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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NFI1
growing stock (stemwood)
protection forest against avalanches (2022) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest
grid: 1.4-km grid
state 1983/85
		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
protection forest against avalanches (2022)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
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	.
inside	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	.	161	73	134	.	2716	18	315	51	0	.	141	58	0	.	0	.	0	.	0	.	0	.	0	.	229	63	116	.	0	.	1052	31	505	39	430	41	681	43	2651	20	623	27	66	55	7	.	0	.	158	81	50	76	37	.	0	.	0	.	12	.	279	45	104	.	285	54	0	.	172	92	616	38	9	100	9	95	0	.	0	.	0	.	0	.	9	.	169	58	0	.	0	.	0	.	0	.	0	.	176	51	225	46	378	50	0	.	0	.	0	.	15	71	0	.	0	.	328	47	314	68	542	40	223	85	351	40	342	41	22	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	73	0	.	1446	21	838	26	1743	21	59	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18811	6
	fir	0	.	0	.	0	.	0	.	0	.	58	.	370	48	219	52	0	.	198	59	0	.	0	.	0	.	0	.	0	.	0	.	-2	81	0	.	0	.	320	53	0	.	54	92	0	.	121	53	0	.	25	72	34	.	0	.	0	.	0	.	29	.	0	.	0	.	56	.	65	73	0	.	53	91	0	.	66	63	38	71	72	72	140	90	0	.	0	.	0	.	0	.	0	.	229	49	0	.	0	.	0	.	0	.	0	.	0	.	122	76	0	.	0	.	0	.	0	.	113	89	52	100	0	.	155	79	0	.	78	77	42	67	56	100	261	45	68	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	93	0	.	165	74	88	71	149	46	23	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3541	13
	pine	0	.	0	.	0	.	0	.	0	.	0	.	135	59	0	.	0	.	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	76	0	.	0	.	11	93	191	55	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	44	100	0	.	0	.	0	.	0	.	42	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	81	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	45	30	77	170	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	953	20
	larch	0	.	0	.	0	.	0	.	0	.	0	.	51	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	79	49	62	43	65	345	33	509	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	70	30	57	47	61	33	97	0	.	0	.	91	58	23	100	0	.	0	.	0	.	37	100	13	100	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1186	23	500	30	353	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3417	11
	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	.	23	100	219	34	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	141	39	97	41	32	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	513	21
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	1	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	.	1	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	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	84
