908	29	2078	29	855	39	797	51	745	52	224	41	1407	27	3881	30	1035	31	1566	39	1547	38	2105	42	770	39	2178	26	1691	30	0	.	15	71	16	70	307	65	141	63	326	45	821	38	1233	44	2556	34	587	42	915	35	1233	45	1115	41	404	66	1340	33	700	50	259	60	2539	24	667	48	1127	50	1285	35	394	66	567	61	82	59	972	51	5160	17	7094	20	6646	18	1207	45	629	40	1220	35	2167	36	2163	33	938	41	577	47	987	35	0	.	201842	3
	fir	527	52	86	61	80	74	765	33	361	44	822	34	1474	26	5825	13	3804	18	3065	18	61	66	111	60	476	42	0	.	255	67	202	49	1196	35	676	52	0	.	333	49	277	49	267	36	184	62	591	41	0	.	3760	20	1297	29	2746	20	1536	25	581	50	609	32	527	41	504	40	1470	37	284	46	420	52	727	39	514	31	108	68	555	34	337	51	712	29	143	51	422	38	522	46	209	56	687	37	366	50	773	36	498	41	562	44	293	45	362	70	16	100	226	52	202	100	0	.	0	.	8	100	177	85	197	61	178	71	911	35	0	.	502	54	467	41	299	46	832	40	89	61	41	100	1064	38	279	66	301	43	215	47	241	50	573	32	364	50	192	55	707	54	115	59	139	59	319	67	383	56	655	31	432	41	46	51	242	43	702	29	285	41	66	78	0	.	53	88	0	.	54477	4
	pine	217	49	245	46	47	54	47	65	0	.	8	100	473	47	46	53	340	33	607	44	46	100	203	72	151	47	0	.	7	100	123	77	0	.	239	62	238	87	0	.	550	49	441	35	30	100	1146	36	2968	26	417	42	0	.	205	66	0	.	59	100	104	100	0	.	0	.	75	100	0	.	403	100	91	100	7	100	0	.	0	.	0	.	17	100	531	48	67	65	113	45	213	69	178	56	111	100	21	77	0	.	54	47	84	62	197	50	6	100	96	72	149	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	363	61	27	100	0	.	0	.	0	.	41	85	7	100	0	.	27	100	0	.	41	72	0	.	55	71	0	.	0	.	86	100	0	.	3653	30	1320	31	1666	31	0	.	72	100	211	42	78	55	195	41	175	55	136	62	47	55	0	.	19587	9
	larch	42	61	78	55	69	100	88	77	0	.	0	.	126	44	23	72	118	75	0	.	0	.	0	.	24	77	0	.	0	.	82	85	0	.	69	91	0	.	7	100	358	51	475	37	374	62	1469	28	3317	14	7	100	14	100	17	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	7	100	101	100	342	58	10	100	0	.	104	62	0	.	0	.	0	.	0	.	0	.	0	.	15	100	50	100	76	100	0	.	115	56	671	37	507	42	231	56	0	.	29	61	1300	30	437	47	334	70	0	.	0	.	7	100	167	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	88	86	0	.	0	.	0	.	0	.	5121	18	2346	22	1104	34	14	100	7	100	17	100	0	.	0	.	64	71	7	100	78	61	0	.	20166	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	22	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	192	49	2277	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	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	.	713	28	484	41	215	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3926	15
	other conifers	17	100	0	.	50	100	17	100	0	.	18	100	0	.	0	.	285	39	0	.	0	.	0	.	0	.	153	.	15	100	0	.	0	.	0	.	0	.	18	100	73	100	0	.	0	.	0	.	191	59	17	100	47	71	63	100	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	17	100	55	100	18	100	46	100	21	100	0	.	35	100	34	100	17	100	86	60	0	.	0	.	0	.	18	100	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	18	100	0	.	52	100	0	.	17	100	0	.	0	.	121	100	128	60	0	.	17	100	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	225	100	17	100	35	71	0	.	0	.	24	77	221	74	17	100	0	.	0	.	0	.	0	.	2288	18
