Selection from the list of topics

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

1. What do the numbers stand for?

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

Example 1: Estimated value and standard error

NFI5

volume of deadwood (stemwood)
ownership (2 categories)

regional demarcation: production region

unit: m³/ha

evaluation area: accessible forest without shrub forest

grid: 1.4 km grid, subgrids 1-5

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

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

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2. What is the purpose of the standard error?

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

The statistical certainty is:

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

Example 2: Calculating confidence intervals

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

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

Procedure

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

Answer

			Step 1	Step 2

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

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

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

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

Data: see example 2

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

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

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3. When are two results statistically different?*

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

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

Example 4: Interpretation of two results from the same inventory

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

	case 2						case 1

case 1

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

Procedure

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

Answer

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

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

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

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

Case 2

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

Procedure

See case 1.

Answer

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

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

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

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

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

Example 5: Interpretation of two results from different inventories

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI1
growing stock (stemwood)
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 1983/85
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	843	46	785	45	442	53	2285	29	1120	42	1322	40	7790	16	3101	21	3269	22	2446	21	225	100	217	75	280	48	0	.	573	50	797	35	2508	28	1255	42	1	100	3091	25	3458	22	2149	23	5088	19	8046	14	5008	15	2086	23	1057	39	568	68	1164	35	187	57	255	46	428	46	399	49	774	46	434	53	1396	31	1712	29	290	54	1422	43	2055	28	1416	34	1033	43	1594	27	996	36	661	44	927	50	199	54	1249	30	1825	30	1190	36	491	63	277	56	500	44	782	39	1380	30	1254	32	-1	100	11	100	38	100	198	78	58	90	184	59	544	46	590	54	1302	34	555	59	595	49	338	50	992	49	0	.	1124	31	349	60	530	47	1238	26	435	46	643	33	810	36	86	69	449	51	196	59	520	77	2988	18	3168	19	4050	19	524	42	466	42	576	44	366	69	619	37	666	47	843	49	523	48	0	.	112676	3
	fir	169	43	32	100	202	76	1196	32	242	67	629	44	672	32	1933	25	1587	23	1563	24	14	72	31	71	188	65	0	.	39	74	82	85	1011	43	363	70	0	.	371	54	215	39	285	51	286	66	620	41	0	.	1781	26	123	61	165	71	165	69	568	52	177	52	432	44	486	40	434	40	147	87	513	51	293	40	321	47	142	59	317	50	88	59	316	48	55	81	322	75	381	51	245	60	208	48	321	57	414	46	506	42	79	76	21	100	71	100	0	0	65	73	93	100	0	.	0	.	11	100	313	66	283	57	59	82	362	60	0	.	170	70	253	43	382	58	107	61	64	87	0	.	528	42	73	71	448	45	142	59	347	44	356	36	371	51	203	77	169	72	151	85	6	100	131	80	303	56	365	41	177	53	118	54	75	79	218	72	294	65	0	.	0	.	109	96	0	.	27562	6
	pine	104	65	354	65	82	50	69	89	0	.	8	100	432	66	24	100	434	47	178	40	0	.	23	100	89	65	0	.	12	100	44	69	0	.	182	86	25	100	0	.	255	56	784	35	0	.	485	37	950	30	425	38	0	.	97	77	0	.	70	100	0	.	0	.	12	100	28	100	0	.	66	100	12	100	31	100	0	.	0	.	0	.	0	.	380	41	110	57	120	45	195	51	180	46	32	100	11	100	0	.	17	100	101	73	160	89	14	100	151	73	49	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	166	55	5	100	0	.	0	.	0	.	0	.	17	100	68	100	17	100	0	.	18	72	0	.	260	81	0	.	0	.	59	100	0	.	556	35	153	40	571	36	0	.	0	.	105	62	40	91	206	59	164	74	107	76	108	71	0	.	9415	9
	larch	3	109	62	72	32	73	138	69	0	.	0	.	173	53	34	100	158	58	0	.	0	.	0	.	29	71	63	.	0	.	63	100	0	.	0	.	0	.	38	100	344	42	254	41	80	63	1162	22	3824	16	17	100	4	100	3	100	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	174	63	40	100	0	.	44	76	0	.	0	.	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	.	225	71	312	36	132	43	154	60	0	.	160	93	429	36	304	39	391	58	0	.	0	.	63	100	61	64	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	2	100	0	.	31	100	0	.	0	.	0	.	0	.	2625	18	1109	24	712	26	52	100	0	.	0	.	0	.	0	.	58	100	38	100	148	62	0	.	13837	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	140	57	1065	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	367	31	150	38	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1741	16
	other conifers	6	100	0	.	10	100	16	83	0	.	0	.	0	.	0	.	16	64	0	.	0	.	0	.	0	.	30	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	84	1	100	24	100	97	100	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	3	100	0	.	0	.	2	100	1	100	7	90	0	.	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	72	0	.	2	100	0	.	0	.	0	.	4	60	0	.	0	.	0	.	0	.	0	.	149	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	398	46
