Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI4
total wood volume (stemwood)
ownership (2 categories) · main tree species
regional demarcation: forest district (2024)
unit: 1000 m³
evaluation area: accessible forest without shrub forest NFI1-NFI5
grid: 1.4 km grid, subgrids 1-5
state 2009/17
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
ownership (2 categories)	main tree species	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%	1000 m³	±%
n/a	n/a	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	spruce	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	fir	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	larch	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other conifers	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	beech	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	maple	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	ash	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	oak	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	other broadleaves	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
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	236	41	521	41	842	43	1369	36	1203	40	1156	41	3937	22	2561	22	2005	24	2684	19	19	86	336	62	270	64	0	.	857	44	1456	36	1801	33	1254	38	0	.	3738	25	4756	20	2748	20	4738	21	9467	14	5864	15	2360	25	733	49	970	40	1294	33	196	51	373	42	425	51	363	40	733	43	922	45	1369	33	1506	29	214	54	872	42	2092	29	1616	38	1135	46	1770	27	943	43	618	46	327	65	264	60	2050	30	1570	32	1114	39	130	71	396	49	651	42	925	43	2271	26	2063	31	0	.	27	100	0	.	331	61	102	68	246	56	646	52	672	54	1885	34	687	66	1077	39	350	56	603	51	37	100	1154	32	123	63	346	56	1617	25	622	42	856	34	1062	39	250	72	259	62	133	83	247	96	3776	19	3945	20	5524	18	422	41	653	38	524	45	277	57	498	55	404	88	611	53	513	36	0	.	116562	3
	fir	309	54	97	76	119	72	1223	35	267	58	928	47	994	36	1716	30	1624	26	2095	24	37	89	101	70	259	67	0	.	99	83	288	62	673	40	601	57	0	.	495	52	309	35	475	43	335	68	876	39	17	100	2577	25	252	45	513	53	241	50	410	64	577	55	493	37	545	45	640	43	195	73	206	54	470	36	628	46	76	74	347	56	234	49	397	44	139	57	391	38	383	56	131	60	221	42	560	49	403	39	549	38	214	100	132	73	152	100	9	100	142	57	178	100	0	.	0	.	0	.	334	60	261	58	174	69	462	51	0	.	294	56	271	47	532	58	423	62	76	69	0	.	892	38	139	92	342	45	376	48	425	47	478	37	274	54	366	68	408	53	372	69	0	.	137	83	469	53	604	43	155	51	99	64	0	.	109	95	44	89	48	100	25	100	185	82	0	.	35111	5
	pine	19	100	35	71	29	76	72	87	0	.	17	100	399	78	5	100	408	41	408	55	0	.	26	76	171	55	0	.	18	100	38	73	0	.	195	92	85	100	0	.	153	51	563	39	0	.	604	35	1138	32	441	38	16	100	106	66	0	.	107	100	29	100	0	.	0	.	51	100	0	.	147	100	22	100	31	100	0	.	0	.	0	.	0	.	300	43	65	83	109	50	55	111	151	49	52	100	8	100	0	.	9	100	109	73	152	74	0	.	95	73	59	100	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	146	73	13	100	0	.	0	.	0	.	22	71	10	100	0	.	37	100	0	.	13	100	0	.	26	100	0	.	0	.	95	100	0	.	548	32	213	45	846	34	0	.	39	100	147	56	47	100	132	59	64	68	129	82	0	.	0	.	9036	10
