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
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	992	34	1525	45	801	49	1319	27	1771	31	2677	30	16124	11	6460	14	6980	17	3929	20	110	75	448	78	490	51	17	.	1281	43	3768	26	3485	25	2667	27	20	100	3353	30	5490	17	4032	21	4798	18	10430	14	7578	15	3873	25	2006	29	2901	24	5809	20	537	78	1489	37	520	38	419	43	1434	29	1857	45	2128	31	2392	32	1395	37	1485	40	2229	28	987	36	2619	30	2043	29	810	41	783	51	745	52	224	41	1283	26	3507	28	935	31	1529	39	1531	38	2105	42	717	39	2111	27	1576	31	0	.	15	71	8	100	300	64	141	63	290	49	740	40	1170	45	2281	34	569	43	826	34	1143	45	990	41	404	66	1251	33	642	50	259	60	2500	24	667	48	1127	50	1112	36	327	73	516	58	82	59	805	53	4509	18	6290	20	5922	18	1171	46	605	41	1220	35	2115	37	2095	33	921	41	577	47	953	36	0	.	189065	3
	fir	461	57	86	61	63	90	715	32	331	42	778	34	1390	25	5720	14	3644	18	2871	19	17	100	111	60	409	42	0	.	255	67	187	53	1143	35	676	52	0	.	252	48	277	49	218	37	135	56	483	40	0	.	3262	20	1257	30	2698	21	1503	25	546	47	609	32	527	41	487	40	1470	37	224	42	398	54	641	39	485	32	50	73	509	34	337	51	632	29	143	51	355	40	381	52	184	55	548	35	335	53	757	36	425	43	562	44	293	45	362	70	0	0	166	54	195	100	0	.	0	.	8	100	177	85	178	63	159	72	823	36	0	.	477	54	399	42	241	48	814	40	74	71	41	100	900	42	263	65	301	43	198	45	241	50	573	32	338	49	192	55	707	54	115	59	106	61	302	70	383	56	538	33	432	41	46	51	242	43	702	29	285	41	66	78	0	.	53	88	0	.	50936	4
	pine	147	60	245	46	47	54	47	65	0	.	8	100	323	49	46	53	340	33	600	45	30	100	203	72	151	47	0	.	7	100	106	74	0	.	239	62	238	87	0	.	478	48	427	35	30	100	1004	36	2768	26	364	42	0	.	205	66	0	.	59	100	104	100	0	.	0	.	75	100	0	.	403	100	45	100	7	100	0	.	0	.	0	.	17	100	531	48	67	65	95	50	171	80	178	56	87	100	21	77	0	.	54	47	84	62	197	50	6	100	88	71	149	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	344	62	8	100	0	.	0	.	0	.	41	85	7	100	0	.	27	100	0	.	24	77	0	.	55	71	0	.	0	.	86	100	0	.	3093	32	1143	32	1382	34	0	.	55	100	211	42	54	58	195	41	168	57	136	62	47	55	0	.	17590	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	321	49	368	40	349	66	1251	28	3143	15	7	100	14	100	17	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	7	100	101	100	270	54	10	100	0	.	104	62	0	.	0	.	0	.	0	.	0	.	0	.	15	100	50	100	76	100	0	.	65	48	603	39	489	43	231	56	0	.	29	61	1284	30	437	47	316	69	0	.	0	.	7	100	167	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	49	77	0	.	0	.	0	.	0	.	4526	18	2170	23	857	28	14	100	7	100	17	100	0	.	0	.	64	71	7	100	78	61	0	.	18304	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	192	49	2128	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	.	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	.	688	28	484	41	197	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3727	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	.	122	65	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	69	61	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	108	58	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	.	2182	19
