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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NFI3–NFI4
increment (gross increment)
ownership (2 categories) · 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 2004/06–2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
ownership (2 categories)	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	.
public	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	14	44	25	65	37	50	45	34	15	66	24	50	69	32	62	25	79	25	80	21	0	100	5	87	-3	132	0	.	42	48	62	38	36	36	43	40	0	0	79	27	124	28	51	43	79	29	148	23	102	29	43	38	24	41	45	48	28	43	14	60	-1	957	12	59	14	39	16	39	37	59	32	32	29	39	12	47	13	102	46	32	27	46	11	64	52	42	21	38	26	42	16	75	5	55	53	34	34	48	17	49	17	55	11	56	32	47	25	66	44	41	55	42	0	.	0	100	0	.	6	79	4	68	11	70	16	72	10	60	30	55	-1	1436	3	391	4	105	40	51	3	100	31	41	12	50	6	54	37	33	26	44	13	48	34	41	10	87	13	48	3	81	1	100	94	31	63	35	107	28	10	46	16	73	11	53	14	64	12	103	17	60	10	47	17	41	0	.	2721	5
	fir	5	54	3	73	4	68	37	34	7	74	29	52	9	89	38	45	63	26	63	26	0	221	4	84	-1	250	0	.	1	128	4	101	29	53	8	145	0	.	9	54	12	45	10	54	12	79	22	41	2	100	56	25	9	55	12	39	11	53	10	89	17	81	11	41	19	45	20	47	8	83	-1	218	10	81	13	47	0	115	11	53	8	64	7	50	6	73	17	49	9	54	5	77	1	309	8	58	17	47	16	49	0	444	12	103	4	100	0	100	5	49	3	100	0	.	0	.	0	.	10	71	-2	232	11	69	11	48	0	.	9	67	10	65	5	210	8	75	3	64	0	.	23	38	7	83	12	47	8	44	13	52	9	70	7	50	10	67	16	57	4	58	0	.	6	92	17	78	21	56	9	54	3	64	0	100	3	80	1	63	2	100	3	100	6	95	0	.	976	7
	pine	0	100	3	48	0	5113	1	100	0	.	1	100	5	81	-1	111	13	46	3	226	0	.	1	92	0	241	0	.	0	100	0	399	0	.	1	89	2	100	0	.	-1	266	0	722	0	.	-6	110	7	180	2	144	0	100	2	69	0	.	-1	100	2	100	0	.	0	0	1	100	0	.	6	100	0	100	-1	100	0	.	0	.	0	.	0	.	8	73	0	153	4	73	-1	109	0	1134	-3	100	0	100	0	.	1	100	0	105	2	101	0	0	6	97	0	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	1	79	0	0	0	.	0	.	0	.	2	100	0	100	0	.	0	100	0	.	0	100	0	.	1	85	0	.	0	.	8	100	0	.	18	46	0	3753	15	82	0	.	-4	100	1	106	0	100	2	65	0	275	1	145	0	71	0	.	100	29
	larch	0	100	2	99	0	71	2	169	0	.	0	.	0	100	0	100	3	86	0	.	0	.	0	.	-2	106	0	.	0	.	2	100	0	.	0	.	0	.	2	91	-3	187	5	74	8	75	4	300	60	29	0	100	0	0	1	73	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	100	0	100	0	.	1	198	0	100	0	.	1	71	0	.	0	.	1	100	0	.	0	.	0	.	0	100	1	100	0	.	0	.	11	62	1	1520	0	1554	2	234	0	.	2	87	20	40	9	87	11	78	0	.	0	.	0	100	5	94	0	.	2	100	0	.	5	100	0	.	0	100	0	0	0	.	0	100	0	.	1	71	0	.	0	100	0	.	0	.	94	24	17	42	22	39	0	100	0	.	1	100	0	100	0	.	1	75	0	100	2	87	0	.	273	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	8	73	19	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	.	4	92	2	75	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	27