	beech	0	.	0	.	0	.	0	.	7	100	0	.	321	49	144	49	0	.	142	46	0	.	0	.	0	.	0	.	0	.	0	.	88	94	0	.	0	.	164	40	240	81	22	100	8	100	10	67	0	.	113	56	60	.	0	.	24	100	56	83	5	.	76	.	0	.	18	.	203	62	0	.	91	72	0	.	43	74	132	55	28	71	6	83	0	.	0	.	0	.	0	.	11	.	163	49	145	.	0	.	0	.	0	.	0	.	0	.	25	77	0	.	5	100	44	.	0	.	88	73	29	100	0	.	51	75	0	.	9	95	4	100	125	74	59	49	56	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	72	0	.	0	.	0	.	96	55	81	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3040	14
	maple	0	.	0	.	0	.	0	.	0	.	0	.	160	39	1	100	0	.	27	74	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	47	0	.	1	100	0	.	3	80	0	.	0	.	13	.	0	.	0	.	0	.	0	.	8	.	0	.	0	.	53	78	0	.	7	100	0	.	29	78	41	56	35	76	27	71	0	.	0	.	0	.	0	.	0	.	9	72	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	15	100	25	89	48	45	10	.	0	.	9	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	6	100	0	.	27	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	614	17
	ash	0	.	0	.	0	.	0	.	0	.	0	.	33	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	34	61	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	6	100	0	.	0	.	0	.	11	100	8	77	0	.	10	76	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	82	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	100	4	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	142	28
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	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	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	90	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	66
	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	.	13	42	1	100	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	8	82	29	58	0	.	0	.	18	62	8	75	23	92	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	3	76	0	.	4	100	13	72	16	81	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	12	93	0	100	0	.	0	.	0	.	0	.	9	68	12	100	0	.	0	.	0	.	0	.	0	.	17	90	4	54	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	36	59	10	63	29	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	289	18
	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	.	167	72	192	.	3800	16	681	37	0	.	527	41	0	.	0	.	0	.	0	.	0	.	0	.	318	58	116	.	0	.	1611	27	837	35	557	39	732	41	3184	18	1551	19	245	45	114	.	0	.	182	83	111	71	71	.	89	.	0	.	86	.	606	43	104	.	447	47	0	.	327	69	889	31	161	71	235	72	0	.	0	.	0	.	0	.	61	.	581	42	145	.	0	.	0	.	0	.	0	.	247	40	483	39	425	46	38	85	44	.	0	.	317	44	116	73	0	.	539	47	314	68	700	37	297	67	593	35	732	34	157	.	0	.	26	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	131	71	0	.	3116	16	1568	21	2613	18	163	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31344	5
outside	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	828	32	1112	30	841	30	2335	20	1376	28	2668	20	12840	9	8019	10	8350	10	2993	16	206	65	195	50	438	43	0	.	1292	28	2369	20	3319	17	2034	21	2	75	2057	22	4679	15	3010	16	5521	13	6708	11	4385	13	3078	15	2226	21	2556	20	3774	17	237	45	1180	21	697	34	501	32	1963	21	695	33	2015	21	1848	23	2122	24	1097	35	2850	19	1527	23	3191	19	1384	22	1257	26	488	36	655	40	351	36	1546	23	2940	18	1446	24	1566	26	1252	30	867	26	513	36	1111	23	789	24	-1	100	120	66	26	81	187	50	55	67	291	47	293	43	604	41	880	29	578	33	674	35	1407	24	1400	27	520	47	934	23	364	41	548	32	1746	17	723	26	1015	25	954	23	101	51	570	45	139	50	1217	33	2275	15	2279	17	3302	16	1120	29	889	33	1532	25	1113	24	1634	21	877	29	1008	29	984	32	0	.	157655	2
