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
total number of stems
giant (yes/no) · main tree species
regional demarcation: forest district (2024)
unit: 1000 n
evaluation area: accessible forest without shrub forest
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	585	37	1534	26	1049	29	721	22	1831	23	2281	21	19116	8	5273	11	4769	13	3073	15	102	50	495	52	310	29	0	.	574	35	3172	23	3636	17	1922	24	4	100	3825	17	7452	12	4959	14	10685	12	14726	9	8747	10	3074	17	1892	22	1903	20	4570	16	528	42	1309	25	450	34	516	34	1765	30	1735	32	2977	21	3604	21	1581	23	1915	30	3635	17	1099	27	2334	18	1582	26	726	28	703	36	371	42	350	32	1966	20	2850	19	1219	23	1123	27	871	32	1329	40	1157	24	2858	19	2206	21	57	100	87	82	4	100	725	32	172	48	753	37	1003	29	1280	29	2128	24	1030	26	1416	23	2724	22	866	32	204	51	1820	26	293	48	270	35	2709	17	1229	36	1051	33	983	26	498	51	502	39	61	44	436	35	7166	11	6829	14	8249	11	1240	28	1111	40	718	27	1099	30	1257	26	536	36	707	38	871	31	0	.	207120	2
	fir	524	40	72	64	258	39	803	37	360	31	824	30	1565	18	5562	10	3714	13	3147	15	80	58	155	43	539	29	0	.	259	44	197	42	1322	26	697	27	0	.	424	40	221	40	436	26	159	48	493	27	103	91	4268	12	1360	23	2204	19	1773	19	333	36	883	24	410	31	365	33	1312	25	255	33	290	43	672	31	570	25	238	39	483	29	468	32	923	23	102	49	724	37	420	35	79	42	741	28	807	31	626	26	555	31	493	34	395	44	280	81	16	80	269	38	111	97	0	.	0	.	4	100	310	42	447	48	257	58	690	31	42	77	409	44	553	31	158	49	1105	27	271	32	66	76	626	26	309	44	622	30	467	31	500	31	584	23	312	33	250	64	158	44	260	48	96	47	445	44	324	37	1467	25	692	31	93	46	207	43	508	23	385	32	41	64	11	100	105	57	0	.	57111	3
	pine	141	47	67	58	105	56	75	40	18	71	5	100	723	32	111	77	305	50	369	43	97	63	85	42	182	34	0	.	14	77	87	74	0	.	122	51	68	76	0	.	661	33	543	32	146	92	1333	25	3866	19	558	38	4	100	66	60	195	69	59	72	42	89	54	100	0	.	52	100	0	.	329	88	46	100	37	60	0	.	387	56	0	.	47	51	255	39	40	56	167	39	82	66	291	49	337	48	4	100	32	100	44	41	36	59	170	45	0	.	209	66	109	90	0	.	0	.	102	100	0	.	0	.	37	73	78	88	18	100	212	50	190	92	0	.	0	.	0	.	31	66	4	100	8	100	20	83	0	.	45	60	0	.	25	58	0	.	0	.	63	100	0	.	3320	21	1497	24	1373	24	0	.	39	82	121	38	41	54	185	33	86	43	113	43	27	62	0	.	20412	7
	larch	74	58	100	59	52	74	32	56	15	77	24	100	149	35	4	100	111	45	63	100	0	.	0	.	8	71	0	.	85	88	42	74	0	.	8	71	0	.	8	71	320	32	742	37	342	37	2918	16	5265	11	19	65	39	71	14	77	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	70	90	4	100	89	95	420	33	5	100	4	100	50	56	20	100	12	100	12	75	0	.	0	.	10	100	14	72	31	88	54	72	10	100	565	31	1207	25	869	32	548	36	130	53	25	71	2132	18	636	33	372	43	0	.	0	.	0	.	242	47	18	81	59	77	21	72	92	73	0	.	13	75	0	.	0	.	44	75	0	.	60	54	0	.	4	100	0	.	200	91	6623	11	2959	15	1969	17	8	100	4	100	43	61	27	61	0	.	35	54	8	71	32	56	0	.	30186	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	34	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	83	4	100	20	100	362	30	3059	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	.	76	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1090	21	714	25	534	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6124	10