	beech	1443	29	737	33	459	35	679	33	39	59	195	57	834	33	1272	29	3636	20	2107	19	522	56	727	36	969	33	0	.	172	86	207	57	377	39	377	63	0	.	705	37	380	52	388	37	46	75	242	59	0	.	2859	19	811	42	258	77	5	72	60	62	67	74	161	58	83	69	158	41	91	79	21	91	1340	38	286	84	294	71	458	39	528	43	369	61	1211	23	802	34	1022	28	664	38	621	32	192	38	256	97	569	44	3	100	81	66	321	46	0	.	80	60	0	.	290	44	145	75	238	38	603	34	427	36	243	50	243	54	0	.	131	68	273	49	46	63	193	54	283	69	0	.	613	37	167	65	313	36	140	45	426	42	738	33	333	57	364	58	156	51	135	55	75	80	0	.	0	.	403	37	94	82	450	53	293	57	13	100	754	37	119	56	313	50	206	53	0	.	39402	5
	maple	312	30	36	55	19	72	104	54	14	100	72	89	119	42	62	40	93	52	350	28	26	70	59	56	74	48	36	.	4	100	3	100	66	51	0	.	0	.	91	35	30	59	15	81	4	100	35	50	0	.	215	30	26	54	4	80	12	78	8	100	17	65	26	100	74	96	11	100	45	92	76	91	34	53	88	77	61	72	273	47	57	62	97	62	31	57	44	74	88	55	57	62	109	55	66	57	6	79	11	100	0	.	31	63	16	100	0	.	2	100	0	.	2	100	0	.	0	.	0	.	9	100	0	.	29	78	0	.	0	.	38	70	13	109	38	51	41	75	0	.	36	64	0	.	9	100	18	63	16	58	55	61	64	55	3	100	0	.	0	.	-2	100	0	.	2	100	43	61	43	73	69	60	29	66	8	100	29	58	0	.	79	75	118	66	0	.	3999	8
	ash	254	38	315	54	100	64	83	66	0	.	60	100	23	100	131	51	120	49	48	61	143	55	0	.	42	88	0	.	65	100	28	71	101	60	22	100	0	.	27	95	0	.	0	.	16	76	2	100	0	.	179	39	35	63	0	.	0	.	0	.	54	78	0	.	0	.	0	.	120	73	14	100	29	100	160	54	88	62	39	53	29	71	31	100	178	46	11	91	36	68	14	69	17	74	78	72	0	.	57	74	6	72	41	100	45	72	7	100	10	100	0	.	0	100	1	100	5	100	13	100	20	80	5	100	2	100	5	100	0	.	105	70	2	100	25	100	20	100	0	.	44	72	9	100	0	.	2	100	84	74	14	92	93	71	5	100	12	100	2	100	105	83	19	100	0	.	87	56	60	100	178	66	79	57	3	81	45	76	30	100	105	65	91	66	0	.	4124	10
	oak	27	59	55	81	147	62	97	48	0	.	0	.	3	82	26	71	263	31	0	.	112	81	8	100	50	60	0	.	11	100	29	94	11	62	8	100	100	82	0	.	0	.	34	100	0	.	1	100	0	.	203	49	29	82	0	.	0	.	36	79	0	.	26	100	0	.	0	.	0	.	0	.	0	.	0	.	85	72	0	.	8	73	0	.	252	45	0	.	19	71	18	100	82	59	0	.	0	.	0	.	4	100	88	86	182	47	0	.	0	.	0	.	26	70	3	100	33	85	45	100	85	59	3	75	0	100	0	.	0	.	5	80	9	100	0	.	22	100	0	.	30	100	201	71	31	72	0	.	255	45	0	.	59	61	107	52	0	.	0	.	21	100	1	100	3	72	34	43	0	.	38	100	0	.	0	.	223	54	65	74	118	69	88	71	0	.	3518	11
	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	.	184	64	48	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	.	30	100	203	91	210	66	109	78	0	.	187	60	205	98	162	48	119	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	58	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1516	24
	other broadleaves	112	58	72	47	141	56	81	93	2	100	9	100	179	51	58	42	279	40	45	38	111	67	14	84	31	50	0	.	15	100	56	68	2	100	104	84	0	.	26	67	30	61	17	77	94	66	262	30	52	60	115	45	17	71	0	.	21	70	9	100	10	59	27	95	16	81	29	74	7	66	20	90	40	74	0	.	103	70	121	50	95	64	8	68	33	63	16	62	36	65	41	57	57	79	60	51	9	85	32	92	121	89	21	94	103	64	25	65	65	53	49	87	92	58	105	59	123	55	184	38	147	64	129	49	13	91	40	100	5	74	62	93	45	66	11	65	19	77	37	100	36	62	49	92	6	83	5	65	66	58	7	71	37	94	42	71	45	93	3	100	16	82	108	42	28	51	268	48	10	85	45	91	0	.	1	100	51	83	24	91	78	85	91	58	0	.	5130	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.