	larch	9	100	52	89	67	98	112	85	0	.	0	.	70	72	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	74	73	0	.	0	.	0	.	67	87	317	49	231	49	214	59	1758	21	4885	15	20	100	0	.	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	15	100	12	100	0	.	115	70	45	100	0	.	33	77	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	0	.	0	.	324	62	537	33	258	41	297	57	0	.	77	80	908	28	431	39	527	59	0	.	0	.	0	.	166	45	0	.	10	100	0	.	46	100	0	.	13	100	0	.	0	.	24	100	0	.	69	73	0	.	11	100	0	.	0	.	2988	16	1394	22	1274	24	55	100	0	.	16	100	2	100	0	.	50	71	20	100	101	60	0	.	18017	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	207	48	1156	21	0	.	0	.	0	.	0	.	0	.	0	.	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	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	.	554	32	166	49	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2174	15
	other conifers	36	77	0	.	43	100	42	69	0	.	0	.	0	.	0	.	150	74	1	100	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	9	100	28	44	-7	100	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	7	86	0	.	0	.	9	100	-19	100	22	71	0	.	0	.	0	.	3	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	6	100	0	.	8	100	0	.	0	.	105	100	21	61	0	.	5	100	0	.	0	.	0	.	185	100	0	.	1	100	0	.	0	.	7	100	13	100	3	72	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1186	31
	beech	900	32	750	34	651	32	811	28	56	64	141	66	822	39	802	28	2547	20	2906	18	434	48	851	36	1013	32	0	.	238	72	259	43	548	38	260	66	0	.	750	32	268	45	406	34	34	66	420	48	0	.	2644	18	699	38	392	56	25	73	151	57	107	46	179	44	128	43	181	41	152	82	36	58	1341	34	340	56	98	60	493	37	965	38	376	63	1821	23	920	33	1051	28	972	37	885	32	326	37	253	88	770	43	172	57	215	59	444	49	0	.	131	63	0	.	769	34	477	53	209	50	951	32	871	32	508	43	137	48	0	.	179	69	440	53	100	65	339	52	105	73	15	100	822	37	122	94	109	49	291	35	387	44	935	33	227	54	351	61	46	67	270	50	38	100	0	.	16	89	560	38	125	65	356	44	337	52	13	100	505	41	225	55	226	41	314	34	0	.	43511	4
	maple	144	39	111	48	108	47	67	49	0	.	72	80	220	40	153	51	161	42	429	27	61	59	81	60	207	46	0	.	73	71	8	110	132	44	21	100	0	.	179	32	66	75	17	159	34	71	116	46	0	.	303	27	60	54	26	64	69	47	47	82	69	48	50	76	100	83	22	60	42	62	24	76	183	75	37	89	41	75	238	55	67	71	173	59	88	52	111	48	139	38	44	61	114	58	91	54	10	100	30	60	56	56	43	65	-7	170	12	100	44	100	19	100	2	100	0	.	22	72	5	59	26	100	0	.	69	65	4	100	0	.	70	53	67	60	58	60	17	82	0	.	99	49	0	.	15	83	12	77	35	81	40	60	73	50	13	100	16	100	0	.	-1	442	0	.	13	82	123	56	82	61	125	63	-28	148	57	77	66	75	203	55	114	68	235	55	0	.	6437	7
	ash	259	38	265	66	170	58	160	65	0	.	80	83	26	97	64	51	306	41	78	65	228	64	32	67	63	81	0	.	161	73	137	77	227	54	38	72	0	.	58	73	8	100	7	183	48	75	49	49	0	.	448	36	24	96	52	76	12	76	58	79	43	58	2	100	5	70	0	.	229	84	0	.	58	81	147	58	202	57	82	59	117	80	0	.	213	42	72	51	52	63	156	57	18	67	172	62	0	.	63	100	86	52	134	66	76	75	22	100	123	100	0	.	17	71	4	100	33	89	82	63	13	100	18	100	19	64	42	74	0	.	122	73	19	87	53	74	0	.	9	71	41	58	70	100	2	100	9	100	23	92	55	71	177	71	2	100	4	100	0	.	182	100	38	68	5	81	136	59	62	81	321	62	92	63	70	57	52	54	146	68	85	54	168	59	0	.	7298	9