	beech	1659	21	954	27	665	36	2081	25	347	44	541	34	3192	24	3594	17	6042	13	5552	17	329	43	1059	35	1648	31	0	.	412	49	1060	32	1674	39	1141	34	0	.	1392	28	880	40	1040	33	42	58	757	45	0	.	3887	18	1179	27	1210	29	1869	26	321	45	534	34	472	42	353	41	554	39	880	52	54	66	1847	34	704	41	1492	41	1246	32	1260	37	1896	30	2608	24	1247	32	1522	28	773	34	1630	28	587	34	298	48	727	32	497	32	296	37	945	27	0	.	184	72	0	.	1304	45	1752	35	1460	35	1887	37	2260	35	1031	42	800	39	0	.	298	66	486	46	235	54	594	49	346	44	103	100	1483	30	123	78	426	44	576	34	514	36	1309	35	347	48	598	45	368	39	194	59	281	62	0	.	36	71	1982	31	254	48	424	55	387	47	1023	30	1217	29	456	39	419	36	356	36	0	.	92468	3
	maple	578	30	235	41	47	58	155	52	43	73	329	49	1068	24	531	27	329	32	1147	24	52	74	237	42	174	41	0	.	116	81	67	56	443	33	107	58	0	.	608	35	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	253	34	101	92	360	65	233	37	153	45	686	41	246	54	815	32	156	71	92	47	277	34	270	39	373	32	267	41	240	49	56	55	211	55	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	515	52	328	45	76	81	0	.	258	39	0	.	19	100	73	60	90	52	174	34	149	61	34	100	17	100	0	.	7	100	0	.	93	65	705	33	98	67	187	57	98	39	530	39	139	43	396	63	225	58	122	62	0	.	19379	5
	ash	238	34	362	46	67	54	134	63	0	.	371	52	325	49	551	29	544	27	302	49	309	69	65	90	108	61	0	.	64	71	307	57	657	40	208	58	52	100	648	54	34	71	42	87	544	72	239	53	0	.	1028	31	456	71	255	45	295	58	141	83	178	79	17	100	0	.	0	.	484	53	0	.	77	64	586	35	477	45	103	50	261	58	552	43	226	57	24	77	103	53	257	60	204	74	462	57	17	100	132	61	362	44	277	42	256	45	113	100	134	100	0	.	36	71	107	64	356	54	85	72	85	60	176	72	157	72	74	61	0	.	133	57	129	63	107	87	230	63	75	80	117	67	17	100	0	.	39	100	77	71	335	81	217	61	57	58	176	53	0	.	436	67	212	50	196	75	303	38	124	62	158	39	250	68	401	42	133	48	124	78	197	54	91	57	0	.	19055	7
	oak	301	61	48	63	35	100	93	47	0	.	0	.	58	73	51	87	608	29	0	.	42	84	29	100	267	62	0	.	15	71	14	100	126	59	98	71	1137	52	0	.	0	.	28	100	0	.	18	100	55	100	274	46	96	54	117	79	118	100	253	91	0	.	21	100	0	.	0	.	22	100	0	.	0	.	27	100	73	85	0	.	43	71	17	100	243	38	0	.	68	70	24	76	334	71	6	100	0	.	0	.	154	49	329	50	191	39	0	.	0	.	0	.	180	54	220	51	907	34	494	88	147	45	182	65	26	76	0	.	26	100	54	74	88	100	0	.	123	85	0	.	76	91	126	61	160	62	0	.	923	53	7	100	134	48	690	54	86	100	0	.	7	100	157	58	265	64	198	35	0	.	7	100	7	100	31	79	86	55	67	52	84	57	44	76	0	.	11035	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	.	894	69	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	.	131	100	411	41	438	65	1558	45	1382	32	2688	28	283	54	549	57	3235	30	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	.	11804	13