	other conifers	2	93	0	.	1	100	4	68	0	.	0	.	0	.	0	.	7	72	0	100	0	.	0	.	0	.	10	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	1	45	-2	100	2	100	6	85	0	100	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	95	0	.	0	.	0	100	-3	100	1	59	0	.	0	.	0	.	0	100	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	100	0	.	0	100	0	.	0	.	6	100	3	82	0	.	0	100	0	.	0	.	0	.	6	100	0	.	0	100	0	.	0	.	0	100	0	0	0	0	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	51	33
	beech	19	48	18	49	22	38	28	32	4	99	1	246	21	74	21	45	66	25	84	24	5	77	17	44	16	45	0	.	6	60	4	119	15	68	8	59	0	.	11	54	3	82	-4	194	-2	93	17	71	0	.	75	25	32	42	31	67	2	80	7	72	4	77	9	66	8	48	6	41	4	76	1	59	32	52	13	72	-10	98	13	44	24	55	4	177	41	40	9	83	28	30	23	56	14	51	14	44	-3	130	16	65	15	57	5	61	16	48	0	.	-6	86	0	.	31	52	20	63	5	50	-11	178	2	1115	25	56	3	65	0	.	3	86	11	66	0	1323	14	61	4	130	5	100	13	66	0	2229	7	52	2	117	15	48	21	49	8	63	10	95	2	67	-1	286	1	100	0	.	2	93	28	56	10	69	13	56	9	52	0	100	11	45	10	50	7	83	17	42	0	.	1153	7
	maple	5	38	2	202	13	54	3	54	0	100	-1	103	4	59	6	103	6	126	11	44	3	63	-3	94	7	52	0	.	2	74	1	197	5	49	1	100	0	.	4	108	5	74	0	1116	3	81	4	71	0	.	2	185	4	55	1	70	2	45	3	193	3	66	0	2550	-1	219	1	63	1	51	0	165	9	92	-3	65	1	85	1	435	2	62	0	1027	4	51	3	65	5	72	-1	194	-1	96	3	82	0	100	0	68	4	59	3	134	-3	128	0	100	2	100	1	100	0	100	0	.	3	83	1	65	-3	100	0	.	1	197	-1	100	0	.	1	92	3	71	-4	71	1	65	0	.	0	497	0	.	0	64	-1	97	2	57	-2	88	2	95	0	100	1	100	0	.	-3	122	0	.	1	67	2	254	3	60	-2	150	-6	104	2	73	5	69	11	51	2	81	16	62	0	.	170	18
	ash	13	47	7	71	12	62	12	87	0	.	3	100	0	4058	2	52	4	45	5	56	7	86	2	85	2	60	0	.	12	88	-2	196	3	197	5	84	0	.	-2	150	0	100	0	1308	2	71	3	53	0	.	18	40	3	80	4	93	1	72	5	79	1	136	0	100	0	76	0	.	9	95	0	0	2	76	3	84	5	59	3	57	7	80	0	0	8	44	5	48	2	65	5	220	1	83	7	65	0	.	0	100	7	64	3	105	3	69	0	100	1	100	0	.	0	140	0	100	2	72	4	52	1	100	1	100	0	59	1	71	0	.	-2	236	1	84	2	86	0	0	1	71	2	108	1	100	0	100	0	100	7	97	3	90	9	74	-3	100	0	100	0	.	4	100	2	71	0	100	4	115	2	94	8	110	-5	104	3	52	4	66	6	66	-3	171	4	63	0	.	268	13
	oak	6	102	0	0	0	0	6	81	0	.	0	.	0	100	3	123	6	62	0	.	1	73	0	.	4	61	0	.	1	82	0	.	2	80	1	100	1	126	0	.	0	.	1	100	0	.	0	100	0	.	4	95	0	100	3	87	0	.	-1	100	0	.	0	100	0	100	0	.	0	.	0	.	0	.	0	.	-1	118	0	.	1	67	0	.	3	132	0	.	0	118	1	73	6	69	0	.	0	.	0	.	1	89	-1	432	3	70	0	.	0	.	0	.	0	321	0	.	2	100	3	75	2	76	3	70	0	100	0	.	0	.	-1	110	2	100	0	.	0	100	0	.	0	87	3	100	3	87	0	.	7	77	0	.	0	105	-4	155	1	100	-2	100	0	.	1	96	1	70	2	67	0	.	3	95	0	100	0	.	4	66	5	80	0	3495	5	54	0	.	95	20
	sweet chestnut	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	59	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	.	17	69	10	92	2	124	0	.	2	129	13	62	7	61	-5	153	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	3	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	49	35