	fir	493	33	148	34	255	45	1306	23	580	33	865	26	1072	21	6784	11	4166	14	2173	16	33	59	157	58	188	43	0	.	245	45	141	62	1121	26	630	35	0	.	181	52	252	39	454	31	237	50	375	37	0	.	2943	15	1120	25	2263	17	1189	22	439	40	774	25	393	33	376	33	1097	24	251	44	326	46	379	39	637	26	254	40	382	37	308	43	1254	27	74	61	513	36	433	33	277	43	428	26	200	40	550	27	482	30	427	37	168	49	162	63	0	0	25	98	87	71	0	.	0	.	17	74	116	54	248	43	183	63	175	42	0	.	65	63	295	36	178	62	394	35	179	44	0	100	431	34	334	48	667	34	201	39	298	37	389	26	369	34	177	60	212	49	130	65	84	55	104	65	138	54	361	29	400	29	323	64	347	33	1194	24	660	33	53	66	183	67	175	58	0	.	49146	3
	pine	215	33	478	36	75	41	239	42	17	100	5	100	268	58	31	58	577	26	240	45	148	61	164	60	296	37	0	.	26	78	183	51	0	.	287	50	25	65	0	.	267	42	689	26	25	100	618	31	902	20	495	28	0	.	97	61	43	64	70	71	13	100	35	100	7	85	17	100	0	.	73	71	2	100	98	58	0	.	112	63	8	87	61	73	285	33	181	44	189	37	116	49	229	34	145	53	7	100	24	100	134	41	107	48	289	38	4	100	109	81	29	100	0	.	0	.	54	100	0	.	0	.	45	98	36	77	0	.	100	49	3	100	0	.	0	.	0	.	6	100	9	100	103	70	16	73	0	.	12	63	0	.	167	71	17	94	0	.	32	100	0	.	1028	24	303	25	316	34	1	100	29	71	275	36	130	38	423	35	299	40	247	40	194	51	0	.	12600	6
	larch	44	72	131	42	23	61	135	47	7	100	44	100	96	61	36	71	114	47	20	100	0	.	0	.	16	71	36	.	92	100	78	71	0	.	57	86	0	.	22	100	382	30	325	30	79	54	1044	17	3030	13	23	72	77	97	2	100	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	6	100	29	100	1	100	303	48	23	100	0	.	71	60	9	100	2	100	1	100	0	.	0	.	0	.	40	72	19	100	12	100	0	.	214	55	315	26	79	46	71	55	35	68	81	98	566	25	284	40	301	42	0	.	0	.	0	.	61	57	1	100	34	100	32	100	0	.	0	.	0	.	12	100	0	.	1	100	0	.	46	61	0	.	0	.	0	.	0	.	2373	14	1268	17	802	23	30	100	8	100	36	100	42	71	0	.	66	63	78	77	129	52	0	.	13416	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	6	100	0	.	108	46	895	20	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	276	30	88	37	35	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1456	14
	other conifers	6	72	0	.	6	100	25	70	1	100	2	100	0	.	0	.	148	51	16	100	0	.	0	.	0	.	17	.	39	100	0	.	0	.	19	100	0	.	1	100	0	.	1	100	0	.	0	.	24	49	1	100	16	90	59	99	0	.	0	.	0	.	1	100	0	.	0	.	2	100	0	.	0	.	83	100	2	100	-1	100	2	100	3	100	17	93	1	100	4	87	4	90	0	.	0	.	0	.	1	100	3	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	63	0	.	1	100	0	.	0	.	0	.	2	60	0	.	1	100	0	.	0	.	0	.	96	86	0	.	5	100	0	.	0	.	2	100	0	.	0	.	0	.	-1	100	17	72	18	63	13	75	0	.	0	.	0	.	0	.	663	25