	oak	134	74	0	.	0	.	156	76	0	.	0	.	6	100	60	71	312	33	0	.	79	100	0	.	132	55	0	.	17	100	0	.	37	68	8	100	168	70	0	.	0	.	59	100	0	.	1	100	0	.	175	46	7	100	79	84	0	.	40	97	0	.	33	100	2	100	0	.	0	.	0	.	0	.	0	.	46	72	0	.	23	70	0	.	159	61	0	.	22	71	18	95	140	51	0	.	0	.	0	.	25	94	77	62	119	76	0	.	0	.	0	.	35	59	0	.	74	96	104	74	52	67	68	90	10	100	0	.	0	.	16	73	26	100	0	.	9	100	0	.	40	84	114	100	95	58	0	.	206	67	0	.	38	78	97	71	25	100	23	100	0	.	12	100	12	61	45	58	0	.	51	81	13	100	0	.	126	64	138	47	166	64	93	64	0	.	3822	11
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	364	56	25	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	.	304	48	125	74	241	62	0	.	255	71	209	54	127	61	337	45	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	71	0	.	0	.	0	.	0	.	0	.	0	.	64	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2067	19
	other broadleaves	140	50	96	45	140	49	95	80	3	100	7	100	59	61	82	46	441	44	85	35	84	55	27	76	42	57	0	.	13	100	357	75	21	67	119	55	3	100	87	51	122	47	35	61	267	40	780	28	202	51	277	41	29	59	35	100	15	85	2	100	10	80	7	100	24	62	48	78	46	74	33	78	37	72	0	.	54	53	134	46	188	74	23	81	37	47	42	50	62	65	122	58	3	100	124	51	3	72	2	149	34	97	75	61	58	79	128	60	203	38	100	82	270	39	162	54	185	42	349	32	153	58	220	45	43	70	55	100	9	71	178	63	305	89	18	100	19	75	126	78	105	52	5	82	7	68	24	60	87	70	21	60	23	100	33	77	77	88	6	100	0	.	206	38	112	45	306	35	12	72	156	67	3	71	35	80	13	64	13	61	135	72	75	65	0	.	8806	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	2	71	21	72	0	.	0	.	1	100	0	.	0	.	0	.	0	.	2	100	1	100	0	.	0	.	0	.	9	100	7	100	5	100	19	94	14	63	3	84	2	100	4	100	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	2	100	14	100	0	.	0	.	0	.	0	.	37	87	0	.	0	.	13	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	11	100	0	.	22	100	0	.	2	100	22	93	0	.	1	100	3	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	16	95	47	59	8	83	0	.	0	.	0	.	0	.	0	.	20	100	0	.	2	100	0	.	343	21
	total	2185	24	1926	27	2175	26	4106	21	1529	39	2403	37	6553	19	5459	19	8103	14	8687	14	941	39	1454	33	2183	29	177	.	1479	38	2618	29	3440	24	2495	30	256	68	5383	22	6022	17	4548	18	5688	19	14664	11	13318	11	9239	14	1884	29	2386	33	1662	32	1010	40	1207	38	1196	36	1210	37	1674	31	1586	37	1829	31	3636	22	1408	37	1392	33	3516	24	3263	28	2103	36	4559	19	2552	26	2453	25	1863	30	1796	29	3388	25	2247	29	2583	29	737	47	1180	38	1647	33	1419	33	3850	20	2808	26	1630	24	670	42	854	36	3326	18	2046	23	2114	27	1408	39	773	49	2520	30	1985	32	2141	32	1251	47	832	47	360	58	3193	24	586	61	958	36	2329	23	1821	30	2384	26	2157	29	1113	46	847	39	900	55	466	69	8281	13	6407	16	9561	14	913	36	1800	33	1114	36	609	53	1436	32	1311	37	1512	35	1685	29	0	.	254370	2
