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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NFI5
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 2018/26
		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	216	44	464	39	866	42	669	47	1279	42	918	41	2652	25	2237	24	1613	25	1933	21	45	81	292	58	289	65	0	.	740	48	1073	38	1314	36	1488	40	1	100	2919	26	4685	20	2747	22	4331	21	8572	13	5542	15	1642	29	511	52	797	38	963	36	139	60	755	43	363	58	347	46	587	51	813	56	1375	33	1744	30	288	51	729	40	1825	32	1236	43	1165	44	1082	34	831	46	294	44	271	54	290	60	1595	31	1338	33	895	39	166	73	295	58	348	58	1041	41	3242	39	1408	32	0	.	45	100	0	.	368	60	94	65	361	49	607	48	627	50	1387	36	855	58	1199	39	259	59	693	44	29	100	951	37	138	68	253	51	1425	26	616	43	728	42	586	46	97	100	212	52	32	75	242	98	3658	20	3461	21	5145	18	518	47	355	47	426	42	280	56	557	54	10	100	427	52	348	50	0	.	102256	4
	fir	249	72	38	100	144	60	862	37	297	65	996	44	777	43	1773	28	1428	24	2081	22	29	71	112	70	221	77	0	.	162	72	296	71	625	44	547	48	0	.	499	48	318	43	419	43	291	61	886	40	0	0	2652	25	333	42	544	51	242	58	361	69	622	54	527	37	545	45	769	43	147	76	193	63	567	39	711	46	44	76	366	54	191	55	406	43	184	59	435	36	280	62	96	59	202	54	762	52	445	39	501	42	287	100	128	76	176	100	18	100	208	52	167	100	0	.	0	.	0	.	354	59	276	60	242	73	464	48	0	.	239	65	289	42	544	56	321	52	125	60	0	.	1018	35	200	84	325	50	374	53	471	48	586	33	299	55	259	69	498	53	140	71	0	.	161	94	314	56	542	39	171	55	66	100	0	.	131	92	28	58	78	100	19	100	32	99	0	.	34722	5
	pine	0	.	21	71	21	100	74	100	0	.	0	.	313	78	50	100	309	43	401	55	8	100	53	83	145	54	0	.	21	100	43	74	0	.	163	98	67	100	0	.	182	50	480	38	0	.	584	42	1090	29	383	37	18	100	152	72	0	.	50	100	29	100	0	.	0	.	44	100	0	.	176	100	42	100	41	100	0	.	0	.	0	.	0	.	302	41	64	81	97	53	4	71	119	51	57	100	8	100	0	.	12	100	100	89	165	79	0	.	46	74	66	100	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	178	71	0	0	0	.	0	.	0	.	14	100	9	100	0	.	20	100	0	.	13	100	0	.	29	100	0	.	0	.	69	100	0	.	484	37	312	37	594	35	0	.	25	100	119	59	49	100	70	61	76	66	32	100	0	.	0	.	8107	10
	larch	3	252	33	83	106	71	95	81	0	.	0	.	34	100	0	.	90	71	0	.	0	.	0	.	15	100	0	.	0	.	57	100	0	.	6	100	0	.	50	100	274	43	200	46	194	64	1608	21	4658	15	22	100	0	.	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	0	.	123	72	45	100	0	.	23	73	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	0	.	0	.	300	60	524	35	322	41	282	53	0	.	81	79	1237	28	459	40	556	59	0	.	0	.	0	.	237	46	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	0	.	2977	17	1554	24	992	25	40	100	0	.	9	100	18	100	0	.	23	71	26	100	101	58	0	.	17744	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	219	53	1256	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	.	8	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	.	577	33	142	49	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2265	16