	other broadleaves	4	102	5	43	5	66	4	53	0	100	0	100	1	147	2	82	29	71	-1	105	4	63	-3	109	1	57	0	.	0	100	25	76	3	92	9	74	0	100	2	50	11	54	0	170	12	46	11	60	7	51	7	44	1	73	1	99	0	397	0	100	0	80	1	100	1	188	1	69	4	90	0	70	-2	105	0	.	2	74	6	69	1	988	1	107	3	46	1	73	1	61	3	65	1	64	2	78	0	84	-1	107	3	99	4	64	3	197	3	60	11	73	-1	167	9	45	2	61	-1	261	18	55	-2	196	-2	148	2	100	3	100	0	72	4	78	4	89	0	100	0	100	9	96	4	68	1	77	1	77	2	73	8	91	1	66	0	100	0	74	2	84	0	100	0	0	9	63	4	42	8	53	0	92	3	97	1	97	1	84	1	58	0	1403	-1	148	2	60	0	.	276	14
	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
	total	69	29	64	33	95	29	141	21	26	51	57	40	109	27	135	22	277	17	244	16	21	45	22	44	23	42	10	.	66	44	95	34	93	31	77	37	4	102	104	25	151	26	63	40	115	23	216	21	198	20	206	17	76	29	106	35	45	37	37	41	25	56	32	40	42	37	43	32	63	44	38	34	83	31	38	41	10	176	81	27	70	38	22	43	126	26	56	33	71	27	52	44	26	48	84	28	48	44	49	37	47	42	36	49	61	37	40	47	82	34	69	37	44	40	23	59	14	56	65	45	18	113	55	42	33	45	14	51	44	44	28	58	18	88	26	49	50	51	30	57	78	27	24	46	30	38	48	31	79	32	46	37	68	32	24	79	35	37	13	79	4	145	228	18	107	27	215	21	35	39	39	50	14	85	25	53	40	36	52	34	20	47	68	31	0	.	6166	3
private	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	7	65	3	83	0	.	2	65	21	69	41	53	250	17	102	22	146	21	21	65	0	0	0	0	1	76	0	.	0	100	23	51	21	48	22	78	0	.	9	99	47	40	14	60	69	35	15	70	2	248	11	47	18	50	73	31	58	26	14	84	27	43	3	99	5	77	23	47	4	76	8	.	9	75	4	246	18	69	12	61	15	57	39	41	13	59	1	100	0	.	0	.	1	90	4	131	7	80	5	62	22	42	37	44	29	51	-2	262	0	.	-3	115	0	.	1	100	1	100	0	.	0	.	0	.	5	63	9	108	3	75	4	88	8	64	27	58	9	66	7	110	5	75	0	.	0	.	9	78	2	100	0	100	6	74	0	182	8	100	0	.	-1	136	18	46	19	52	7	130	3	94	15	79	4	82	32	43	34	36	13	75	9	75	5	96	0	.	1527	6
	fir	3	75	-4	100	0	.	4	68	6	92	11	87	27	54	127	23	125	24	10	98	0	100	2	100	3	94	0	.	4	100	1	78	23	73	3	106	0	.	2	90	0	100	1	90	0	.	3	100	0	.	36	38	55	36	112	23	35	52	4	71	14	40	0	83	1	100	20	61	2	85	0	.	7	72	6	74	2	100	7	66	5	146	9	76	0	.	2	100	0	.	2	77	5	51	0	.	7	53	1	81	5	116	14	46	18	68	0	.	0	.	0	.	0	.	0	.	-1	100	0	.	0	.	0	.	5	62	0	.	7	77	10	56	1	100	4	201	-2	100	6	78	1	100	4	.	0	.	0	100	0	100	0	100	4	82	0	.	0	.	0	.	8	71	0	.	2	82	5	86	5	67	3	85	8	68	37	44	10	59	1	100	0	.	0	.	0	.	877	8
	pine	0	76	0	.	0	.	0	0	0	.	0	.	1	69	0	137	4	57	0	89	0	100	-9	100	1	123	0	.	0	.	2	100	0	.	1	245	0	98	0	.	0	100	1	71	0	100	0	151	0	145	0	0	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	2	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	-1	132	4	87	0	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	10	85	3	84	-2	104	0	.	0	.	1	62	3	82	1	58	2	72	2	100	0	100	0	.	28	48