	other broadleaves	270	48	510	34	246	54	161	78	30	71	129	64	1218	27	536	30	1064	38	356	33	382	47	92	51	180	47	0	.	220	58	290	38	275	41	303	52	34	71	561	42	382	41	347	57	1083	35	2643	29	1189	46	1146	29	208	35	80	72	418	36	9	100	240	50	119	100	177	83	128	60	125	65	195	81	106	47	0	0	405	45	498	41	493	59	348	44	94	56	93	67	120	62	574	48	127	47	273	42	102	41	473	80	418	56	165	37	183	58	671	58	512	39	224	57	1099	32	1803	32	3292	27	1335	31	605	47	1042	34	276	45	257	100	181	62	418	48	377	40	156	54	88	51	224	72	85	58	20	100	17	100	115	52	330	66	115	50	272	69	306	66	144	58	39	100	31	81	1426	30	741	35	1611	27	84	66	201	54	83	67	196	47	17	100	260	53	202	73	131	46	0	.	38805	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	4705	19	4042	25	2091	31	4810	18	2523	30	4851	24	23840	10	17512	10	19954	10	14758	13	1272	39	2243	34	3450	26	171	.	2385	34	5882	21	7804	19	5508	21	1481	47	6839	20	8151	15	6569	18	7162	16	18053	11	17026	11	14539	12	5444	20	7670	17	10357	16	1909	34	3353	24	1758	30	1687	35	3719	23	3844	30	3278	29	5486	22	3500	24	4254	27	5587	21	3657	28	6896	22	6183	19	2722	25	3366	25	3067	26	3619	24	3300	24	4943	25	2780	25	3838	25	3173	28	4466	25	1719	32	4300	20	3105	23	4442	25	5375	22	8826	18	5852	20	4462	25	6448	20	3142	28	1509	40	3666	28	2678	26	2428	29	3142	35	1927	34	1010	46	4286	23	1190	41	1227	32	3501	23	2916	27	3640	27	2712	26	2204	34	2014	37	516	55	1673	40	15230	12	11818	14	13813	13	2178	33	1689	30	2539	26	5273	23	4184	22	2522	27	1847	31	1876	30	0	.	474351	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	22	58	0	0	20	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	7	100	7	100	11	100	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	.	15	71	7	100	14	71	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	400	14
	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	0	0	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	.	163	24
	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	22	58	0	0	20	73	0	.	0	.	0	.	0	.	0	.	7	100	0	.	23	58	9	100	16	100	7	100	21	74	52	81	8	100	0	.	8	100	24	75	57	58	0	0	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	.	58	39	29	61	21	58	0	.	7	100	0	.	8	100	0	.	14	71	7	100	0	.	0	.	1014	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	992	34	1525	45	801	49	1319	27	1771	31	2677	30	16151	11	6482	14	7002	17	3936	20	110	75	455	77	490	51	17	.	1281	43	3781	26	3485	25	2675	27	20	100	3389	29	5498	17	4039	21	4832	18	10454	14	7578	15	3880	25	2013	29	2901	24	5809	20	537	78	1489	37	520	38	419	43	1442	29	1857	45	2128	31	2392	32	1403	37	1491	40	2250	28	987	36	2638	30	2043	29	810	41	783	51	745	52	224	41	1283	26	3507	28	942	31	1529	39	1531	38	2112	42	724	39	2122	26	1576	31	0	.	15	71	8	100	300	64	141	63	290	49	740	40	1178	45	2281	34	576	42	834	34	1152	45	990	41	404	66	1251	33	642	50	259	60	2500	24	667	48	1127	50	1112	36	327	73	516	58	82	59	805	53	4523	18	6297	20	5936	18	1171	46	605	41	1220	35	2115	37	2095	33	928	41	577	47	953	36	0	.	189464	3
	fir	461	57	86	61	63	90	723	32	331	42	778	34	1390	25	5772	13	3657	18	2878	19	17	100	111	60	409	42	0	.	255	67	187	53	1143	35	676	52	0	.	252	48	277	49	218	37	135	56	490	40	0	.	3269	20	1257	30	2698	21	1503	25	546	47	609	32	534	41	487	40	1470	37	224	42	398	54	641	39	485	32	50	73	509	34	337	51	632	29	143	51	355	40	381	52	184	55	548	35	342	52	757	36	433	43	562	44	293	45	362	70	0	0	166	54	195	100	0	.	0	.	8	100	198	77	178	63	159	72	823	36	0	.	477	54	409	41	248	49	814	40	74	71	41	100	900	42	263	65	301	43	198	45	241	50	573	32	338	49	192	55	707	54	115	59	106	61	302	70	383	56	538	33	432	41	46	51	242	43	710	29	285	41	66	78	0	.	53	88	0	.	51100	4