	beech	1969	17	996	20	776	26	1171	23	215	40	769	27	1777	19	2128	16	4463	12	2352	15	615	34	1003	25	1367	20	0	.	343	44	638	30	771	26	1141	27	3	100	803	30	295	37	347	33	34	69	281	38	0	.	3583	12	1090	25	823	28	859	27	243	37	242	36	264	47	168	39	323	27	291	35	70	59	1073	31	440	37	650	36	628	31	776	26	993	24	1260	16	1486	23	1126	21	691	26	1379	19	332	27	69	69	676	29	243	38	174	37	864	24	0	.	95	44	0	.	429	28	504	28	324	27	512	25	421	30	390	29	189	45	0	.	84	65	287	33	66	49	252	41	492	36	14	74	628	28	240	41	196	34	156	30	376	30	873	23	406	35	413	36	213	39	63	62	461	33	0	0	11	100	313	28	212	44	702	32	625	34	811	24	930	23	436	35	456	28	385	32	0	.	58036	3
	maple	364	25	63	44	49	49	94	40	36	52	146	37	347	23	143	25	238	30	292	22	80	46	165	33	64	36	20	.	27	92	40	64	104	33	27	71	7	100	202	28	59	45	12	64	21	50	32	43	0	.	472	18	120	44	29	59	103	32	35	51	36	51	44	62	45	86	17	52	86	37	45	75	54	34	166	34	67	48	182	41	79	39	325	25	54	35	86	59	92	36	48	48	207	29	85	37	51	41	36	68	177	46	43	38	38	68	0	.	1	100	0	.	28	77	12	72	22	58	17	106	5	100	0	.	41	52	15	72	0	.	65	48	67	50	76	34	79	46	0	.	41	45	24	82	7	75	25	57	22	46	104	37	82	44	5	73	4	73	0	.	11	79	2	100	11	51	55	36	84	44	61	44	106	44	189	34	130	30	36	59	59	60	75	61	0	.	7016	5
	ash	319	24	335	35	172	37	79	47	100	86	208	34	90	38	320	29	501	27	129	40	150	45	16	72	60	53	0	.	67	64	74	56	208	35	246	39	4	80	119	57	27	95	0	.	74	58	7	81	16	72	622	21	147	38	85	40	72	41	63	84	30	78	0	.	1	100	18	100	156	44	36	50	30	68	402	28	112	37	49	47	164	54	128	35	146	37	38	55	44	62	56	53	139	39	128	43	13	76	91	52	98	34	239	35	147	44	10	83	5	100	15	81	17	71	15	117	123	33	22	54	10	82	30	48	26	93	24	84	10	100	92	48	16	86	24	85	137	43	82	77	24	72	127	60	0	.	1	100	50	69	155	45	113	48	9	53	98	58	0	.	128	50	34	53	3	71	87	39	221	40	170	44	144	42	172	35	149	31	99	57	123	41	109	40	0	.	9252	5
	oak	207	32	350	34	180	40	169	30	0	.	12	100	28	86	22	58	627	17	9	100	137	44	50	92	70	46	0	.	27	59	40	65	40	55	116	47	306	41	0	.	7	100	76	80	11	100	104	86	21	100	374	37	59	44	86	71	31	74	77	48	0	.	17	86	0	.	0	.	1	100	0	.	8	100	105	83	69	56	0	0	8	61	8	74	320	32	58	68	27	64	67	53	125	37	8	87	0	.	22	100	63	52	184	51	239	28	0	.	0	.	0	.	15	68	85	47	216	33	77	55	105	40	23	106	11	102	0	.	12	100	58	70	5	98	0	.	71	65	0	0	28	70	323	45	169	50	0	.	245	40	55	85	79	62	116	39	26	66	0	.	107	45	13	74	21	66	32	43	21	100	21	100	14	81	15	100	134	54	109	39	130	45	152	47	0	.	7056	6
	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	.	209	38	50	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	292	44	155	52	174	40	516	28	884	21	549	40	220	30	722	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	5	100	0	.	0	.	14	100	0	.	39	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3883	10