	larch	-1	100	0	100	0	.	0	100	0	.	0	.	4	115	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	72	0	.	0	.	1	93	-2	130	-1	100	3	71	-3	83	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	0	0	.	0	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	-1	100	0	.	0	.	-2	100	0	.	0	.	0	0	0	.	0	.	0	.	0	97	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	24	45	-2	475	1	107	0	.	0	100	0	.	0	.	0	.	0	.	0	.	-2	100	0	.	26	53
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	-3	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	606	4	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	605
	other conifers	0	.	0	.	0	.	0	.	0	.	0	76	0	100	0	.	7	63	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	96	-2	111	0	100	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	82	3	76	0	100	0	.	0	.	0	.	0	.	19	57
	beech	14	73	3	60	0	.	29	47	3	160	10	66	-12	232	65	25	87	21	9	48	-2	100	-7	117	1	256	0	.	2	77	18	50	12	91	33	54	0	.	4	64	9	65	6	60	0	.	3	118	0	.	30	55	23	47	12	38	13	63	11	115	2	64	1	71	1	186	9	64	2	104	0	.	7	68	-5	117	6	127	14	46	7	82	25	42	-2	327	6	146	0	.	5	80	4	51	0	1518	0	277	5	68	8	65	10	54	17	54	0	.	0	.	0	.	0	.	16	72	12	74	0	.	0	.	1	100	14	83	0	.	4	92	2	59	7	79	-1	131	8	59	3	73	3	103	0	.	0	.	1	77	0	100	7	97	12	90	4	72	1	147	0	.	5	66	0	.	0	.	10	82	-1	203	2	72	1	467	28	41	12	51	11	71	6	62	1	162	0	.	704	9
	maple	4	96	-1	114	0	.	0	100	1	87	7	67	8	90	1	715	-2	143	-2	246	1	100	1	77	0	100	0	.	0	.	1	104	11	61	1	61	0	.	1	184	1	83	0	100	-1	100	0	.	0	.	3	46	6	61	2	59	1	445	1	76	3	115	0	.	0	.	1	70	0	1099	0	.	2	122	-2	129	-10	139	3	79	8	71	12	41	0	.	0	100	0	.	1	71	0	1336	4	56	2	70	2	88	1	85	8	92	1	73	0	.	0	.	0	.	0	.	1	89	0	108	0	.	0	.	0	100	5	58	0	.	0	.	4	98	-1	196	0	103	1	100	0	.	1	73	0	.	0	.	0	100	0	73	4	89	0	100	0	100	0	.	0	.	0	100	0	100	2	100	2	113	0	100	2	78	2	79	4	108	-3	95	0	.	0	100	0	.	0	.	105	25
	ash	0	100	3	129	0	.	0	.	0	.	7	63	3	86	4	130	13	37	1	74	1	100	1	100	3	100	0	.	1	100	8	73	1	264	5	184	-2	100	-2	125	1	100	0	0	-4	173	1	100	0	.	9	50	15	52	4	45	6	70	1	100	0	.	0	.	0	.	0	100	3	148	0	.	1	62	-3	177	2	154	0	100	-12	100	8	53	0	100	1	100	0	.	3	112	3	65	-2	416	0	100	3	71	11	116	8	53	-6	114	0	.	0	.	0	.	0	100	1	62	6	137	0	.	14	92	4	59	4	58	0	100	0	.	17	74	-5	118	0	100	3	148	7	98	0	.	0	.	0	.	0	.	0	.	-2	119	3	100	2	63	0	894	0	.	1	119	-8	120	1	71	2	97	2	100	2	71	1	63	3	55	5	56	0	.	1	100	0	100	0	.	161	24
	oak	6	92	0	651	0	.	1	123	0	.	0	.	1	100	0	.	4	36	0	.	0	0	0	100	0	.	0	.	0	.	-1	100	0	0	-1	114	2	199	0	.	0	.	0	.	0	.	0	.	2	100	3	89	1	85	2	100	2	100	0	100	0	.	0	.	0	.	0	.	0	100	0	.	0	.	2	100	0	.	0	.	0	.	0	100	0	72	0	.	0	.	0	.	0	71	0	100	0	.	0	.	2	71	9	102	4	77	0	.	0	.	0	.	2	100	3	69	11	47	0	100	-2	126	1	94	0	100	0	.	0	100	0	.	0	.	0	.	-3	100	0	.	0	.	2	.	0	.	0	.	4	72	1	100	0	100	6	67	0	.	0	.	0	100	-1	143	-1	181	0	87	0	.	0	.	1	74	-2	100	0	.	0	100	0	100	0	.	0	.	69	25
