Selection from the list of topics

What do the numbers stand for?
What is the purpose of the standard error?
When are two results statistically different?
When is it not sufficient to consider only the standard error?
Two types of changes?
Change analyses: When are the results (not) additive?
Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?
Why are no standard error values shown in some tables?
Negative values for growing stock, increment or fellings?

1. What do the numbers stand for?

The tables mainly show results that were calculated using statistical methods from the data of the sample inventory of the National Forest Inventory (NFI). Such results always consist of two figures: (1) the estimated value and (2) the sampling error, the so-called standard error.

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	30	10	17	13	37	9	36	6	26	10	30	4
private	27	17	20	12	50	10	32	8	39	13	34	5
total	29estimated value	9standard error	19	9	44	7	35	5	29	8	32	3

The tables usually show the relative (percentage) standard error (“±%”), but occasionally – in the case of estimated percentages – the absolute standard error (“±”).

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2. What is the purpose of the standard error?

The standard error can be used to define confidence intervals around the estimated value, which contain the true value of the population with a certain statistical certainty.

The statistical certainty is:

68% if the confidence interval is calculated using the standard error
(68% confidence interval = estimated value ± standard error), and
95% if the confidence interval is calculated using twice the standard error
(95% confidence interval = estimated value ± 2 × standard error*)

Example 2: Calculating confidence intervals

LFI5
volume of deadwood (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura
ownership (2 classes)	m³/ha	±%
public	30	10
private	27	17
total	29	9

Question
What are the 68% and 95% confidence intervals of the above estimates?

Procedure

Calculate the absolute standard error (= relative standard error × estimated value / 100)
Calculate the confidence intervals
- 68% confidence interval = estimated value ± absolute standard error
- 95% confidence interval = estimated value ± 2 × absolute standard error

Answer

			Step 1	Step 2

ownership (2 categories)	volume of deadwood Jura NFI5
	estimated value	standard error		confidence interval
		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha
public	30	10	3	27-33	24-36
private	27	17	5	22-32	17-37
total	29	9	3	26-32calculated with the standard error, i.e. 29 ± 3	23-25calculated with twice the standard error, i.e. 29 ± 2 × 3

The volume of deadwood in the Jura is between 26 and 32 m³/ha with a statistical certainty of 68% and between 23 and 35 m³/ha with a statistical certainty of 95%.

The 95% confidence interval is larger than the 68% confidence interval. This results in higher statistical certainty with the 95% confidence interval.

Example 3: Visualisation of the 68% and 95% confidence intervals

Data: see example 2

When interpreting the results, one must define the level of statistical certainty that one would like to apply to the statement.

The 68% confidence interval is generally used in the National Forest Inventory (NFI).

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3. When are two results statistically different?*

This can be checked by comparing the confidence intervals of two estimated values:

If the 68% confidence intervals of two estimates do not overlap, it can be assumed with some certainty that the two populations differ.
If the 95% confidence intervals of two estimates do not overlap, it can be assumed with high certainty that the two populations differ.

Example 4: Interpretation of two results from the same inventory

NFI5
growing stock (stemwood)
ownership (2 categories)
regional demarcation: production region
unit: m³/ha
evaluation area: accessible forest without shrub forest
grid: 1.4 km grid, subgrids 1-5
state 2018/26
	production region
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
ownership (2 categories)	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
public	331	3	311	3	405	4	300	3	256	5	314	2
private	387	5	432	4	454	4	348	4	285	6	398	2
total	345	2	363	2	431	2	314	2	262	4	343	1

	case 2						case 1

case 1

Question
Is the growing stock per hectare greater in the Alps than in the Southern Alps?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap

Answer

ownership (2 categories)	growing stock NFI5
	Alps					Southern Alps
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	314	2	6	308-320	302-326	262	4	10	252-272	242-282

Neither the 68%-confidence intervals nor the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has no influence on the result.

Overall, this means that there is high statistical certainty that the growing stock per hectare is greater in the Alps than in the Southern Alps.

Case 2

Question
Is the growing stock per hectare greater in the Plateau than in the Jura?

Procedure

See case 1.

Answer

ownership (2 categories)	growing stock NFI5
	Jura					Plateau
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±m³/ha	m³/ha	m³/ha	m³/ha	±%	±m³/ha	m³/ha	m³/ha
total	345	2	7	338-352	331-359	363	2	7	356-370	349-377

The 68%-confidence intervals do not overlap (pairs of values in ).
In contrast, the 95%-confidence intervals overlap (pairs of values in ).

In this case, the level of statistical certainty selected has an influence on the interpretation of the results. At the 68% confidence interval level, the conclusion is that the growing stock per hectare is higher in the Plateau than in the Jura. At the 95% confidence interval level, however, this conclusion cannot be drawn.

Considering both confidence interval levels, it can be said that there is some, but not high, statistical certainty that the growing stock per hectare is greater in the Plateau than in the Jura.

The confidence intervals can also be used to determine whether a value estimated with a sample from the National Forest Inventory (NFI) deviates from a target value (e.g. from Swiss forest policy) or whether a change between two inventories is statistically certain.

