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	23-35calculated 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 and the total forest (pairs of values in ), but they do in the private 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 and the total forest of the Plateau has decreased. For the private 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 and total forest.

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Procedure

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

Answer

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

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

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

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

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

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

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

Example 8: Standard error and combination of variable expressions

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

The standard error increases as the level of detail increases.

Large standard errors are to be expected if:

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

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

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

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5. Two types of changes?

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

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

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

The results are:

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

Example 9: : Additivity/non-additivity of changes

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

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

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

Example 10: Average number of years per domain

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

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

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

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

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

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

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

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

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

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

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NFI3
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 2004/06
		forest district (2024)
		AG01		AG02		AG03		AG04		AI01		AR01		BE01		BE02		BE03		BE04		BSBL01		BSBL02		BSBL03		BSBL04		FR01		FR02		FR03		FR04		GE01		GL01		GR01		GR02		GR03		GR04		GR05		JU01		LU01		LU02		LU03		NE01		NE02		NE03		NE04		NE06		NW01		OW01		OW02		SG01		SG02		SG03		SG04		SG05		SH01		SO01		SO02		SO03		SO04		SZ01		SZ02		SZ03		TG01		TG02		TG03		TI01		TI02		TI03		TI04		TI05		TI06		TI07		TI08		TI09		UR01		UR02		UR03		VD02		VD03		VD04		VD05		VD06		VD07		VD08		VD09		VD11		VD12		VD14		VD15		VD16		VD18		VD20		VD22		VS01		VS02		VS03		ZG01		ZH01		ZH02		ZH03		ZH04		ZH05		ZH06		ZH07		lakes		Switzerland