	other conifers	54	53	13	58	12	100	106	45	40	100	21	71	198	68	0	.	229	29	148	87	36	100	0	.	0	.	42	.	4	100	0	.	0	.	0	.	0	.	10	100	160	88	0	.	0	.	142	58	778	32	50	53	79	78	43	57	9	100	0	.	0	.	9	100	0	.	0	.	21	100	0	.	10	100	86	50	22	71	48	65	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	.	60	100	212	55	4	100	40	79	0	.	0	.	0	.	36	61	11	100	35	76	0	.	0	.	30	75	51	100	61	62	21	100	10	100	99	49	147	76	28	75	0	.	0	.	0	.	0	.	4015	12
	beech	1522	16	1041	22	610	24	2011	22	299	34	765	26	3711	19	3531	12	4100	10	4708	13	261	40	980	24	1937	21	0	.	587	30	799	27	1328	26	1135	26	0	.	1517	22	874	27	1021	24	91	66	956	32	0	.	4372	13	1346	24	959	27	1648	22	443	28	607	32	367	26	368	30	576	26	728	34	176	48	1345	27	598	25	1048	31	1524	24	1542	25	1723	21	2200	17	1203	22	1375	23	935	29	1611	20	1123	22	340	41	711	28	658	24	213	37	1052	23	0	.	367	37	0	.	1855	26	1599	27	1430	26	3095	20	2655	26	1667	29	606	36	4	100	182	62	633	35	194	42	595	34	504	28	145	75	1527	27	297	39	256	36	646	29	461	28	1110	24	750	27	386	34	215	42	167	46	318	40	80	100	231	63	1962	22	379	33	495	31	491	26	853	24	860	20	506	30	352	27	463	26	0	.	92910	3
	maple	600	31	260	26	495	30	221	32	396	68	333	49	1665	16	589	19	906	23	1109	19	152	46	284	27	313	29	0	.	119	52	141	45	570	26	244	39	39	79	860	24	159	34	261	52	311	46	338	38	15	74	1207	19	353	28	166	35	371	24	292	60	216	32	252	52	149	65	68	41	266	29	86	42	591	43	280	29	258	43	515	31	347	34	514	25	229	42	223	33	341	30	100	38	438	29	567	30	219	33	282	41	481	36	236	40	140	39	190	71	299	57	10	100	165	50	108	51	369	33	245	38	98	56	23	61	261	40	23	67	40	80	363	38	667	32	721	25	273	40	19	100	206	31	136	80	79	39	102	42	155	34	250	30	323	36	9	100	42	60	4	100	83	52	151	49	275	40	1015	20	110	44	184	47	247	41	349	29	204	31	368	47	155	53	331	62	0	.	28218	4
	ash	651	26	321	34	274	39	91	51	94	51	315	37	573	31	448	25	765	20	345	45	206	39	127	44	166	39	0	.	134	55	258	52	405	33	552	32	48	100	442	31	137	45	70	55	574	41	448	29	211	90	1102	20	286	42	381	33	359	34	85	60	62	78	14	77	20	71	25	71	295	55	102	51	135	35	613	30	421	41	206	40	300	41	332	34	273	52	86	40	101	41	210	43	209	42	561	35	232	80	116	50	366	31	232	32	233	34	170	52	195	100	189	54	200	41	292	37	652	28	712	30	67	47	255	46	147	55	129	55	21	100	140	44	248	44	337	43	281	43	137	43	53	50	169	41	11	100	21	100	58	58	164	59	245	38	46	47	131	53	0	.	209	44	524	41	315	43	432	26	191	38	146	33	278	44	521	33	380	34	148	46	159	58	86	42	0	.	23470	4
	oak	238	36	164	61	27	54	313	33	0	.	4	100	76	47	18	82	406	23	35	74	46	51	208	82	145	52	0	.	28	59	9	100	67	48	93	48	389	41	0	.	4	100	105	73	4	100	172	45	34	100	168	38	50	51	74	58	19	70	306	63	0	.	11	100	20	100	0	.	11	100	0	.	5	100	81	78	36	66	15	76	28	61	0	.	249	28	57	93	112	46	105	60	226	63	14	78	0	.	6	100	70	47	92	42	86	39	0	.	21	100	0	.	166	41	332	31	711	30	425	67	450	42	167	46	19	71	4	100	22	100	143	78	230	71	0	.	69	83	0	.	110	77	102	47	600	47	0	.	522	50	27	87	145	70	357	52	70	84	7	100	78	59	238	44	410	46	627	34	10	71	8	71	24	47	18	81	138	78	71	47	62	45	66	43	0	.	10876	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	.	979	39	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	.	98	100	971	34	553	42	1875	33	1459	28	3479	21	792	30	1217	34	2961	26	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	83	0	.	0	.	0	.	0	.	50	100	0	.	243	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	14834	9
