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–NFI5
increment (gross increment)
protection forest against hillslope debris flows/landslides (2022) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI4/NFI5
grid: 1.4 km grid, subgrids 1-5
change 2009/17–2018/26
		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 hillslope debris flows/landslides (2022)	main tree species	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%	1000 m³/yr	±%
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	.	0	.	16	63	37	33	0	1362	0	100	5	74	0	.	1	100	0	.	0	.	0	.	0	.	3	84	0	.	0	.	9	72	12	55	26	52	25	39	63	30	28	40	4	66	0	.	0	.	0	.	0	.	5	71	0	.	-1	182	-2	110	11	95	0	.	13	75	9	62	4	72	12	52	0	.	3	59	-1	182	0	.	0	.	0	.	0	.	0	1580	0	.	0	.	1	81	0	.	0	.	11	54	6	73	6	50	0	.	0	100	1	100	7	77	0	100	0	.	-4	.	5	.	20	42	10	92	2	100	10	100	1	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	3	.	0	.	0	.	-3	.	0	.	29	55	46	32	43	29	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	476	10
	fir	0	.	0	.	0	.	0	.	0	.	15	77	3	138	4	84	5	77	12	51	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	113	0	.	2	100	0	.	8	100	0	0	13	58	0	100	0	.	0	.	0	.	4	63	0	.	4	100	10	62	0	100	0	.	5	100	0	100	1	100	0	136	0	.	8	54	0	.	0	.	0	.	0	.	0	.	9	85	0	.	3	118	1	86	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-2	150	4	107	8	96	2	100	0	.	0	.	3	75	4	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	2	.	0	.	-1	100	0	219	8	82	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	145	18
	pine	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	70	0	.	2	61	7	92	1	67	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	65	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	76	4	45	3	63	0	.	0	.	0	.	-1	.	0	.	0	.	0	.	0	.	0	.	30	28
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	677	5	78	1	78	-1	425	9	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	83	0	100	0	100	0	.	0	100	12	66	0	0	0	.	0	.	0	.	0	.	1	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	55	14	68	5	57	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	57	26
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	471	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	-2	115	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	387
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	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	100	0	.	0	.	0	.	0	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	.	1	58