	other conifers	42	75	0	.	71	100	85	72	0	.	0	.	0	.	0	.	220	71	2	100	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	6	100	22	47	7	100	74	100	274	87	2	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	8	100	0	.	0	.	1	100	5	100	0	.	0	.	11	100	6	100	40	77	0	.	0	.	0	.	4	100	0	.	0	.	2	100	0	.	0	.	0	.	0	.	0	.	0	.	81	100	0	.	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	56	100	27	62	0	.	6	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	6	100	0	.	3	64	0	.	0	.	14	89	1	100	0	.	0	.	0	.	0	.	0	.	1360	30
	beech	783	27	703	34	688	34	763	28	52	51	136	65	675	37	982	29	2367	19	2805	18	309	47	633	32	559	42	0	.	261	69	215	55	643	36	220	72	0	.	777	34	288	44	383	34	25	61	504	43	0	.	2532	19	608	39	423	62	8	81	119	53	61	40	226	39	99	51	88	42	139	80	53	60	1088	35	487	56	186	59	621	38	720	39	415	52	1787	23	897	33	744	31	755	37	800	32	334	40	273	78	839	42	215	55	162	57	356	61	0	.	157	65	0	.	816	34	481	46	324	45	963	31	1025	32	544	43	173	50	0	.	165	65	501	51	29	73	341	56	158	74	0	.	764	34	165	89	81	49	310	39	335	42	782	35	309	46	396	58	90	61	256	59	0	.	0	.	21	100	621	36	148	60	387	45	386	52	5	100	359	42	289	47	177	41	242	40	0	.	41605	4
	maple	233	32	158	39	53	82	14	155	0	.	96	57	216	39	242	45	310	27	474	26	96	56	90	67	136	60	0	.	60	74	17	74	149	42	26	100	0	.	151	36	92	50	56	70	35	71	119	43	0	.	336	32	91	45	20	91	84	46	76	71	80	47	83	64	158	79	26	59	73	55	35	78	135	80	61	66	50	86	263	46	79	84	255	51	97	55	164	46	168	42	65	43	156	46	112	51	21	72	38	58	96	56	65	53	14	87	34	100	59	100	11	100	5	63	0	.	32	72	9	50	54	100	5	74	57	69	16	85	1	100	68	50	75	52	105	58	37	66	2	100	132	48	10	100	31	63	18	73	66	42	63	64	96	52	38	71	10	100	0	.	60	75	0	.	17	92	205	47	76	57	164	51	14	131	43	65	69	58	243	55	53	72	166	64	0	.	7868	7
	ash	258	45	185	67	32	83	64	98	0	.	108	100	32	100	80	56	247	50	41	61	137	82	18	60	65	86	0	.	124	87	192	87	315	58	84	60	0	.	46	56	17	100	10	61	17	100	46	43	0	.	327	37	2	100	45	71	7	100	47	78	17	94	4	100	3	100	0	.	329	83	0	.	109	81	112	70	116	67	76	60	30	80	34	88	153	52	103	54	56	58	73	58	8	100	142	63	6	100	73	100	82	54	7	100	93	74	18	100	211	100	2	100	15	82	8	100	35	127	123	64	1	100	22	92	26	64	45	80	0	.	62	69	31	87	48	75	0	.	12	71	24	74	0	.	9	77	12	100	32	90	21	71	141	71	2	100	18	100	0	.	0	.	53	71	2	100	201	52	78	75	364	57	41	78	46	58	76	54	173	65	38	70	99	77	0	.	6266	11
	oak	191	70	0	.	0	.	158	74	0	.	0	.	6	100	68	73	262	32	0	.	78	100	0	.	107	57	0	.	9	100	0	.	52	79	30	71	78	72	0	.	0	.	64	100	0	.	0	0	0	.	154	48	8	100	78	79	0	.	34	100	0	.	35	100	0	.	0	.	0	.	0	.	0	.	0	.	52	89	0	.	17	71	0	.	140	50	13	100	11	100	31	86	130	62	0	.	0	.	0	.	25	93	69	77	152	79	0	.	0	.	0	.	28	66	0	.	113	77	71	76	55	67	73	82	0	0	0	.	0	.	14	72	23	100	0	.	11	100	2	100	44	82	54	96	82	56	0	.	201	68	0	.	5	100	68	73	0	.	41	100	0	.	14	100	20	60	50	69	0	.	17	100	14	100	0	.	98	85	157	45	95	62	81	76	0	.	3480	12