	total	3275	23	2447	27	1634	28	4748	21	1417	39	2294	36	10233	14	6640	18	9855	14	6737	14	1154	38	1079	34	1751	28	128	.	891	44	1310	32	4076	25	2312	33	126	73	4349	22	4718	18	3924	19	5614	18	11179	12	10948	11	7880	14	2126	28	1190	43	1367	36	940	41	580	41	1101	36	1103	38	1436	33	843	45	2106	31	3460	23	1176	41	2200	35	3437	23	2265	29	1854	35	3777	20	2303	27	2364	25	2169	30	1473	29	1998	27	2522	29	2405	29	721	48	661	45	1398	33	1083	32	2268	24	1787	26	672	30	265	48	795	38	1990	22	1494	24	1135	33	1192	36	635	50	1841	31	1358	33	1095	40	712	38	1441	40	37	100	2432	25	914	43	1375	35	1545	24	1651	29	1813	26	2207	27	810	42	831	49	546	54	740	59	6798	14	4916	16	6595	14	959	39	1363	35	1168	36	647	64	2221	31	1127	40	1681	35	1482	32	0	.	223318	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	171	59	675	65	0	.	404	56	877	45	1717	34	7614	15	4677	17	4933	18	772	30	58	100	5	100	342	90	0	.	303	97	1792	36	690	39	1297	33	0	.	432	60	1746	34	324	52	1107	35	560	48	160	82	583	41	897	43	2343	25	3274	23	22	100	761	32	332	71	172	61	1256	38	477	52	172	.	740	74	1047	39	216	86	491	47	605	51	1937	33	77	72	154	100	10	100	0	.	77	63	106	73	859	49	293	56	875	40	858	36	478	39	0	.	95	100	37	.	0	.	24	100	11	100	0	.	0	.	0	.	97	77	563	65	71	84	131	65	424	55	533	42	325	61	339	90	120	63	0	.	0	.	521	47	105	.	25	100	111	89	21	100	110	84	0	.	466	87	294	49	227	44	712	37	103	100	427	66	685	49	897	32	936	34	322	57	130	87	310	75	0	.	58940	5
	fir	52	79	31	71	0	.	131	64	267	54	404	48	543	41	4787	16	2936	23	759	39	0	.	0	.	24	100	0	.	100	100	0	.	118	62	219	86	0	.	26	80	25	100	3	100	0	.	71	100	0	.	852	30	991	34	2485	21	1078	33	70	86	404	43	32	100	14	100	384	55	140	80	0	.	284	77	457	46	45	100	88	88	157	99	814	40	0	.	146	100	0	.	55	77	168	57	0	.	249	58	55	71	79	45	271	53	201	77	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	112	75	0	.	16	100	93	61	5	100	410	53	69	100	0	100	16	84	203	.	0	.	12	100	0	.	22	100	10	100	0	.	72	100	0	.	55	92	0	.	0	.	137	62	78	76	12	100	328	48	962	33	261	41	41	94	128	100	16	100	0	.	23093	7
	pine	90	58	159	77	0	.	33	77	0	.	0	.	94	72	31	71	306	43	145	80	59	100	25	100	116	63	0	.	0	.	116	100	0	.	303	69	20	76	0	.	0	.	47	100	45	100	41	100	100	78	8	100	0	.	75	100	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	79	100	23	71	102	100	6	100	0	.	0	.	0	.	0	.	0	.	76	54	64	78	99	50	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	0	.	0	.	0	.	11	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	283	44	163	42	43	71	0	.	28	100	254	57	77	57	120	65	210	67	76	100	57	83	0	.	3609	13
	larch	8	100	15	100	0	.	56	72	0	.	0	.	66	100	32	100	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	100	86	0	.	0	.	99	100	64	78	0	.	51	73	59	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	59	.	0	.	0	.	-8	100	0	.	0	.	17	100	0	.	0	.	0	.	65	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	516	40	463	39	258	47	0	.	15	100	0	.	0	.	0	.	0	.	0	.	72	100	0	.	2045	18