	beech	0	.	0	.	0	.	0	.	0	.	1	79	14	49	3	102	3	75	6	98	2	.	8	58	4	.	0	.	0	.	0	.	7	74	0	.	0	.	4	90	-1	174	3	52	0	100	0	100	0	.	6	48	6	72	0	.	0	.	0	.	0	1105	0	.	0	100	1	75	5	100	0	.	1	71	2	82	13	67	8	70	-5	.	1	475	2	93	0	.	0	.	1	.	0	.	2	91	0	.	4	112	2	77	0	.	0	.	0	.	0	573	0	.	-1	117	6	73	1	129	0	.	2	100	0	100	0	.	0	.	7	60	3	156	0	228	0	100	10	72	0	.	3	74	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	.	0	.	0	.	0	.	-3	198	4	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	0	.	149	17
	maple	0	.	0	.	0	.	0	.	0	.	1	83	6	42	0	.	4	100	2	65	0	.	0	100	0	.	0	.	0	.	0	.	0	61	0	.	0	.	3	168	-1	100	1	101	2	100	0	100	0	.	2	76	1	100	0	.	0	.	0	.	2	59	0	.	0	84	0	100	1	100	0	.	0	100	-2	116	15	98	0	200	0	.	5	60	0	.	0	.	0	.	0	.	0	.	0	96	0	.	0	100	0	71	0	.	0	.	0	100	0	.	0	.	0	100	0	.	0	95	0	.	0	100	0	.	0	.	0	.	0	100	0	106	0	2281	0	100	0	.	0	.	2	89	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	4	78	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	52	35
	ash	0	.	0	.	0	.	0	.	0	.	1	100	2	90	0	.	1	88	1	100	1	.	0	.	0	.	0	.	0	.	0	.	6	107	0	.	0	.	1	68	0	100	-1	100	5	92	0	.	0	.	5	74	0	0	0	.	0	.	0	.	1	76	0	.	0	100	0	.	0	100	0	.	0	100	2	78	3	73	1	100	0	.	1	202	0	.	0	.	0	.	0	.	0	.	-1	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	1	90	0	.	0	100	0	.	1	100	0	156	0	.	0	.	0	.	1	100	0	100	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	85	0	100	0	77	2	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	28
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	2	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	1	83	0	585	3	110	0	226	1	62	0	0	0	.	0	.	1	100	0	73	0	100	0	.	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	90	1	74	0	80	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	41
	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	.	2	100	0	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	100	7	59	0	100	3	78	5	58	12	56	1	115	0	85	10	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	67	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	41	27
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	100	2	197	0	.	1	100	1	66	0	.	0	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	100	-2	71	9	89	6	64	2	54	2	103	1	71	0	87	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	0	.	1	70	0	0	1	76	0	74	0	.	1	70	0	0	0	.	0	.	0	.	0	.	2	99	0	.	0	.	0	.	0	.	0	.	0	76	2	95	0	86	4	62	4	65	7	44	1	100	4	72	9	68	0	.	0	.	1	74	0	71	0	100	0	100	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	110	4	39	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	71	20
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0
	total	0	.	0	.	0	.	0	.	0	.	35	62	67	27	7	113	17	60	27	41	3	.	9	61	4	.	0	.	0	.	0	.	17	53	0	.	0	.	19	49	9	82	47	41	39	37	76	29	47	34	32	40	8	71	0	.	0	.	0	.	13	54	0	.	4	73	10	65	18	72	0	.	21	62	12	58	39	59	21	49	-4	.	17	46	1	83	0	.	0	.	1	.	1	.	12	102	0	.	8	113	5	71	0	.	0	.	12	55	21	42	6	52	8	53	14	50	24	39	21	49	9	54	19	50	-4	.	5	.	30	36	18	61	10	90	13	95	15	71	0	.	8	72	7	.	0	.	0	.	0	.	0	.	4	.	0	.	0	.	4	.	0	.	48	37	67	29	64	30	5	.	0	.	0	.	1	.	1	.	0	.	0	.	0	.	0	.	1074	7