	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	.	1	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	0	.	-5	100	4	83	7	58	17	100	9	78	8	74	37	72	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	.	68	42
	other broadleaves	6	62	2	94	0	.	0	66	0	100	2	58	17	37	4	115	10	55	3	78	0	100	0	79	0	100	0	.	0	76	0	72	1	61	2	98	0	0	2	65	0	76	-1	172	6	101	3	67	11	72	1	63	3	50	2	73	3	65	0	.	0	77	0	.	0	.	0	.	0	100	0	.	0	.	1	98	0	90	1	65	1	100	-2	148	0	100	0	.	0	.	0	100	1	100	2	62	0	149	-2	88	14	85	1	73	1	66	0	100	0	.	0	.	1	76	10	111	21	47	0	100	1	592	3	81	1	70	0	.	0	100	0	89	1	73	1	75	2	75	0	100	0	100	0	.	0	.	0	100	0	0	0	84	3	100	3	97	0	100	0	.	1	100	5	46	5	42	3	86	0	100	0	77	3	140	1	67	0	0	0	72	0	131	0	100	0	.	161	16
	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
	total	39	36	8	86	0	.	37	39	31	60	77	40	295	18	302	16	393	14	43	51	1	72	-13	133	8	97	0	.	8	74	52	37	69	41	68	45	0	1795	15	80	60	33	19	55	70	38	29	45	13	75	92	28	121	27	207	21	117	25	30	69	47	34	4	88	7	70	52	36	11	67	8	.	26	54	12	154	17	105	39	42	24	85	93	27	14	88	11	95	0	.	11	72	14	45	8	60	16	47	14	48	63	46	93	35	65	36	-1	280	0	.	-8	71	6	71	39	45	65	37	9	77	21	68	46	58	33	45	10	107	15	71	37	49	10	77	30	53	17	84	24	71	10	68	7	.	0	.	9	72	6	58	11	82	28	53	14	52	9	140	0	.	18	52	48	38	34	44	29	54	9	61	24	64	21	54	109	28	59	32	27	53	18	57	5	138	0	.	3746	4
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	36	28	58	37	50	47	32	36	49	65	38	318	15	164	17	226	16	102	22	0	100	5	87	-3	155	0	.	41	47	84	31	57	29	64	38	0	0	87	26	171	23	65	36	149	23	163	22	105	29	54	31	42	32	118	27	86	23	28	52	28	47	15	51	19	35	39	32	39	53	39	32	38	35	16	74	31	58	58	29	43	36	50	35	64	36	23	36	26	42	17	75	5	49	57	33	40	42	22	40	39	34	48	36	61	34	24	72	44	41	53	44	0	.	1	152	1	100	6	79	4	68	11	70	21	58	19	57	32	51	3	400	10	113	28	52	46	42	10	82	36	37	12	48	6	54	45	30	28	41	14	47	40	36	9	92	21	49	3	81	0	1684	112	27	82	29	114	28	13	41	31	54	15	44	47	36	46	38	30	47	20	44	21	38	0	.	4249	4
	fir	8	44	0	1057	4	68	41	31	13	59	40	45	36	46	167	21	189	18	73	26	0	104	5	67	1	412	0	.	6	82	5	78	51	44	11	108	0	.	11	48	12	45	10	50	12	79	25	38	2	100	92	21	63	32	123	21	46	41	14	67	30	46	11	40	20	43	39	39	9	68	-1	218	17	56	19	39	2	110	19	41	12	69	16	49	6	73	19	45	9	54	7	62	7	74	8	58	24	36	17	46	5	120	26	53	22	58	0	100	5	49	3	100	0	.	0	.	-1	100	10	71	-2	232	11	69	16	38	0	.	16	51	20	43	5	179	12	78	1	566	6	78	24	37	12	64	12	47	8	43	13	52	10	68	11	44	10	67	16	57	4	58	8	71	6	92	19	72	26	48	14	42	6	53	8	67	40	41	11	54	2	80	3	100	6	95	0	.	1857	5
