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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NFI4
number of stems
giant (yes/no) · main tree species
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 2009/17
		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	543	39	1465	27	1021	30	677	23	1664	24	1995	22	15841	8	4770	11	4547	14	2728	16	79	53	425	52	234	33	0	.	489	36	2817	22	2916	19	1758	25	4	100	3211	17	6768	12	4476	14	9538	13	12938	9	8103	10	2758	17	1573	23	1738	21	3582	15	489	44	1295	25	423	34	516	34	1619	32	1552	34	2625	21	3288	21	1435	24	1763	30	3057	18	939	26	2075	18	1441	26	678	27	619	36	330	42	308	35	1861	21	2391	18	1054	23	1082	26	807	32	1261	39	1046	25	2547	19	2041	22	57	100	68	88	4	100	651	30	158	48	702	38	919	30	1206	30	1694	25	906	27	1280	24	2347	23	755	34	185	50	1634	27	250	50	219	36	2505	17	1107	35	944	35	841	26	473	53	428	38	52	45	384	38	5954	11	6021	14	6972	11	1124	29	1012	40	650	27	1050	30	1223	27	493	36	688	39	792	31	0	.	182949	2
	fir	478	43	72	64	258	39	734	39	350	30	716	30	1404	18	5142	10	3448	14	2901	15	70	63	155	43	512	29	0	.	240	43	176	45	1214	28	641	27	0	.	353	41	205	42	394	26	124	47	434	27	93	100	3848	12	1324	23	2115	20	1615	20	304	36	861	24	391	32	365	33	1244	24	224	32	233	47	621	32	482	27	224	39	440	29	453	32	899	23	102	49	683	38	392	37	79	42	632	29	723	32	575	27	504	33	479	35	395	44	280	81	16	80	257	39	108	96	0	.	0	.	4	100	303	42	425	50	224	56	620	33	42	77	334	41	503	31	137	51	1024	28	211	32	66	76	543	27	294	42	578	31	442	32	445	33	501	24	302	33	223	70	158	44	246	49	92	48	385	46	308	37	1189	23	642	31	93	46	196	43	421	24	370	32	31	59	11	100	105	57	0	.	52447	3
	pine	111	51	57	55	88	59	70	40	18	71	5	100	542	35	106	80	253	58	342	44	69	54	85	42	146	35	0	.	14	77	42	71	0	.	84	48	64	80	0	.	613	34	465	32	146	92	1089	27	2988	19	444	43	4	100	66	60	155	83	51	76	33	87	26	100	0	.	28	100	0	.	318	91	15	100	37	60	0	.	325	56	0	.	38	59	245	40	40	56	138	42	56	81	210	51	261	48	4	100	21	100	44	41	36	59	160	46	0	.	152	75	96	92	0	.	0	.	93	100	0	.	0	.	33	72	68	86	18	100	207	50	175	100	0	.	0	.	0	.	14	77	4	100	8	100	16	80	0	.	45	60	0	.	25	58	0	.	0	.	63	100	0	.	2612	23	1056	25	962	28	0	.	35	80	102	41	28	55	175	33	86	43	103	43	27	62	0	.	16359	7
	larch	74	58	91	65	52	74	32	56	15	77	24	100	119	37	4	100	101	43	34	100	0	.	0	.	8	71	0	.	74	100	38	80	0	.	4	100	0	.	8	71	276	31	699	39	309	40	2465	16	4888	11	19	65	39	71	14	77	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	59	89	4	100	89	95	366	36	5	100	4	100	50	56	20	100	12	100	12	75	0	.	0	.	10	100	14	72	31	88	54	72	10	100	485	33	1099	26	775	32	530	36	30	62	25	71	2000	18	546	32	345	43	0	.	0	.	0	.	214	50	18	81	59	77	21	72	92	73	0	.	13	75	0	.	0	.	24	72	0	.	45	58	0	.	4	100	0	.	200	91	5881	11	2656	15	1505	17	8	100	4	100	43	61	27	61	0	.	35	54	8	71	32	56	0	.	26855	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	26	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	83	4	100	20	100	352	30	2804	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	100	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	38	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1004	21	670	25	485	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5668	10