	other broadleaves	378	40	308	31	110	43	53	77	28	64	23	55	357	24	142	26	287	26	117	26	142	40	105	62	58	31	0	.	85	63	64	52	30	38	86	60	21	71	94	37	22	43	45	47	159	37	342	23	43	44	350	21	88	38	37	63	61	48	71	58	7	51	16	92	9	81	32	50	96	64	42	49	50	44	4	80	150	40	159	36	112	44	89	38	26	48	51	43	39	44	124	47	120	38	78	42	30	43	86	51	160	43	93	47	119	42	47	75	118	29	89	53	261	32	307	24	539	24	312	24	157	38	263	29	44	71	28	78	31	51	71	51	96	59	34	51	50	52	24	89	38	52	111	64	27	76	6	44	37	57	18	53	88	77	35	52	58	60	0	.	27	59	203	30	81	38	332	33	20	46	73	49	77	43	58	42	42	59	119	44	71	58	161	43	0	.	9554	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4822	13	3921	16	2486	18	5607	14	2360	24	4741	17	16878	8	17626	8	19472	7	8341	10	1512	25	1855	21	2558	18	73	.	2243	24	3627	18	5593	15	4643	17	369	39	3478	18	5993	13	4966	13	6160	13	9828	9	9365	9	11939	8	4944	16	6036	14	6131	14	1235	26	2283	19	1468	27	1127	26	3467	17	1578	24	2607	20	3470	18	4086	17	2414	23	4666	16	3008	18	6052	15	3638	15	3681	18	2443	18	2040	21	2978	16	2521	18	3660	18	2905	20	2891	19	2273	23	2727	18	804	28	2070	17	1243	20	994	18	1596	18	2287	14	2359	15	1506	18	2248	17	813	27	671	37	1193	26	1511	22	1104	28	2221	21	2440	22	645	42	2135	19	1625	25	1643	24	2134	16	1765	20	2609	18	2434	18	874	28	1192	31	364	47	2035	24	6325	10	4203	13	5674	11	2111	23	2276	23	3172	19	3743	17	4115	16	2095	22	2354	22	2363	22	0	.	329734	1
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	828	32	1112	30	841	30	2335	20	1537	26	2801	19	15555	8	8334	10	8350	10	3134	15	206	65	195	50	438	43	0	.	1292	28	2369	20	3548	17	2150	21	2	75	3109	18	5184	14	3441	15	6202	13	9359	9	5008	11	3144	14	2233	21	2556	20	3932	16	287	39	1218	21	697	34	501	32	1975	21	974	27	2118	21	2133	21	2122	24	1269	33	3466	17	1536	23	3200	19	1384	22	1257	26	488	36	655	40	359	35	1714	22	2940	18	1446	24	1566	26	1252	30	867	26	689	30	1336	21	1167	23	-1	100	120	66	26	81	203	46	55	67	291	47	621	32	918	35	1422	24	801	33	1025	26	1749	21	1422	26	520	47	935	23	364	41	548	32	1746	17	723	26	1015	25	954	23	101	51	570	45	210	41	1217	33	3721	12	3118	14	5045	12	1179	28	889	33	1532	25	1113	24	1634	21	877	29	1008	29	984	32	0	.	176466	2
	fir	493	33	148	34	255	45	1306	23	580	33	924	25	1442	20	7003	10	4166	14	2371	15	33	59	157	58	188	43	0	.	245	45	141	62	1119	27	630	35	0	.	501	38	252	39	508	29	237	50	496	31	0	.	2968	15	1154	24	2263	17	1189	22	439	40	804	25	393	33	376	33	1153	23	316	38	326	46	431	36	637	26	320	34	420	34	380	37	1394	26	74	61	513	36	433	33	277	43	428	26	430	32	550	27	482	30	427	37	168	49	162	63	0	0	147	66	87	71	0	.	0	.	17	74	229	52	301	39	183	63	331	43	0	.	143	51	337	33	233	53	655	28	247	42	0	100	438	33	334	48	667	34	201	39	298	37	389	26	369	34	177	60	212	49	144	59	84	55	269	52	226	43	510	25	424	28	323	64	347	33	1194	24	660	33	53	66	183	67	175	58	0	.	52687	3
	pine	215	33	478	36	75	41	239	42	17	100	5	100	403	43	31	58	577	26	258	43	148	61	164	60	296	37	0	.	26	78	183	51	0	.	287	50	25	65	0	.	304	38	689	26	25	100	629	31	1093	19	512	27	0	.	97	61	43	64	70	71	13	100	35	100	7	85	17	100	0	.	73	71	9	81	98	58	0	.	112	63	8	87	104	60	285	33	181	44	189	37	116	49	271	32	145	53	7	100	24	100	134	41	107	48	289	38	13	74	190	56	29	100	0	.	0	.	54	100	0	.	0	.	45	98	36	77	0	.	133	44	3	100	0	.	0	.	0	.	6	100	9	100	103	70	16	73	0	.	12	63	0	.	167	71	17	94	0	.	32	100	0	.	1157	22	333	23	487	27	1	100	29	71	275	36	130	38	423	35	299	40	247	40	194	51	0	.	13553	6