Example 5: Interpretation of two results from different inventories

ownership (2 categories)	growing stock Plateau*
	NFI4		NFI5
	m³/ha	±%	m³/ha	±%
public	340	3	309	3
private	447	4	432	4
total	386	2	363	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Calculate the absolute standard errors
Calculate the confidence intervals depending on the desired level of statistical certainty:
- 68% confidence intervals (some certainty; absolute standard error)
- 95% confidence intervals (high certainty; twice the absolute standard error)
Assess whether the confidence intervals corresponding to the desired level of statistical certainty do not overlap.

Answer

ownership (2 categories)	growing stock Plateau*
	NFI4					NFI5
	estimated value	standard error		confidence interval		estimated value	standard error		confidence interval
		relative	absolute	68%	95%		relative	absolute	68%	95%
	m³/ha	±%	±	m³/ha	m³/ha	m³/ha	±%	±	m³/ha	m³/ha
public	340	3	11	329-351	318-362	309	3	11	298-320	287-331
private	447	4	16	431-463	415-479	432	4	17	415-449	398-466
total	386	2	8	378-394	370-402	363	2	9	354-372	345-381
* accessible forest without shrub forest NFI4/NFI5

The 68%-confidence intervals do not overlap in the public forest (pairs of values in ), but they do in the private forest and the total forest (pairs of values in ). At the certainty level of the 68% confidence intervals, it can be concluded that the growing stock per hectare in the public forest of the Plateau has decreased. For the private forest, as well as for the total forest, the decrease is not statistically certain at this level.

The 95%-confidence intervals overlap in all three categories. At the certainty level of the 95% confidence intervals, it is therefore not certain that the growing stock has decreased, even for the public forest.

See also examples 6 and 7, in which changes between two inventories are interpreted using the more sensitive metric of net change.

For many target variables in the NFI, it is possible to determine whether a change between two inventories is statistically certain, not only by comparing the states in the two inventories (see example 5), but also by analysing the net change** between the two inventories.

To do this, one must first define the level of statistical certainty that one would like to apply to the statement (as done above):

A net change has occurred with some certainty if the relative standard error is less than 100% or if the confidence interval calculated with the absolute standard error (68% confidence interval) does not include the value 0.
A net change has occurred with high certainty if twice the relative standard error is less than 100% or if the confidence interval calculated with twice the absolute standard error (95% confidence interval) does not include the value 0.

Example 6: Interpretation of net changes with the relative standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	m³/ha	±%
public	-34	30
private	-9	147
total	-23	34
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Determine whether:
- the relative standard error is less than 100% (certainty level of the 68% confidence interval)
- twice the relative standard error is less than 100% (certainty level of the 95% confidence interval)

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
		single	double
	m³/ha	±%	±%
public	-34	30	60
private	-9	147	294
total	-23	34	68
* accessible forest without shrub forest NFI4/NFI5

The single relative standard error is less than 100% for both the public forest and the total forest (highlighted values). For the private forest, however, it is greater than 100% (highlighted values). At the certainty level of the 68% confidence interval, it can be concluded that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain at this level.

The same conclusion is reached in this example with the certainty level of the 95% confidence interval, as twice the relative standard error is again less than 100% for both the public forest and the total forest.

Overall, this means that there is high statistical certainty that the growing stock per hectare has decreased in the public forest in the Plateau, as well as overall. For private forests, on the other hand, the decrease in stock is not statistically certain (as twice the relative standard error and the relative standard error are both less than 100%).

Example 7: Interpretation of net changes with the absolute standard error

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error
	%	±
public	-10	3
private	-2	3
total	-6	2
* accessible forest without shrub forest NFI4/NFI5

Question
Has the growing stock per hectare in the Plateau decreased from NFI4 to NFI5?

Procedure

Define the level of statistical certainty (68% or 95% confidence interval)
Calculate the confidence interval based on the defined certainty level
- 68% confidence interval with the absolute standard error
- 95% confidence interval with twice the absolute standard error
Determine whether the corresponding confidence interval does not include the value 0

Answer

ownership (2 categories)	change in growing stock from NFI4 to NFI5, Plateau*
	estimated value	standard error		confidence interval
		single	double	68%	95%
	%	±	±	%	%
public	-10	3	6	-13 to -7	-16 to -4
private	-2	3	6	-5 to +1	-8 to +4
total	-6	2	4	-8 to -4	-10 to -2
* accessible forest without shrub forest NFI4/NFI5

Neither the 68% nor the 95% confidence interval includes the value 0 for the public forest or for the total forest. At both certainty levels, this leads to the conclusion that the growing stock in the Plateau has decreased in the public forest and in total. For the private forest, on the other hand, the decrease in stock is not statistically certain (as the confidence intervals include the value 0).

Net changes can be interpreted using the absolute or relative standard error (see examples 6 and 7). It is advisable to use the absolute standard error if the estimated value in the table is shown as a percentage (%; as in example 7), and the relative standard error if the estimated value in the table is shown in absolute terms (m³, m³/ha, pc, m²; as in example 6). This means that the standard error can be taken directly from the table and does not need to be converted.

To assess whether a change is statistically certain, it is advisable to analyse the net change between the two inventories wherever possible. Net changes are more sensitive than status comparisons, which makes it easier to detect differences statistically. See examples 6 and 7 (net change) and 7 (comparison of two results from different inventories).

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4. When is it not sufficient to consider only the standard error?

The National Forest Inventory (NFI) is a sample-based large-scale inventory whose sample areas are arranged on a grid with a mesh size of 1.4 km × 1.4 km. The NFI was designed in such a way that the growing stock for Switzerland as a whole can be predicted with a maximum standard error of 1%.