	other conifers	54	53	13	58	12	100	96	47	30	100	21	71	177	69	0	.	229	29	148	87	36	100	0	.	0	.	42	.	4	100	0	.	0	.	0	.	0	.	10	100	100	91	0	.	0	.	122	54	639	31	50	53	79	78	33	68	9	100	0	.	0	.	9	100	0	.	0	.	21	100	0	.	10	100	86	50	22	71	38	78	72	72	152	100	21	100	20	100	99	91	79	68	0	.	10	100	0	.	10	100	20	100	0	.	120	84	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	10	100	0	.	0	.	155	59	0	.	10	100	13	100	0	.	50	100	173	57	4	100	40	79	0	.	0	.	0	.	36	61	11	100	35	76	0	.	0	.	30	75	41	100	51	66	21	100	10	100	99	49	147	76	28	75	0	.	0	.	0	.	0	.	3664	12
	beech	1472	16	1004	23	581	25	1931	22	290	35	729	27	3534	19	3336	12	3937	10	4529	13	254	41	895	24	1832	21	0	.	566	31	771	27	1314	27	1077	26	0	.	1394	22	795	27	943	24	91	66	937	32	0	.	4225	13	1252	23	937	27	1471	23	433	28	586	32	357	26	368	30	535	27	666	34	166	46	1212	26	594	25	922	31	1391	24	1430	25	1611	21	2138	17	1195	22	1285	23	872	30	1534	20	1033	22	318	44	687	29	645	24	213	37	1032	23	0	.	362	37	0	.	1784	26	1536	27	1355	26	2980	20	2597	26	1551	29	587	36	4	100	182	62	553	34	184	43	562	33	478	29	131	75	1451	28	283	39	256	36	636	29	417	29	1058	24	728	26	360	36	215	42	167	46	274	40	80	100	211	60	1830	23	366	33	491	31	477	26	821	25	807	21	476	31	348	27	425	26	0	.	88343	3
	maple	587	31	250	27	495	30	198	34	396	68	301	51	1604	17	589	19	822	24	1100	19	152	46	271	28	303	30	0	.	115	53	121	40	559	27	226	41	39	79	807	24	159	34	261	52	301	46	338	38	15	74	1126	19	332	27	166	35	350	25	282	59	216	32	233	52	149	65	68	41	255	28	86	42	591	43	280	29	244	44	492	31	337	34	491	24	191	41	223	33	341	30	100	38	381	28	548	31	219	33	261	42	481	36	236	40	140	39	178	71	289	56	10	100	165	50	97	51	348	34	235	39	98	56	23	61	261	40	23	67	40	80	353	39	657	32	683	26	273	40	19	100	206	31	136	80	79	39	102	42	155	34	241	31	298	35	9	100	32	67	4	100	72	50	151	49	275	40	950	19	110	44	184	47	219	41	305	29	190	31	358	46	155	53	331	62	0	.	27142	4
	ash	602	28	292	33	274	39	81	53	76	46	271	40	521	31	424	24	729	20	324	47	192	40	116	43	166	39	0	.	134	55	258	52	395	33	542	32	48	100	419	32	137	45	70	55	522	40	415	29	211	90	1020	19	276	42	326	35	316	34	75	59	62	78	14	77	20	71	25	71	274	57	67	47	135	35	544	30	409	42	178	39	290	41	320	33	273	52	86	40	101	41	190	42	180	42	513	36	232	80	116	50	355	31	228	33	223	35	113	50	195	100	189	54	180	41	281	38	543	29	708	30	56	52	233	45	147	55	129	55	21	100	120	42	228	41	318	44	269	44	137	43	53	50	149	40	11	100	21	100	48	67	137	62	235	39	46	47	121	54	0	.	209	44	505	41	295	44	373	25	177	38	146	33	268	46	471	34	365	35	129	49	159	58	82	43	0	.	21936	4
	oak	217	39	143	56	27	54	284	32	0	.	4	100	76	47	18	82	386	22	35	74	37	59	168	79	117	50	0	.	14	79	9	100	56	54	64	44	336	42	0	.	4	100	95	71	4	100	162	47	34	100	144	35	46	54	66	62	19	70	276	64	0	.	11	100	20	100	0	.	11	100	0	.	5	100	81	78	15	75	15	76	28	61	0	.	220	28	57	93	92	47	85	61	215	61	14	78	0	.	6	100	70	47	92	42	82	40	0	.	21	100	0	.	146	42	291	32	560	31	346	64	398	39	157	48	19	71	4	100	22	100	112	81	220	72	0	.	69	83	0	.	110	77	91	48	535	48	0	.	453	51	27	87	126	79	335	52	66	89	7	100	59	52	238	44	401	46	604	36	10	71	8	71	16	50	18	81	102	71	71	47	62	45	62	45	0	.	9727	8
	sweet chestnut	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	673	42	25	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	100	732	34	457	46	1578	34	1014	29	2127	22	678	30	797	31	2300	28	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	89	0	.	0	.	0	.	0	.	30	100	0	.	195	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10778	10