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	397	47	459	36	798	46	1526	36	1250	38	1336	39	6165	19	3034	21	1788	26	2723	19	17	92	271	71	678	61	5	.	958	43	1298	36	1687	33	1342	36	0	.	3737	28	4152	21	2883	20	4601	20	9582	14	5707	14	2410	25	946	42	984	40	1512	32	100	64	432	46	496	45	386	46	652	46	586	51	1258	35	1734	31	210	82	773	39	2116	28	1515	39	1167	48	1868	26	1005	40	535	47	267	57	261	57	1709	30	1901	31	1061	39	245	55	370	51	510	40	869	40	2096	27	1790	32	0	.	29	100	0	.	316	66	89	70	211	63	538	51	540	53	1902	35	852	52	1048	40	300	58	933	55	14	100	1121	30	328	71	477	67	1451	25	428	48	836	33	902	40	211	71	493	55	184	63	323	96	3344	20	4080	19	5041	18	495	46	610	38	660	45	247	57	530	46	693	56	566	55	512	36	0	.	118469	3
	fir	254	62	77	76	100	70	1150	33	243	63	736	46	1038	33	1576	28	1519	25	1823	22	39	82	48	86	341	57	0	.	100	86	302	60	924	46	545	56	0	.	432	53	280	37	463	44	283	66	758	40	6	100	2338	26	305	44	275	40	179	48	378	58	538	52	433	37	502	46	726	44	149	71	213	51	502	33	532	48	79	73	375	49	206	49	350	45	126	59	380	45	378	54	97	57	351	51	475	51	343	37	437	44	239	89	96	61	122	100	0	.	150	61	160	100	0	.	0	.	0	.	354	60	335	58	139	71	386	50	0	.	227	67	327	48	541	54	383	61	48	60	0	.	704	41	85	96	365	53	291	53	367	51	435	38	206	60	369	64	540	56	308	76	0	.	118	91	435	55	465	43	121	54	105	61	23	100	84	100	47	95	77	100	5	100	146	84	0	.	32506	5
	pine	18	100	406	59	41	71	65	86	0	.	14	100	453	65	33	75	324	47	382	49	0	.	16	71	159	54	0	.	17	100	50	62	0	.	186	94	91	100	0	.	215	49	639	35	0	.	689	34	1234	31	454	37	15	100	134	68	0	.	114	100	27	100	0	.	0	.	39	100	0	.	84	100	33	100	36	100	0	.	0	.	0	.	0	.	290	42	68	85	97	50	68	87	179	43	83	100	8	100	0	.	6	100	107	74	192	77	0	.	96	76	58	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	155	74	12	100	0	.	0	.	0	.	13	85	9	100	0	.	36	100	0	.	13	100	0	.	93	79	0	.	0	.	30	100	0	.	493	31	278	39	767	33	0	.	68	100	160	57	44	100	208	55	74	56	139	74	53	75	0	.	9876	9
	larch	7	100	42	86	65	100	198	72	0	.	0	.	95	55	15	100	135	58	0	.	0	.	0	.	36	79	0	.	0	.	61	71	0	.	0	.	0	.	49	100	353	49	288	47	180	53	1748	22	4584	15	20	100	0	.	21	89	0	.	0	.	0	.	0	.	42	100	0	.	0	.	0	.	8	100	11	100	0	.	158	58	43	100	0	.	41	71	0	.	0	.	14	100	0	.	0	.	0	.	52	100	11	100	0	.	0	.	245	68	530	40	243	40	289	58	0	.	66	79	826	30	428	38	456	61	0	.	0	.	22	100	192	52	0	.	0	.	0	.	16	100	0	.	12	100	0	.	0	.	22	100	0	.	59	73	0	.	0	.	0	.	0	.	2680	16	1347	23	1139	24	55	100	0	.	10	100	0	.	0	.	47	76	19	100	121	61	0	.	17097	7
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	164	56	1142	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	.	4	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	.	542	31	163	53	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2087	16
	other conifers	29	76	0	.	48	100	18	73	0	.	0	.	0	.	0	.	30	59	2	71	0	.	0	.	0	.	153	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	22	45	3	100	44	100	185	92	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	6	100	0	.	0	.	7	100	3	100	18	77	0	.	0	.	0	.	3	100	0	.	0	.	1	100	0	.	0	.	0	.	0	.	0	.	0	.	42	100	0	.	0	.	0	.	0	.	5	100	0	.	6	100	0	.	0	.	33	100	13	65	0	.	3	100	0	.	0	.	0	.	141	100	0	.	0	.	0	.	0	.	7	100	0	.	2	74	0	.	0	.	7	100	0	.	0	.	0	.	0	.	0	.	0	.	850	33