	beech	1701	22	970	27	665	36	2131	25	395	44	541	34	3394	23	3601	17	6092	13	5639	17	329	43	1094	35	1707	31	0	.	481	52	1077	32	1695	39	1191	34	0	.	1507	28	898	40	1071	33	42	58	757	45	0	.	4050	18	1212	27	1227	29	1886	26	321	45	534	34	472	42	353	41	554	39	880	52	54	66	1879	35	704	41	1546	41	1262	32	1355	36	1921	30	2643	24	1280	32	1556	28	841	36	1683	29	609	33	298	48	748	32	497	32	296	37	963	27	0	.	184	72	0	.	1304	45	1788	36	1594	34	1887	37	2317	35	1058	43	816	39	0	.	298	66	486	46	235	54	594	49	346	44	103	100	1557	30	123	78	426	44	576	34	531	35	1326	35	347	48	598	45	368	39	194	59	281	62	0	.	36	71	1982	31	254	48	442	57	387	47	1023	30	1274	28	472	39	419	36	373	35	0	.	94602	3
	maple	585	29	235	41	47	58	171	53	43	73	329	49	1130	24	531	27	329	32	1164	24	52	74	237	42	174	41	0	.	116	81	67	56	443	33	107	58	0	.	680	34	207	49	67	72	181	62	141	56	0	.	679	22	181	49	124	43	346	33	44	62	199	37	65	90	244	80	58	57	269	33	101	92	360	65	233	37	153	45	717	41	246	54	840	32	156	71	92	47	277	34	270	39	398	32	291	38	240	49	56	55	228	54	121	47	192	56	16	100	90	100	18	100	34	71	96	71	76	62	0	.	60	74	0	.	300	46	7	100	0	.	444	53	551	49	328	45	76	81	0	.	258	39	0	.	19	100	73	60	107	46	174	34	166	65	34	100	17	100	0	.	7	100	0	.	93	65	742	34	98	67	187	57	98	39	530	39	155	42	396	63	225	58	122	62	0	.	19814	5
	ash	238	34	378	46	67	54	134	63	0	.	442	52	343	49	551	29	561	27	302	49	309	69	65	90	108	61	0	.	64	71	324	55	657	40	208	58	52	100	726	56	34	71	42	87	562	70	239	53	0	.	1080	31	456	71	272	46	323	59	141	83	178	79	17	100	0	.	0	.	484	53	0	.	77	64	604	36	477	45	103	50	279	55	552	43	226	57	24	77	103	53	257	60	221	70	462	57	17	100	132	61	362	44	277	42	256	45	113	100	134	100	0	.	36	71	107	64	374	53	85	72	85	60	176	72	157	72	74	61	0	.	151	56	144	57	107	87	230	63	75	80	117	67	17	100	0	.	39	100	77	71	387	83	234	59	57	58	176	53	0	.	436	67	212	50	196	75	303	38	124	62	158	39	250	68	401	42	166	43	158	69	197	54	91	57	0	.	19631	7
	oak	318	58	48	63	35	100	93	47	0	.	0	.	79	60	68	74	625	30	0	.	62	66	29	100	267	62	0	.	15	71	14	100	147	64	98	71	1191	52	0	.	0	.	28	100	0	.	18	100	55	100	308	43	96	54	117	79	118	100	272	91	0	.	21	100	0	.	0	.	22	100	0	.	0	.	27	100	73	85	0	.	43	71	17	100	278	38	0	.	153	81	24	76	355	73	6	100	0	.	0	.	154	49	345	49	191	39	0	.	0	.	0	.	180	54	220	51	987	34	513	89	165	44	182	65	26	76	0	.	26	100	54	74	106	100	0	.	123	85	0	.	76	91	126	61	160	62	0	.	975	52	7	100	134	48	740	55	86	100	0	.	14	100	157	58	300	63	198	35	0	.	7	100	7	100	48	85	86	55	67	52	84	57	62	81	0	.	11724	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	.	1034	67	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	.	149	100	411	41	481	61	1796	45	1656	32	3403	27	301	54	629	61	3723	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	94	100	0	.	82	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13817	13