	pine	1	68	3	48	0	5113	1	100	0	.	1	100	6	73	-1	141	16	38	3	215	0	100	-7	112	0	303	0	.	0	100	2	112	0	.	2	141	2	95	0	.	-1	328	1	536	0	100	-6	114	7	176	2	144	0	100	2	68	0	.	-1	100	1	100	0	.	0	0	1	100	0	.	6	100	0	100	-1	100	0	.	0	.	0	.	0	100	10	64	0	358	4	73	-1	109	0	1134	-3	100	0	100	0	.	0	6991	4	81	2	86	0	0	6	97	0	100	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	2	63	0	0	0	.	0	.	0	.	2	94	0	100	0	.	0	100	0	.	0	94	0	.	1	85	0	.	0	.	8	100	0	.	28	43	3	201	13	96	0	.	-4	100	1	66	3	71	3	47	2	110	3	85	0	59	0	.	127	25
	larch	-1	117	2	80	0	71	3	140	0	.	0	.	4	107	0	100	3	86	0	.	0	.	0	.	-2	106	0	.	0	.	2	100	0	.	1	72	0	.	2	91	-2	272	4	122	7	86	6	169	57	31	0	100	0	100	1	73	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	1	100	0	100	1	100	1	198	0	100	0	100	1	60	0	.	0	.	1	100	0	.	0	.	0	.	0	100	1	100	1	100	0	.	11	62	1	1520	0	760	2	234	0	.	0	8845	20	40	9	87	11	78	0	.	0	.	0	100	5	89	0	.	2	100	0	.	5	100	0	.	0	100	0	0	0	.	0	100	0	.	1	71	0	.	0	100	0	.	4	100	118	21	16	63	24	37	0	100	0	100	1	100	0	100	0	.	1	75	0	100	1	358	0	.	298	13
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	-3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	8	73	19	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	.	4	93	6	65	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	34	30
	other conifers	2	93	0	.	1	100	4	68	0	.	0	76	0	100	0	.	14	48	0	71	0	.	0	.	0	.	10	.	0	0	0	.	0	.	0	.	0	.	0	100	0	100	0	.	0	.	0	100	1	45	-2	103	2	100	6	85	0	100	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	9	96	-3	111	0	100	0	95	0	.	0	100	0	100	-3	100	1	59	0	.	0	.	0	.	0	100	0	.	0	.	0	85	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	0	0	.	0	100	0	.	0	100	0	.	0	.	7	100	3	82	0	.	0	100	0	.	0	.	0	.	6	100	0	.	0	100	0	.	0	.	0	130	0	0	0	0	0	.	0	.	1	74	3	76	0	100	0	.	0	.	0	.	0	.	71	29
	beech	34	42	21	43	22	38	58	29	7	87	11	65	10	300	88	21	154	16	92	22	3	137	12	89	18	45	0	.	8	49	22	46	28	56	43	45	0	.	16	43	12	53	2	330	-2	93	20	62	0	.	105	24	55	31	44	50	15	55	18	74	6	52	9	60	8	46	15	42	7	61	1	59	40	44	9	130	-3	427	27	32	31	47	29	43	40	45	14	74	28	29	28	49	18	41	14	47	-3	120	21	51	23	44	15	41	33	36	0	.	-6	86	0	.	30	52	35	48	17	53	-11	178	2	1115	26	53	17	68	0	.	8	64	13	59	7	91	15	65	14	56	8	68	16	57	0	610	7	52	3	91	16	47	28	44	20	61	14	69	3	71	-1	286	7	58	0	.	2	93	38	47	9	76	15	51	10	57	28	41	23	34	21	45	13	53	18	41	0	.	1858	5
	maple	10	48	1	697	13	54	2	59	0	119	6	75	13	61	7	125	4	231	9	68	5	53	-2	132	7	52	0	.	2	74	2	95	16	44	2	56	0	.	5	93	7	61	1	565	2	115	4	71	0	.	5	74	11	43	2	46	3	130	3	161	6	73	0	2550	-1	219	1	47	1	225	0	165	11	81	-5	64	-10	149	5	123	11	58	11	47	4	51	3	61	5	70	-1	286	-1	156	7	48	2	65	2	77	4	52	11	76	-1	346	0	100	2	100	1	100	0	100	1	89	3	76	1	65	-3	100	0	100	5	56	-1	100	0	.	4	82	1	222	-4	76	2	55	0	.	1	224	0	.	0	64	-1	101	2	55	2	193	2	95	0	79	1	100	0	.	-2	119	0	100	3	79	4	143	3	55	0	3909	-4	183	6	76	2	191	11	51	2	70	16	62	0	.	275	15