	larch	44	72	131	42	23	61	135	47	7	100	44	100	147	45	36	71	114	47	20	100	0	.	0	.	16	71	36	.	92	100	78	71	0	.	57	86	0	.	22	100	403	29	374	27	122	42	1389	15	3539	12	23	72	77	97	2	100	0	.	0	.	0	.	0	.	19	100	0	.	0	.	0	.	6	100	29	100	1	100	344	44	23	100	0	.	71	60	9	100	2	100	1	100	0	.	0	.	0	.	40	72	19	100	12	100	0	.	248	48	344	24	126	37	104	49	35	68	81	98	658	23	307	38	301	42	0	.	0	.	37	100	74	50	11	89	34	100	32	100	0	.	0	.	0	.	12	100	0	.	1	100	0	.	46	61	0	.	0	.	0	.	0	.	3559	12	1768	15	1155	18	30	100	8	100	36	100	42	71	0	.	66	63	78	77	129	52	0	.	16833	5
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	6	100	0	.	130	42	1114	17	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	418	24	185	28	67	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1969	12
	other conifers	6	72	0	.	6	100	25	70	1	100	2	100	1	100	0	.	148	51	16	100	0	.	0	.	0	.	17	.	39	100	0	.	0	.	19	100	0	.	1	100	0	.	1	100	0	.	0	.	24	49	1	100	16	90	59	99	0	.	0	.	0	.	1	100	0	.	0	.	2	100	0	.	0	.	83	100	3	82	-1	100	2	100	3	100	17	93	1	100	4	87	4	90	0	.	0	.	0	.	1	100	3	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	63	0	.	1	100	0	.	0	.	0	.	2	60	0	.	1	100	0	.	0	.	0	.	96	86	0	.	5	100	0	.	0	.	9	77	0	.	0	.	0	.	-1	100	17	72	18	63	13	75	0	.	0	.	0	.	0	.	670	24
	beech	1969	17	996	20	776	26	1171	23	222	39	769	27	2098	18	2273	15	4463	12	2493	14	615	34	1003	25	1367	20	0	.	343	44	638	30	858	25	1141	27	3	100	967	26	535	42	369	31	42	59	292	36	0	.	3696	12	1151	24	823	28	884	26	299	34	247	35	340	43	168	39	341	26	494	33	70	59	1164	29	440	37	694	34	760	27	804	25	999	24	1260	16	1486	23	1126	21	691	26	1389	19	496	24	214	71	676	29	243	38	174	37	864	24	0	.	120	38	0	.	434	28	548	27	324	27	600	23	450	28	390	29	239	39	0	.	94	59	291	33	190	51	311	35	548	34	14	74	637	28	240	41	196	34	156	30	376	30	873	23	406	35	413	36	213	39	100	47	461	33	0	0	11	100	409	25	294	42	702	32	625	34	811	24	930	23	436	35	456	28	385	32	0	.	61077	3
	maple	364	25	63	44	49	49	94	40	36	52	146	37	507	20	144	25	238	30	319	21	80	46	165	33	64	36	20	.	27	92	40	64	104	33	27	71	7	100	234	25	59	45	13	60	21	50	35	40	0	.	472	18	133	41	29	59	103	32	35	51	36	51	53	54	45	86	17	52	139	38	45	75	61	32	166	34	96	41	223	35	115	36	351	23	54	35	86	59	92	36	48	48	207	29	94	34	51	41	36	68	177	46	43	38	38	68	17	100	1	100	0	.	28	77	12	72	22	58	17	106	5	100	0	.	45	48	15	72	0	.	80	43	92	44	123	27	90	43	0	.	50	41	24	82	7	75	25	57	22	46	104	37	82	44	5	73	4	73	7	100	11	79	8	82	11	51	83	31	84	44	61	44	106	44	189	34	130	30	36	59	59	60	75	61	0	.	7630	4