	other broadleaves	322	45	527	34	246	54	161	78	30	71	129	64	1418	26	586	28	1082	38	429	29	382	47	92	51	214	46	0	.	298	62	355	36	275	41	366	49	34	71	593	42	398	41	419	63	1342	34	3026	27	1230	46	1303	29	288	36	126	59	527	36	55	100	240	50	119	100	177	83	128	60	125	65	241	84	142	50	18	100	467	44	567	37	493	59	348	44	94	56	110	59	154	50	574	48	127	47	273	42	190	61	515	81	549	50	165	37	183	58	688	57	594	39	224	57	1214	31	1934	31	3614	26	1533	29	732	46	1151	32	276	45	276	100	217	63	518	45	392	40	222	43	142	52	379	82	128	48	20	100	17	100	115	52	364	61	149	46	324	72	306	66	160	53	39	100	31	81	1584	30	811	34	1678	27	84	66	201	54	83	67	229	47	50	58	293	51	254	66	172	48	0	.	43455	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	5013	18	4140	25	2157	31	4933	18	2678	30	5125	24	26063	10	17913	10	20273	10	15294	13	1350	38	2278	34	3628	25	171	.	2565	35	6307	21	7962	19	5658	21	1535	48	7658	21	8693	15	7273	17	8157	16	20043	11	17939	11	15586	12	5654	19	7826	17	10916	16	2009	34	3372	24	1775	30	1704	35	3762	23	4079	30	3535	28	5819	22	3613	24	4508	26	6118	21	3947	27	7313	21	6287	19	2884	25	3691	25	3219	26	3872	25	3524	24	5420	26	3017	25	4022	25	3206	27	4483	25	1875	31	4585	19	3288	24	4795	25	5815	22	10102	18	6110	20	4762	25	7145	20	3325	28	1590	40	4020	27	2901	25	2659	28	3315	35	2121	35	1165	46	4674	23	1265	42	1227	32	3557	23	3054	28	3744	27	3038	26	2320	34	2082	38	516	55	1880	40	17237	12	13081	14	15307	13	2213	33	1748	31	2539	26	5399	23	4392	22	2630	27	1898	31	1984	30	0	.	505328	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	.	0	.	0	.	0	.	28	50	22	58	23	58	8	100	0	.	7	100	0	.	0	.	0	.	13	71	0	.	8	100	0	.	36	53	7	100	7	100	35	45	24	59	0	.	7	100	7	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	8	100	6	100	29	50	6	100	20	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	7	100	18	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	7	100	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50	7	100	14	71	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	433	13
	fir	0	.	0	.	0	.	8	100	0	.	0	.	0	.	52	38	14	71	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	7	100	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	8	100	0	.	0	.	0	.	0	.	10	100	7	100	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	.	171	23
	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	.	28	50	0	.	0	.	0	.	0	.	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	.	22	74	22	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	33
	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	.	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	.	21	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	28	50
	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	.	7	100	0	.	7	100	0	.	0	.	8	100	0	.	8	100	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	13	100	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	.	7	100	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	.	94	32