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	369	54	24	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	.	346	47	129	84	229	60	0	.	372	63	231	53	66	74	153	69	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	66	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1994	20
	other broadleaves	118	44	133	37	140	44	80	81	0	.	12	75	35	51	98	45	437	32	93	34	88	57	40	71	25	62	0	.	0	0	158	74	39	60	132	62	6	100	72	59	70	52	43	74	345	42	517	37	283	74	265	43	35	59	36	93	17	71	0	.	13	75	7	100	-2	100	24	60	37	55	6	76	32	54	0	.	59	57	90	55	108	85	20	86	37	51	94	69	5	75	151	52	2	100	116	62	2	100	3	100	110	94	60	63	47	80	92	67	390	52	106	85	265	41	146	54	270	40	220	37	139	42	248	43	32	69	11	100	7	86	47	76	306	92	13	90	33	57	182	89	55	58	3	75	4	75	19	61	116	71	20	62	18	100	32	84	82	100	5	100	0	.	145	41	113	41	309	29	7	63	156	62	0	0	42	62	21	59	27	52	105	73	81	73	0	.	8208	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	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	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	2093	23	1735	26	2121	26	2864	24	1628	41	2267	36	4739	22	5530	18	7283	14	7830	14	790	37	1239	33	1563	36	252	.	1377	39	2051	30	3135	23	2698	30	152	85	4517	22	5948	17	4401	18	5236	19	13429	11	12876	11	8320	15	1680	29	2390	32	1324	36	826	42	1576	36	1251	36	1192	39	1538	33	1538	42	1837	32	3743	22	1714	36	1235	34	3364	24	2432	31	2294	34	3806	21	2613	25	1661	29	1505	28	1707	30	3118	26	2093	29	2410	29	1008	44	887	37	1353	36	1504	32	5184	29	2220	25	1640	23	680	37	1226	32	3657	18	2167	24	2217	28	1358	37	699	47	1985	32	2074	32	2224	33	1107	38	1057	39	363	59	3025	24	592	59	809	34	2159	24	1851	30	2200	26	1540	30	891	42	936	37	544	60	302	87	8074	14	5956	17	8773	14	1039	38	1534	34	1023	33	615	47	1279	31	1075	33	972	32	1150	34	0	.	235875	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	135	65	31	100	0	.	210	71	1285	47	1177	42	11473	12	3326	19	2986	22	752	35	0	.	0	.	125	78	0	.	35	100	1257	42	980	38	776	44	3	100	234	58	2491	28	913	50	1975	34	975	44	431	72	597	37	653	38	1129	31	2604	25	178	100	740	35	153	72	200	75	1021	39	759	47	193	.	620	63	913	39	669	63	711	45	706	53	1549	33	306	51	0	.	0	.	0	.	30	95	111	54	843	51	336	65	811	40	474	44	478	49	58	.	23	100	58	94	0	.	37	100	30	100	0	.	0	.	0	.	299	59	951	60	219	67	118	61	391	55	915	38	225	83	108	73	183	59	0	.	0	.	467	48	189	100	12	100	146	85	37	97	88	100	0	.	182	89	687	43	751	32	768	40	180	100	397	55	268	68	1072	37	1301	35	74	106	13	100	230	59	0	.	58831	5