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	15	34	51	37	38	46	33	33	49	39	40	38	256	14	148	17	188	16	46	30	5	85	6	68	9	48	0	.	14	47	42	27	74	25	60	35	0	75	48	35	156	19	89	25	152	19	187	16	108	19	41	27	33	36	60	29	69	23	9	59	36	34	8	45	14	50	25	27	22	39	14	72	49	34	34	33	42	39	71	30	13	59	54	31	29	25	23	41	2	211	15	52	7	46	13	40	78	26	23	34	25	35	26	42	43	34	11	54	52	40	20	40	5	100	2	100	0	0	7	47	2	71	23	36	16	37	32	45	20	67	27	37	48	31	29	42	25	49	2	57	27	45	12	68	6	43	30	34	15	63	24	54	4	126	12	58	16	52	0	100	10	65	73	22	54	30	83	22	38	39	26	36	35	37	40	38	50	29	8	63	17	53	26	37	0	.	3614	3
	fir	11	68	1	78	5	79	32	33	17	42	20	41	41	27	188	16	150	17	68	27	2	80	4	60	11	48	0	.	11	52	13	52	12	147	28	45	0	100	3	104	12	53	7	48	-1	513	8	72	1	100	93	23	59	30	110	23	65	31	11	45	23	38	17	36	13	54	51	39	6	74	4	77	23	41	30	41	1	74	12	44	28	64	35	29	6	62	20	36	9	56	4	50	19	47	15	67	22	37	11	52	11	120	18	43	15	65	1	100	5	58	1	100	0	.	0	.	1	100	7	62	6	72	6	59	6	75	1	100	0	1496	17	41	5	65	22	53	11	65	2	92	28	37	4	58	15	46	8	55	11	49	20	34	10	48	10	77	20	48	9	83	4	100	5	95	9	49	20	42	18	39	4	57	7	48	25	34	15	34	4	87	1	100	5	59	0	.	1720	5
	pine	-3	104	1	71	0	71	2	96	0	.	0	100	1	85	8	80	2	154	5	93	1	71	5	68	3	58	0	.	0	100	1	69	0	.	2	144	1	87	0	.	7	48	2	166	0	100	10	60	19	38	7	101	0	100	5	73	0	.	1	100	0	.	0	.	0	.	2	100	0	100	4	100	0	.	1	100	0	.	-1	112	0	.	1	100	12	56	1	73	-2	77	2	89	1	68	2	76	0	100	0	.	1	63	1	160	4	73	0	.	-4	100	0	282	0	.	0	100	0	.	0	.	0	.	0	100	0	.	0	100	0	100	0	.	0	.	0	.	0	.	0	79	0	100	0	.	0	100	0	.	0	94	0	.	0	100	0	.	0	.	2	100	0	.	2	94	6	77	2	100	0	.	3	100	3	46	0	298	5	58	2	73	1	100	1	100	0	.	136	15
	larch	2	112	-2	92	8	75	-2	120	0	.	0	.	4	64	0	100	3	84	0	.	0	.	0	.	0	94	0	.	0	.	2	100	0	.	2	73	0	.	-1	100	12	58	3	146	9	66	58	23	97	17	0	100	0	100	1	71	0	100	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	100	0	100	0	.	2	88	1	100	0	.	1	86	0	.	0	.	0	100	0	.	0	.	0	.	1	100	1	100	1	100	3	100	1	346	9	84	12	54	9	99	0	.	1	79	35	27	8	49	6	108	0	.	0	.	0	0	9	54	0	.	0	.	0	.	4	100	0	.	1	100	0	.	0	.	1	100	0	.	1	124	0	.	1	100	0	.	5	100	54	28	34	32	10	47	1	100	1	100	0	100	2	88	0	.	1	85	1	100	2	65	0	.	412	9
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	71	0	.	1	100	4	71	31	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	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	.	13	36	3	80	1	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	55	22