	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	7	100	7	100	0	.	0	.	36	54
	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	.	41	100	8	100	0	.	8	100	24	75	36	72	0	.	49	55	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	165	36
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15	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	.	9	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	.	24	73
	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	.	7	100	0	.	14	71	0	.	0	.	36	45	84	30	60	44	15	71	8	100	7	100	0	.	0	.	0	.	13	71	0	.	24	74	0	.	36	53	7	100	7	100	41	41	38	45	28	50	14	71	20	74	0	.	0	.	0	.	0	.	7	100	0	.	8	100	0	.	0	.	0	.	8	100	6	100	29	50	6	100	20	73	0	.	0	.	0	.	0	.	0	.	7	100	0	.	23	58	9	100	16	100	7	100	21	74	59	72	8	100	0	.	8	100	24	75	57	58	8	100	49	55	0	.	7	100	7	100	17	72	14	71	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	71	35	29	61	21	58	0	.	7	100	0	.	8	100	0	.	14	71	7	100	0	.	0	.	1056	10
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	1046	33	1573	44	851	47	1326	27	1848	31	2836	29	17632	11	6703	14	7058	17	4095	19	110	75	455	77	507	52	17	.	1314	43	4076	27	3547	25	2712	27	20	100	3830	30	5899	17	4471	20	5475	18	11453	14	7857	15	3971	25	2070	29	2929	24	6180	20	537	78	1509	37	537	38	419	43	1485	29	2016	45	2318	31	2527	31	1451	36	1572	40	2555	27	1170	36	2928	29	2078	29	855	39	797	51	745	52	224	41	1407	27	3881	30	1042	31	1566	39	1547	38	2112	42	778	38	2196	26	1691	30	0	.	15	71	16	70	307	65	141	63	326	45	821	38	1240	44	2556	34	594	41	922	35	1241	45	1115	41	404	66	1340	33	700	50	259	60	2539	24	667	48	1127	50	1285	35	394	66	567	61	82	59	972	51	5188	17	7101	20	6660	18	1207	45	629	40	1220	35	2167	36	2163	33	945	41	577	47	987	35	0	.	202275	3
	fir	527	52	86	61	80	74	773	33	361	44	822	34	1474	26	5877	13	3818	18	3072	18	61	66	111	60	476	42	0	.	255	67	202	49	1196	35	676	52	0	.	333	49	277	49	267	36	184	62	598	40	0	.	3767	20	1297	29	2746	20	1536	25	581	50	609	32	534	41	504	40	1470	37	284	46	420	52	727	39	514	31	108	68	555	34	337	51	712	29	143	51	422	38	522	46	209	56	687	37	373	50	773	36	507	41	562	44	293	45	362	70	16	100	226	52	202	100	0	.	0	.	8	100	198	77	205	61	178	71	911	35	0	.	502	54	477	40	306	47	832	40	89	61	41	100	1064	38	279	66	301	43	215	47	241	50	573	32	364	50	192	55	707	54	115	59	139	59	319	67	383	56	655	31	432	41	46	51	242	43	710	29	285	41	66	78	0	.	53	88	0	.	54648	4
	pine	217	49	245	46	47	54	47	65	0	.	8	100	473	47	46	53	340	33	607	44	46	100	203	72	151	47	0	.	7	100	123	77	0	.	239	62	238	87	0	.	550	49	441	35	30	100	1146	36	2968	26	417	42	0	.	205	66	0	.	59	100	104	100	0	.	0	.	75	100	0	.	403	100	91	100	7	100	0	.	0	.	0	.	17	100	531	48	67	65	113	45	213	69	178	56	111	100	21	77	0	.	54	47	84	62	197	50	6	100	96	72	149	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	363	61	27	100	0	.	0	.	0	.	41	85	7	100	0	.	27	100	0	.	41	72	0	.	55	71	0	.	0	.	86	100	0	.	3653	30	1320	31	1666	31	0	.	72	100	211	42	78	55	195	41	175	55	136	62	47	55	0	.	19587	9