	other broadleaves	824	49	566	28	269	31	280	32	47	60	187	46	1814	18	653	23	1068	19	622	21	149	38	149	44	299	31	0	.	194	50	185	47	192	34	352	63	189	47	460	30	522	29	343	37	2259	24	2586	21	1261	33	1063	20	266	35	94	46	266	37	87	51	115	46	48	73	95	52	107	51	203	36	175	40	333	44	123	46	209	37	611	31	377	46	312	32	175	40	322	54	149	43	297	47	117	49	431	36	269	35	406	57	334	50	393	34	242	41	1077	39	1060	23	779	31	1360	25	2068	22	3497	18	2077	22	1105	21	1589	19	155	45	495	62	265	44	453	39	361	34	669	42	267	53	173	53	279	41	195	41	49	52	106	55	193	53	144	39	132	57	171	57	69	49	11	100	58	49	1652	19	1762	21	2382	17	31	58	159	46	248	39	172	44	109	43	145	38	160	41	156	37	0	.	48920	4
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	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	4960	16	3953	17	3080	17	4384	16	2886	21	4253	17	25659	7	15042	8	15530	8	12763	10	1037	26	2264	21	3615	17	42	.	1845	22	4416	18	6647	14	4748	17	681	36	6662	13	9620	11	7752	12	13315	11	22511	7	21059	7	14697	8	5190	16	5554	15	7784	12	1998	22	3167	18	1512	25	1536	24	3626	19	3205	22	3670	20	6364	16	3665	17	3946	21	6912	14	3931	19	5902	16	4855	15	3324	18	3229	19	2099	22	3577	17	5394	16	4018	16	3080	19	3543	18	2453	21	3551	20	2995	21	6715	14	4493	15	5802	15	5385	15	8557	13	9987	11	6180	16	7167	14	2775	23	1922	27	2994	20	3388	18	3099	21	5674	17	2343	22	868	35	4452	19	1423	26	1782	27	3813	16	2887	21	3052	20	2854	18	1628	31	1128	29	551	42	1348	28	18522	8	13696	9	17497	8	2489	21	2141	24	2320	19	3460	19	3370	16	1823	22	1695	23	2012	22	0	.	494789	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	.	4	100	12	58	0	.	0	.	74	28	44	30	20	45	21	67	4	100	4	100	0	.	0	.	4	100	4	100	13	58	4	100	0	.	40	37	32	39	51	50	67	28	75	27	15	50	16	61	4	100	0	.	5	100	0	.	0	.	0	.	0	.	8	100	4	100	4	100	29	53	0	.	5	100	20	45	5	100	50	54	0	.	0	.	0	.	0	.	0	.	8	71	4	100	12	76	0	.	4	100	8	100	16	60	44	39	24	47	0	.	0	.	0	.	4	100	0	.	8	71	0	.	13	73	4	100	16	60	35	45	13	58	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	35	50	15	61	24	41	0	.	0	.	4	100	5	100	4	100	8	71	8	100	0	.	0	.	964	8
	fir	0	.	4	100	8	71	12	58	0	.	6	100	16	61	38	37	29	44	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	4	100	0	.	15	61	0	.	12	58	0	.	16	50	0	.	16	50	0	.	0	.	4	100	4	100	8	71	4	100	0	.	12	75	0	.	0	.	0	.	0	.	7	100	0	.	4	100	13	58	0	.	4	100	4	100	0	.	0	.	4	100	0	.	8	71	0	.	0	.	8	100	0	.	4	100	0	.	0	.	0	.	0	.	6	100	4	100	4	100	0	.	0	.	4	100	5	100	0	0	4	100	4	100	0	.	7	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	4	100	4	100	4	100	0	.	4	100	4	100	0	.	0	.	4	100	9	71	0	.	0	.	0	.	0	.	0	.	347	12
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	8	71
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	72	4	100	16	50	63	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	4	100	4	100	6	100	0	.	0	.	26	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	30	34	36	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	245	15
	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	.	8	71	20	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	50	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	30
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	50