If results are requested for individual regions or for individual classes, the standard error quickly becomes much larger. This is because the number of sample areas and subjects (e.g. sample trees, young forest plants, pieces of deadwood) analysed for the queried combination of variable expressions* is considerably lower than for the total.

Example 8: Standard error and combination of variable expressions

region	growing stock
	total		maple		sycamore		Norway maple
	estimated value	standard error	estimated value	standard error	estimated value	standard error	estimated value	standard error
	m³/ha	±%	m³/ha	±%	m³/ha	±%	m³/ha	±%
Switzerland	343	1	12	5	11	5	1	20
production region "Plateau"	363	2	15	10	14	11	0	50
Canton of Aargau	289	7	15	20	13	20	1	63

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

forest districts are selected as the regional demarcation or results for small cantons (e.g. Appenzell Innerrhoden, Nidwalden) are queried,
a classification variable with many classes is selected (e.g. tree species in 56 classes),
several classification variables are combined with each other (e.g. main tree species and development stage).

Large standard errors are an indication that the estimate may have been based on too small a number of sample areas or subjects and that the result of the estimate therefore may not be reliable.

For the target variable “number of stems of young forest plants with browsing damage” (browsing intensity), an uncertain estimate is not necessarily recognisable by a large standard error. This is because this target variable is a quotient estimator**, which is – due to the survey method – only based on a small number of assessed young forest plants for rarer tree species (e.g. pine, larch, Arolla pine, oak, chestnut). Accordingly, for this target variable one should always check how many young forest plants were assessed for the individual estimates. As a rule of thumb, at least 30 young forest plants per estimate should have been assessed for browsing in order to obtain a reliable estimate.

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5. Two types of changes?

There are two types of changes in the National Forest Inventory (NFI):

The first type of change involves specific target variables for change components such as increment, fellings or mortality. These target variables are only available for two consecutive measurement cycles, e.g. NFI4 to NFI5. When change components are analysed, the classification variable expression of the second measurement cycle is generally assigned to the first measurement cycle. These analyses therefore do not take into account any change in the expression (e.g. from private to public ownership) from the earlier to the later measurement cycle.
In the second type of change, the difference in target variables, such as stem number, growing stock or forest area, is used to calculate the net change between two measurement cycles. These target variables are usually used to represent states, e.g. that in NFI5, but can also show the net change between any two measurement cycles, e.g. between NFI1 and NFI5. In these net change analyses, the change in expression is taken into account for some of the classification variables, including “tree condition” and “forest, non-forest”. In this way, it can be seen, for example, that the forest area has increased. For the remaining classification variables, the expressions are regarded as static. This means that the most recent state – whether it was assessed in a measurement cycle (e.g. “primary forest function NFI5”) or taken from an external data source (e.g. “protection forest [2022]”) – is assigned to all measurement cycles.

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6. Change analyses: When are the results (not) additive?

The results are:

additive if they are reported as the total change between two measurement dates. For example, the fellings for the various production regions reported in cubic metres (m³) can be added together to obtain the total fellings for Switzerland.
not additive if they are reported as a change per year (e.g. m³/year). For example, the fellings for the various production regions reported in cubic metres per year (m³/year) cannot be added together to obtain the total fellings for Switzerland.

Example 9: : Additivity/non-additivity of changes

	growing stock: total change between NFI4 and NFI5 (in 1000 m³)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
lower altitude zone	-3759.0	28	-4909.1	36	-1025.2	133	-317.1	195	3464.1	31	-6546.3	42
higher altitude zone	574.3	117	-328.0	62	1093.0	123	7128.4	21	666.7	93	9134.3	24
total	-3184.7	40	-5237.1	34	67.9	2805	6811.3	23	4130.8	30	2588.1	136
	* accessible forest without shrub forest NFI4/NFI5
	additive

	growing stock: annual change between NFI4 and NFI5 (in 1000 m³/year)*
	Jura		Plateau		Pre-Alps		Alps		Southern Alps		Switzerland
higher/lower altitude zone	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%	1000 m³/year	±%
lower altitude zone	-421.1	28	-560.6	36	-115.7	133	-35.9	195	389.5	31	-740.7	42
higher altitude zone	64.7	117	-38.3	62	122.4	123	810.4	21	74.9	93	1032.7	24
total	-357.3	40	-598.4	34	7.6	2805	773.8	23	464.4	30	292.7	136
	* accessible forest without shrub forest NFI4/NFI5
	not additive

The non-additivity of changes expressed per year arises because the total change is divided by the average number of years between the two measurement dates in the domain* under consideration, and this average number of years varies slightly depending on the domain.

Example 10: Average number of years per domain

domain		mean number of years*
production region	higher/lower altitude zone
Mittelland	-	8.75
Plateau	higher altitude zone	8.56
Plateau	lower altitude zone	8.76
Southern Alps	-	8.90
Southern Alps	higher altitude zone	8.90
Southern Alps	lower altitude zone	8.89
* between the NFI4 and NFI5 measurements in the accessible forest without shrub forest

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7. Why is a dot (“.”) sometimes given in the cells for standard error or estimated value?