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	204	62	42	59	0	.	204	69	1195	46	1871	35	13020	12	4602	17	3841	20	872	35	0	.	0	.	163	79	0	.	28	100	1473	37	1654	36	599	38	0	.	426	57	2314	29	744	47	2198	31	833	47	319	75	521	34	846	37	1253	33	4126	25	196	91	908	37	246	73	166	78	1086	39	519	53	235	.	569	60	686	47	487	64	674	42	607	53	2036	36	198	62	0	.	0	.	0	.	44	69	260	61	632	57	322	56	720	40	614	52	717	42	148	72	0	.	23	93	0	.	37	100	24	100	0	.	0	.	0	.	280	58	801	59	108	86	223	52	386	57	927	39	397	60	150	59	150	59	15	.	0	.	447	49	170	100	11	100	207	79	41	72	226	100	0	.	158	88	726	44	663	32	660	46	147	97	573	58	169	56	1000	35	1361	32	412	62	98	72	101	88	0	.	66111	5
	fir	80	72	5	100	0	.	172	59	290	50	729	50	916	37	4842	17	3227	22	963	45	1	100	20	100	90	71	0	.	168	100	45	71	584	59	152	93	0	.	41	73	45	85	14	74	0	.	103	100	0	.	1252	33	1251	36	2629	23	1442	29	138	76	584	47	21	100	41	100	733	51	171	82	0	.	443	68	365	45	76	100	165	66	329	65	814	39	0	.	127	100	0	.	77	82	154	50	0	.	367	59	64	72	321	43	166	46	314	70	0	.	0	.	0	.	0	.	0	.	27	100	0	.	0	.	0	.	174	65	0	.	109	88	210	61	9	100	641	50	13	100	65	88	55	100	97	.	0	.	3	100	1	100	27	100	105	91	0	.	0	.	0	.	166	69	0	.	17	91	200	57	78	71	75	80	315	47	931	31	409	38	11	100	0	.	0	.	0	.	28267	6
	pine	76	79	0	.	0	.	0	.	0	.	0	.	78	63	58	71	175	67	37	84	47	100	-17	100	34	74	0	.	0	.	169	100	0	.	157	79	43	81	0	.	16	100	75	86	48	100	39	79	109	74	6	100	0	.	64	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	43	100	66	88	34	100	0	.	0	.	0	.	0	.	0	.	0	.	80	65	50	71	45	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	38	100	0	.	0	.	0	.	0	.	15	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	419	56	241	38	48	71	0	.	0	.	100	68	64	54	119	59	106	74	66	100	45	100	0	.	2802	15
	larch	5	100	24	100	0	.	33	100	0	.	0	.	103	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	36	76	0	.	0	.	70	71	80	73	-3	100	111	72	93	66	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	14	100	0	.	10	100	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	0	.	107	100	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	1885	40	1043	35	224	65	0	.	22	100	0	.	0	.	0	.	0	.	0	.	76	100	0	.	4073	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	86	80	60	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	96	53
	other conifers	0	.	0	.	0	.	0	.	0	.	8	78	1	100	0	.	213	59	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	11	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	294	99	-12	114	-16	100	0	.	0	.	57	100	0	.	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	3	71	33	74	6	100	0	.	0	.	0	.	0	.	607	53
	beech	638	50	122	60	0	.	1300	46	203	55	432	38	1753	33	2237	21	2022	21	515	38	38	100	198	95	357	57	0	.	61	84	525	45	486	50	1044	40	0	.	236	71	417	69	188	67	0	.	76	73	0	.	1028	35	700	39	431	39	1381	30	281	68	117	55	52	73	96	73	257	52	293	48	0	.	206	67	230	47	940	48	388	53	163	47	1139	32	313	58	457	62	103	.	133	76	493	56	98	66	15	65	198	65	385	37	191	54	323	34	0	.	0	.	0	.	0	.	747	41	438	43	0	.	0	.	26	100	483	54	0	.	53	77	160	76	89	71	55	72	353	59	61	76	346	50	6	.	0	.	20	72	19	100	183	95	298	90	227	71	130	71	0	.	226	71	0	.	0	.	358	51	140	63	81	79	81	58	877	31	430	41	490	62	223	75	104	80	0	.	29961	6