	other conifers	2	79	0	.	3	100	5	75	0	.	1	71	0	95	0	.	20	39	0	100	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	91	0	44	2	90	2	94	8	77	0	100	0	.	0	.	0	100	0	.	0	.	0	.	0	100	0	.	5	91	3	88	0	124	0	100	0	.	4	100	0	100	3	100	1	76	0	.	0	0	0	.	0	100	0	.	0	.	0	86	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	5	100	1	62	0	.	0	100	0	.	0	.	0	.	2	100	0	.	1	100	0	.	0	.	0	100	0	0	0	100	0	.	0	.	1	64	3	74	0	72	0	.	0	.	0	.	0	.	79	20
	beech	40	24	23	28	19	41	45	30	11	56	8	64	48	40	90	21	137	15	64	21	7	52	15	42	24	38	0	.	5	60	30	35	12	58	21	64	0	.	16	48	7	55	11	54	2	92	16	50	0	.	50	23	22	40	43	42	18	44	6	43	3	47	13	55	6	67	8	63	5	124	2	61	-3	237	24	48	5	67	30	41	20	60	49	39	35	30	34	28	14	41	15	44	14	38	4	105	10	61	11	54	14	42	3	88	31	30	0	.	6	71	0	.	13	37	10	68	19	38	37	42	26	35	10	107	11	72	1	100	0	100	8	58	2	65	9	68	9	76	3	66	14	34	2	88	2	99	7	39	6	97	15	53	9	67	6	89	14	47	6	75	1	146	0	.	1	70	10	47	4	65	9	35	17	38	23	43	26	31	21	42	13	49	10	48	0	.	1517	5
	maple	10	42	8	49	2	80	3	40	1	99	9	61	10	74	13	41	19	29	14	35	2	130	4	47	2	236	0	.	2	72	3	53	3	124	3	55	0	100	9	41	4	62	6	74	3	76	1	195	0	100	7	107	5	55	3	39	2	168	6	69	5	72	5	96	7	83	1	68	1	330	1	84	1	567	1	324	4	58	11	48	2	154	14	33	3	53	6	48	4	53	2	110	10	86	5	44	1	103	4	75	7	45	-1	788	7	45	3	100	3	71	0	100	1	79	-1	148	1	76	1	45	6	71	3	73	2	102	3	61	0	.	0	5092	4	47	8	84	1	60	1	78	3	60	0	.	2	82	1	87	6	62	0	495	3	69	3	76	1	100	0	.	8	69	1	100	2	59	12	41	2	99	5	64	5	63	15	38	1	230	11	58	2	57	10	56	0	.	383	9
	ash	12	39	4	68	2	111	2	53	0	.	5	110	1	242	15	43	11	41	0	221	0	1112	2	48	2	176	0	.	-4	130	17	60	16	52	3	209	1	100	11	48	0	100	2	95	4	56	2	41	0	.	10	37	9	45	0	1075	2	45	2	66	0	100	0	100	0	100	0	100	9	50	0	.	6	80	20	64	4	70	1	58	8	79	6	77	8	73	5	47	1	68	-2	297	1	87	8	50	2	90	4	77	6	60	9	69	8	44	1	100	10	100	1	82	0	1207	1	64	5	79	6	59	-1	121	3	50	6	74	2	58	0	.	-1	563	4	66	2	73	13	94	4	90	1	58	1	90	1	90	0	100	1	84	1	245	1	362	2	61	4	58	0	.	5	62	1	86	3	56	10	60	4	70	4	72	3	77	5	36	12	53	5	56	2	46	1	113	0	.	361	10
	oak	12	72	2	66	0	.	6	63	0	.	0	.	1	75	1	105	14	39	0	.	0	100	0	100	0	346	0	.	0	100	1	100	2	86	5	54	11	77	0	.	0	.	1	100	0	.	2	100	2	100	2	128	4	97	0	357	1	100	0	218	0	.	0	100	0	.	0	.	0	100	0	.	0	.	1	100	2	100	0	100	0	60	0	0	6	51	0	100	0	71	1	75	5	58	0	100	0	.	0	.	1	62	9	87	3	96	0	.	0	.	0	.	1	56	0	91	5	45	0	71	4	75	3	68	1	85	0	100	0	.	0	.	0	.	0	.	4	100	0	100	0	100	3	82	1	115	0	.	4	67	1	100	0	164	3	76	0	100	2	100	2	85	0	100	1	62	4	59	0	100	1	80	1	64	1	71	3	81	3	71	2	50	1	84	0	.	144	14
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	7	60	0	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	.	7	71	7	70	9	71	4	50	15	73	2	202	0	867	11	97	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	1	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	30