	fir	0	0	2	100	0	.	103	68	312	50	556	63	1443	30	4760	17	3416	21	478	37	3	100	24	100	152	52	0	.	185	100	71	71	321	79	53	59	4	100	2	100	65	100	10	100	0	.	117	100	0	.	1237	33	1175	38	2658	22	1321	29	161	75	545	44	29	100	51	100	982	48	221	79	0	.	499	69	309	63	50	100	143	70	576	59	838	35	0	.	78	100	0	.	44	71	170	51	0	.	245	73	78	69	250	49	207	43	329	70	0	.	0	.	0	.	0	.	0	.	34	100	0	.	0	.	0	.	85	58	0	.	73	102	190	68	11	100	805	53	76	95	75	100	20	100	0	.	0	.	6	100	1	100	34	100	159	88	0	.	0	.	0	.	200	68	1	100	207	87	396	59	98	75	74	73	265	53	896	34	394	36	16	100	0	.	54	72	0	.	28445	6
	pine	26	100	0	.	0	.	0	.	0	.	0	.	54	61	60	73	139	60	-1	100	38	100	13	100	40	72	0	.	0	.	2	100	0	.	102	80	0	0	0	.	20	100	96	82	52	100	55	72	64	81	0	0	0	.	76	98	0	.	0	.	0	.	0	.	0	.	0	.	4	100	0	.	0	.	0	.	0	.	0	.	0	.	53	100	28	100	35	100	0	.	0	.	0	.	0	.	0	.	0	.	72	78	17	100	37	73	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	46	100	0	.	0	.	0	.	0	.	16	100	0	.	0	.	0	.	0	.	10	100	0	.	0	.	0	.	0	.	0	.	0	.	374	53	96	49	30	74	0	.	0	.	117	71	43	61	122	56	77	71	55	100	56	100	0	.	2126	16
	larch	28	100	22	100	0	.	0	.	0	.	0	.	125	77	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	34	100	0	.	0	.	74	72	100	81	43	124	173	58	292	70	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	37	100	1	100	0	.	0	.	14	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	29	100	27	100	0	.	0	.	147	100	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	68	100	1722	43	1022	36	283	58	0	.	30	100	0	.	2	100	0	.	0	.	0	.	76	100	0	.	4369	21
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	39	72	72	65	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	129	44
	other conifers	0	.	0	.	0	.	0	.	0	.	14	74	1	100	0	.	302	55	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	7	100	0	.	0	.	0	.	0	.	4	100	1	100	1	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	344	98	12	72	-19	100	2	100	0	.	66	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	1	100	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	.	4	71	46	73	9	81	0	.	0	.	0	.	0	.	803	48
	beech	440	58	115	66	0	.	1076	48	270	57	358	44	1723	31	2312	23	1919	22	607	39	0	.	252	90	565	43	0	.	31	76	534	49	424	52	1038	40	0	.	158	70	387	69	208	70	0	.	53	72	0	.	884	38	825	37	430	42	1347	30	163	61	110	60	68	71	119	71	253	52	347	50	0	.	224	63	306	49	746	53	456	49	243	43	1076	37	371	54	547	61	50	.	152	81	484	53	97	68	45	64	239	62	374	38	189	53	438	35	0	.	25	100	0	.	0	.	845	39	419	49	239	100	0	.	0	.	570	52	0	.	93	71	120	99	103	68	91	73	456	59	71	73	364	50	1	.	0	.	26	72	20	100	164	86	237	85	196	73	145	71	0	.	210	76	0	.	4	100	240	66	166	63	111	70	130	59	874	33	522	39	457	68	175	74	189	52	0	.	30316	6