	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	.	24	96	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	70
	other conifers	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	195	64	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	35	100	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	137	100	1	100	-1	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	27	70	2	100	0	.	0	.	0	.	0	.	414	46
	beech	423	48	179	49	0	.	634	49	193	63	390	46	1346	31	1719	25	1543	19	341	39	113	77	172	78	108	79	0	.	99	93	414	48	255	61	641	43	0	.	222	69	402	78	99	93	0	.	71	74	0	.	828	35	559	47	460	35	971	33	178	67	96	49	67	75	57	71	203	56	315	65	0	.	105	56	158	55	614	54	221	62	116	66	767	38	241	54	551	67	81	71	67	85	463	48	70	71	1	100	97	65	221	42	133	61	494	47	0	.	0	.	0	.	8	77	530	39	149	62	0	.	0	.	72	86	87	64	0	.	32	97	44	57	75	64	36	73	169	66	0	.	219	62	7	.	0	.	29	70	12	.	121	94	0	.	119	71	39	64	0	.	43	77	0	.	0	.	115	56	60	72	53	62	54	57	784	31	453	44	317	63	124	66	137	53	0	.	21389	6
	maple	173	69	38	100	0	.	0	.	5	100	127	48	416	34	87	34	101	56	100	60	34	100	65	79	0	627	0	.	0	.	26	92	45	71	3	96	0	.	105	49	63	65	0	.	16	100	0	.	0	.	198	37	52	65	6	67	128	41	16	75	48	67	0	.	0	.	4	72	60	41	0	.	27	73	142	42	65	72	70	58	38	81	301	31	0	.	2	100	14	75	9	119	91	49	36	60	61	55	2	100	171	69	22	71	47	83	0	.	0	.	0	.	0	.	21	72	2	71	0	.	0	.	0	.	33	74	11	100	0	.	70	72	105	56	40	61	0	.	0	.	17	91	0	.	0	.	0	.	0	.	58	84	8	100	0	.	0	.	0	.	0	.	3	100	0	.	45	60	24	100	11	77	23	68	207	36	88	55	15	100	18	81	0	.	0	.	3814	9
	ash	4	100	145	56	0	.	0	.	0	.	152	56	87	60	327	44	274	47	0	.	25	100	12	100	37	100	0	.	27	100	96	69	73	54	206	58	6	100	172	64	42	100	0	.	108	72	1	120	0	.	435	42	136	61	84	55	68	57	117	84	0	.	0	.	0	.	0	.	114	63	0	.	3	100	319	45	61	61	4	100	21	81	110	49	0	.	15	100	0	.	44	100	89	64	78	84	0	.	99	70	132	41	220	49	58	61	0	.	0	.	0	.	0	.	5	87	42	61	0	.	0	.	31	71	41	97	0	.	0	.	32	76	26	90	3	100	117	74	41	100	0	.	0	.	0	.	0	.	0	.	110	72	8	100	2	100	118	77	0	.	17	100	6	86	9	76	15	74	91	100	26	63	3	100	236	40	98	43	69	86	58	71	57	62	0	.	5264	9
	oak	80	84	58	73	0	.	86	63	0	.	0	.	0	0	13	100	195	31	0	.	5	100	0	.	48	100	0	.	19	100	35	100	-1	100	69	89	281	66	0	.	0	.	0	.	0	.	0	.	34	100	115	100	49	72	142	71	4	100	52	80	0	.	0	.	0	.	0	.	2	100	0	.	0	.	35	100	0	.	0	.	0	.	9	100	51	88	0	.	0	.	0	.	19	72	11	100	0	.	0	.	92	58	198	71	116	48	0	.	0	.	0	.	1	100	77	81	292	42	27	76	9	.	42	100	18	100	0	.	22	100	0	.	0	.	0	.	67	100	0	.	0	.	9	.	0	.	0	.	0	.	16	100	2	100	27	72	0	.	0	.	0	.	2	84	25	100	5	76	0	.	0	.	0	.	25	100	0	.	85	57	11	100	29	100	0	.	2607	14
	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	.	40	82	14	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	.	11	75	0	.	77	48	487	30	707	33	40	96	55	.	915	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2372	17