In the calculation of a results table, data is not always available for all combinations of variable expressions*. In most cases, this indicates that the variable estimated with the relevant target variable does not occur or only occurs very rarely. The value 0 is then usually used. However, as this value is not based on any direct measurements, a dot (“.”) is given for the associated standard error. If reference is made to the assumed value of 0 in the calculation, e.g. in the case of percentages or certain change estimates, no value can be used. In this case, a dot (“.”) is given for the estimated value and the standard error.

For example, no Arolla pines have yet been found and measured in the Plateau in the National Forest Inventory (NFI) (growing stock of Arolla pine by production region). It can therefore be assumed that the values are missing because the Arolla pine actually does not occur in the Plateau, and therefore the growing stock in that region must be 0.

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8. Why are no standard error values shown in some tables?

The data in these tables originates from a complete survey and not from a sample inventory. Accordingly, it is not necessary to specify a standard error because there is no uncertainty due to sampling.

The forest road network survey that the National Forest Inventory (NFI) periodically carries out with the local forestry services is an example of such a complete survey.

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9. Negative values for growing stock, increment or fellings?

A negative value is possible if the result could only be calculated on the basis of a few trees. The estimate is therefore not reliable. This is also reflected in the standard error, which is usually extremely large for negative stock, increment or felling values.

Negative values can occur because, when the stemwood volume of the tally trees is determined, the volume estimated solely on the basis of the diameter at breast height (DBH) (tariff volume) is adjusted using the volume of the tally trees estimated on the basis of DBH, diameter at 7 m height and tree height. This procedure enables an unbiased and more accurate calculation of the stemwood volume. However, it can lead to negative values for all target variables that are based on the stemwood volume of the tally trees (e.g. growing stock, increment, fellings) if the number of tally trees in the selected combination of variable expressions* is small.