	maple	133	63	24	100	0	.	20	64	25	75	170	57	554	24	222	35	88	49	228	47	45	95	27	73	34	51	0	.	0	.	65	61	185	50	64	68	0	.	218	62	70	62	7	100	51	100	0	.	3	100	172	39	107	46	31	60	216	41	24	73	100	60	0	.	0	.	18	73	64	74	0	.	29	71	113	72	191	64	138	54	164	54	432	34	3	100	4	100	6	.	35	76	130	70	60	72	56	77	49	97	218	74	-11	249	92	59	0	.	0	.	0	.	0	.	22	100	7	87	0	.	2	100	5	100	96	60	0	.	0	.	117	70	93	62	69	60	0	.	7	100	26	68	0	.	0	.	13	100	13	75	17	100	14	100	0	.	0	.	0	.	12	100	9	100	32	93	112	47	24	100	48	76	49	60	286	32	43	86	0	.	17	100	82	88	0	.	5893	8
	ash	0	.	194	59	0	.	9	100	0	.	146	60	70	123	372	54	398	39	46	75	0	.	15	100	72	82	0	.	42	100	218	100	169	50	200	48	9	100	214	53	42	100	0	.	110	81	58	100	0	.	443	42	417	66	54	72	51	60	23	95	0	.	0	.	0	.	6	100	182	71	0	.	8	71	472	53	154	66	21	100	108	100	224	50	78	91	35	100	0	.	103	77	84	60	180	69	4	100	169	89	198	46	255	59	145	57	0	.	0	.	0	.	9	100	27	77	171	63	0	.	24	100	84	54	135	86	5	100	0	.	119	68	55	67	18	100	320	65	85	95	0	.	0	.	0	.	0	.	0	.	68	100	67	100	68	66	29	100	0	.	70	71	21	60	61	69	4	100	37	100	66	85	7	100	147	50	150	50	0	.	32	100	20	100	0	.	7694	10
	oak	158	89	80	70	0	.	102	69	0	.	0	.	12	100	0	0	280	34	0	.	0	.	4	100	0	.	0	.	0	.	60	100	0	.	79	80	306	71	0	.	0	.	0	.	0	.	0	.	85	100	179	73	80	100	99	100	40	100	18	100	0	.	0	.	0	.	0	.	5	100	0	.	0	.	28	100	0	.	0	.	0	.	0	.	70	100	0	.	0	.	0	.	29	71	19	100	0	.	0	.	83	63	483	84	151	54	0	.	0	.	0	.	19	100	119	69	361	38	15	100	56	72	29	62	50	100	0	.	36	100	0	.	0	.	0	.	97	100	0	.	0	.	75	.	0	.	0	.	202	73	25	100	5	100	163	63	0	.	0	.	41	94	33	82	91	80	23	84	0	.	0	.	36	71	15	100	0	.	19	100	15	100	11	100	0	.	3986	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	.	100	100	18	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	32	.	12	100	166	100	161	76	397	37	818	35	237	89	189	73	1300	32	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	3462	17
	other broadleaves	153	73	42	96	0	.	19	75	0	.	63	82	428	34	116	34	127	49	17	76	9	100	13	86	22	100	0	.	23	87	73	65	22	71	45	70	1	100	84	70	13	52	12	78	26	71	81	47	174	74	128	52	91	59	27	65	86	53	0	.	17	67	0	.	0	.	0	.	44	91	0	.	5	100	3	100	12	80	41	73	28	100	105	44	2	100	0	.	0	.	73	100	14	100	34	57	17	73	55	93	144	67	79	58	28	58	0	.	51	100	0	.	10	100	324	48	956	31	20	100	130	73	175	74	34	68	0	.	24	94	6	100	6	100	25	81	7	100	13	100	3	100	0	.	0	.	4	100	0	.	2	100	100	100	15	100	40	100	0	.	13	100	192	48	111	42	36	84	0	.	16	89	161	64	22	91	0	.	15	78	21	100	32	94	0	.	5162	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	.	0	.	0	.	0	.	0	.	0	.	0	.	0	0	0	0	0	.	0	.	0	.	0	.	0	0	0	.	0	.	0	0	0	.	0	0