	beech	999	30	878	31	583	35	1035	29	34	70	234	59	869	44	799	28	2745	21	2646	18	547	42	718	39	1080	31	0	.	158	80	323	38	498	37	405	59	0	.	846	33	270	45	449	34	60	68	354	44	0	.	2587	19	520	38	404	52	12	56	95	69	133	45	200	46	59	63	170	48	129	84	21	67	1199	33	384	63	187	73	473	37	851	42	364	65	1730	23	917	34	829	28	839	37	920	33	231	38	271	91	683	41	100	70	184	62	401	51	0	.	162	56	0	.	580	39	354	59	261	48	1027	34	870	35	404	45	290	63	0	.	153	67	492	46	118	63	261	54	73	71	-12	100	783	35	122	100	173	56	282	39	450	43	913	31	178	55	374	56	98	61	275	53	33	100	0	.	4	100	367	35	105	76	399	43	376	46	10	100	561	37	220	53	195	42	288	44	0	.	42663	4
	maple	189	40	90	40	36	111	70	55	32	100	92	69	191	40	107	49	189	35	415	27	66	55	59	78	167	44	0	.	60	71	7	61	105	47	15	100	0	.	196	28	33	83	14	76	16	76	76	55	0	.	346	28	44	54	18	75	46	51	28	62	45	49	51	63	118	91	19	62	35	62	61	86	125	69	64	66	39	73	233	49	53	74	175	59	66	53	95	51	93	37	74	57	166	46	72	50	11	85	23	71	47	56	36	54	10	100	11	100	36	100	11	100	9	100	0	.	7	61	2	70	40	100	0	.	77	64	11	100	0	.	68	57	43	77	84	56	43	77	0	.	87	49	0	.	12	90	16	71	42	48	66	56	123	72	0	.	12	100	0	.	20	100	0	.	10	91	121	47	61	64	149	51	39	72	50	73	43	70	144	57	103	70	137	57	0	.	6093	7
	ash	261	43	293	64	140	56	75	74	0	.	82	75	8	63	111	57	263	40	127	48	234	66	16	85	57	81	0	.	96	96	153	68	242	64	8	71	0	.	168	57	6	100	17	79	40	78	29	59	0	.	405	33	85	76	22	72	6	82	34	130	46	61	0	.	1	100	0	.	243	89	0	.	30	85	217	51	166	56	98	53	61	76	11	100	172	44	62	59	48	60	100	68	33	62	128	61	0	.	64	100	48	58	154	66	57	77	20	100	96	100	0	.	16	73	11	83	26	100	66	67	-26	124	15	100	9	83	33	76	0	.	155	66	14	88	35	100	0	.	5	71	57	81	60	100	0	.	7	100	23	100	114	73	109	72	18	100	3	100	0	.	160	100	9	100	2	100	104	53	50	79	299	58	184	61	53	56	41	66	103	97	153	60	195	61	0	.	6936	9
	oak	120	72	0	.	0	.	155	72	0	.	0	.	6	100	51	94	235	35	0	.	123	79	0	.	123	55	0	.	48	80	0	.	32	67	5	100	285	64	0	.	0	.	50	100	0	.	1	100	0	.	195	43	19	73	48	100	0	.	49	97	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	78	71	0	.	16	75	0	.	140	54	0	.	21	70	17	85	135	68	0	.	0	.	0	.	20	95	114	66	128	65	0	.	0	.	0	.	29	69	13	72	55	94	78	88	48	57	9	98	8	100	0	.	0	.	7	81	13	100	0	.	20	100	0	.	54	88	95	100	64	60	0	.	227	58	0	.	67	67	100	73	19	100	31	100	0	.	9	83	9	71	34	57	0	.	23	100	11	100	0	.	151	53	107	43	166	67	94	58	0	.	3785	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	.	329	56	23	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	.	227	76	71	64	237	66	0	.	230	71	85	71	146	56	244	58	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	0	.	0	.	51	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1656	22
	other broadleaves	115	62	85	44	116	52	87	89	4	100	13	100	79	51	91	44	320	51	100	36	155	59	56	73	70	41	0	.	17	100	222	73	7	74	86	55	5	100	130	52	82	46	33	60	197	43	717	29	194	54	243	48	21	55	32	96	12	64	0	.	10	74	2	100	25	60	43	80	19	58	32	83	53	79	0	.	46	60	121	42	184	63	19	78	98	64	38	54	54	66	118	54	107	75	103	52	5	58	7	72	8	85	49	62	90	75	112	64	144	37	83	82	235	39	278	48	161	48	237	32	170	63	210	45	44	69	46	100	8	72	195	64	294	87	23	71	16	82	81	76	46	47	1	100	1	100	13	50	65	57	15	59	42	100	56	60	56	97	6	100	0	.	155	46	105	43	336	35	10	71	141	61	-4	151	12	100	8	100	15	47	169	78	67	63	0	.	8169	8