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NFI2–NFI3
net increment
protection forest against avalanches (2022) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³/yr
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
change 1993/95–2004/06
		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/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	.	5	71	-3	.	26	49	11	77	0	.	1	371	0	.	0	.	0	.	0	.	0	.	0	.	-1	822	0	.	0	.	17	43	7	65	5	81	8	63	20	94	27	41	2	100	0	.	0	.	4	.	1	.	2	.	0	.	0	.	-2	.	0	228	-3	.	-6	158	0	.	3	95	15	51	0	.	3	77	0	.	0	.	0	.	0	.	0	.	5	70	0	.	0	.	0	.	0	.	0	.	7	55	3	74	7	53	0	.	0	.	0	.	1	100	0	.	0	.	1	199	2	.	9	84	5	100	6	85	3	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	.	0	.	18	47	28	52	30	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	269	15
	fir	0	.	0	.	0	.	0	.	0	.	2	.	14	50	2	81	0	.	1	93	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	67	0	.	1	100	0	.	2	59	0	100	4	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	.	2	91	0	.	1	140	0	.	2	71	3	60	0	.	2	100	0	.	0	.	0	.	0	.	0	.	6	64	0	.	0	.	0	.	0	.	0	.	0	.	1	71	0	.	0	.	0	.	0	.	-2	100	-1	100	0	.	1	545	0	.	1	145	1	100	2	100	8	58	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	.	0	.	0	100	2	100	0	2720	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	63	21
	pine	0	.	0	.	0	.	0	.	0	.	0	.	-1	89	0	.	0	.	-2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	85	0	.	0	.	-1	100	0	529	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	-1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	535	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	52	1	185	3	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	84
	larch	0	.	0	.	0	.	0	.	0	.	0	.	-3	102	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	2	75	0	147	4	106	13	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	67	1	86	2	79	2	.	0	.	0	.	2	114	-1	100	0	.	0	.	0	.	0	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	20	47	9	49	7	43	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	64	23
	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	.	4	47	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	79	1	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	45
	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	100	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	1751
	beech	0	.	0	.	0	.	0	.	-2	100	0	.	7	78	15	59	0	.	2	81	0	.	0	.	0	.	0	.	0	.	0	.	0	112	0	.	0	.	9	57	2	72	1	100	-1	100	1	100	0	.	0	100	0	.	0	.	1	.	2	.	0	.	0	.	0	.	0	.	1	179	0	.	10	77	0	.	-2	119	-4	94	0	.	-1	131	0	.	0	.	0	.	0	.	0	.	-2	170	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	59	1	100	0	.	0	100	0	.	1	80	0	.	0	100	1	166	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	.	0	.	0	.	0	.	3	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	51	29
	maple	0	.	0	.	0	.	0	.	0	.	0	.	2	145	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	328	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	58	0	.	0	100	0	.	0	74	-2	65	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	100	0	.	0	.	1	100	0	100	1	60	0	.	0	.	1	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	159	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	61
	ash	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	100	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	2	81	0	.	4	100	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	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	405	1	100	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	50
	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	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	88
	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	51	0	100	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	100	0	.	0	100	1	67	-1	136	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	128	0	.	0	100	0	.	0	.	0	.	0	34896	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	68	2	67	0	58	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	37
	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
	total	0	.	0	.	0	.	0	.	4	84	-1	.	46	36	28	56	0	.	3	134	0	.	0	.	0	.	0	.	0	.	0	.	0	12130646	0	.	0	.	30	39	12	44	8	60	7	87	28	72	44	31	6	81	0	.	0	.	5	.	3	.	2	.	0	.	0	.	4	.	4	67	-3	.	5	247	0	.	4	119	15	40	0	.	8	71	0	.	0	.	0	.	0	.	0	.	10	75	3	.	0	.	0	.	0	.	0	.	9	48	5	61	10	51	2	.	0	.	0	.	4	102	-1	142	0	.	2	253	2	.	11	76	8	78	8	72	13	61	0	.	0	.	3	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	-1	.	0	.	47	32	44	38	45	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	484	11