	total	1072	40	510	43	0	.	1539	39	1892	42	2483	33	15894	11	11167	13	9656	15	2127	28	94	71	350	84	1022	39	0	.	317	78	2279	35	2101	30	2391	28	323	71	909	41	3169	25	1346	41	2258	31	1616	34	1067	43	3643	23	3353	25	4504	21	5666	19	567	56	1511	34	250	71	370	68	2281	32	1626	35	193	.	1385	53	2489	34	1872	41	1490	36	1827	41	4278	23	937	42	699	59	56	.	408	74	941	43	502	57	1211	43	927	48	2149	29	1723	35	1728	28	90	.	111	64	371	71	200	73	1772	27	2800	21	511	63	401	58	1593	30	1269	43	956	60	491	61	670	50	659	43	1924	39	1181	54	374	68	595	45	76	.	0	.	516	48	436	62	323	74	727	51	479	52	302	71	0	.	796	54	3110	29	2451	26	1891	32	505	63	742	53	1037	44	3404	25	2539	28	658	52	327	71	752	42	0	.	151214	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	351	37	495	37	866	42	879	39	2564	31	2096	30	14125	11	5563	14	4598	17	2685	18	45	81	292	58	414	51	0	.	775	46	2330	29	2295	26	2264	30	4	75	3152	24	7176	16	3660	21	6306	18	9547	13	5973	15	2239	23	1164	31	1925	24	3567	20	317	62	1495	27	516	46	547	40	1608	31	1573	37	1568	31	2364	28	1201	32	1398	36	2536	26	1942	34	2714	27	1388	28	831	46	294	44	271	54	320	55	1706	29	2182	28	1232	34	977	36	769	35	826	38	1098	39	3265	38	1466	31	0	.	82	71	30	100	368	60	94	65	361	49	905	38	1578	41	1607	32	974	52	1590	32	1174	32	918	39	137	62	1134	32	138	68	253	51	1892	23	805	40	741	42	732	40	135	77	300	47	32	75	425	68	4345	18	4213	18	5913	16	699	43	752	37	694	37	1352	32	1858	29	84	94	439	50	578	38	0	.	161087	3
	fir	249	72	40	96	144	60	965	34	609	41	1551	36	2219	24	6533	14	4844	17	2559	19	32	64	136	60	373	50	0	.	347	63	366	59	946	40	600	44	4	100	501	48	383	40	429	42	291	61	1003	38	0	0	3889	20	1507	31	3202	20	1564	26	522	53	1167	35	555	35	596	42	1751	33	368	56	193	63	1066	38	1019	37	94	64	509	43	768	47	1244	27	184	59	514	34	280	62	140	46	372	37	762	52	690	36	578	38	536	58	335	39	506	58	18	100	208	52	167	100	0	.	0	.	34	100	354	59	276	60	242	73	549	42	0	.	313	55	479	37	556	55	1126	41	202	52	75	100	1037	35	200	84	325	50	380	52	472	47	619	32	458	47	259	69	498	53	140	71	200	68	162	93	521	48	938	34	269	44	140	61	265	53	1027	32	422	34	94	85	19	100	86	58	0	.	63168	4
	pine	26	100	21	71	21	100	74	100	0	.	0	.	367	67	109	60	449	35	400	55	45	85	66	69	186	45	0	.	21	100	45	71	0	.	265	67	67	100	0	.	201	46	576	34	52	100	639	38	1154	28	383	37	18	100	229	58	0	.	50	100	29	100	0	.	0	.	44	100	4	100	176	100	42	100	41	100	0	.	0	.	0	.	53	100	330	39	100	63	97	53	4	71	119	51	57	100	8	100	0	.	85	68	117	77	202	66	0	.	46	74	66	100	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	224	60	0	0	0	.	0	.	0	.	30	71	9	100	0	.	20	100	0	.	23	71	0	.	29	100	0	.	0	.	69	100	0	.	858	31	408	30	624	33	0	.	25	100	236	46	92	60	192	42	153	49	86	73	56	100	0	.	10232	8
	larch	30	94	55	64	106	71	95	81	0	.	0	.	158	64	0	.	90	71	0	.	0	.	0	.	27	71	0	.	0	.	57	100	0	.	40	87	0	.	50	100	348	37	300	41	237	57	1781	20	4950	14	22	100	5	100	22	72	1	100	0	.	0	.	0	.	41	100	0	.	0	.	0	.	18	100	13	100	37	100	123	71	45	100	0	.	36	59	0	.	0	.	20	100	0	.	0	.	0	.	57	100	14	100	29	100	27	100	300	60	524	35	469	42	282	53	0	.	86	75	1237	28	459	40	556	59	0	.	0	.	0	.	237	46	0	.	20	100	0	.	66	100	0	.	22	100	0	.	0	.	0	.	0	.	57	73	0	.	21	100	0	.	68	100	4698	19	2576	20	1275	23	40	100	30	100	9	100	20	89	0	.	23	71	26	100	178	54	0	.	22114	6