	other broadleaves	6	43	13	42	7	48	3	60	0	100	2	80	9	28	15	30	20	35	1	45	4	50	1	63	1	59	0	.	1	92	11	48	6	51	7	47	2	56	3	46	8	56	5	77	21	41	8	95	26	56	3	100	6	55	1	140	5	59	0	100	1	51	0	100	-1	104	1	71	2	166	0	76	2	47	0	100	1	58	3	71	7	57	2	79	2	50	7	84	1	70	8	49	1	61	2	77	5	86	5	53	9	109	8	40	-5	119	2	49	11	79	3	74	2	57	9	50	31	46	39	38	-5	151	8	74	1	148	3	124	0	79	-4	107	6	87	1	77	0	1218	9	88	4	76	2	77	0	73	0	412	4	70	1	93	2	84	1	55	3	68	0	.	0	100	14	42	13	50	9	27	1	75	2	84	6	72	3	42	2	62	2	59	5	53	5	71	0	.	448	9
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0
	total	106	20	100	24	83	28	128	19	78	31	84	30	372	13	478	11	564	10	198	16	21	45	38	37	51	31	7	.	30	43	119	24	124	24	131	25	17	57	89	26	208	17	125	20	192	16	305	13	284	13	215	16	141	21	231	18	164	19	35	32	68	26	43	31	39	41	88	28	45	32	24	50	79	30	117	29	63	33	129	22	80	35	161	22	106	21	96	23	32	41	46	31	59	31	49	27	119	23	58	27	74	28	73	29	111	23	19	44	99	26	44	29	39	39	26	36	78	28	136	20	45	32	71	28	42	33	42	38	20	68	57	31	70	27	72	34	63	39	31	43	79	25	25	47	26	38	47	28	48	35	63	33	32	41	36	43	60	33	18	69	35	41	163	16	126	20	162	17	69	32	55	27	78	26	117	24	114	21	55	28	43	30	62	29	0	.	8931	2
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	15	34	51	37	38	46	33	33	49	38	56	33	294	13	147	17	188	16	51	28	5	85	7	59	9	48	0	.	14	47	42	27	77	24	60	35	0	75	57	32	167	18	115	22	176	17	250	14	136	17	45	26	33	36	60	29	69	23	9	56	42	31	8	45	13	56	24	31	33	41	14	72	62	31	43	29	45	36	83	27	13	59	57	30	29	26	23	41	2	211	15	52	7	46	13	61	78	26	23	34	25	34	26	42	43	34	22	38	59	37	25	32	5	100	2	100	1	100	13	45	2	64	23	36	12	59	38	41	40	39	36	37	50	30	39	40	26	47	2	57	27	45	11	68	6	43	30	34	15	63	24	54	7	86	12	58	16	52	-3	99	10	65	102	22	100	22	126	17	38	39	26	36	35	37	41	38	50	28	8	63	17	53	26	37	0	.	4090	3
	fir	11	68	1	78	5	79	32	33	17	42	35	41	44	27	193	16	155	16	80	24	2	80	4	60	11	48	0	.	11	52	13	52	12	146	28	45	0	100	5	79	12	53	9	44	-1	513	16	61	1	100	106	21	59	29	110	23	65	31	11	45	26	34	17	36	17	47	61	34	6	69	4	77	28	38	30	40	2	62	13	42	28	65	43	26	6	62	20	36	9	56	4	50	19	47	24	52	22	37	14	48	12	107	18	43	15	65	1	100	8	53	1	100	0	.	0	.	1	100	7	62	6	72	6	59	6	78	1	100	-2	187	21	39	14	65	24	50	11	65	2	92	31	34	8	60	15	46	8	55	11	49	20	34	11	45	10	77	20	48	10	72	4	100	5	107	8	53	28	38	18	39	4	57	7	48	26	34	15	34	4	87	1	100	5	59	0	.	1864	5
	pine	-3	104	1	71	0	71	2	96	0	.	0	100	2	66	8	80	2	154	5	93	1	71	5	68	3	58	0	.	0	100	1	69	0	.	2	144	1	87	0	.	7	48	4	85	0	100	12	51	26	37	8	88	0	100	5	73	0	.	1	100	1	100	0	.	0	.	2	100	0	100	4	100	0	100	1	100	0	.	-1	112	0	.	1	100	12	56	1	73	-2	77	2	85	1	68	2	76	0	100	0	.	1	63	1	160	4	73	0	.	-2	194	0	282	0	.	0	100	0	.	0	.	0	.	0	100	0	.	0	100	4	63	0	0	0	.	0	.	0	.	0	79	0	100	0	.	0	100	0	.	0	94	0	.	0	100	0	.	0	.	2	100	0	.	7	59	10	48	4	55	0	.	3	100	3	46	-1	119	5	58	2	73	1	100	1	100	0	.	166	14