	maple	140	64	23	100	0	.	8	100	18	71	178	64	569	29	172	36	67	51	157	57	67	100	22	83	6	100	0	.	0	.	46	70	170	53	45	53	0	.	163	51	70	67	5	100	34	100	0	.	0	.	160	35	96	48	42	60	240	39	32	72	102	66	0	.	0	.	11	70	89	41	0	.	42	73	150	47	27	191	124	55	155	58	405	33	0	.	5	100	7	.	32	77	72	56	104	59	56	56	22	93	208	66	58	83	82	68	0	.	0	.	0	.	0	.	50	72	20	69	0	.	0	.	2	100	78	58	0	.	0	.	121	76	113	61	65	60	10	100	0	.	36	78	0	.	0	.	12	100	10	76	48	75	16	100	0	.	0	.	0	.	9	100	2	100	22	100	132	46	22	100	51	76	59	61	247	37	44	64	0	.	18	100	0	.	0	.	5468	8
	ash	22	100	195	57	0	.	0	.	0	.	281	55	143	77	458	48	378	37	40	94	52	100	12	100	65	100	0	.	37	100	223	76	112	50	358	47	28	100	238	50	38	100	0	.	108	71	53	100	0	.	497	42	591	76	177	52	111	54	18	100	0	.	0	.	0	.	4	100	230	79	0	.	10	69	375	46	120	61	14	100	81	100	312	46	2	100	30	100	0	.	104	90	83	60	164	75	4	100	209	81	335	46	296	53	144	49	0	.	0	.	0	.	9	100	45	65	129	76	0	.	57	71	76	60	114	76	5	100	0	.	180	68	42	71	5	100	171	73	42	92	0	.	0	.	0	.	0	.	0	.	68	88	53	100	51	67	217	71	0	.	54	71	27	61	46	73	41	68	109	100	62	78	31	64	267	45	199	42	0	.	28	100	22	100	0	.	8900	10
	oak	153	91	62	72	0	.	84	71	0	.	0	.	11	100	0	.	279	31	0	.	0	.	3	100	0	.	0	.	0	.	54	100	0	.	55	83	351	77	0	.	0	.	0	.	0	.	0	.	72	100	155	94	111	80	101	100	36	100	14	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	32	100	0	.	0	.	0	.	0	.	88	92	0	.	0	.	0	.	26	71	16	100	0	.	0	.	94	58	410	83	170	49	0	.	0	.	0	.	14	100	120	58	392	35	14	100	50	61	15	81	34	100	0	.	31	100	0	.	0	.	0	.	62	100	0	.	0	.	72	.	0	.	0	.	208	71	20	100	4	100	159	63	0	.	0	.	25	100	16	74	81	84	15	61	0	.	0	.	29	71	11	100	0	.	17	100	16	100	0	.	0	.	3786	16
	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	.	79	100	18	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	.	39	100	0	.	178	100	224	75	634	31	1061	29	228	82	255	61	1386	35	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4133	16
	other broadleaves	155	64	26	69	0	.	12	89	1	100	59	79	526	31	84	32	206	48	17	71	6	100	12	86	12	100	0	.	27	85	6	71	19	54	23	64	1	100	125	63	9	64	62	63	164	76	102	57	188	68	90	52	96	69	33	62	73	48	0	.	22	65	0	.	0	.	0	.	30	100	0	.	0	.	6	100	22	61	46	70	51	100	101	46	3	100	0	.	0	.	67	100	13	100	38	66	15	78	72	102	199	69	46	74	20	57	5	100	0	.	0	.	21	79	345	48	927	31	13	100	189	79	185	71	35	56	0	.	18	100	14	67	9	62	28	79	26	71	15	100	2	100	0	.	0	.	4	100	0	.	6	74	114	100	19	75	63	100	0	.	12	100	199	49	117	38	31	59	0	.	16	90	103	58	45	52	0	.	21	88	23	91	2	100	0	.	5494	10
	indeterminable	4	100	0	.	0	.	0	.	0	.	10	100	120	38	6	100	16	100	0	.	4	100	0	.	13	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	12	100	0	.	0	.	2	100	0	.	0	.	0	.	3	100	23	100	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	8	100	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	80	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	263	23