	beech	4	100	4	100	4	100	4	100	0	.	0	.	12	58	4	100	4	100	0	.	0	0	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	9	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	73	0	.	0	.	5	100	0	.	0	.	12	58	0	.	4	100	4	100	0	.	0	.	8	71	0	.	4	100	0	.	0	.	4	100	0	.	15	78	4	100	0	.	0	.	0	.	4	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	16	61	15	100	0	.	6	100	0	.	4	100	0	.	0	.	180	18
	maple	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	71	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	43
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	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	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	45
	oak	4	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	6	100	6	100	0	.	0	.	0	.	4	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	9	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	8	71	4	100	0	.	88	24
	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	.	37	55	4	100	0	.	38	89	25	52	29	54	0	.	20	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	158	29
	other broadleaves	10	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	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	.	22	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	81	39
	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	18	51	12	58	16	50	28	43	0	.	6	100	111	24	92	22	80	29	25	59	4	100	16	59	6	100	0	.	8	71	4	100	25	41	13	58	0	.	62	29	32	39	77	36	70	27	136	19	99	26	45	35	4	100	0	.	10	71	4	100	8	71	4	100	10	100	20	83	7	100	4	100	29	53	11	73	16	59	20	45	14	58	63	46	7	71	20	45	4	100	4	100	4	100	12	58	4	100	34	40	4	100	17	71	42	60	24	52	93	31	32	39	20	63	42	81	25	52	65	33	4	100	37	42	0	.	13	73	12	58	21	52	35	45	17	50	12	58	0	.	7	100	17	61	0	.	4	100	4	100	0	.	4	100	0	.	12	58	4	100	4	100	108	24	57	28	52	30	0	.	20	53	23	70	17	50	10	72	12	58	20	53	4	100	0	.	2190	5
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	543	39	1465	27	1025	30	689	23	1664	24	1995	22	15915	8	4813	11	4567	13	2749	16	83	51	429	52	234	33	0	.	493	36	2821	22	2929	19	1762	25	4	100	3251	17	6800	12	4527	14	9605	13	13013	9	8118	10	2774	17	1577	23	1738	21	3588	15	489	44	1295	25	423	34	516	34	1627	32	1555	34	2630	21	3317	21	1435	24	1767	30	3077	18	944	26	2125	17	1441	26	678	27	619	36	330	42	308	35	1869	21	2395	18	1066	23	1082	26	811	32	1269	39	1063	25	2591	19	2065	22	57	100	68	88	4	100	655	30	158	48	710	38	919	30	1219	29	1698	25	922	27	1315	23	2360	23	763	34	185	50	1634	27	250	50	219	36	2505	17	1107	35	944	35	841	26	473	53	432	38	52	45	384	38	5989	11	6036	14	6995	11	1124	29	1012	40	654	27	1055	30	1227	27	501	36	696	38	792	31	0	.	183914	2
	fir	478	43	76	61	266	38	747	39	350	30	722	30	1420	18	5180	10	3477	14	2904	15	70	63	155	43	512	29	0	.	240	43	176	45	1218	27	645	27	0	.	368	40	205	42	406	26	124	47	451	26	93	100	3863	12	1324	23	2115	20	1620	20	309	35	870	24	395	31	365	33	1256	24	224	32	233	47	621	32	482	27	231	40	440	29	457	32	912	23	102	49	686	38	396	37	79	42	632	29	727	32	575	27	512	33	479	35	395	44	288	79	16	80	261	39	108	96	0	.	0	.	4	100	309	41	429	51	228	55	620	33	42	77	338	41	507	31	137	51	1027	28	215	32	66	76	549	27	294	42	578	31	446	32	445	33	501	24	302	33	223	70	162	43	250	48	96	47	385	46	312	37	1193	23	642	31	93	46	200	42	430	24	370	32	31	59	11	100	105	57	0	.	52794	3