	larch	2	112	-2	92	8	75	-2	120	0	.	0	.	4	64	0	100	3	84	0	.	0	.	0	.	0	94	0	.	0	.	2	100	0	.	2	73	0	.	-1	100	12	58	7	74	10	59	58	23	106	16	0	100	0	100	1	71	0	100	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	100	0	100	2	100	1	118	1	100	0	.	1	86	0	.	0	.	0	100	0	.	0	.	0	.	1	100	1	100	1	100	3	100	1	251	10	82	12	53	9	98	0	.	0	282	47	26	8	49	6	108	0	.	0	.	0	0	10	51	0	.	1	100	0	.	4	100	0	.	1	100	0	.	0	.	1	100	0	.	1	124	0	.	1	100	0	.	5	100	63	25	48	30	15	36	1	100	1	100	0	100	2	88	0	.	1	85	1	100	2	65	0	.	469	8
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	71	0	.	1	100	4	71	31	33	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	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	.	13	35	1	319	1	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	56	23
	other conifers	2	79	0	.	3	100	5	75	0	.	1	71	0	95	0	.	20	39	0	71	0	.	0	.	0	.	7	.	0	.	0	.	0	.	0	.	0	.	0	100	0	100	0	.	0	.	0	91	0	44	2	90	2	94	8	77	0	100	0	.	0	.	0	100	0	.	0	.	0	.	0	100	0	100	5	91	3	88	0	124	0	838	0	.	4	100	0	100	3	100	1	76	0	.	0	0	0	.	0	100	0	.	0	.	0	86	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	100	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	5	100	1	62	0	.	0	100	0	.	0	.	0	.	2	100	0	.	1	100	0	.	0	.	0	100	0	0	0	100	0	.	0	.	1	64	3	74	0	72	0	.	0	.	0	.	0	.	80	20
	beech	40	24	23	28	19	41	45	30	11	56	9	56	62	33	93	20	141	15	70	21	9	46	23	34	28	35	0	.	5	60	30	35	19	45	21	64	0	.	20	42	6	68	13	44	2	107	16	50	0	.	56	21	28	35	43	42	18	44	6	44	3	51	13	55	6	64	8	58	10	79	2	61	-2	408	27	45	19	53	38	35	15	83	49	39	37	29	34	28	14	41	16	41	15	37	5	78	10	61	16	50	16	38	3	88	31	30	0	.	6	70	0	.	12	40	16	50	20	37	37	42	27	33	10	103	11	72	1	100	7	59	11	59	2	70	9	65	19	52	3	66	17	31	6	71	2	99	7	39	6	97	15	53	10	65	6	89	14	47	10	61	1	146	0	.	1	70	7	102	8	60	9	35	17	38	24	43	27	30	21	42	13	49	10	48	0	.	1665	4
	maple	10	42	8	49	2	80	3	40	1	99	10	54	16	50	13	41	23	29	16	32	2	130	4	45	2	236	0	.	2	72	3	53	4	110	3	55	0	100	12	53	4	74	7	64	5	61	1	173	0	100	8	87	6	49	3	39	2	168	6	69	7	54	5	96	8	77	1	59	2	216	1	84	1	459	0	977	20	79	11	48	3	133	19	29	3	53	6	48	4	53	2	110	10	85	5	41	1	103	4	73	7	47	-1	788	7	45	3	94	3	71	0	100	1	74	-1	148	1	59	1	45	6	68	3	73	2	102	3	61	0	100	1	449	4	49	8	83	1	60	1	78	5	50	0	100	2	82	1	87	6	62	0	495	3	69	3	76	1	100	0	.	8	69	1	100	2	58	17	36	1	139	5	64	5	63	15	38	1	230	11	58	2	57	10	56	0	.	435	9