	ash	319	24	335	35	172	37	79	47	100	86	208	34	123	34	320	29	501	27	129	40	150	45	16	72	60	53	0	.	67	64	74	56	209	35	246	39	4	80	153	47	33	80	0	.	74	58	7	81	16	72	622	21	147	38	85	40	72	41	63	84	30	78	5	100	1	100	18	100	162	42	36	50	30	68	402	28	123	35	57	42	164	54	138	33	146	37	38	55	44	62	56	53	139	39	129	42	13	76	91	52	98	34	239	35	147	44	10	83	5	100	15	81	17	71	15	117	123	33	22	54	10	82	30	48	26	93	24	84	10	100	92	48	16	86	42	60	137	43	82	77	24	72	127	60	0	.	1	100	50	69	155	45	113	48	9	53	98	58	1	100	128	50	35	52	7	64	93	37	221	40	170	44	144	42	172	35	149	31	99	57	123	41	109	40	0	.	9394	5
	oak	207	32	350	34	180	40	169	30	0	.	12	100	28	85	22	58	627	17	9	100	137	44	50	92	70	46	0	.	27	59	40	65	40	55	116	47	306	41	0	.	7	100	76	80	11	100	105	86	21	100	374	37	59	44	86	71	31	74	77	48	0	.	17	86	0	.	0	.	1	100	0	.	8	100	105	83	69	56	0	0	8	61	8	74	320	32	58	68	27	64	67	53	125	37	8	87	0	.	22	100	63	52	184	51	239	28	0	.	0	.	0	.	15	68	85	47	216	33	77	55	105	40	23	106	11	102	0	.	12	100	58	70	15	74	0	.	71	65	0	0	28	70	323	45	169	50	0	.	245	40	55	85	79	62	116	39	26	66	0	.	107	45	13	74	21	66	39	39	21	100	21	100	14	81	15	100	134	54	109	39	130	45	152	47	0	.	7073	6
	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	.	209	38	50	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	16	100	292	44	155	52	174	40	516	28	884	21	549	40	220	30	722	26	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	100	0	.	5	100	0	.	0	.	14	100	0	.	39	52	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3883	10
	other broadleaves	378	40	308	31	110	43	53	77	28	64	23	55	371	23	144	26	287	26	118	26	142	40	105	62	58	31	0	.	85	63	64	52	32	35	86	60	21	71	102	35	50	38	45	47	159	37	361	22	51	39	373	21	88	38	37	63	61	48	76	54	7	51	16	92	9	81	32	50	96	64	42	49	53	41	4	80	155	39	172	33	128	40	89	38	26	48	51	43	39	44	124	47	120	38	87	39	30	43	86	51	160	43	93	47	119	42	59	62	118	29	89	53	261	32	307	24	539	24	321	24	170	36	263	29	44	71	28	78	31	51	71	51	113	52	38	46	50	52	24	89	38	52	111	64	27	76	6	44	37	57	18	53	88	77	35	52	58	60	1	100	27	59	238	27	91	34	361	31	20	46	73	49	77	43	58	42	42	59	119	44	71	58	161	43	0	.	9843	5
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	4822	13	3921	16	2486	18	5607	14	2528	23	4933	17	20678	7	18306	8	19472	7	8868	10	1512	25	1855	21	2558	18	73	.	2243	24	3627	18	5912	14	4759	16	369	39	5089	15	6830	12	5523	13	6892	12	13012	8	10916	8	12184	8	5058	15	6036	14	6314	13	1346	24	2355	18	1558	26	1127	26	3553	17	2184	21	2711	20	3917	17	4086	17	2742	22	5555	14	3168	18	6287	15	3638	15	3681	18	2443	18	2040	21	3039	16	3102	17	3805	17	2905	20	2891	19	2273	23	2727	18	1052	24	2553	15	1668	19	1032	18	1640	18	2287	14	2676	14	1623	17	2248	17	1352	25	985	33	1892	22	1808	21	1697	22	2953	18	2597	22	645	42	2161	19	1625	25	1643	24	2134	16	1765	20	2609	18	2434	18	874	28	1192	31	495	39	2035	24	9441	9	5770	11	8287	10	2274	22	2276	23	3172	19	3743	17	4115	16	2095	22	2354	22	2363	22	0	.	361078	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. 2292009. Birmensdorf, Swiss Federal Research Institute WSL

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