	Arolla pine	0	.	0	.	0	.	0	.	0	.	0	.	12	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	19	100	0	.	0	.	225	52	1256	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	.	8	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	.	617	31	214	39	44	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	2393	15
	other conifers	42	75	0	.	71	100	85	72	0	.	14	74	1	100	0	.	522	44	3	71	0	.	0	.	0	.	252	.	0	.	0	.	0	.	0	.	0	.	3	100	7	100	0	.	0	.	6	100	22	47	11	74	75	99	275	86	2	100	0	.	0	.	8	100	0	.	0	.	0	.	0	.	8	100	344	98	12	72	-19	104	7	79	0	.	66	100	11	100	6	100	40	77	0	.	0	.	0	.	4	100	0	.	0	.	5	72	0	.	0	.	0	.	0	.	0	.	0	.	81	100	1	100	0	.	0	.	0	.	7	100	0	.	10	100	0	.	0	.	56	100	27	62	0	.	6	100	0	.	0	.	0	.	0	.	0	.	6	100	0	.	0	.	6	100	4	100	3	64	0	.	0	.	18	71	47	71	9	81	0	.	0	.	0	.	0	.	2163	26
	beech	1223	27	818	30	688	34	1839	30	323	48	494	36	2398	25	3294	18	4287	14	3412	16	309	47	885	34	1123	30	0	.	292	63	749	39	1067	30	1258	35	0	.	935	31	675	44	591	33	25	61	557	39	0	.	3416	17	1433	27	852	37	1355	30	282	42	170	41	294	34	218	45	341	40	486	42	53	60	1313	31	793	39	932	44	1077	30	964	31	1491	30	2159	21	1444	31	794	30	907	34	1285	28	430	35	317	67	1078	35	589	31	351	39	793	34	0	.	182	58	0	.	816	34	1326	30	743	34	1202	32	1025	32	544	43	743	42	0	.	258	49	621	45	132	56	432	47	614	48	71	73	1128	28	166	88	81	49	335	37	355	40	946	32	546	45	592	45	234	50	256	59	210	76	0	.	25	86	861	32	314	43	498	38	515	42	879	33	881	28	746	45	352	42	432	32	0	.	71921	3
	maple	366	30	182	36	53	82	34	74	25	75	266	42	771	21	465	29	399	24	702	23	140	49	117	54	170	49	0	.	60	74	82	51	334	33	91	56	0	.	370	39	162	39	63	63	86	66	119	43	3	100	508	24	198	32	51	51	301	32	100	57	181	39	83	64	158	79	45	46	136	46	35	78	164	67	174	52	241	54	400	35	244	45	687	29	101	53	168	45	174	40	99	39	286	40	172	41	77	59	87	60	314	54	54	81	106	52	34	100	59	100	11	100	5	63	22	100	39	61	9	50	56	96	9	62	153	46	16	85	1	100	184	48	168	41	175	43	37	66	9	78	158	42	10	100	31	63	32	59	79	37	80	55	109	47	38	71	10	100	0	.	71	65	9	100	50	69	317	35	100	50	212	43	64	55	329	29	111	48	243	55	70	60	248	51	0	.	13760	5
	ash	258	45	380	44	32	83	74	87	0	.	254	55	102	90	451	46	645	31	87	49	137	82	34	56	138	59	0	.	166	70	409	67	484	42	284	38	9	100	260	44	60	77	10	61	127	72	104	59	0	.	770	29	419	66	99	51	57	54	70	61	17	94	4	100	3	100	6	100	511	59	0	.	116	76	584	45	270	48	97	52	138	80	258	45	231	46	138	48	56	58	176	51	92	55	323	48	11	72	242	69	279	36	262	58	238	45	18	100	211	100	2	100	24	63	35	64	205	56	123	64	25	95	107	47	161	73	50	72	0	.	181	51	86	53	66	61	320	65	97	84	24	74	0	.	9	77	12	100	32	90	89	78	208	58	70	64	47	73	0	.	70	71	75	54	63	67	205	50	115	60	430	50	48	68	194	40	226	38	173	65	71	59	119	66	0	.	13959	7