	pine	147	60	245	46	47	54	47	65	0	.	8	100	323	49	46	53	340	33	600	45	30	100	203	72	151	47	0	.	7	100	106	74	0	.	239	62	238	87	0	.	478	48	427	35	30	100	1004	36	2768	26	364	42	0	.	205	66	0	.	59	100	104	100	0	.	0	.	75	100	0	.	403	100	45	100	7	100	0	.	0	.	0	.	17	100	531	48	67	65	95	50	171	80	178	56	87	100	21	77	0	.	54	47	84	62	197	50	6	100	88	71	149	100	0	.	0	.	0	.	0	.	0	.	18	100	0	.	0	.	344	62	8	100	0	.	0	.	0	.	41	85	7	100	0	.	27	100	0	.	24	77	0	.	55	71	0	.	0	.	86	100	0	.	3093	32	1143	32	1382	34	0	.	55	100	211	42	54	58	195	41	168	57	136	62	47	55	0	.	17590	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	321	49	368	40	349	66	1251	28	3171	15	7	100	14	100	17	100	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	7	100	101	100	270	54	10	100	0	.	104	62	0	.	0	.	0	.	0	.	0	.	0	.	15	100	50	100	76	100	0	.	79	47	603	39	489	43	231	56	0	.	29	61	1284	30	437	47	316	69	0	.	0	.	7	100	167	46	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	100	0	.	49	77	0	.	0	.	0	.	0	.	4548	18	2191	22	857	28	14	100	7	100	17	100	0	.	0	.	64	71	7	100	78	61	0	.	18391	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	15	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	199	49	2128	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	.	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	.	709	28	484	41	197	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3755	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	.	122	65	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	69	61	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	108	58	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	.	2182	19
	beech	1659	21	960	27	665	36	2088	25	347	44	541	34	3200	24	3594	17	6050	13	5552	17	337	42	1059	35	1648	31	0	.	412	49	1060	32	1674	39	1157	34	0	.	1392	28	880	40	1040	33	49	58	757	45	0	.	3887	18	1192	27	1210	29	1869	26	321	45	534	34	472	42	353	41	554	39	880	52	54	66	1847	34	704	41	1492	41	1246	32	1260	37	1896	30	2608	24	1247	32	1522	28	773	34	1630	28	587	34	298	48	734	31	497	32	296	37	945	27	0	.	184	72	0	.	1304	45	1752	35	1460	35	1887	37	2260	35	1031	42	800	39	0	.	306	65	486	46	235	54	594	49	346	44	103	100	1483	30	123	78	426	44	576	34	514	36	1309	35	347	48	598	45	368	39	194	59	281	62	0	.	36	71	1990	31	254	48	424	55	387	47	1023	30	1217	29	456	39	419	36	356	36	0	.	92563	3
	maple	578	30	235	41	47	58	155	52	43	73	329	49	1068	24	531	27	329	32	1147	24	52	74	237	42	174	41	0	.	116	81	67	56	443	33	107	58	0	.	608	35	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	253	34	101	92	360	65	233	37	153	45	686	41	246	54	815	32	156	71	92	47	277	34	270	39	373	32	267	41	240	49	56	55	211	55	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	515	52	328	45	76	81	0	.	258	39	0	.	19	100	73	60	90	52	174	34	149	61	34	100	17	100	0	.	7	100	0	.	93	65	705	33	98	67	187	57	98	39	530	39	139	43	396	63	225	58	122	62	0	.	19379	5