	indeterminable	0	.	0	.	0	.	0	.	0	.	2	74	23	71	0	.	0	.	7	87	0	.	0	.	0	.	0	.	2	100	1	100	0	.	1	100	0	.	5	100	13	81	0	.	11	74	37	63	17	67	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	2	100	15	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	9	100	0	.	0	.	0	.	0	.	4	71	0	.	0	.	0	.	0	.	0	.	12	100	1	100	0	.	3	100	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	31	96	26	77	25	87	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	265	22
	total	2390	24	2331	26	1927	28	4380	21	1564	36	2509	35	8927	17	5818	18	7548	15	8224	14	1180	36	1184	36	2710	28	158	.	1455	38	2417	28	3494	24	2591	28	381	59	5564	23	5414	18	4835	18	5386	19	14493	11	12928	11	9001	14	1998	28	2125	32	1777	32	798	42	1231	38	1221	36	1133	40	1650	32	1162	40	1669	32	3686	22	1453	39	1370	34	3589	23	2937	29	2085	37	4531	20	2573	26	2059	25	1612	30	2152	33	2802	26	2539	28	2329	30	724	46	1112	40	1510	33	1257	32	3547	21	2421	26	1394	25	685	38	805	34	3037	19	2100	24	1694	30	1352	35	629	48	2471	31	2313	30	2084	33	1086	49	1135	48	149	55	2874	24	705	54	1132	40	2060	23	1638	31	2379	24	1929	28	1129	41	1221	49	834	55	537	71	7388	14	6459	16	8515	14	898	37	1794	32	1465	34	501	53	1587	30	1479	35	1517	36	1613	29	0	.	250453	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	174	69	22	73	0	.	348	58	1174	44	1710	33	9751	13	4618	17	4681	19	874	34	0	.	73	100	42	100	0	.	24	100	1515	35	1164	38	585	39	0	.	527	62	2421	34	597	48	1870	31	786	44	296	80	508	34	739	40	1719	29	4550	23	107	100	929	34	324	73	141	80	1185	40	631	48	193	.	660	73	1156	38	449	64	399	55	537	54	2284	33	126	71	129	100	11	100	0	.	48	71	233	64	976	51	473	63	673	44	829	40	984	42	50	.	0	.	35	100	0	.	31	100	31	71	0	.	0	.	0	.	241	60	736	62	102	81	283	46	364	57	815	40	333	61	122	66	121	56	12	.	0	.	433	47	157	100	10	100	208	73	46	71	165	100	0	.	161	96	708	40	427	35	717	43	144	96	509	58	179	62	1218	33	1199	34	319	64	165	100	70	85	0	.	64354	5
	fir	133	53	28	100	0	.	202	61	245	51	745	48	741	37	4868	16	3358	23	950	41	0	.	10	100	28	100	0	.	138	100	23	100	261	57	134	98	0	.	31	71	43	88	9	71	1	100	91	100	0	.	1323	30	1068	36	2763	24	1385	27	119	76	443	48	21	93	24	100	554	51	184	84	0	.	349	82	367	44	82	100	149	72	343	66	1119	35	0	.	172	100	0	.	67	83	135	50	0	.	396	53	76	72	294	44	282	63	310	71	0	.	0	.	0	.	0	.	0	.	30	100	0	.	0	.	0	.	137	66	0	.	65	98	129	65	0	.	599	48	11	100	32	100	46	100	160	.	0	.	1	100	1	100	25	100	45	92	0	.	0	.	0	.	115	72	0	.	9	100	170	55	97	59	56	89	361	51	710	39	428	39	8	100	0	.	0	.	0	.	27297	6
	pine	286	77	0	.	0	.	0	.	0	.	0	.	61	71	57	71	296	47	43	87	47	100	49	78	31	78	0	.	0	.	167	100	0	.	190	78	50	83	0	.	14	100	78	87	47	100	38	78	110	74	9	100	0	.	64	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	65	88	51	100	0	.	0	.	0	.	0	.	0	.	0	.	83	66	58	97	63	53	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	42	100	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	9	100	0	.	0	.	0	.	0	.	0	.	0	.	370	53	190	41	57	74	0	.	0	.	123	58	73	58	126	58	90	74	125	100	44	100	0	.	3261	15