	other broadleaves	888	47	599	27	282	30	303	33	91	52	196	45	2338	17	700	22	1141	19	704	21	169	36	160	41	356	29	0	.	258	43	204	43	229	29	454	52	229	46	584	27	677	28	427	36	2703	23	3123	19	1373	32	1308	19	321	31	116	40	546	36	97	51	145	48	48	73	106	53	126	45	222	36	296	46	397	45	123	46	268	33	752	33	429	42	348	30	188	38	332	53	149	43	310	46	145	42	523	32	290	35	541	52	382	45	456	35	242	41	1316	36	1188	23	1044	30	1586	24	2336	21	3959	18	2740	20	1235	21	1838	19	177	42	554	61	318	42	582	35	510	30	773	39	286	50	250	51	314	37	215	43	67	42	127	49	193	53	155	37	152	58	181	55	93	49	21	100	68	49	2044	19	2068	22	2885	17	52	53	178	46	301	35	221	40	119	42	154	37	174	44	165	36	0	.	58041	4
	indeterminable	40	50	0	.	0	.	0	.	0	.	23	61	177	27	20	71	37	44	60	47	21	100	0	.	4	100	0	.	21	100	9	100	0	.	11	100	0	.	19	71	16	50	28	54	105	46	87	38	64	51	30	58	10	100	21	100	37	64	10	100	0	.	0	.	0	.	11	100	0	.	13	75	12	100	4	100	21	70	41	65	0	.	10	100	0	.	10	100	39	71	0	.	30	74	17	64	0	.	0	.	0	.	10	100	0	.	4	100	0	.	0	.	9	100	20	100	51	73	35	53	41	62	10	100	8	100	0	.	36	59	68	39	20	71	30	58	9	100	0	.	4	100	0	.	10	100	0	.	0	.	0	.	0	.	9	100	9	100	0	.	4	100	87	34	50	41	71	37	10	100	0	.	0	.	0	.	4	100	9	100	0	.	9	100	0	.	1681	9
	total	5317	15	4172	17	3168	17	4675	16	3144	21	4792	17	30325	7	16257	8	16492	8	13763	10	1169	25	2494	22	3959	17	42	.	2084	22	4917	18	7558	14	5239	16	778	35	7687	13	10721	10	8595	12	15140	10	26078	7	23539	7	16156	8	5740	16	5947	14	9526	12	2152	22	3263	18	1617	25	1548	24	3933	19	3532	21	4270	19	6982	16	3976	17	4365	20	8026	14	4291	18	6387	16	5149	15	3442	18	3518	19	2284	22	4041	17	5925	15	4571	16	3486	19	3669	18	2594	21	3662	20	3526	20	7583	13	5130	15	6462	15	6363	15	10789	13	11220	11	7016	16	8350	14	2989	23	2055	27	3601	20	3943	18	3476	20	6356	17	2579	22	1005	35	4876	18	1544	26	1974	27	4072	16	3206	21	3341	19	3144	18	1746	30	1258	30	585	41	1492	27	21848	7	15723	9	20888	8	2713	21	2267	24	2529	18	3785	18	3560	16	1953	22	1742	23	2156	21	0	.	555000	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	.	78	27	52	28	20	45	21	67	4	100	4	100	0	.	0	.	4	100	4	100	13	58	4	100	0	.	43	35	32	39	51	50	67	28	79	26	15	50	16	61	4	100	0	.	5	100	0	.	0	.	0	.	0	.	8	100	4	100	8	71	29	53	0	.	5	100	24	41	9	72	50	54	0	.	0	.	0	.	0	.	0	.	8	71	4	100	12	76	0	.	4	100	8	100	16	60	48	37	24	47	0	.	0	.	0	.	8	71	0	.	12	74	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	.	43	45	19	52	24	41	0	.	0	.	4	100	5	100	4	100	8	71	8	100	0	.	0	.	1020	8
	fir	0	.	4	100	8	71	12	58	0	.	6	100	16	61	38	37	34	40	8	71	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	13	76	4	100	0	.	0	.	4	100	5	100	4	100	4	100	8	71	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	.	372	11
	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	20	45	67	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	9	72	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	.	66	28	34	36	8	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	264	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	.	19	45	8	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	60	29