	other broadleaves	135	66	116	53	0	.	8	100	1	100	9	100	203	45	105	39	104	37	24	54	30	76	49	88	4	100	0	.	61	95	43	93	10	71	21	89	3	100	77	62	8	84	49	71	80	91	111	75	24	64	198	46	46	48	56	70	17	47	82	79	3	100	0	.	0	0	0	.	25	95	0	.	0	.	2	100	70	64	47	57	17	87	80	59	4	100	0	.	2	100	64	100	52	73	16	86	9	83	103	69	105	58	105	70	12	71	0	.	17	75	0	.	128	65	203	38	475	39	60	62	39	.	137	69	58	85	0	.	10	100	15	72	106	92	16	66	26	93	0	.	0	100	0	.	0	.	2	100	0	.	1	100	115	100	5	100	43	96	0	.	0	.	93	53	20	59	11	78	9	100	29	58	25	72	54	60	5	74	38	69	20	75	31	100	0	.	4181	10
	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	1136	35	1416	39	0	.	1351	36	1344	43	2802	30	10369	13	11778	13	10592	14	2141	27	324	76	328	66	679	59	0	.	609	62	2521	31	1190	34	2894	27	310	66	1036	43	2385	29	586	43	1356	32	947	37	391	52	3217	23	2731	28	5650	19	5541	20	537	56	1312	30	431	71	246	63	1848	31	1134	39	172	.	1161	60	2297	30	1072	45	929	37	955	49	4096	24	398	49	971	59	113	71	240	71	958	39	316	54	1178	43	649	45	1751	31	1893	31	1506	32	0	.	123	86	96	.	214	49	1347	26	1691	22	127	60	103	.	1215	29	447	52	574	65	173	71	451	52	741	44	1038	40	773	58	390	82	371	51	219	.	0	.	564	48	117	.	353	59	254	65	176	66	382	58	0	.	581	73	1256	25	926	30	1341	31	365	65	600	53	1372	40	3269	23	1963	27	1097	38	566	55	709	48	0	.	127770	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	1014	40	1461	38	442	53	2689	26	1997	30	3039	26	15404	11	7778	13	8203	14	3219	18	282	82	221	74	622	54	0	.	876	47	2589	27	3198	24	2552	27	1	100	3523	23	5204	18	2473	21	6195	17	8606	14	5168	15	2669	20	1954	29	2911	24	4438	19	209	52	1016	26	760	40	571	39	2031	29	911	37	1568	30	2453	30	1337	32	1639	39	2546	25	2022	28	2969	26	1671	26	1150	34	671	44	927	50	276	43	1355	28	2684	25	1483	31	1366	34	1135	30	978	29	782	39	1475	29	1291	31	-1	100	35	76	49	81	198	78	58	90	184	59	641	41	1153	42	1373	33	686	49	1019	36	871	32	1318	40	339	90	1244	29	349	60	530	47	1759	23	540	42	667	32	921	33	107	59	559	44	196	59	986	57	3282	17	3395	18	4762	17	627	38	892	38	1261	34	1263	30	1556	25	988	37	973	44	833	41	0	.	171616	2
	fir	221	38	62	62	202	76	1327	30	508	43	1033	33	1214	25	6720	13	4523	17	2322	20	14	72	31	71	213	59	0	.	140	75	82	85	1129	39	582	54	0	.	397	51	239	36	287	50	286	66	690	38	0	.	2634	20	1113	31	2650	20	1243	30	638	47	582	33	464	41	500	39	819	33	287	59	513	51	577	43	777	33	187	51	404	43	246	67	1130	31	55	81	469	60	381	51	301	51	375	36	321	57	663	36	560	39	158	44	291	50	272	63	0	0	65	73	93	100	0	.	0	.	31	74	313	66	283	57	59	82	474	49	0	.	186	64	347	35	387	57	517	44	133	67	0	100	543	41	275	76	448	45	154	55	347	44	378	35	381	50	203	77	241	59	151	85	61	84	131	80	303	56	502	34	255	44	131	50	403	42	1180	30	555	39	41	94	128	100	124	84	0	.	50654	4