	larch	15	100	21	100	0	.	29	100	0	.	0	.	69	117	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	71	0	.	0	.	65	71	90	77	11	100	95	73	105	72	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	22	100	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	40	100	0	.	0	.	0	.	110	100	0	.	0	.	20	100	0	.	0	.	0	.	0	.	0	.	0	.	89	86	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1893	38	1005	35	236	59	0	.	21	100	0	.	0	.	0	.	0	.	0	.	109	100	0	.	4089	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	18	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	52	66	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	72	51
	other conifers	0	.	0	.	0	.	0	.	0	.	6	80	0	.	0	.	388	48	0	.	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	.	240	100	4	100	-16	100	0	.	0	.	69	100	0	.	0	.	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	13	100	0	.	0	.	0	.	1	100	15	103	5	100	0	.	0	.	0	.	0	.	747	42
	beech	871	47	127	58	0	.	1220	44	201	56	349	38	1952	30	2388	26	2298	21	548	37	73	100	318	73	236	73	0	.	62	88	445	52	421	58	788	45	0	.	405	65	360	70	145	71	0	.	59	71	0	.	910	36	553	43	456	37	1488	29	184	63	119	52	45	74	91	71	221	51	255	47	0	.	138	65	278	51	1103	47	312	61	144	54	1250	38	344	62	574	62	189	71	108	75	468	56	111	57	26	68	182	60	357	36	147	48	500	46	0	.	0	.	0	.	20	100	639	41	284	45	9	100	0	.	14	100	207	56	0	.	9	141	61	60	31	100	69	71	354	68	44	83	316	52	5	.	0	.	16	76	16	100	130	100	220	89	197	71	120	71	0	.	168	71	0	.	1	100	407	47	145	66	70	81	107	59	1005	31	462	40	418	63	181	75	96	76	0	.	29640	6
	maple	119	55	64	85	0	.	9	100	14	71	86	69	490	28	186	34	77	64	225	47	65	100	45	65	0	.	0	.	0	.	38	75	115	56	32	63	0	.	173	61	63	66	3	100	38	100	16	100	0	.	147	36	33	72	36	56	245	51	28	72	76	70	0	.	0	.	7	70	92	39	0	.	29	71	208	42	90	63	109	53	94	62	402	40	0	.	4	100	18	75	54	72	150	67	70	59	93	60	9	100	226	65	14	100	97	71	0	.	0	.	0	.	0	.	44	71	7	84	0	.	0	.	1	100	43	58	0	.	0	.	104	73	117	63	71	55	5	100	0	.	26	93	0	.	0	.	12	100	9	77	18	100	16	100	4	100	0	.	0	.	10	100	0	.	10	100	121	46	21	100	36	75	44	59	227	35	81	54	0	.	14	100	0	.	0	.	5229	9
	ash	23	87	186	69	0	.	0	.	0	.	255	61	124	72	483	44	331	35	0	.	52	100	9	100	50	100	0	.	26	100	183	78	106	54	332	64	43	100	543	80	30	100	7	100	137	73	58	92	0	.	571	47	596	82	149	65	82	60	15	100	0	.	0	.	0	.	2	100	215	71	0	.	5	100	449	43	150	52	12	100	132	98	272	43	0	.	9	100	0	.	80	85	152	78	207	87	3	100	188	82	358	49	336	54	229	45	0	.	0	.	0	.	0	.	33	83	104	96	0	.	26	100	69	63	100	84	3	100	0	.	91	71	63	76	14	73	171	68	-1	424	0	.	0	.	0	.	0	.	0	.	0	.	26	100	40	72	215	75	0	.	47	74	82	82	40	79	29	62	129	100	69	72	25	67	249	44	173	45	0	.	22	100	20	100	0	.	9028	11
	oak	361	91	61	77	0	.	103	58	0	.	0	.	7	100	0	.	339	30	0	.	0	.	3	100	0	.	0	.	0	.	56	100	0	.	63	87	380	74	0	.	0	.	0	.	0	.	0	.	56	100	181	79	120	81	162	100	52	100	16	100	0	.	0	.	0	.	0	.	4	100	0	.	0	.	18	100	0	.	0	.	0	.	11	100	86	93	0	.	0	.	0	.	23	71	15	100	0	.	0	.	86	59	377	73	145	48	0	.	0	.	0	.	4	100	94	65	393	37	30	71	50	70	55	97	37	100	0	.	33	100	0	.	0	.	0	.	85	100	0	.	0	.	49	.	0	.	0	.	192	72	16	100	4	100	167	62	0	.	0	.	22	100	22	85	96	74	14	61	0	.	0	.	22	71	25	100	0	.	16	100	13	100	0	.	0	.	4165	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	.	70	100	19	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	.	44	.	35	.	213	100	199	74	609	33	985	30	298	72	102	80	1391	31	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	33	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3998	16