	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	8	71	0	.	4	100	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	.	188	17
	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	.	42	81	25	52	34	49	0	.	28	55	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	.	175	27
	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	115	24	100	21	88	27	28	53	8	71	16	59	6	100	0	.	8	71	4	100	25	41	13	58	0	.	66	28	32	39	77	36	70	27	144	19	103	26	45	35	4	100	0	.	10	71	4	100	8	71	4	100	10	100	20	83	7	100	8	71	29	53	11	73	16	59	24	41	18	51	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	97	30	32	39	24	55	46	74	25	52	73	30	13	76	48	39	0	.	13	73	12	58	21	52	39	42	17	50	16	50	0	.	7	100	17	61	0	.	4	100	4	100	0	.	4	100	0	.	12	58	4	100	4	100	128	22	66	29	52	30	0	.	20	53	23	70	17	50	10	72	12	58	20	53	4	100	0	.	2322	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	585	37	1534	26	1053	29	733	22	1831	23	2281	21	19194	8	5325	11	4789	13	3093	15	106	49	499	52	310	29	0	.	578	35	3176	23	3649	17	1926	24	4	100	3868	17	7484	12	5010	14	10751	12	14805	9	8762	10	3090	17	1896	22	1903	20	4575	16	528	42	1309	25	450	34	516	34	1773	30	1739	32	2985	21	3633	21	1581	23	1920	30	3658	17	1108	27	2384	18	1582	26	726	28	703	36	371	42	350	32	1974	20	2855	19	1231	23	1123	27	875	32	1337	39	1173	24	2907	19	2229	21	57	100	87	82	4	100	732	32	172	48	765	37	1003	29	1292	29	2132	24	1046	26	1451	23	2737	22	874	32	204	51	1820	26	293	48	270	35	2709	17	1229	36	1051	33	983	26	498	51	506	39	61	44	436	35	7209	11	6849	14	8273	11	1240	28	1111	40	722	26	1104	30	1261	26	543	36	716	37	871	31	0	.	208140	2
	fir	524	40	76	61	266	38	816	36	360	31	830	30	1581	18	5601	10	3748	13	3155	15	80	58	155	43	539	29	0	.	259	44	197	42	1327	26	701	27	0	.	439	39	221	40	447	26	159	48	510	26	103	91	4284	12	1360	23	2204	19	1777	19	337	36	891	24	414	30	365	33	1324	25	255	33	290	43	672	31	570	25	245	40	483	29	471	32	936	23	102	49	727	37	424	35	79	42	741	28	811	31	626	26	562	31	493	34	395	44	288	79	16	80	273	38	111	97	0	.	0	.	4	100	316	41	460	48	260	57	690	31	42	77	413	44	558	30	162	49	1108	27	279	32	66	76	632	26	309	44	622	30	471	31	500	31	584	23	312	33	250	64	162	43	264	47	100	46	445	44	328	36	1471	24	692	31	93	46	211	42	517	23	385	32	41	64	11	100	105	57	0	.	57483	3
	pine	141	47	67	58	105	56	75	40	18	71	5	100	723	32	111	77	305	50	369	43	97	63	85	42	182	34	0	.	14	77	87	74	0	.	122	51	68	76	0	.	661	33	547	32	146	92	1333	25	3866	19	558	38	4	100	66	60	195	69	59	72	42	89	54	100	0	.	52	100	0	.	329	88	46	100	37	60	0	.	387	56	0	.	47	51	255	39	40	56	167	39	82	66	291	49	337	48	4	100	32	100	44	41	36	59	170	45	0	.	209	66	109	90	0	.	0	.	102	100	0	.	0	.	37	73	78	88	18	100	212	50	190	92	0	.	0	.	0	.	31	66	4	100	8	100	20	83	0	.	45	60	0	.	25	58	0	.	0	.	63	100	0	.	3320	21	1497	24	1373	24	0	.	39	82	121	38	45	50	185	33	86	43	113	43	27	62	0	.	20420	7