	pine	194	44	513	51	82	50	102	65	0	.	8	100	526	56	55	59	740	33	322	42	59	100	48	71	204	46	0	.	12	100	159	75	0	.	485	54	45	65	0	.	255	56	831	33	45	100	526	35	1049	28	434	37	0	.	171	62	0	.	70	100	0	.	0	.	14	85	28	100	0	.	66	100	12	100	31	100	0	.	0	.	0	.	79	100	403	39	212	56	127	43	195	51	180	46	32	100	11	100	0	.	93	48	165	54	260	58	14	100	151	73	49	100	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	187	50	5	100	0	.	0	.	0	.	11	100	17	100	68	100	17	100	0	.	18	72	0	.	260	81	2	100	0	.	59	100	0	.	839	28	316	29	613	33	0	.	28	100	359	44	117	49	326	44	374	50	184	61	166	54	0	.	13024	7
	larch	11	78	76	61	32	73	194	54	0	.	0	.	239	47	66	71	163	56	0	.	0	.	0	.	29	71	63	.	0	.	63	100	0	.	100	86	0	.	38	100	443	40	318	36	80	63	1214	21	3883	15	17	100	4	100	3	100	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	0	.	0	.	0	.	183	60	40	100	0	.	44	76	0	.	0	.	0	.	0	.	0	.	0	.	40	100	0	.	22	100	0	.	225	71	312	36	190	43	154	60	0	.	153	98	429	36	304	39	408	56	0	.	0	.	63	100	125	54	0	.	0	.	0	.	0	.	0	.	0	.	21	100	0	.	2	100	0	.	31	100	0	.	0	.	0	.	0	.	3141	17	1573	20	971	23	52	100	15	100	0	.	0	.	0	.	58	100	38	100	221	53	0	.	15882	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	140	57	1065	23	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	391	30	168	36	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1783	16
	other conifers	6	100	0	.	10	100	16	83	0	.	3	100	0	.	0	.	211	60	0	.	0	.	0	.	0	.	30	.	0	.	0	.	0	.	35	100	0	.	1	100	0	.	0	.	0	.	0	.	3	84	1	100	24	100	97	100	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	137	100	6	84	-1	100	3	100	0	.	2	100	2	100	1	100	7	90	0	.	0	.	0	.	1	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	72	0	.	2	100	0	.	0	.	0	.	4	60	0	.	0	.	0	.	0	.	0	.	149	100	0	.	0	.	0	.	0	.	15	77	0	.	0	.	0	.	0	.	11	100	27	70	2	100	0	.	0	.	0	.	0	.	813	32
	beech	1867	25	916	28	459	35	1313	29	232	53	585	36	2180	23	2990	19	5179	15	2448	17	636	48	899	33	1077	30	0	.	271	64	621	37	633	34	1018	36	0	.	927	32	782	47	486	35	46	75	312	48	0	.	3687	17	1371	31	718	36	977	33	238	53	163	42	228	47	140	50	361	36	406	53	21	91	1446	36	444	57	908	43	679	33	644	37	1136	32	1452	21	1353	34	1103	26	732	35	1083	27	261	33	257	97	665	39	224	41	215	45	815	34	0	.	80	60	0	.	299	43	675	35	387	33	603	34	427	36	315	43	330	43	0	.	163	58	317	43	121	46	229	47	452	50	0	.	832	32	174	63	313	36	169	39	439	41	859	31	333	57	483	47	195	43	135	55	118	58	0	.	0	.	518	31	154	57	502	47	347	49	796	30	1206	28	437	48	437	40	343	38	0	.	60791	3
	maple	485	31	74	58	19	72	104	54	19	78	198	44	535	28	149	26	194	39	450	26	60	64	124	49	74	47	36	.	4	100	29	82	111	42	3	96	0	.	196	30	93	48	15	81	20	83	35	50	0	.	413	23	78	47	10	51	140	38	25	60	65	52	26	100	74	96	15	76	105	46	76	91	61	44	230	39	125	51	344	39	95	49	398	28	31	57	45	71	103	48	66	56	199	37	102	42	67	50	14	84	171	69	53	47	63	67	0	.	2	100	0	.	2	100	21	72	2	71	0	.	9	100	0	.	62	54	11	100	0	.	108	53	119	51	78	40	41	75	0	.	53	52	0	.	9	100	18	63	16	58	113	52	72	51	3	100	0	.	0	.	-2	100	3	100	2	100	89	43	67	59	79	53	52	47	215	35	118	43	15	100	97	63	118	66	0	.	7814	6