	other broadleaves	167	56	22	66	0	.	19	71	1	100	53	85	509	32	140	43	228	47	24	67	3	100	10	88	0	.	0	.	39	76	6	74	11	64	13	82	1	100	221	95	8	58	79	65	137	67	93	53	123	67	125	51	84	84	18	61	77	57	0	.	22	63	0	.	0	.	0	.	30	100	0	.	0	.	3	71	35	67	38	82	41	100	166	56	3	100	0	.	0	.	67	100	11	100	43	74	16	72	97	78	198	66	47	66	13	69	0	.	25	.	0	.	22	76	192	39	898	32	82	62	163	98	191	71	28	54	0	.	20	100	13	73	1	100	23	77	33	65	23	100	2	100	0	.	0	.	4	100	0	.	12	100	111	100	72	93	76	100	0	.	6	100	231	47	78	43	27	55	19	100	14	100	85	73	65	49	0	.	12	87	21	88	1	100	0	.	5593	10
	indeterminable	0	.	0	.	0	.	0	.	0	.	1	100	87	39	0	.	16	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	18	94	0	.	0	.	2	100	0	.	0	.	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	67	0	.	0	.	15	100	0	.	0	.	1	100	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	174	27
	total	2149	44	531	44	0	.	1931	37	1636	42	3204	28	13808	12	12739	13	12011	14	2664	29	240	82	516	62	388	71	0	.	289	73	2434	31	2078	34	2176	29	474	73	1902	52	3033	30	1007	39	2240	28	1308	35	710	49	3776	23	3193	30	5366	21	7893	19	470	53	1589	31	391	73	257	71	1970	31	1412	36	193	.	1182	61	2717	30	1937	36	1003	42	1292	46	5542	24	697	48	939	59	219	71	376	70	986	43	679	49	1509	41	1025	48	2275	29	2129	36	2347	29	93	.	60	.	357	72	245	67	1643	28	2761	21	419	57	342	80	1736	29	793	44	739	62	273	72	769	48	580	48	1593	39	992	53	234	62	512	46	226	.	0	.	466	47	384	60	211	87	630	50	527	53	576	71	0	.	528	64	3341	26	1922	26	1780	29	555	63	776	55	948	39	3586	23	2475	28	862	46	541	74	340	73	0	.	157648	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	571	39	482	34	798	46	1874	31	2424	29	3047	25	15916	11	7652	13	6469	15	3597	16	17	92	344	60	720	58	5	.	982	42	2813	25	2850	25	1927	28	0	.	4264	26	6573	18	3480	18	6470	17	10368	13	6004	14	2919	21	1685	29	2704	23	6062	19	208	60	1360	27	820	40	528	40	1837	30	1217	35	1451	33	2395	30	1366	35	1222	34	2515	25	2052	32	3451	27	1995	25	1135	37	547	46	267	57	309	49	1943	28	2877	27	1533	33	918	35	1199	32	1494	31	918	38	2096	27	1825	31	0	.	60	71	31	71	316	66	89	70	211	63	778	40	1276	42	2004	33	1135	41	1411	33	1115	33	1266	43	136	60	1242	28	339	69	477	67	1884	22	585	44	846	32	1109	36	257	60	659	49	184	63	484	72	4052	18	4506	17	5759	17	640	41	1119	34	839	38	1465	29	1730	27	1012	43	731	48	582	33	0	.	182823	2
	fir	387	45	105	62	100	70	1352	30	489	40	1480	33	1779	25	6445	14	4877	17	2773	20	39	82	58	73	369	53	0	.	238	68	324	57	1185	38	679	49	0	.	464	50	323	34	471	44	283	66	849	37	6	100	3661	19	1373	30	3038	22	1564	25	497	47	981	36	454	36	526	44	1281	33	333	56	213	51	851	39	898	33	161	62	524	41	549	45	1468	29	126	59	552	44	378	54	164	47	486	39	475	51	739	33	513	39	533	46	378	49	432	58	0	.	150	61	160	100	0	.	0	.	30	100	354	60	335	58	139	71	523	41	0	.	292	56	456	39	541	54	983	38	59	52	32	100	751	39	245	73	365	53	292	53	368	51	460	36	251	52	369	64	540	56	308	76	115	72	118	91	444	54	635	34	219	40	161	51	384	48	794	36	475	36	85	91	5	100	146	84	0	.	59803	4
	pine	304	73	406	59	41	71	65	86	0	.	14	100	514	58	90	53	621	33	425	45	47	100	65	61	190	47	0	.	17	100	218	78	0	.	376	61	141	71	0	.	229	47	717	33	47	100	727	33	1344	29	463	36	15	100	198	56	0	.	114	100	27	100	0	.	0	.	39	100	0	.	84	100	33	100	36	100	0	.	0	.	0	.	40	100	355	38	119	65	97	50	68	87	179	43	83	100	8	100	0	.	89	62	165	59	255	59	0	.	96	76	58	100	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	198	62	12	100	0	.	0	.	0	.	27	66	9	100	0	.	36	100	0	.	22	72	0	.	93	79	0	.	0	.	30	100	0	.	863	29	469	29	824	31	0	.	68	100	283	41	117	52	334	41	164	48	264	61	98	61	0	.	13137	8