	ash	13	45	11	64	12	62	12	87	0	.	10	54	3	131	7	86	17	30	6	48	8	78	3	73	5	60	0	.	13	77	6	111	4	159	11	107	-2	100	-5	97	2	87	0	1308	-2	295	4	47	0	.	27	31	18	45	9	52	7	61	6	72	1	136	0	100	0	76	0	100	12	80	0	0	3	62	0	5424	7	59	3	52	-6	219	8	53	8	43	5	44	2	65	8	144	4	54	6	129	0	100	4	65	18	75	11	48	-3	231	0	100	1	100	0	.	1	83	1	54	7	109	4	52	14	83	5	53	4	53	2	60	0	.	15	86	-4	151	2	86	3	148	7	89	2	108	1	100	0	100	0	100	7	97	1	224	12	60	-1	323	1	618	0	.	5	83	-6	161	1	62	6	81	4	69	10	90	-4	127	6	38	9	42	6	66	-2	217	4	60	0	.	429	12
	oak	13	68	0	651	0	0	7	69	0	.	0	.	1	113	3	123	10	41	0	.	1	73	0	100	4	61	0	.	1	82	-1	100	2	80	-1	248	3	133	0	.	0	.	1	100	0	.	0	100	2	100	7	67	1	83	5	66	2	100	-2	83	0	.	0	100	0	100	0	.	0	100	0	.	0	.	2	100	-1	118	0	.	1	67	0	100	4	119	0	.	0	118	1	73	6	65	0	100	0	.	0	.	3	56	8	115	7	54	0	.	0	.	0	.	2	97	4	69	13	42	3	71	1	470	4	59	0	294	0	.	0	100	-1	110	2	100	0	.	-3	104	0	.	0	87	5	71	3	87	0	.	11	55	1	100	0	105	2	303	1	100	-2	100	0	100	0	709	0	7166	3	62	0	.	3	95	1	62	-2	100	4	66	5	78	0	624	5	54	0	.	165	16
	sweet chestnut	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	51	0	223	0	.	0	.	0	.	0	.	0	.	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	17	69	6	166	7	69	8	58	19	90	22	48	14	48	33	86	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	100	0	.	0	.	0	.	0	.	0	100	0	.	3	77	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	117	28
	other broadleaves	11	55	7	43	5	66	4	49	0	82	2	58	18	36	6	81	39	55	1	183	4	59	-2	126	1	52	0	.	1	69	25	75	4	72	11	62	0	100	4	42	11	52	-1	133	19	46	14	49	18	49	8	39	5	41	3	59	3	78	0	100	1	61	1	100	1	188	1	69	4	89	0	70	-2	105	1	98	2	70	7	58	2	347	-1	258	3	45	1	73	1	61	3	60	2	55	4	51	0	73	-3	72	17	71	5	56	4	140	3	55	11	73	-1	167	10	41	12	91	20	52	18	55	-1	488	2	221	3	75	3	100	0	60	4	73	5	77	1	61	2	64	9	91	4	68	1	77	1	77	2	73	8	91	1	53	4	91	3	113	2	96	0	100	1	100	14	43	9	30	12	45	0	213	3	85	4	100	2	54	1	58	0	193	-1	145	2	58	0	.	437	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	0	0	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	0	0	.	0	0
	total	110	22	71	30	95	29	178	18	57	40	134	28	405	15	440	13	672	11	286	15	21	44	13	135	31	39	10	.	72	39	147	25	162	25	144	29	4	118	118	24	211	20	82	33	185	20	245	19	211	19	299	14	198	20	314	18	163	21	67	38	72	29	37	37	48	33	96	25	71	38	46	33	108	27	50	49	27	92	120	23	94	36	115	23	140	25	66	31	72	27	62	39	40	35	92	27	64	35	63	30	110	32	128	29	125	26	39	49	82	34	63	41	52	35	61	36	79	32	74	41	38	66	101	35	66	32	23	51	59	37	64	37	28	63	57	36	64	44	54	45	87	25	31	43	30	38	58	28	85	30	56	34	98	27	37	52	45	42	13	79	23	49	276	17	141	23	244	19	43	33	63	39	34	47	134	25	99	24	79	29	39	37	73	30	0	.	9919	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. 2285521. Birmensdorf, Swiss Federal Research Institute WSL

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