	total	1479	34	498	43	0	.	1812	39	1707	42	3569	28	17234	11	12458	13	10422	14	2602	28	215	78	251	87	740	51	0	.	320	74	2541	31	3025	33	2481	27	424	75	1231	39	3002	26	1168	40	2549	29	1403	37	799	46	3712	22	3695	27	4733	21	7433	19	679	51	1733	33	319	74	304	70	2100	31	1338	39	235	.	1269	51	2138	34	1690	37	1409	36	1386	44	4865	23	733	48	652	58	110	.	414	71	884	44	680	47	1089	44	888	47	2340	29	1876	38	1818	29	192	71	0	.	308	72	267	72	1979	27	3034	20	255	77	550	58	1690	32	1199	40	806	59	356	71	931	45	650	44	1721	40	1031	53	347	60	589	46	190	.	0	.	486	49	418	60	364	69	796	51	496	52	636	72	0	.	679	58	3330	27	2311	25	1711	31	496	61	881	54	890	37	3476	23	2567	27	1057	46	471	64	350	71	0	.	159962	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	440	36	563	38	842	43	1573	32	2398	30	3027	26	16958	11	7163	13	5847	16	3556	17	19	86	336	62	433	49	0	.	885	43	2929	26	3454	24	1853	28	0	.	4164	23	7070	16	3492	19	6935	17	10300	13	6183	14	2881	21	1579	30	2223	25	5420	20	391	52	1281	28	671	42	529	37	1819	29	1441	34	1604	32	2075	26	900	38	1360	35	2767	24	2224	31	3171	28	1969	25	943	43	618	46	327	65	308	52	2310	27	2202	28	1436	33	850	36	1010	37	1368	29	1074	38	2271	26	2086	31	0	.	64	71	24	100	331	61	102	68	246	56	926	40	1473	41	1992	32	910	52	1463	32	1277	32	1000	39	187	51	1304	29	137	57	346	56	2063	22	792	39	867	33	1269	35	291	63	485	57	133	83	405	68	4502	17	4608	17	6184	17	569	40	1227	34	693	36	1277	30	1859	28	816	54	709	47	614	33	0	.	182672	2
	fir	389	45	102	72	119	72	1395	31	557	38	1657	34	1910	25	6558	14	4850	17	3057	21	38	86	120	61	348	53	0	.	267	70	333	54	1258	35	753	49	0	.	537	49	354	32	489	42	335	68	978	37	17	100	3829	19	1503	31	3142	21	1683	25	548	52	1160	36	514	35	587	42	1372	33	365	55	206	54	913	38	992	33	153	62	512	43	563	43	1211	30	139	57	518	38	383	56	208	48	376	32	560	49	770	35	614	35	535	47	298	41	466	57	9	100	142	57	178	100	0	.	0	.	27	100	334	60	261	58	174	69	636	41	0	.	402	47	481	37	540	57	1063	39	89	61	65	88	947	36	235	68	342	45	379	48	427	47	505	35	379	46	366	68	408	53	372	69	166	69	137	83	486	51	804	35	233	41	174	50	315	47	1040	30	453	35	59	83	25	100	185	82	0	.	63378	4
	pine	95	67	35	71	29	76	72	87	0	.	17	100	478	66	62	66	582	35	446	51	47	100	9	292	205	47	0	.	18	100	207	83	0	.	352	62	129	72	0	.	169	47	638	36	48	100	642	33	1247	30	447	37	16	100	170	56	0	.	107	100	29	100	0	.	0	.	51	100	0	.	147	100	22	100	31	100	0	.	0	.	0	.	43	100	366	39	99	64	109	50	55	111	151	49	52	100	8	100	0	.	89	59	159	55	197	59	0	.	95	73	59	100	0	.	0	.	0	.	0	.	0	.	15	100	0	.	0	.	184	62	13	100	0	.	0	.	0	.	36	58	10	100	0	.	37	100	0	.	22	71	0	.	26	100	0	.	0	.	95	100	0	.	967	30	454	29	894	33	0	.	39	100	248	43	112	52	250	42	169	53	196	64	45	100	0	.	11839	8
	larch	14	73	75	68	67	98	144	69	0	.	0	.	173	53	16	100	150	59	0	.	0	.	0	.	25	72	0	.	0	.	74	73	0	.	36	76	0	.	67	87	387	42	312	41	211	60	1869	20	4978	15	20	100	4	100	15	73	0	.	0	.	0	.	0	.	43	100	0	.	0	.	0	.	15	100	12	100	30	100	129	64	45	100	10	100	39	66	0	.	0	.	17	100	0	.	0	.	0	.	52	100	9	100	46	100	0	.	324	62	537	33	365	41	297	57	0	.	86	72	908	28	431	39	527	59	0	.	0	.	0	.	180	42	0	.	10	100	0	.	46	100	0	.	13	100	0	.	0	.	24	100	0	.	69	73	0	.	11	100	0	.	29	100	4873	18	2438	19	1499	22	55	100	22	100	16	100	2	100	0	.	50	71	20	100	177	55	0	.	22090	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	-7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	17	100	0	.	0	.	211	47	1156	21	0	.	0	.	0	.	0	.	0	.	0	.	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	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	.	573	31	246	38	68	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2270	14