	ash	258	37	460	41	100	64	83	66	0	.	212	49	111	52	458	35	394	36	48	61	168	49	12	100	79	66	0	.	92	76	123	56	174	41	228	53	6	100	199	57	42	100	0	.	124	63	3	77	0	.	613	31	171	50	84	55	68	57	117	84	54	78	0	.	0	.	0	.	234	48	14	100	32	90	480	35	149	44	43	49	50	53	141	44	178	46	26	70	36	68	58	78	105	55	156	55	0	.	155	52	138	39	260	44	103	46	7	100	10	100	0	.	0	100	6	72	47	56	13	100	20	80	36	62	43	93	5	100	0	.	138	57	27	85	28	90	137	65	41	100	44	72	9	100	0	.	2	100	84	74	125	65	101	65	6	78	130	71	2	100	122	73	25	79	9	76	102	49	151	72	204	58	82	55	239	39	142	38	99	67	164	49	148	47	0	.	9388	7
	oak	107	65	113	54	147	62	183	39	0	.	0	.	3	82	38	58	459	22	0	.	117	78	8	100	97	57	0	.	30	73	64	69	10	67	78	80	381	53	0	.	0	.	34	100	0	.	1	100	34	100	318	48	77	55	142	71	4	100	89	57	0	.	26	100	0	.	0	.	2	100	0	.	0	.	35	100	85	72	0	.	8	73	9	100	303	40	0	.	19	71	18	100	101	50	11	100	0	.	0	.	96	56	286	56	298	34	0	.	0	.	0	.	27	68	81	78	324	38	72	68	95	54	45	94	18	103	0	.	22	100	5	80	9	100	0	.	89	79	0	.	30	100	209	68	31	72	0	.	255	45	16	100	61	59	134	44	0	.	0	.	21	100	3	66	28	89	39	39	0	.	38	100	0	.	25	100	223	54	150	45	129	63	117	59	0	.	6125	9
	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	.	225	54	62	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	30	100	215	87	210	66	186	50	487	30	895	29	245	83	217	43	1034	29	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	25	100	0	.	58	59	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3888	13
	other broadleaves	247	45	188	37	141	56	89	85	3	72	18	71	381	34	164	29	383	31	69	31	141	55	63	71	35	46	0	.	76	78	100	56	12	61	124	72	3	100	103	49	38	51	66	56	175	55	374	30	75	46	312	33	63	40	56	70	38	44	91	72	13	51	27	95	16	81	29	74	32	75	20	90	40	74	2	100	173	49	168	39	113	56	89	53	37	57	16	62	37	62	106	65	110	54	76	44	18	59	135	57	226	55	126	60	115	58	25	65	82	45	49	87	219	45	309	32	597	33	243	32	186	54	266	43	72	71	40	100	15	73	78	76	151	67	27	47	45	63	37	100	36	62	49	92	6	83	7	54	66	58	8	62	152	79	46	65	89	67	3	100	16	82	201	33	48	39	279	46	19	65	74	60	25	72	56	58	56	77	62	55	98	69	122	50	0	.	9311	6
	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	4411	19	3863	22	1634	28	6099	18	2760	29	5097	23	20601	10	18418	10	20447	9	8878	12	1477	34	1407	30	2429	26	128	.	1500	36	3832	23	5266	21	5206	21	435	51	5385	19	7103	16	4510	18	6971	16	12126	11	11339	11	11098	11	4856	20	6841	17	6908	17	1476	33	1892	23	1533	32	1348	33	3284	22	1977	29	2278	29	4621	23	3473	24	3272	28	4367	20	3220	25	5951	19	4175	19	3274	26	2477	24	2410	28	2431	23	2314	24	3700	24	3054	25	2472	26	2554	26	2905	23	1083	32	2391	23	1883	25	886	25	1612	23	2486	19	2117	21	1598	23	2351	22	1639	30	1209	40	2014	29	1808	28	1836	29	1749	28	2214	33	427	76	2803	23	1133	39	1375	35	2109	21	1768	28	2166	24	2461	25	986	37	1214	38	546	54	1321	46	8055	12	5842	14	7936	13	1324	33	1964	29	2540	27	3917	22	4184	21	2224	28	2246	30	2191	27	0	.	351089	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. 2293508. Birmensdorf, Swiss Federal Research Institute WSL

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