	oak	349	55	80	70	0	.	260	53	0	.	0	.	17	74	68	73	542	23	0	.	78	100	4	100	107	57	0	.	9	100	60	100	52	79	109	61	384	58	0	.	0	.	64	100	0	.	0	0	85	100	332	45	88	91	177	66	40	100	53	74	0	.	35	100	0	.	0	.	5	100	0	.	0	.	28	100	52	89	0	.	17	71	0	.	210	47	13	100	11	100	31	86	158	52	19	100	0	.	0	.	108	53	552	74	303	48	0	.	0	.	0	.	47	57	119	69	474	33	85	65	111	49	102	61	50	100	0	.	36	100	14	72	23	100	0	.	108	91	2	100	44	82	129	71	82	56	0	.	403	50	25	100	10	71	231	49	0	.	41	100	41	94	47	65	111	66	73	54	0	.	17	100	50	58	15	100	98	85	176	41	110	55	92	68	0	.	7465	10
	sweet chestnut	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5	100	0	.	0	.	468	48	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	.	32	100	358	45	294	67	390	47	397	37	1190	31	468	52	254	57	1453	29	0	.	0	.	0	.	0	.	6	100	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	31	100	0	.	66	61	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	0	.	5456	12
	other broadleaves	271	45	174	36	140	44	98	67	0	.	75	69	463	32	213	27	564	27	110	31	97	52	54	57	48	57	0	.	23	87	231	55	61	46	177	49	8	86	155	46	83	45	54	60	371	39	598	33	457	54	393	34	126	45	63	60	103	46	0	.	30	50	7	100	-2	100	24	60	81	55	6	76	38	48	3	100	71	49	131	44	136	70	125	40	39	48	94	69	5	75	224	48	16	87	151	50	19	68	59	88	254	55	140	43	76	55	92	67	441	48	106	85	275	40	471	37	1225	26	239	35	269	40	423	40	66	48	11	100	31	75	53	69	312	91	38	61	40	50	194	84	58	56	3	75	4	75	24	53	116	71	22	57	118	86	46	66	122	75	5	100	13	100	337	33	224	29	345	28	7	63	172	57	161	64	64	51	21	59	42	44	127	63	114	59	0	.	13369	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	.	0	0	0	.	0	.	0	0	0	0	0	0	0	.	0	.	0	0	0	.	0	.	0	0	0	0	0	.	0	.	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	3166	20	2245	22	2121	26	4404	20	3520	29	4750	24	20632	9	16697	10	16939	10	9958	12	884	34	1589	32	2585	26	252	.	1694	35	4330	23	5236	18	5089	21	475	55	5426	19	9118	14	5748	17	7494	16	15046	10	13943	10	11963	12	5033	19	6895	17	6990	17	1393	33	3088	24	1501	32	1562	34	3819	23	3164	27	2030	30	5129	22	4202	25	3108	28	4855	20	4259	25	6573	19	4744	18	3312	23	1717	28	1913	27	2648	24	3620	24	3304	24	3337	25	3157	24	2610	26	3080	22	1593	31	5295	29	2591	24	1840	22	2452	22	4026	17	4168	18	2569	22	3810	20	2627	28	1655	40	2476	28	2743	27	2883	27	3031	29	2238	34	738	45	3620	21	669	53	809	34	2675	22	2286	27	2523	25	2267	26	1370	33	1238	33	544	60	1098	46	11185	13	8408	14	10664	13	1544	33	2276	28	2060	28	4019	22	3819	21	1733	29	1299	30	1903	26	0	.	387089	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. 2276588. Birmensdorf, Swiss Federal Research Institute WSL

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