	ash	238	34	362	46	67	54	134	63	0	.	371	52	325	49	562	29	544	27	302	49	309	69	65	90	108	61	0	.	64	71	307	57	657	40	208	58	52	100	648	54	34	71	42	87	544	72	239	53	0	.	1028	31	456	71	255	45	295	58	141	83	178	79	17	100	0	.	0	.	484	53	0	.	77	64	586	35	477	45	103	50	261	58	552	43	226	57	24	77	103	53	257	60	204	74	462	57	17	100	132	61	362	44	277	42	256	45	113	100	134	100	0	.	36	71	107	64	356	54	85	72	85	60	176	72	157	72	74	61	0	.	133	57	129	63	107	87	230	63	75	80	117	67	17	100	0	.	39	100	77	71	335	81	217	61	57	58	183	53	0	.	436	67	212	50	196	75	303	38	124	62	158	39	250	68	401	42	133	48	124	78	197	54	91	57	0	.	19072	7
	oak	301	61	48	63	35	100	93	47	0	.	0	.	58	73	51	87	608	29	0	.	42	84	29	100	267	62	0	.	15	71	14	100	126	59	98	71	1137	52	0	.	0	.	28	100	0	.	18	100	55	100	274	46	96	54	117	79	118	100	253	91	0	.	21	100	0	.	0	.	22	100	0	.	0	.	27	100	73	85	0	.	43	71	17	100	243	38	0	.	68	70	24	76	334	71	6	100	0	.	0	.	154	49	344	50	191	39	0	.	0	.	0	.	180	54	220	51	907	34	494	88	147	45	182	65	26	76	0	.	26	100	54	74	88	100	0	.	123	85	0	.	76	91	126	61	160	62	0	.	923	53	7	100	134	48	690	54	86	100	0	.	7	100	157	58	265	64	198	35	0	.	14	71	7	100	31	79	86	55	74	48	91	55	44	76	0	.	11071	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	.	894	69	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	.	131	100	452	41	446	65	1558	45	1390	32	2712	28	319	51	549	57	3283	30	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	.	11969	13
	other broadleaves	270	48	510	34	246	54	161	78	30	71	129	64	1218	27	536	30	1080	38	356	33	382	47	92	51	180	47	0	.	220	58	290	38	275	41	303	52	34	71	561	42	382	41	347	57	1083	35	2643	29	1189	46	1146	29	208	35	80	72	418	36	9	100	240	50	119	100	177	83	128	60	125	65	195	81	106	47	0	0	405	45	498	41	493	59	348	44	94	56	93	67	120	62	574	48	127	47	273	42	102	41	473	80	427	56	165	37	183	58	671	58	512	39	224	57	1099	32	1803	32	3292	27	1335	31	605	47	1042	34	276	45	257	100	181	62	418	48	377	40	156	54	88	51	224	72	85	58	20	100	17	100	115	52	330	66	115	50	272	69	306	66	144	58	39	100	31	81	1426	30	741	35	1611	27	84	66	201	54	83	67	196	47	17	100	260	53	202	73	131	46	0	.	38829	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	4705	19	4049	25	2091	31	4824	18	2523	30	4851	24	23876	10	17596	10	20014	10	14773	13	1280	39	2250	34	3450	26	171	.	2385	34	5895	21	7804	19	5531	21	1481	47	6876	20	8158	15	6577	18	7203	16	18092	11	17054	11	14553	12	5465	20	7670	17	10357	16	1909	34	3353	24	1765	30	1687	35	3727	23	3844	30	3278	29	5486	22	3509	24	4260	26	5608	21	3657	28	6915	22	6183	19	2722	25	3366	25	3067	26	3619	24	3307	24	4943	25	2803	25	3846	25	3188	28	4473	25	1740	32	4352	19	3113	23	4442	25	5383	22	8849	18	5909	20	4462	25	6497	20	3142	28	1516	40	3673	28	2695	26	2442	29	3151	35	1927	34	1010	46	4286	23	1190	41	1227	32	3501	23	2916	27	3640	27	2712	26	2204	34	2020	37	516	55	1673	40	15288	12	11847	14	13834	13	2178	33	1695	30	2539	26	5281	22	4184	22	2535	27	1853	31	1876	30	0	.	475365	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. 2297826. Birmensdorf, Swiss Federal Research Institute WSL

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