	larch	74	58	100	59	52	74	32	56	15	77	24	100	149	35	4	100	111	45	63	100	0	.	0	.	8	71	0	.	85	88	42	74	0	.	8	71	0	.	8	71	320	32	753	37	346	38	2939	16	5332	11	19	65	39	71	14	77	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	70	90	4	100	89	95	420	33	5	100	4	100	50	56	24	85	12	100	12	75	0	.	0	.	10	100	14	72	31	88	54	72	10	100	573	31	1211	25	873	31	557	36	130	53	25	71	2158	18	636	33	372	43	0	.	0	.	0	.	242	47	18	81	59	77	21	72	92	73	0	.	13	75	0	.	0	.	44	75	0	.	60	54	0	.	4	100	0	.	200	91	6689	11	2992	15	1977	16	8	100	4	100	43	61	27	61	0	.	35	54	8	71	32	56	0	.	30450	4
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	34	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	83	4	100	20	100	370	30	3079	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	.	76	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1109	21	722	25	538	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6184	10
	other conifers	54	53	13	58	12	100	106	45	40	100	21	71	198	68	0	.	233	28	148	87	36	100	0	.	0	.	42	.	8	100	0	.	0	.	0	.	0	.	10	100	160	88	0	.	0	.	142	58	778	32	50	53	79	78	43	57	9	100	0	.	0	.	9	100	0	.	0	.	21	100	0	.	10	100	86	50	22	71	48	65	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	.	60	100	212	55	4	100	40	79	0	.	0	.	0	.	40	59	11	100	35	76	0	.	0	.	30	75	51	100	61	62	21	100	10	100	99	49	147	76	28	75	0	.	0	.	0	.	0	.	4031	12
	beech	1526	16	1045	22	614	24	2015	22	299	34	765	26	3723	19	3535	12	4108	10	4708	13	265	40	980	24	1937	21	0	.	587	30	799	27	1332	26	1135	26	0	.	1517	22	874	27	1021	24	91	66	963	32	0	.	4381	13	1346	24	959	27	1648	22	443	28	607	32	367	26	368	30	576	26	728	34	176	48	1345	27	609	25	1048	31	1524	24	1547	25	1723	21	2200	17	1215	21	1375	23	939	29	1615	20	1123	22	340	41	720	28	658	24	217	37	1052	23	0	.	371	37	0	.	1869	26	1603	27	1430	26	3095	20	2655	26	1672	29	606	36	4	100	186	61	633	35	194	42	595	34	504	28	145	75	1527	27	297	39	256	36	646	29	461	28	1110	24	750	27	386	34	215	42	167	46	318	40	80	100	231	63	1966	22	379	33	511	31	506	26	853	24	866	21	506	30	356	27	463	26	0	.	93098	3
	maple	600	31	260	26	495	30	221	32	396	68	333	49	1674	16	589	19	912	23	1109	19	152	46	284	27	313	29	0	.	119	52	141	45	570	26	244	39	39	79	867	23	159	34	261	52	311	46	342	37	15	74	1207	19	353	28	166	35	371	24	292	60	216	32	252	52	149	65	68	41	269	29	86	42	591	43	280	29	258	43	515	31	347	34	514	25	229	42	223	33	341	30	100	38	438	29	567	30	219	33	282	41	481	36	236	40	140	39	190	71	299	57	10	100	165	50	108	51	369	33	245	38	98	56	23	61	261	40	23	67	40	80	363	38	667	32	721	25	273	40	19	100	206	31	136	80	79	39	102	42	155	34	250	30	323	36	9	100	42	60	4	100	83	52	151	49	275	40	1015	20	110	44	184	47	247	41	349	29	204	31	368	47	155	53	331	62	0	.	28248	4
	ash	651	26	321	34	274	39	91	51	94	51	315	37	573	31	454	25	765	20	345	45	206	39	127	44	166	39	0	.	134	55	258	52	405	33	552	32	48	100	442	31	137	45	70	55	574	41	448	29	211	90	1102	20	286	42	381	33	359	34	85	60	62	78	14	77	20	71	25	71	295	55	102	51	135	35	613	30	421	41	206	40	300	41	332	34	273	52	86	40	101	41	210	43	209	42	561	35	232	80	116	50	370	31	232	32	233	34	170	52	195	100	189	54	200	41	292	37	652	28	712	30	67	47	255	46	147	55	129	55	21	100	140	44	248	44	337	43	281	43	137	43	53	50	173	40	11	100	21	100	58	58	164	59	245	38	46	47	135	52	0	.	209	44	524	41	315	43	432	26	191	38	150	33	278	44	521	33	380	34	148	46	159	58	86	42	0	.	23492	4