	other conifers	36	77	0	.	43	100	42	69	0	.	8	78	1	100	0	.	362	46	2	71	0	.	0	.	0	.	177	.	0	.	0	.	0	.	0	.	0	.	2	100	11	100	0	.	0	.	9	100	28	44	-5	145	60	100	198	89	1	100	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	294	99	-12	114	-16	100	7	86	0	.	57	100	9	100	-19	100	22	71	0	.	0	.	0	.	3	100	0	.	0	.	4	74	0	.	0	.	0	.	0	.	0	.	0	.	50	100	0	.	0	.	0	.	0	.	6	100	0	.	8	100	0	.	0	.	105	100	21	61	0	.	5	100	0	.	0	.	0	.	185	100	0	.	1	100	0	.	0	.	8	86	13	100	3	72	0	.	0	.	13	80	33	74	6	100	0	.	0	.	0	.	0	.	1793	27
	beech	1538	28	872	30	651	32	2111	30	259	45	573	33	2575	26	3038	17	4569	14	3421	16	472	44	1049	34	1370	28	0	.	300	60	784	33	1034	31	1304	35	0	.	986	30	685	46	594	31	34	66	496	42	0	.	3672	16	1399	27	823	34	1406	30	432	48	224	36	231	37	224	40	438	34	446	42	36	58	1547	31	570	38	1038	43	882	31	1129	33	1514	28	2133	21	1377	30	1154	27	1106	34	1378	28	424	32	268	83	968	37	557	31	406	40	767	32	0	.	131	63	0	.	769	34	1224	32	646	33	951	32	871	32	534	41	620	43	0	.	231	56	600	44	188	48	394	46	458	48	75	64	1168	30	129	90	109	49	311	33	406	42	1118	32	524	56	578	46	176	55	270	50	264	63	0	.	16	89	918	30	265	45	436	38	418	44	890	30	935	29	715	46	449	43	419	32	0	.	73472	3
	maple	284	37	133	44	108	47	75	45	18	71	250	51	788	23	325	31	228	33	586	25	128	59	104	51	213	45	0	.	73	71	54	61	302	36	66	48	0	.	342	29	136	50	22	123	67	61	116	46	0	.	464	21	156	36	68	44	309	32	78	57	171	43	50	76	100	83	33	46	131	34	24	76	224	62	187	41	68	88	361	40	222	46	578	29	88	52	116	47	146	36	76	48	186	41	195	40	67	49	52	52	264	53	101	55	74	76	12	100	44	100	19	100	2	100	50	72	42	50	5	59	26	100	2	100	148	43	4	100	0	.	192	52	180	44	124	43	27	64	0	.	135	41	0	.	15	83	25	63	45	65	88	49	89	45	13	100	16	100	0	.	8	140	2	100	35	69	255	36	105	53	175	50	31	176	304	33	111	52	203	55	132	61	235	55	0	.	11905	5
	ash	281	36	459	45	170	58	160	65	0	.	361	46	169	67	523	43	683	27	118	53	280	55	44	56	129	64	0	.	198	62	361	56	339	39	397	43	28	100	296	42	46	85	7	183	157	54	102	57	0	.	945	27	615	73	228	44	123	49	76	65	43	58	2	100	5	70	4	100	459	58	0	.	67	70	522	37	321	42	96	52	198	62	312	46	215	42	102	47	52	63	261	49	101	51	336	48	4	100	272	66	421	38	429	42	220	41	22	100	123	100	0	.	25	58	49	61	162	63	82	63	70	61	94	52	133	65	47	67	0	.	301	50	61	56	58	67	171	73	51	77	41	58	70	100	2	100	9	100	23	92	123	58	230	60	52	65	221	70	0	.	236	79	65	47	51	67	176	48	171	70	384	53	122	50	337	37	251	35	146	68	113	47	189	53	0	.	16198	6