	larch	22	75	63	66	65	100	227	64	0	.	0	.	164	59	15	100	135	58	0	.	0	.	0	.	36	79	0	.	0	.	61	71	0	.	39	71	0	.	49	100	418	43	378	40	191	51	1843	21	4689	15	20	100	2	100	21	89	0	.	0	.	0	.	0	.	42	100	0	.	0	.	0	.	8	100	11	100	22	100	158	58	43	100	0	.	44	66	0	.	0	.	14	100	0	.	0	.	0	.	52	100	11	100	40	100	0	.	245	68	530	40	353	41	289	58	0	.	85	65	826	30	428	38	456	61	0	.	0	.	22	100	281	44	0	.	0	.	0	.	16	100	0	.	12	100	0	.	0	.	22	100	0	.	59	73	0	.	0	.	0	.	0	.	4573	18	2352	20	1375	22	55	100	21	100	10	100	0	.	0	.	47	76	19	100	230	57	0	.	21186	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	18	75	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	11	100	0	.	0	.	164	56	1142	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	.	4	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	.	544	31	215	43	62	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2159	15
	other conifers	29	76	0	.	48	100	18	73	0	.	6	80	0	.	0	.	418	45	2	71	0	.	0	.	0	.	153	.	0	.	0	.	0	.	0	.	0	.	2	100	11	100	0	.	0	.	8	100	22	45	5	74	44	100	185	92	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	3	100	240	100	4	100	-16	100	6	100	0	.	69	100	7	100	3	100	18	77	0	.	0	.	0	.	3	100	0	.	0	.	8	86	0	.	0	.	0	.	0	.	0	.	0	.	42	100	0	.	0	.	0	.	0	.	5	100	0	.	6	100	0	.	0	.	33	100	13	65	0	.	3	100	0	.	0	.	0	.	141	100	0	.	0	.	0	.	0	.	7	100	13	100	2	74	0	.	0	.	8	86	15	103	5	100	0	.	0	.	0	.	0	.	1598	26
	beech	1870	27	1005	28	583	35	2255	27	235	49	582	32	2820	25	3188	21	5042	15	3194	16	620	39	1036	35	1316	28	0	.	220	63	768	34	919	33	1193	36	0	.	1251	30	630	45	594	31	60	68	413	38	0	.	3497	16	1072	29	860	31	1500	29	280	48	252	34	245	40	150	50	391	36	384	42	21	67	1337	31	662	42	1290	41	785	33	996	37	1613	33	2074	22	1491	31	1019	26	947	33	1387	29	342	32	297	83	864	35	457	32	332	40	901	34	0	.	162	56	0	.	600	38	993	34	545	32	1036	34	870	35	417	44	497	44	0	.	162	64	553	42	149	54	330	45	427	57	31	122	1099	29	127	96	173	56	298	37	466	41	1043	30	399	55	572	44	218	48	275	53	201	61	0	.	5	77	774	29	250	50	470	39	484	38	1015	31	1023	27	637	45	376	42	384	38	0	.	72303	3
	maple	308	32	154	42	36	111	79	50	46	72	178	48	681	23	293	28	266	31	640	24	131	57	104	53	167	44	0	.	60	71	45	64	220	37	47	53	0	.	370	32	95	52	16	66	54	74	92	49	0	.	492	22	77	44	54	45	291	44	56	47	121	47	51	63	118	91	25	49	128	33	61	86	154	57	272	36	128	49	342	38	147	48	577	33	66	53	99	49	111	33	127	45	316	40	142	38	104	54	31	59	273	55	50	48	106	66	11	100	36	100	11	100	9	100	44	71	14	53	2	70	40	100	1	100	119	46	11	100	0	.	172	50	160	50	155	40	48	69	0	.	114	43	0	.	12	90	28	59	51	42	84	49	139	65	4	100	12	100	0	.	30	75	0	.	21	68	241	33	82	54	185	44	83	46	277	32	123	43	144	57	117	63	137	57	0	.	11322	5
	ash	283	40	480	47	140	56	75	74	0	.	337	49	132	68	594	38	594	26	127	48	286	57	25	65	107	63	0	.	122	78	336	52	348	47	339	63	43	100	711	62	36	85	24	63	176	59	86	64	0	.	976	31	681	73	171	57	88	56	49	95	46	61	0	.	1	100	2	100	458	58	0	.	35	74	666	33	316	38	111	48	194	71	283	42	172	44	71	53	48	60	180	53	185	65	334	59	3	100	253	66	407	44	490	43	286	39	20	100	96	100	0	.	16	73	44	65	130	79	66	67	0	46674	84	55	108	78	36	71	0	.	246	49	77	64	49	74	171	68	4	149	57	81	60	100	0	.	7	100	23	100	114	73	135	61	58	58	217	74	0	.	207	79	91	74	41	76	133	43	179	75	369	49	209	54	302	37	214	38	103	97	175	54	215	56	0	.	15964	7