	pine	111	51	57	55	88	59	70	40	18	71	5	100	542	35	106	80	253	58	342	44	69	54	85	42	146	35	0	.	14	77	42	71	0	.	84	48	64	80	0	.	613	34	469	32	146	92	1089	27	2988	19	444	43	4	100	66	60	155	83	51	76	33	87	26	100	0	.	28	100	0	.	318	91	15	100	37	60	0	.	325	56	0	.	38	59	245	40	40	56	138	42	56	81	210	51	261	48	4	100	21	100	44	41	36	59	160	46	0	.	152	75	96	92	0	.	0	.	93	100	0	.	0	.	33	72	68	86	18	100	207	50	175	100	0	.	0	.	0	.	14	77	4	100	8	100	16	80	0	.	45	60	0	.	25	58	0	.	0	.	63	100	0	.	2612	23	1056	25	962	28	0	.	35	80	102	41	31	50	175	33	86	43	103	43	27	62	0	.	16367	7
	larch	74	58	91	65	52	74	32	56	15	77	24	100	119	37	4	100	101	43	34	100	0	.	0	.	8	71	0	.	74	100	38	80	0	.	4	100	0	.	8	71	276	31	709	38	313	40	2481	16	4952	11	19	65	39	71	14	77	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	59	89	4	100	89	95	366	36	5	100	4	100	50	56	24	85	12	100	12	75	0	.	0	.	10	100	14	72	31	88	54	72	10	100	492	33	1103	26	779	32	536	36	30	62	25	71	2026	18	546	32	345	43	0	.	0	.	0	.	214	50	18	81	59	77	21	72	92	73	0	.	13	75	0	.	0	.	24	72	0	.	45	58	0	.	4	100	0	.	200	91	5939	11	2690	15	1513	17	8	100	4	100	43	61	27	61	0	.	35	54	8	71	32	56	0	.	27100	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	26	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	83	4	100	20	100	360	30	2824	16	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	100	0	.	48	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	38	79	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1019	21	674	25	489	42	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5720	10
	other conifers	54	53	13	58	12	100	96	47	30	100	21	71	177	69	0	.	233	28	148	87	36	100	0	.	0	.	42	.	8	100	0	.	0	.	0	.	0	.	10	100	100	91	0	.	0	.	122	54	639	31	50	53	79	78	33	68	9	100	0	.	0	.	9	100	0	.	0	.	21	100	0	.	10	100	86	50	22	71	38	78	72	72	152	100	24	86	20	100	99	91	79	68	0	.	10	100	0	.	10	100	20	100	0	.	120	84	0	.	0	.	0	.	0	.	0	.	0	.	9	100	0	.	10	100	0	.	0	.	155	59	0	.	10	100	13	100	0	.	50	100	173	57	4	100	40	79	0	.	0	.	0	.	40	59	11	100	35	76	0	.	0	.	30	75	41	100	51	66	21	100	10	100	99	49	147	76	28	75	0	.	0	.	0	.	0	.	3680	12
	beech	1475	16	1008	23	585	25	1935	22	290	35	729	27	3546	19	3340	12	3941	10	4529	13	254	41	895	24	1832	21	0	.	566	31	771	27	1318	26	1077	26	0	.	1394	22	795	27	943	24	91	66	945	32	0	.	4234	13	1252	23	937	27	1471	23	433	28	586	32	357	26	368	30	535	27	666	34	166	46	1212	26	604	25	922	31	1391	24	1435	25	1611	21	2138	17	1207	21	1285	23	875	30	1537	20	1033	22	318	44	695	29	645	24	217	37	1032	23	0	.	367	37	0	.	1799	26	1540	27	1355	26	2980	20	2597	26	1555	29	587	36	4	100	186	61	553	34	184	43	562	33	478	29	131	75	1451	28	283	39	256	36	636	29	417	29	1058	24	728	26	360	36	215	42	167	46	274	40	80	100	211	60	1834	23	366	33	507	31	492	26	821	25	813	21	476	31	352	27	425	26	0	.	88523	3
	maple	587	31	250	27	495	30	198	34	396	68	301	51	1612	17	589	19	828	24	1100	19	152	46	271	28	303	30	0	.	115	53	121	40	559	27	226	41	39	79	814	24	159	34	261	52	301	46	342	37	15	74	1126	19	332	27	166	35	350	25	282	59	216	32	233	52	149	65	68	41	259	28	86	42	591	43	280	29	244	44	492	31	337	34	491	24	191	41	223	33	341	30	100	38	381	28	548	31	219	33	261	42	481	36	236	40	140	39	178	71	289	56	10	100	165	50	97	51	348	34	235	39	98	56	23	61	261	40	23	67	40	80	353	39	657	32	683	26	273	40	19	100	206	31	136	80	79	39	102	42	155	34	241	31	298	35	9	100	32	67	4	100	72	50	151	49	275	40	950	19	110	44	184	47	219	41	305	29	190	31	358	46	155	53	331	62	0	.	27171	4