	oak	242	36	168	60	27	54	313	33	0	.	4	100	76	47	18	82	410	22	35	74	46	51	214	80	151	51	0	.	28	59	9	100	71	45	98	46	389	41	0	.	4	100	105	73	4	100	172	45	34	100	172	37	50	51	74	58	19	70	306	63	0	.	11	100	20	100	0	.	11	100	0	.	5	100	81	78	40	60	15	76	28	61	0	.	253	28	57	93	112	46	105	60	226	63	14	78	0	.	12	100	70	47	101	45	90	37	0	.	21	100	0	.	166	41	332	31	711	30	425	67	450	42	167	46	19	71	4	100	22	100	143	78	230	71	0	.	69	83	0	.	110	77	106	47	600	47	0	.	525	49	27	87	145	70	357	52	70	84	7	100	78	59	238	44	410	46	627	34	10	71	8	71	24	47	18	81	138	78	74	45	70	45	70	41	0	.	10965	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	.	979	39	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	.	98	100	1008	33	557	43	1875	33	1501	27	3504	21	826	30	1217	34	2988	26	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	114	83	0	.	0	.	0	.	0	.	50	100	0	.	248	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	15008	9
	other broadleaves	899	47	599	27	282	30	303	33	91	52	196	45	2338	17	700	22	1154	19	704	21	169	36	165	41	356	29	0	.	258	43	204	43	229	29	454	52	229	46	584	27	677	28	427	36	2703	23	3132	19	1373	32	1308	19	321	31	116	40	546	36	97	51	145	48	48	73	116	49	126	45	222	36	296	46	397	45	123	46	268	33	752	33	429	42	348	30	188	38	332	53	149	43	310	46	145	42	523	32	290	35	541	52	382	45	456	35	265	39	1316	36	1188	23	1044	30	1586	24	2336	21	3959	18	2740	20	1235	21	1838	19	177	42	554	61	318	42	582	35	510	30	773	39	286	50	250	51	314	37	223	42	67	42	127	49	193	53	155	37	152	58	181	55	93	49	21	100	68	49	2044	19	2068	22	2889	17	52	53	178	46	301	35	221	40	119	42	154	37	174	44	165	36	0	.	58122	4
	indeterminable	40	50	0	.	0	.	0	.	0	.	23	61	177	27	20	71	37	44	60	47	21	100	0	.	4	100	0	.	21	100	9	100	0	.	11	100	0	.	19	71	16	50	28	54	105	46	87	38	64	51	30	58	10	100	21	100	37	64	10	100	0	.	0	.	0	.	11	100	0	.	13	75	12	100	4	100	21	70	41	65	0	.	10	100	0	.	10	100	39	71	0	.	30	74	17	64	0	.	0	.	0	.	10	100	0	.	4	100	0	.	0	.	9	100	20	100	51	73	35	53	41	62	10	100	8	100	0	.	36	59	68	39	20	71	30	58	9	100	0	.	4	100	0	.	10	100	0	.	0	.	0	.	0	.	9	100	9	100	0	.	4	100	87	34	50	41	71	37	10	100	0	.	0	.	0	.	4	100	9	100	0	.	9	100	0	.	1681	9
	total	5335	15	4183	17	3184	17	4703	16	3144	21	4798	17	30440	7	16357	8	16581	8	13792	10	1177	25	2510	22	3965	17	42	.	2092	22	4921	18	7583	14	5252	16	778	35	7754	13	10753	10	8672	11	15210	10	26222	7	23642	7	16201	8	5744	16	5947	14	9535	12	2157	22	3272	18	1620	25	1557	24	3953	19	3540	21	4278	19	7011	16	3987	17	4381	20	8049	14	4309	18	6450	16	5156	15	3461	18	3521	19	2287	22	4044	17	5937	15	4575	16	3520	19	3673	18	2611	21	3704	20	3550	20	7680	13	5162	15	6486	15	6409	15	10814	13	11293	11	7029	16	8398	14	2989	23	2067	27	3613	20	3964	18	3514	20	6373	17	2595	22	1005	35	4883	18	1561	26	1974	27	4076	16	3210	21	3341	19	3149	18	1746	30	1271	30	589	41	1496	27	21976	7	15789	9	20940	8	2713	21	2287	24	2552	18	3802	18	3570	16	1965	22	1762	23	2160	21	0	.	557321	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. 2296898. Birmensdorf, Swiss Federal Research Institute WSL

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