	oak	481	70	61	77	0	.	258	49	0	.	0	.	13	71	51	94	574	22	0	.	123	79	3	100	123	55	0	.	48	80	56	100	32	67	67	81	665	50	0	.	0	.	50	100	0	.	1	100	56	100	376	44	138	71	210	80	52	100	65	77	0	.	33	100	0	.	0	.	4	100	0	.	0	.	18	100	78	71	0	.	16	75	11	100	226	48	0	.	21	70	17	85	158	59	15	100	0	.	0	.	106	51	490	58	273	40	0	.	0	.	0	.	32	62	107	58	448	34	108	66	98	45	64	85	45	83	0	.	33	100	7	81	13	100	0	.	104	83	0	.	54	88	144	74	64	60	0	.	419	45	16	100	71	63	267	47	19	100	31	100	22	100	31	64	105	68	48	44	0	.	23	100	32	58	25	100	151	53	123	39	180	62	94	58	0	.	7950	10
	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	.	399	50	42	71	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	44	100	262	67	284	76	436	49	609	33	1215	28	383	58	248	46	1634	27	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	8	100	0	.	0	.	0	.	0	.	33	100	0	.	51	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5654	12
	other broadleaves	283	42	106	38	116	52	107	74	6	79	66	72	588	29	231	31	548	35	124	32	158	59	66	63	70	41	0	.	55	61	228	71	18	49	99	49	6	80	351	63	89	42	112	49	334	37	810	26	317	42	369	36	105	68	50	66	89	50	0	.	33	49	2	100	25	60	43	80	49	65	32	83	53	79	3	71	81	44	159	37	225	55	185	51	101	62	38	54	54	66	185	50	118	69	146	43	21	56	104	73	206	63	96	45	103	66	112	64	169	35	83	82	257	37	470	32	1059	28	319	28	333	57	401	41	73	47	46	100	28	75	207	60	295	87	46	52	50	52	104	63	48	46	1	100	1	100	16	45	65	57	26	55	154	77	129	58	132	71	6	100	6	100	386	34	183	31	363	32	30	70	154	57	81	77	77	44	8	100	27	46	190	70	68	61	0	.	13762	6
	indeterminable	0	.	0	.	0	.	0	.	0	.	3	59	110	34	0	.	16	100	7	87	0	.	0	.	0	.	0	.	2	100	1	100	0	.	1	100	0	.	5	100	31	64	0	.	11	74	39	60	17	67	0	.	0	.	0	.	25	71	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	6	76	15	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	9	100	0	.	0	.	0	.	8	67	4	71	0	.	15	100	0	.	0	.	1	100	12	100	6	82	0	.	3	100	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	1	100	0	.	0	.	0	.	31	96	26	77	27	81	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	439	17
	total	4539	24	2862	23	1927	28	6311	18	3200	27	5713	22	22736	10	18557	10	19559	10	10889	12	1420	33	1700	31	3098	26	158	.	1744	34	4851	21	5572	20	4768	20	854	48	7466	21	8447	15	5842	16	7626	15	15801	11	13638	10	12778	12	5191	21	7491	18	9671	16	1268	33	2820	24	1612	32	1390	35	3620	22	2574	27	1862	30	4869	23	4170	24	3308	25	4592	20	4229	24	7628	20	5228	18	3512	25	2278	24	1988	28	3138	26	3480	23	4048	23	3353	26	2999	25	3241	27	3857	22	1351	30	3607	21	2778	25	1639	24	2327	22	3566	17	3456	18	2442	23	3430	20	2144	27	1368	40	2745	28	3082	25	2664	28	2678	30	2128	35	383	43	3386	22	931	47	1132	40	2526	20	2022	28	2590	23	2559	25	1656	33	1798	40	834	55	1064	48	10730	12	8381	13	10295	13	1454	33	2570	28	2413	25	4087	21	4063	20	2341	28	2058	33	1953	27	0	.	408101	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. 2281227. Birmensdorf, Swiss Federal Research Institute WSL

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