	oak	287	60	62	72	0	.	240	55	0	.	0	.	17	75	60	71	591	23	0	.	79	100	3	100	132	55	0	.	17	100	54	100	37	68	64	74	519	57	0	.	0	.	59	100	0	.	1	100	72	100	330	50	118	76	180	67	36	100	54	76	0	.	33	100	2	100	0	.	5	100	0	.	0	.	32	100	46	72	0	.	23	70	0	.	247	51	0	.	22	71	18	95	165	45	16	100	0	.	0	.	118	50	486	70	290	42	0	.	0	.	0	.	49	51	120	58	466	33	118	66	101	45	83	75	44	81	0	.	31	100	16	73	26	100	0	.	70	88	0	.	40	84	186	72	95	58	0	.	414	49	20	100	43	71	256	47	25	100	23	100	25	100	28	60	94	73	60	46	0	.	51	81	41	58	11	100	126	64	155	43	182	59	93	64	0	.	7608	10
	sweet chestnut	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	444	49	43	72	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	100	304	48	302	66	464	48	634	31	1316	27	437	50	381	45	1723	30	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	71	0	.	0	.	0	.	0	.	32	100	0	.	64	63	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	6200	12
	other broadleaves	295	41	122	38	140	49	107	72	4	77	67	71	585	29	165	28	647	34	102	31	90	52	40	58	55	50	0	.	40	66	362	74	40	43	142	47	4	75	212	43	130	44	97	46	431	38	883	26	390	42	367	34	125	54	68	60	88	42	2	100	32	51	7	100	24	62	48	78	76	60	33	78	37	72	6	100	76	41	180	39	239	62	123	40	39	44	42	50	62	65	189	52	15	84	162	42	18	66	74	99	233	61	121	47	78	61	132	58	203	38	100	82	291	37	508	37	1112	26	363	31	342	50	406	41	78	46	55	100	28	70	193	59	313	86	45	62	45	52	141	71	107	51	5	82	7	68	28	53	87	70	27	49	136	85	52	56	140	66	6	100	12	100	405	31	229	29	337	32	12	72	172	61	106	57	81	45	13	64	34	58	158	63	76	64	0	.	14300	6
	indeterminable	4	100	0	.	0	.	0	.	0	.	11	85	141	34	6	100	16	100	1	100	4	100	0	.	13	100	0	.	2	100	1	100	0	.	10	100	0	.	9	100	18	73	5	100	19	94	16	56	3	84	2	100	4	100	3	100	28	83	0	.	0	.	0	.	0	.	9	100	0	.	13	100	0	.	0	.	2	100	14	100	0	.	6	100	0	.	0	.	37	87	0	.	0	.	13	100	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	7	100	2	100	11	100	0	.	22	100	0	.	2	100	30	73	4	100	1	100	3	100	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	21	76	47	59	10	69	0	.	0	.	0	.	0	.	0	.	20	100	0	.	2	100	0	.	606	15
	total	3664	19	2425	23	2175	26	5918	19	3236	29	5971	22	23787	9	17917	10	18525	10	11290	12	1156	35	1705	31	2923	25	177	.	1799	33	5159	21	6465	20	4976	20	680	53	6615	19	9024	14	5715	17	8237	16	16066	10	14117	10	12951	11	5578	20	7119	18	9094	17	1689	31	2940	24	1515	33	1514	32	3775	22	2924	27	2064	30	4905	21	3546	25	3082	25	4925	20	4649	23	6968	19	5292	18	3204	24	2564	24	2277	28	2680	24	4069	22	3336	24	3471	25	3077	25	3056	28	3465	21	1612	30	3850	20	3116	25	1897	23	2648	23	3888	17	3581	18	2596	22	3804	21	2607	28	1579	38	2876	28	2916	26	2791	27	2972	31	1862	36	707	41	3782	22	775	52	958	36	2815	21	2239	27	2748	24	2953	25	1610	35	1483	38	900	55	1145	44	11611	12	8717	13	11272	13	1410	32	2681	28	2004	26	4086	21	4004	21	2368	29	1984	30	2035	27	0	.	414332	1

Table citation

Abegg, M.; Ahles, P.; Allgaier Leuch, B.; Cioldi, F.; Didion, M.; Düggelin, C.; Fischer, C.; Herold, A.; Meile, R.; Rohner, B.; Rösler, E.; Speich, S.; Temperli, C.; Traub, B.,
2023: Swiss national forest inventory - Result table No. 2282336. Birmensdorf, Swiss Federal Research Institute WSL

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