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	21	42	26	42	30	54	24	71	33	38	55	39	78	51	135	19	184	16	8	283	1	65	9	49	11	50	0	.	20	71	64	31	-1	4762	57	30	0	100	47	46	87	26	57	30	67	29	114	19	55	32	57	55	18	75	64	27	106	37	6	95	37	34	9	43	14	44	24	42	18	54	20	41	11	137	-2	1122	32	44	27	44	18	40	59	51	53	34	30	36	-2	241	24	50	4	58	19	43	39	70	20	39	40	33	40	37	49	39	10	54	30	40	5	112	0	0	1	88	0	100	3	81	0	16317	4	103	3	98	11	45	15	54	8	64	26	37	21	47	28	44	8	82	12	90	13	70	9	65	31	27	10	71	17	38	7	139	13	69	18	45	2	90	18	54	21	59	48	26	63	26	24	47	28	37	33	40	67	50	63	32	23	47	14	58	32	31	0	.	2723	5
	fir	15	59	5	55	4	59	18	52	13	46	25	49	17	43	149	17	136	18	29	42	0	74	0	723	3	113	0	.	6	62	10	61	21	61	14	51	0	.	3	58	1	316	8	39	-2	252	5	67	0	.	76	21	33	35	95	23	23	60	15	42	25	34	13	41	10	60	30	40	0	2980	-4	155	15	41	5	277	1	100	5	58	17	44	17	54	4	69	18	40	12	51	3	41	-3	372	0	364	23	40	11	64	25	44	14	38	13	59	0	0	1	78	4	100	0	.	0	.	1	100	4	70	3	72	5	74	10	54	0	.	1	63	9	43	0	3771	18	41	2	122	0	100	18	48	7	89	13	43	6	55	8	57	9	38	5	61	11	63	21	52	4	78	5	70	5	84	4	57	11	57	7	41	5	54	10	60	17	66	13	45	3	92	0	100	4	70	0	.	1179	6
	pine	3	71	13	79	3	63	1	79	0	.	0	100	2	73	2	53	-2	450	4	118	0	100	4	68	1	204	0	.	0	100	0	441	0	.	0	725	1	253	0	.	1	49	3	109	1	100	15	41	6	105	1	252	0	100	3	62	0	.	3	100	2	100	0	.	0	0	1	100	0	.	-2	100	0	.	0	100	0	.	0	.	0	.	0	.	5	111	-1	79	0	475	1	68	-2	76	3	100	-2	73	0	.	2	58	-2	204	4	59	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	3	76	0	100	0	.	0	.	0	.	0	74	0	100	0	.	0	100	0	.	0	71	0	.	2	78	0	0	0	.	-2	100	0	.	18	39	9	35	1	167	0	.	2	100	3	93	-2	91	4	43	3	55	1	161	0	140	0	.	123	18
	larch	1	122	2	78	2	100	4	81	0	.	0	.	1	237	0	75	6	82	0	.	0	.	0	.	1	88	0	.	0	.	-2	247	0	.	1	81	0	.	1	100	10	50	1	402	7	63	20	41	64	23	0	100	0	71	1	81	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	100	0	100	1	100	-1	127	-1	100	0	.	12	96	0	.	0	.	0	100	0	.	0	.	0	.	1	100	1	100	3	100	0	.	0	121	8	56	6	46	2	53	0	.	1	65	18	35	5	54	-2	378	0	.	0	.	0	.	8	48	0	.	0	.	0	.	1	100	0	.	0	100	0	0	0	.	0	100	0	.	1	74	0	.	0	.	0	.	0	.	47	37	27	23	7	44	0	100	0	100	1	100	0	.	0	.	0	943	0	100	4	55	0	.	294	12
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	217	12	44	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	3	41	6	57	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	26	28
	other conifers	2	95	0	.	3	100	2	76	0	.	0	71	0	.	0	0	16	41	0	71	0	.	0	.	0	.	8	.	0	100	0	.	0	.	0	0	0	.	0	100	0	100	0	.	0	.	0	.	1	61	0	71	3	100	6	96	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	9	100	0	.	0	100	0	100	0	.	3	100	0	100	0	100	1	69	0	.	0	.	0	.	0	100	0	.	0	.	0	78	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	100	0	.	0	100	0	.	0	100	0	.	0	.	2	100	0	120	0	.	0	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	1	100	0	0	0	0	0	.	0	.	1	73	-1	136	0	100	0	.	0	.	0	.	0	.	60	25
	beech	43	30	22	39	35	40	56	26	6	45	15	40	50	39	54	29	131	22	52	24	5	92	27	44	21	34	0	.	7	48	11	62	17	34	28	35	0	.	9	74	-1	278	15	35	0	91	7	61	0	.	61	27	30	35	30	30	26	45	5	67	12	40	5	113	6	65	11	40	10	70	1	71	1	1690	22	49	15	43	11	95	31	34	11	79	47	27	30	35	18	50	12	42	22	37	11	44	0	3591	16	43	13	39	11	48	24	36	0	.	5	67	0	.	13	64	27	43	8	45	18	50	22	39	12	48	9	53	0	.	2	192	9	49	3	69	4	72	-4	130	1	168	7	75	-2	123	8	62	8	42	11	59	28	35	9	49	11	42	9	42	5	73	7	59	0	.	0	92	11	42	6	53	5	51	19	38	7	81	30	33	7	63	15	40	14	38	0	.	1399	5
	maple	5	41	6	50	0	8481	1	174	3	88	4	138	4	92	10	34	12	42	9	46	4	52	1	66	3	62	0	.	3	71	0	192	4	53	3	53	0	.	10	41	3	49	-1	214	1	115	3	59	0	.	8	49	2	93	2	47	11	70	3	64	1	131	0	160	3	73	1	52	5	72	0	193	7	71	6	70	1	574	-2	139	1	90	4	133	4	51	4	53	3	54	2	46	10	50	1	90	2	55	1	66	10	49	4	107	3	70	1	100	2	100	1	100	0	90	0	177	1	55	0	70	1	100	0	100	4	60	0	100	0	.	1	43	6	45	1	57	2	56	0	.	3	63	0	.	0	72	0	71	1	39	3	54	2	100	0	100	3	100	0	.	1	72	0	0	1	71	4	41	-1	268	4	50	3	78	2	161	3	46	9	85	1	75	5	80	0	.	244	9
	ash	2	171	6	103	4	63	1	200	0	.	5	72	0	828	16	38	24	27	4	54	4	80	1	68	0	1447	0	.	3	70	10	64	9	57	10	71	3	100	17	66	1	71	0	81	3	61	3	61	0	.	20	32	17	75	4	67	2	50	1	179	2	76	0	0	0	100	0	100	6	93	0	0	1	63	25	39	11	45	1	70	5	78	7	46	7	48	3	48	1	59	6	88	4	61	8	67	0	100	10	73	21	52	10	55	9	72	0	100	5	100	0	.	1	73	1	55	5	153	3	102	1	124	1	81	2	56	2	71	0	.	4	50	2	61	1	86	2	55	0	824	1	58	1	100	0	.	0	100	-4	117	3	54	3	59	3	63	5	65	0	.	9	97	2	78	0	167	-1	391	7	79	10	49	13	65	-2	383	6	48	-3	204	11	56	4	48	0	.	386	10