	ash	602	28	292	33	274	39	81	53	76	46	271	40	521	31	429	24	729	20	324	47	192	40	116	43	166	39	0	.	134	55	258	52	395	33	542	32	48	100	419	32	137	45	70	55	522	40	415	29	211	90	1020	19	276	42	326	35	316	34	75	59	62	78	14	77	20	71	25	71	274	57	67	47	135	35	544	30	409	42	178	39	290	41	320	33	273	52	86	40	101	41	190	42	180	42	513	36	232	80	116	50	359	31	228	33	223	35	113	50	195	100	189	54	180	41	281	38	543	29	708	30	56	52	233	45	147	55	129	55	21	100	120	42	228	41	318	44	269	44	137	43	53	50	153	39	11	100	21	100	48	67	137	62	235	39	46	47	125	53	0	.	209	44	505	41	295	44	373	25	177	38	150	33	268	46	471	34	365	35	129	49	159	58	82	43	0	.	21958	4
	oak	220	39	146	55	27	54	284	32	0	.	4	100	76	47	18	82	390	22	35	74	37	59	174	77	123	48	0	.	14	79	9	100	61	50	69	42	336	42	0	.	4	100	95	71	4	100	162	47	34	100	149	34	46	54	66	62	19	70	276	64	0	.	11	100	20	100	0	.	11	100	0	.	5	100	81	78	19	63	15	76	28	61	0	.	224	28	57	93	92	47	85	61	215	61	14	78	0	.	12	100	70	47	101	45	86	39	0	.	21	100	0	.	146	42	291	32	560	31	346	64	398	39	157	48	19	71	4	100	22	100	112	81	220	72	0	.	69	83	0	.	110	77	96	47	535	48	0	.	457	51	27	87	126	79	335	52	66	89	7	100	59	52	238	44	401	46	604	36	10	71	8	71	16	50	18	81	102	71	74	45	70	45	66	43	0	.	9815	8
	sweet chestnut	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	673	42	25	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	70	100	769	33	461	46	1578	34	1052	28	2153	22	707	29	797	31	2320	27	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	86	89	0	.	0	.	0	.	0	.	30	100	0	.	199	70	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10937	10
	other broadleaves	834	49	566	28	269	31	280	32	47	60	187	46	1814	18	653	23	1081	19	622	21	149	38	154	42	299	31	0	.	194	50	185	47	192	34	352	63	189	47	460	30	522	29	343	37	2259	24	2595	21	1261	33	1063	20	266	35	94	46	266	37	87	51	115	46	48	73	104	48	107	51	203	36	175	40	333	44	123	46	209	37	611	31	377	46	312	32	175	40	322	54	149	43	297	47	117	49	431	36	269	35	406	57	334	50	393	34	265	39	1077	39	1060	23	779	31	1360	25	2068	22	3497	18	2077	22	1105	21	1589	19	155	45	495	62	265	44	453	39	361	34	669	42	267	53	173	53	279	41	204	41	49	52	106	55	193	53	144	39	132	57	171	57	69	49	11	100	58	49	1652	19	1762	21	2386	17	31	58	159	46	248	39	172	44	109	43	145	38	160	41	156	37	0	.	49001	4
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	0	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	4978	16	3964	17	3096	17	4412	16	2886	21	4259	17	25770	7	15134	8	15610	8	12787	10	1041	26	2280	21	3621	17	42	.	1853	22	4420	18	6671	14	4761	17	681	36	6724	13	9652	10	7828	12	13386	11	22647	7	21158	7	14742	8	5195	16	5554	15	7793	12	2002	22	3175	18	1516	25	1546	23	3646	19	3212	22	3675	20	6393	16	3675	17	3962	21	6932	14	3945	19	5965	16	4863	15	3343	18	3233	19	2103	22	3581	17	5406	16	4022	16	3114	19	3547	18	2471	21	3594	20	3019	21	6808	13	4524	15	5822	15	5427	15	8582	13	10052	11	6184	16	7204	14	2775	23	1935	27	3006	20	3409	18	3134	20	5691	17	2354	22	868	35	4458	19	1440	26	1782	27	3817	16	2890	21	3052	20	2858	18	1628	31	1140	29	556	42	1352	28	18630	8	13753	9	17549	8	2489	21	2161	24	2343	19	3478	19	3379	16	1835	22	1715	23	2016	22	0	.	496978	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. 2297824. Birmensdorf, Swiss Federal Research Institute WSL

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