	larch	42	61	78	55	69	100	88	77	0	.	0	.	126	44	23	72	118	75	0	.	0	.	0	.	24	77	0	.	0	.	82	85	0	.	69	91	0	.	7	100	358	51	475	37	374	62	1469	28	3346	14	7	100	14	100	17	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	7	100	101	100	342	58	10	100	0	.	104	62	0	.	0	.	0	.	0	.	0	.	0	.	15	100	50	100	76	100	0	.	129	52	671	37	507	42	231	56	0	.	29	61	1300	30	437	47	334	70	0	.	0	.	7	100	167	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	88	86	0	.	0	.	0	.	0	.	5143	18	2368	22	1104	34	14	100	7	100	17	100	0	.	0	.	64	71	7	100	78	61	0	.	20253	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	22	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	199	49	2277	21	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	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	.	734	28	484	41	215	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3954	15
	other conifers	17	100	0	.	50	100	17	100	0	.	18	100	0	.	0	.	285	39	0	.	0	.	0	.	0	.	153	.	15	100	0	.	0	.	0	.	0	.	18	100	73	100	0	.	0	.	0	.	191	59	17	100	47	71	63	100	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	17	100	55	100	18	100	46	100	21	100	0	.	35	100	34	100	17	100	86	60	0	.	0	.	0	.	18	100	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	18	100	0	.	52	100	0	.	17	100	0	.	0	.	121	100	128	60	0	.	17	100	0	.	0	.	0	.	23	100	0	.	0	.	0	.	0	.	225	100	17	100	35	71	0	.	0	.	24	77	221	74	17	100	0	.	0	.	0	.	0	.	2288	18
	beech	1701	22	977	27	665	36	2138	25	395	44	541	34	3402	23	3601	17	6100	13	5639	17	337	42	1094	35	1707	31	0	.	481	52	1077	32	1695	39	1207	34	0	.	1507	28	898	40	1071	33	49	58	757	45	0	.	4050	18	1225	27	1227	29	1886	26	321	45	534	34	472	42	353	41	554	39	880	52	54	66	1879	35	704	41	1546	41	1262	32	1355	36	1921	30	2643	24	1280	32	1556	28	841	36	1683	29	609	33	298	48	755	31	497	32	296	37	963	27	0	.	184	72	0	.	1304	45	1788	36	1594	34	1887	37	2317	35	1058	43	816	39	0	.	306	65	486	46	235	54	594	49	346	44	103	100	1557	30	123	78	426	44	576	34	531	35	1326	35	347	48	598	45	368	39	194	59	281	62	0	.	36	71	1990	31	254	48	442	57	387	47	1023	30	1274	28	472	39	419	36	373	35	0	.	94696	3
	maple	585	29	235	41	47	58	171	53	43	73	329	49	1130	24	531	27	329	32	1164	24	52	74	237	42	174	41	0	.	116	81	67	56	443	33	107	58	0	.	680	34	207	49	67	72	181	62	141	56	0	.	679	22	181	49	124	43	346	33	44	62	199	37	65	90	244	80	58	57	269	33	101	92	360	65	233	37	153	45	717	41	246	54	840	32	156	71	92	47	277	34	270	39	398	32	291	38	240	49	56	55	228	54	121	47	192	56	16	100	90	100	18	100	34	71	96	71	76	62	0	.	60	74	0	.	300	46	7	100	0	.	444	53	551	49	328	45	76	81	0	.	258	39	0	.	19	100	73	60	107	46	174	34	166	65	34	100	17	100	0	.	7	100	0	.	93	65	742	34	98	67	187	57	98	39	530	39	155	42	396	63	225	58	122	62	0	.	19814	5