	oak	10	70	2	66	1	100	5	65	0	.	0	.	0	100	0	1230	18	35	0	.	1	99	0	100	-2	116	0	.	0	631	1	100	1	72	2	78	12	52	0	.	0	.	1	100	0	.	0	.	0	100	0	2511	-1	205	7	78	2	100	1	85	0	.	1	100	0	.	0	.	0	100	0	.	0	.	0	100	0	259	0	.	1	81	0	100	1	175	0	.	0	298	1	82	-2	188	0	100	0	.	0	.	2	48	11	64	4	34	0	.	0	.	0	.	1	64	3	54	6	61	-1	148	5	46	0	1259	1	71	0	.	1	100	0	225	0	100	0	.	0	1043	0	.	1	108	-3	62	2	58	0	.	7	55	1	100	2	73	9	56	1	100	2	100	2	100	1	64	3	72	1	75	0	.	1	100	1	58	1	89	5	60	1	358	3	79	-2	150	0	.	127	15
	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	.	5	52	0	73	0	.	0	.	0	.	0	.	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	7	108	-1	336	8	57	-8	82	9	157	-6	153	4	80	14	79	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	100	0	.	1	128	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	68
	other broadleaves	2	140	9	60	2	120	3	67	0	84	2	105	11	36	7	56	9	64	3	47	2	77	0	300	1	68	0	.	-1	85	11	71	0	109	2	63	0	155	10	73	1	100	0	228	11	63	16	41	11	50	6	84	5	61	1	226	0	905	0	100	1	54	0	0	0	293	2	93	1	58	0	192	2	108	0	71	-2	119	-3	98	10	59	5	74	6	79	1	65	1	65	2	245	3	67	6	59	0	895	-4	60	13	53	4	46	0	789	5	80	9	48	5	88	11	54	5	91	18	43	-5	115	18	70	3	86	2	88	0	100	3	100	8	84	10	91	0	82	0	774	2	124	1	104	0	70	0	100	0	92	-1	253	0	303	-1	73	1	253	2	72	0	.	0	125	9	63	2	90	9	57	9	91	3	56	4	99	1	195	0	79	0	1081	5	75	1	105	0	.	317	11
	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
	total	104	23	91	26	83	28	115	25	54	32	106	30	163	30	373	14	534	11	109	27	19	44	42	36	39	32	8	.	39	42	106	26	51	66	118	21	17	58	98	34	104	24	86	24	89	24	190	15	148	19	229	19	105	26	213	19	169	32	35	37	80	25	28	37	34	37	70	27	40	44	15	70	37	60	67	56	59	34	39	43	82	25	104	33	141	22	85	25	33	40	51	33	38	39	47	27	62	50	53	31	126	24	95	27	105	25	17	43	67	28	20	43	37	34	30	48	48	34	35	52	59	31	38	43	31	38	13	42	26	41	47	33	47	35	45	36	31	51	15	57	43	35	16	67	33	35	46	24	33	41	61	25	35	40	47	38	59	37	10	68	41	44	108	24	101	18	109	20	52	38	59	28	87	27	89	42	125	22	42	38	50	31	62	26	0	.	6914	3
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	21	42	26	42	30	54	24	71	38	34	52	42	104	40	146	19	184	16	8	262	1	65	9	49	11	50	0	.	20	71	64	31	-1	2463	57	30	0	100	64	36	93	25	62	29	76	27	134	21	83	25	58	54	18	75	64	27	109	36	7	81	40	32	9	43	14	44	22	47	18	53	17	53	5	348	-2	1122	35	41	42	33	18	40	62	48	53	34	30	36	-2	241	24	50	4	58	23	37	39	70	20	39	40	33	40	37	49	39	18	39	34	37	13	56	0	0	1	88	0	100	4	66	0	16317	4	103	4	93	13	41	24	46	13	54	32	34	23	42	28	44	8	82	13	90	13	70	9	65	31	27	10	71	17	38	7	139	13	69	18	45	1	163	18	54	39	38	76	25	93	21	24	47	28	37	33	40	67	50	63	32	23	47	14	58	32	31	0	.	2992	5
	fir	15	59	5	55	4	59	18	52	13	46	27	45	30	33	151	17	136	18	31	40	0	74	0	723	3	113	0	.	6	62	10	61	21	61	14	51	0	.	7	45	1	316	9	37	-2	252	7	50	0	100	80	21	33	35	95	23	23	60	15	42	25	34	13	41	10	60	36	37	2	240	-4	155	15	39	5	277	3	58	7	43	17	44	20	49	4	69	18	40	12	51	3	41	-3	372	6	63	23	40	11	64	25	44	14	38	13	59	0	0	3	52	4	100	0	.	0	.	1	100	2	153	2	121	5	74	10	59	0	.	2	67	10	40	2	194	26	33	2	122	0	100	18	46	7	89	13	43	6	55	8	57	9	38	5	61	11	63	21	52	3	108	5	70	5	83	6	50	12	59	7	41	5	54	10	60	17	66	13	45	3	92	0	100	4	70	0	.	1240	5
	pine	3	71	13	79	3	63	1	79	0	.	0	100	1	165	2	53	-2	450	1	309	0	100	4	68	1	204	0	.	0	100	0	441	0	.	0	725	1	253	0	.	4	60	3	109	1	100	14	43	7	104	1	224	0	100	3	62	0	.	3	100	2	100	0	.	0	0	1	100	0	.	-2	100	0	100	0	100	0	.	0	.	0	.	-1	100	5	111	-1	79	0	475	1	68	-2	76	3	100	-2	73	0	.	2	58	-2	204	4	59	0	100	0	535	1	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	3	76	0	100	0	.	0	.	0	.	0	74	0	100	0	.	0	100	0	.	0	71	0	.	2	78	0	0	0	.	-2	100	0	.	21	35	9	36	4	64	0	.	2	100	3	93	-2	91	4	43	3	55	1	161	0	140	0	.	129	18
	larch	1	122	2	78	2	100	4	81	0	.	0	.	-2	149	0	75	6	82	0	.	0	.	0	.	1	88	0	.	0	.	-2	247	0	.	1	81	0	.	1	100	10	49	3	149	7	66	24	38	76	21	0	100	0	71	1	81	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	100	0	100	1	100	-1	127	-1	100	0	.	12	96	0	.	0	.	0	100	0	.	0	.	0	.	1	100	1	100	3	100	0	.	1	90	8	53	8	40	5	55	0	.	1	65	20	33	4	69	-2	378	0	.	0	.	0	100	9	43	0	.	0	.	0	.	1	100	0	.	0	100	0	0	0	.	0	100	0	.	1	74	0	.	0	.	0	.	0	.	67	30	37	21	14	31	0	100	0	100	1	100	0	.	0	.	0	943	0	100	4	55	0	.	360	10