	ash	12	39	4	68	2	111	2	53	0	.	6	91	3	120	15	43	12	37	1	117	1	337	2	48	2	176	0	.	-4	130	17	60	22	48	3	209	1	100	11	45	1	73	1	238	10	56	2	41	0	.	16	35	9	45	0	1075	2	45	2	66	1	63	0	100	0	71	0	100	9	49	0	.	6	78	22	59	7	51	2	51	8	79	6	72	8	73	5	47	1	68	-2	297	1	87	6	63	2	90	4	77	7	53	9	69	8	44	1	100	10	100	1	82	0	194	1	64	5	83	6	59	0	308	3	47	5	74	2	58	0	.	0	2144	4	60	2	73	16	77	4	90	1	58	1	90	1	90	0	100	1	84	1	245	1	362	2	61	4	58	0	.	5	62	3	60	3	54	10	58	6	57	4	72	3	77	5	36	12	52	5	56	2	46	1	113	0	.	401	9
	oak	12	72	2	66	0	.	6	63	0	.	0	.	1	75	1	105	16	36	0	.	0	100	0	100	0	346	0	.	0	100	1	100	2	86	5	54	11	77	0	.	0	.	1	100	0	.	2	100	2	100	2	122	4	97	0	357	1	100	0	218	0	.	0	100	0	100	0	.	0	100	0	.	0	.	1	100	2	100	0	100	0	60	0	0	6	51	0	100	0	71	1	75	5	58	0	100	0	.	0	.	1	62	9	87	3	96	0	.	0	100	0	.	2	56	0	1091	8	50	0	1054	5	59	3	68	1	85	0	100	1	100	0	73	0	100	0	.	4	95	0	100	1	71	3	82	1	115	0	.	4	67	1	100	0	164	3	76	0	100	2	100	2	85	2	85	2	48	4	56	0	100	1	80	1	64	1	71	3	81	3	71	2	50	1	84	0	.	155	13
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	10	51	0	163	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	14	46	7	67	12	56	9	39	28	47	4	133	1	432	21	61	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	1	100	0	.	1	68	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	105	20
	other broadleaves	6	43	13	42	7	48	3	60	0	100	2	74	11	42	15	30	22	33	3	40	4	50	1	58	1	59	0	.	1	92	11	48	6	51	7	47	2	56	3	43	6	74	15	62	27	35	9	80	28	53	4	78	6	54	1	140	5	59	0	100	1	46	0	100	-1	148	1	71	2	166	0	76	3	39	0	100	2	48	3	64	7	57	3	59	2	50	7	84	1	70	8	47	1	61	4	62	5	86	5	53	9	109	8	40	-5	119	2	42	13	66	3	72	6	44	13	40	38	38	40	37	0	2388	16	50	1	148	3	124	1	62	-3	111	6	83	2	65	0	303	9	88	4	76	2	77	0	73	0	412	4	70	1	93	2	84	1	55	3	68	0	100	0	100	15	40	17	39	13	34	1	75	2	84	6	72	3	42	2	62	2	59	5	53	5	71	0	.	518	8
	indeterminable	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	0	0	0	0	0	0	.	0	0	0	0	0	.	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	0	0	.	0	0
	total	106	20	100	24	83	28	128	19	77	30	119	28	439	12	486	11	582	10	225	15	24	41	48	32	55	30	7	.	30	43	119	24	141	22	131	25	17	57	108	23	217	16	172	18	231	15	382	12	331	12	247	14	148	21	231	18	164	19	35	32	80	23	43	31	43	38	97	26	62	31	24	50	100	27	129	27	102	31	150	20	76	38	178	20	107	20	96	23	32	41	48	30	60	30	61	29	119	23	66	27	79	26	73	29	111	23	31	34	120	22	50	26	47	33	41	29	102	22	157	18	54	28	90	24	38	39	48	36	50	35	76	28	80	26	85	32	77	35	31	43	87	24	32	43	26	38	47	28	48	35	63	33	36	38	36	43	60	33	22	60	35	41	211	15	193	16	226	15	74	31	55	27	78	26	119	24	115	21	55	28	43	30	62	29	0	.	10005	2

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. 2302746. Birmensdorf, Swiss Federal Research Institute WSL

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