	ash	238	34	378	46	67	54	134	63	0	.	442	52	343	49	562	29	561	27	302	49	309	69	65	90	108	61	0	.	64	71	324	55	657	40	208	58	52	100	726	56	34	71	42	87	562	70	239	53	0	.	1080	31	456	71	272	46	323	59	141	83	178	79	17	100	0	.	0	.	484	53	0	.	77	64	604	36	477	45	103	50	279	55	552	43	226	57	24	77	103	53	257	60	221	70	462	57	17	100	132	61	362	44	277	42	256	45	113	100	134	100	0	.	36	71	107	64	374	53	85	72	85	60	176	72	157	72	74	61	0	.	151	56	144	57	107	87	230	63	75	80	117	67	17	100	0	.	39	100	77	71	387	83	234	59	57	58	183	53	0	.	436	67	212	50	196	75	303	38	124	62	158	39	250	68	401	42	166	43	158	69	197	54	91	57	0	.	19649	7
	oak	318	58	48	63	35	100	93	47	0	.	0	.	79	60	68	74	625	30	0	.	62	66	29	100	267	62	0	.	15	71	14	100	147	64	98	71	1191	52	0	.	0	.	28	100	0	.	18	100	55	100	308	43	96	54	117	79	118	100	272	91	0	.	21	100	0	.	0	.	22	100	0	.	0	.	27	100	73	85	0	.	43	71	17	100	278	38	0	.	153	81	24	76	355	73	6	100	0	.	0	.	154	49	361	48	191	39	0	.	0	.	0	.	180	54	220	51	987	34	513	89	165	44	182	65	26	76	0	.	26	100	54	74	106	100	0	.	123	85	0	.	76	91	126	61	160	62	0	.	975	52	7	100	134	48	740	55	86	100	0	.	14	100	157	58	300	63	198	35	0	.	14	71	7	100	48	85	86	55	74	48	91	55	62	81	0	.	11760	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	.	1034	67	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	.	149	100	452	41	489	62	1796	45	1664	32	3427	27	337	51	629	61	3771	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	94	100	0	.	82	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13982	12
	other broadleaves	322	45	527	34	246	54	161	78	30	71	129	64	1418	26	586	28	1097	37	429	29	382	47	92	51	214	46	0	.	298	62	355	36	275	41	366	49	34	71	593	42	398	41	419	63	1342	34	3026	27	1230	46	1303	29	288	36	126	59	527	36	55	100	240	50	119	100	177	83	128	60	125	65	241	84	142	50	18	100	467	44	567	37	493	59	348	44	94	56	110	59	154	50	574	48	127	47	273	42	190	61	515	81	558	50	165	37	183	58	688	57	594	39	224	57	1214	31	1934	31	3614	26	1533	29	732	46	1151	32	276	45	276	100	217	63	518	45	392	40	222	43	142	52	379	82	128	48	20	100	17	100	115	52	364	61	149	46	324	72	306	66	160	53	39	100	31	81	1584	30	811	34	1678	27	84	66	201	54	83	67	229	47	50	58	293	51	254	66	172	48	0	.	43479	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	5013	18	4147	25	2157	31	4948	18	2678	30	5125	24	26099	10	17997	10	20333	10	15308	13	1358	38	2285	34	3628	25	171	.	2565	35	6320	21	7962	19	5681	21	1535	48	7695	21	8701	15	7280	17	8198	16	20081	11	17967	11	15600	12	5674	19	7826	17	10916	16	2009	34	3372	24	1782	30	1704	35	3769	23	4079	30	3535	28	5819	22	3621	24	4514	26	6147	21	3953	27	7333	21	6287	19	2884	25	3691	25	3219	26	3872	25	3531	24	5420	26	3040	25	4031	25	3221	27	4490	25	1896	30	4644	19	3296	24	4795	25	5823	22	10125	17	6167	19	4770	25	7194	20	3325	28	1597	40	4028	27	2918	25	2673	28	3324	35	2121	35	1165	46	4674	23	1265	42	1227	32	3557	23	3054	28	3744	27	3038	26	2320	34	2089	38	516	55	1880	40	17309	12	13110	14	15328	13	2213	33	1755	31	2539	26	5407	23	4392	22	2644	27	1905	31	1984	30	0	.	506383	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. 2296759. Birmensdorf, Swiss Federal Research Institute WSL

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