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	217	16	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	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	.	8	49	6	51	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	24
	other conifers	2	95	0	.	3	100	2	76	0	.	0	71	0	.	0	0	16	41	0	71	0	.	0	.	0	.	8	.	0	100	0	.	0	.	0	0	0	.	0	100	0	100	0	.	0	.	0	100	1	61	0	71	3	100	6	96	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	9	100	0	100	0	100	0	100	0	.	3	100	0	100	0	100	1	69	0	.	0	.	0	.	0	100	0	.	0	.	0	78	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	100	0	.	0	100	0	.	0	100	0	.	0	.	2	100	0	120	0	.	0	100	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	1	100	0	0	0	0	0	.	0	.	1	73	-1	136	0	100	0	.	0	.	0	.	0	.	60	25
	beech	43	30	22	39	35	40	56	26	4	79	15	40	58	35	69	26	131	22	54	23	5	92	27	44	21	34	0	.	7	48	11	62	17	33	28	35	0	.	19	46	1	245	16	34	-1	155	8	57	0	.	60	27	30	35	30	30	28	43	7	56	12	40	5	113	6	65	11	39	11	65	1	71	10	122	22	49	13	51	7	158	31	34	11	85	47	27	30	35	18	50	12	42	22	37	9	60	2	119	16	43	13	39	11	48	24	36	0	.	5	67	0	.	13	64	27	43	8	45	21	44	24	38	12	48	9	53	0	.	3	147	9	49	4	64	5	67	-4	130	1	168	8	67	-2	123	8	62	8	42	11	59	28	35	9	49	11	42	9	42	6	67	7	59	0	.	0	92	14	36	6	53	5	51	19	38	7	81	30	33	7	63	15	40	14	38	0	.	1445	5
	maple	5	41	6	50	0	8481	1	174	3	88	4	138	5	78	10	34	12	42	9	45	4	52	1	66	3	62	0	.	3	71	0	192	4	52	3	53	0	.	10	43	3	49	-1	214	1	115	3	54	0	.	8	49	2	93	2	47	11	70	3	64	1	131	0	160	3	73	1	52	5	67	0	193	8	68	6	70	1	462	-3	79	1	90	4	133	4	51	4	53	3	54	2	46	10	50	1	97	2	55	1	66	10	49	4	107	3	70	1	100	2	100	1	100	0	90	0	177	1	55	0	70	1	100	0	100	4	55	0	100	0	.	2	42	6	45	2	41	2	56	0	.	3	53	0	.	0	72	0	71	1	39	3	54	2	100	0	100	3	100	0	.	1	72	0	0	1	71	5	45	-1	268	4	50	3	78	2	161	3	46	9	85	1	75	5	80	0	.	249	9
	ash	2	171	6	103	4	63	1	200	0	.	5	72	0	617	16	38	24	27	5	50	4	80	1	68	0	1447	0	.	3	70	10	64	9	56	10	71	3	100	16	68	1	71	0	81	3	61	3	56	0	.	20	32	17	75	4	67	2	50	1	179	2	76	0	0	0	100	0	100	6	89	0	0	1	63	25	39	11	45	4	56	5	78	11	45	7	48	3	48	1	59	6	88	4	61	8	64	0	100	10	73	21	52	10	55	9	72	0	100	5	100	0	.	1	73	1	55	5	153	3	102	1	124	1	81	2	56	2	71	0	.	4	50	2	61	1	72	2	55	0	824	1	58	1	100	0	.	0	100	-4	117	3	54	3	59	3	63	5	65	0	.	9	97	2	89	1	86	-1	424	7	79	10	49	13	65	-2	383	6	48	-3	204	11	56	4	48	0	.	392	9
	oak	10	70	2	66	1	100	5	65	0	.	0	.	0	83	0	1230	18	35	0	.	1	99	0	100	-2	116	0	.	0	631	1	100	1	72	2	78	12	52	0	.	0	.	1	100	0	.	0	100	0	100	0	2511	-1	205	7	78	2	100	1	85	0	.	1	100	0	.	0	.	0	100	0	.	0	.	0	100	0	259	0	.	1	81	0	100	1	175	0	.	0	298	1	82	-2	188	0	100	0	.	0	.	2	48	11	64	4	34	0	.	0	.	0	.	1	64	3	54	6	61	-1	148	5	46	0	1259	1	71	0	.	1	100	0	225	0	100	0	.	0	1043	0	.	1	108	-3	62	2	58	0	.	7	55	1	100	2	73	9	56	1	100	2	100	2	100	1	64	3	72	2	66	0	.	1	100	1	58	1	89	5	60	1	358	3	79	-2	150	0	.	127	15
	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	.	5	52	0	73	0	.	0	.	0	.	0	.	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	7	108	-1	336	8	57	-8	82	9	157	-6	153	4	80	14	79	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	100	0	.	1	128	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	68
	other broadleaves	2	140	9	60	2	120	3	67	0	84	2	105	11	36	7	55	9	64	3	43	2	77	0	300	1	68	0	.	-1	85	11	71	0	109	2	63	0	155	10	70	1	152	0	228	12	60	18	38	10	55	7	78	5	61	1	226	0	905	0	100	1	54	0	0	0	293	2	93	1	58	0	192	2	107	0	71	-2	119	-2	113	10	59	5	74	6	79	1	65	1	65	2	245	3	67	6	60	0	895	-4	60	13	53	4	46	0	789	5	79	9	48	5	88	11	54	5	91	18	43	-5	115	18	70	3	86	2	88	0	100	3	100	8	84	10	91	0	66	0	774	2	124	1	104	0	70	0	100	0	92	-1	253	0	303	-1	73	1	253	2	72	0	100	0	125	10	55	4	54	10	55	9	91	3	56	4	99	1	195	0	79	0	1081	5	75	1	105	0	.	323	11
	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
	total	104	23	91	26	83	28	115	25	57	30	105	30	208	25	401	13	534	11	111	26	19	44	42	36	39	32	8	.	39	42	106	26	51	66	118	21	17	58	127	28	115	21	94	23	96	23	218	16	193	16	234	19	105	26	213	19	174	31	38	35	82	24	28	37	34	37	73	26	44	41	12	92	42	61	67	56	63	32	54	33	82	25	111	31	141	22	85	25	33	40	51	33	38	39	57	26	65	48	53	31	126	24	95	27	105	25	26	33	72	26	30	33	39	32	30	48	48	34	39	47	59	32	38	43	33	40	15	39	37	37	55	30	55	32	58	31	31	51	15	57	45	34	16	67	33	35	46	24	33	41	61	25	35	40	47	38	59	37	10	74	41	44	155	19	144	17	154	17	52	38	59	28	87	27	89	42	125	22	42	38	50	31	62	26	0	.	7389	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. 2283790. Birmensdorf, Swiss Federal Research Institute WSL

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